harshalmore31
1 month ago
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# Deploying Azure OpenAI in Production: A Comprehensive Guide
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In today's fast-paced digital landscape, leveraging cutting-edge technologies has become essential for businesses to stay competitive and provide exceptional services to their customers. One such technology that has gained significant traction is Azure OpenAI, a powerful platform that allows developers to integrate advanced natural language processing (NLP) capabilities into their applications. Whether you're building a chatbot, a content generation system, or any other AI-powered solution, Azure OpenAI offers a robust and scalable solution for production-grade deployment.
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In this comprehensive guide, we'll walk through the process of setting up and deploying Azure OpenAI in a production environment. We'll dive deep into the code, provide clear explanations, and share best practices to ensure a smooth and successful implementation.
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## Prerequisites:
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Before we begin, it's essential to have the following prerequisites in place:
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1. **Python**: You'll need to have Python installed on your system. This guide assumes you're using Python 3.6 or later.
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2. **Azure Subscription**: You'll need an active Azure subscription to access Azure OpenAI services.
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3. **Azure OpenAI Resource**: Create an Azure OpenAI resource in your Azure subscription.
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4. **Python Packages**: Install the required Python packages, including `python-dotenv` and `swarms`.
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## Setting up the Environment:
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To kick things off, we'll set up our development environment and install the necessary dependencies.
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1. **Create a Virtual Environment**: It's a best practice to create a virtual environment to isolate your project dependencies from the rest of your system. You can create a virtual environment using `venv` or any other virtual environment management tool of your choice.
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```
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python -m venv myenv
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```
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2. **Activate the Virtual Environment**: Activate the virtual environment to ensure that any packages you install are isolated within the environment.
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```
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source myenv/bin/activate # On Windows, use `myenv\Scripts\activate`
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```
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3. **Install Required Packages**: Install the `python-dotenv` and `swarms` packages using pip.
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```
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pip install python-dotenv swarms
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```
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4. **Create a `.env` File**: In the root directory of your project, create a new file called `.env`. This file will store your Azure OpenAI credentials and configuration settings.
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```
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AZURE_OPENAI_ENDPOINT=<your_azure_openai_endpoint>
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AZURE_OPENAI_DEPLOYMENT=<your_azure_openai_deployment_name>
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OPENAI_API_VERSION=<your_openai_api_version>
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AZURE_OPENAI_API_KEY=<your_azure_openai_api_key>
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AZURE_OPENAI_AD_TOKEN=<your_azure_openai_ad_token>
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```
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Replace the placeholders with your actual Azure OpenAI credentials and configuration settings.
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## Connecting to Azure OpenAI:
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Now that we've set up our environment, let's dive into the code that connects to Azure OpenAI and interacts with the language model.
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```python
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import os
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from dotenv import load_dotenv
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from swarms import AzureOpenAI
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# Load the environment variables
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load_dotenv()
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# Create an instance of the AzureOpenAI class
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model = AzureOpenAI(
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azure_endpoint=os.getenv("AZURE_OPENAI_ENDPOINT"),
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deployment_name=os.getenv("AZURE_OPENAI_DEPLOYMENT"),
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openai_api_version=os.getenv("OPENAI_API_VERSION"),
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openai_api_key=os.getenv("AZURE_OPENAI_API_KEY"),
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azure_ad_token=os.getenv("AZURE_OPENAI_AD_TOKEN")
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)
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```
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## Let's break down this code:
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1. **Import Statements**: We import the necessary modules, including `os` for interacting with the operating system, `load_dotenv` from `python-dotenv` to load environment variables, and `AzureOpenAI` from `swarms` to interact with the Azure OpenAI service.
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2. **Load Environment Variables**: We use `load_dotenv()` to load the environment variables stored in the `.env` file we created earlier.
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3. **Create AzureOpenAI Instance**: We create an instance of the `AzureOpenAI` class by passing in the required configuration parameters:
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- `azure_endpoint`: The endpoint URL for your Azure OpenAI resource.
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- `deployment_name`: The name of the deployment you want to use.
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- `openai_api_version`: The version of the OpenAI API you want to use.
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- `openai_api_key`: Your Azure OpenAI API key, which authenticates your requests.
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- `azure_ad_token`: An optional Azure Active Directory (AAD) token for additional security.
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Querying the Language Model:
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With our connection to Azure OpenAI established, we can now query the language model and receive responses.
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```python
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# Define the prompt
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prompt = "Analyze this load document and assess it for any risks and create a table in markdwon format."
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# Generate a response
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response = model(prompt)
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print(response)
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```
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## Here's what's happening:
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1. **Define the Prompt**: We define a prompt, which is the input text or question we want to feed into the language model.
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2. **Generate a Response**: We call the `model` instance with the `prompt` as an argument. This triggers the Azure OpenAI service to process the prompt and generate a response.
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3. **Print the Response**: Finally, we print the response received from the language model.
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Running the Code:
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To run the code, save it in a Python file (e.g., `main.py`) and execute it from the command line:
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```
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python main.py
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```
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## Best Practices for Production Deployment:
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While the provided code serves as a basic example, there are several best practices to consider when deploying Azure OpenAI in a production environment:
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1. **Secure Credentials Management**: Instead of storing sensitive credentials like API keys in your codebase, consider using secure storage solutions like Azure Key Vault or environment variables managed by your cloud provider.
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2. **Error Handling and Retries**: Implement robust error handling and retry mechanisms to handle potential failures or rate-limiting scenarios.
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3. **Logging and Monitoring**: Implement comprehensive logging and monitoring strategies to track application performance, identify issues, and gather insights for optimization.
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4. **Scalability and Load Testing**: Conduct load testing to ensure your application can handle anticipated traffic volumes and scale appropriately based on demand.
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5. **Caching and Optimization**: Explore caching strategies and performance optimizations to improve response times and reduce the load on the Azure OpenAI service.
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6. **Integration with Other Services**: Depending on your use case, you may need to integrate Azure OpenAI with other Azure services or third-party tools for tasks like data processing, storage, or analysis.
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7. **Compliance and Security**: Ensure your application adheres to relevant compliance standards and security best practices, especially when handling sensitive data.
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## Conclusion:
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Azure OpenAI is a powerful platform that enables developers to integrate advanced natural language processing capabilities into their applications. By following the steps outlined in this guide, you can set up a production-ready environment for deploying Azure OpenAI and start leveraging its capabilities in your projects.
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Remember, this guide serves as a starting point, and there are numerous additional features and capabilities within Azure OpenAI that you can explore to enhance your applications further. As with any production deployment, it's crucial to follow best practices, conduct thorough testing, and implement robust monitoring and security measures.
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With the right approach and careful planning, you can successfully deploy Azure OpenAI in a production environment and unlock the power of cutting-edge language models to drive innovation and provide exceptional experiences for your users.
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@ -1,976 +0,0 @@
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## Building Analyst Agents with Swarms to write Business Reports
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> Jupyter Notebook accompanying this post is accessible at: [Business Analyst Agent Notebook](https://github.com/kyegomez/swarms/blob/master/examples/demos/business_analysis_swarm/business-analyst-agent.ipynb)
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Solving a business problem often involves preparing a Business Case Report. This report comprehensively analyzes the problem, evaluates potential solutions, and provides evidence-based recommendations and an implementation plan to effectively address the issue and drive business value. While the process of preparing one requires an experienced business analyst, the workflow can be augmented using AI agents. Two candidates stick out as areas to work on:
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- Developing an outline to solve the problem
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- Doing background research and gathering data
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In this post, we will explore how Swarms agents can be used to tackle a busuiness problem by outlining the solution, conducting background research and generating a preliminary report.
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Before we proceed, this blog uses 3 API tools. Please obtain the following keys and store them in a `.env` file in the same folder as this file.
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- **[OpenAI API](https://openai.com/blog/openai-api)** as `OPENAI_API_KEY`
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- **[TavilyAI API](https://app.tavily.com/home)** `TAVILY_API_KEY`
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- **[KayAI API](https://www.kay.ai/)** as `KAY_API_KEY`
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```python
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import dotenv
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dotenv.load_dotenv() # Load environment variables from .env file
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```
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### Developing an Outline to solve the problem
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Assume the business problem is: **How do we improve Nike's revenue in Q3 2024?** We first create a planning agent to break down the problem into dependent sub-problems.
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#### Step 1. Defining the Data Model and Tool Schema
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Using Pydantic, we define a structure to help the agent generate sub-problems.
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- **QueryType:** Questions are either standalone or involve a combination of multiple others
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- **Query:** Defines structure of a question.
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- **QueryPlan:** Allows generation of a dependency graph of sub-questions
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```python
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import enum
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from typing import List
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from pydantic import Field, BaseModel
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class QueryType(str, enum.Enum):
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"""Enumeration representing the types of queries that can be asked to a question answer system."""
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SINGLE_QUESTION = "SINGLE"
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MERGE_MULTIPLE_RESPONSES = "MERGE_MULTIPLE_RESPONSES"
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class Query(BaseModel):
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"""Class representing a single question in a query plan."""
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id: int = Field(..., description="Unique id of the query")
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question: str = Field(
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...,
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description="Question asked using a question answering system",
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)
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dependencies: List[int] = Field(
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default_factory=list,
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description="List of sub questions that need to be answered before asking this question",
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)
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node_type: QueryType = Field(
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default=QueryType.SINGLE_QUESTION,
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description="Type of question, either a single question or a multi-question merge",
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)
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class QueryPlan(BaseModel):
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"""Container class representing a tree of questions to ask a question answering system."""
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query_graph: List[Query] = Field(
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..., description="The query graph representing the plan"
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)
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def _dependencies(self, ids: List[int]) -> List[Query]:
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"""Returns the dependencies of a query given their ids."""
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return [q for q in self.query_graph if q.id in ids]
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```
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Also, a `tool_schema` needs to be defined. It is an instance of `QueryPlan` and is used to initialize the agent.
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```python
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tool_schema = QueryPlan(
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query_graph = [query.dict() for query in [
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Query(
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id=1,
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question="How do we improve Nike's revenue in Q3 2024?",
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dependencies=[2],
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node_type=QueryType('SINGLE')
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),
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# ... other queries ...
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]]
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)
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```
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#### Step 2. Defining the Planning Agent
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We specify the query, task specification and an appropriate system prompt.
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```python
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from swarm_models import OpenAIChat
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from swarms import Agent
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query = "How do we improve Nike's revenue in Q3 2024?"
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task = f"Consider: {query}. Generate just the correct query plan in JSON format."
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system_prompt = (
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"You are a world class query planning algorithm "
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"capable of breaking apart questions into its "
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"dependency queries such that the answers can be "
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"used to inform the parent question. Do not answer "
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"the questions, simply provide a correct compute "
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"graph with good specific questions to ask and relevant "
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"dependencies. Before you call the function, think "
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"step-by-step to get a better understanding of the problem."
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)
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llm = OpenAIChat(
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temperature=0.0, model_name="gpt-4", max_tokens=4000
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)
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```
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Then, we proceed with agent definition.
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```python
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# Initialize the agent
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agent = Agent(
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agent_name="Query Planner",
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system_prompt=system_prompt,
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# Set the tool schema to the JSON string -- this is the key difference
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tool_schema=tool_schema,
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llm=llm,
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max_loops=1,
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autosave=True,
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dashboard=False,
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streaming_on=True,
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verbose=True,
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interactive=False,
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# Set the output type to the tool schema which is a BaseModel
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output_type=tool_schema, # or dict, or str
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metadata_output_type="json",
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# List of schemas that the agent can handle
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list_base_models=[tool_schema],
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function_calling_format_type="OpenAI",
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function_calling_type="json", # or soon yaml
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)
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```
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#### Step 3. Obtaining Outline from Planning Agent
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We now run the agent, and since its output is in JSON format, we can load it as a dictionary.
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```python
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generated_data = agent.run(task)
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```
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At times the agent could return extra content other than JSON. Below function will filter it out.
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```python
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def process_json_output(content):
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# Find the index of the first occurrence of '```json\n'
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start_index = content.find('```json\n')
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if start_index == -1:
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# If '```json\n' is not found, return the original content
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return content
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# Return the part of the content after '```json\n' and remove the '```' at the end
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return content[start_index + len('```json\n'):].rstrip('`')
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# Use the function to clean up the output
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json_content = process_json_output(generated_data.content)
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import json
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# Load the JSON string into a Python object
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json_object = json.loads(json_content)
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# Convert the Python object back to a JSON string
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json_content = json.dumps(json_object, indent=2)
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# Print the JSON string
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print(json_content)
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```
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Below is the output this produces
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```json
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{
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"main_query": "How do we improve Nike's revenue in Q3 2024?",
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"sub_queries": [
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{
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"id": "1",
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"query": "What is Nike's current revenue trend?"
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},
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{
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"id": "2",
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"query": "What are the projected market trends for the sports apparel industry in 2024?"
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},
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{
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"id": "3",
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"query": "What are the current successful strategies being used by Nike's competitors?",
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"dependencies": [
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"2"
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]
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},
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{
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"id": "4",
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"query": "What are the current and projected economic conditions in Nike's major markets?",
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"dependencies": [
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"2"
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]
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},
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{
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"id": "5",
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"query": "What are the current consumer preferences in the sports apparel industry?",
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"dependencies": [
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"2"
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]
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},
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{
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"id": "6",
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"query": "What are the potential areas of improvement in Nike's current business model?",
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"dependencies": [
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"1"
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]
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},
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{
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"id": "7",
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"query": "What are the potential new markets for Nike to explore in 2024?",
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"dependencies": [
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"2",
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"4"
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]
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},
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{
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"id": "8",
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"query": "What are the potential new products or services Nike could introduce in 2024?",
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"dependencies": [
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"5"
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]
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},
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{
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"id": "9",
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"query": "What are the potential marketing strategies Nike could use to increase its revenue in Q3 2024?",
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"dependencies": [
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"3",
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"5",
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"7",
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"8"
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]
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},
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{
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"id": "10",
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"query": "What are the potential cost-saving strategies Nike could implement to increase its net revenue in Q3 2024?",
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"dependencies": [
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"6"
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]
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}
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]
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}
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```
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The JSON dictionary is not convenient for humans to process. We make a directed graph out of it.
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```python
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import networkx as nx
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import matplotlib.pyplot as plt
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import textwrap
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import random
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# Create a directed graph
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G = nx.DiGraph()
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# Define a color map
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color_map = {}
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# Add nodes and edges to the graph
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for sub_query in json_object['sub_queries']:
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# Check if 'dependencies' key exists in sub_query, if not, initialize it as an empty list
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if 'dependencies' not in sub_query:
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sub_query['dependencies'] = []
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# Assign a random color for each node
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color_map[sub_query['id']] = "#{:06x}".format(random.randint(0, 0xFFFFFF))
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G.add_node(sub_query['id'], label=textwrap.fill(sub_query['query'], width=20))
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for dependency in sub_query['dependencies']:
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G.add_edge(dependency, sub_query['id'])
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# Draw the graph
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pos = nx.spring_layout(G)
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nx.draw(G, pos, with_labels=True, node_size=800, node_color=[color_map[node] for node in G.nodes()], node_shape="o", alpha=0.5, linewidths=40)
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# Prepare labels for legend
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labels = nx.get_node_attributes(G, 'label')
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handles = [plt.Line2D([0], [0], marker='o', color=color_map[node], label=f"{node}: {label}", markersize=10, linestyle='None') for node, label in labels.items()]
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# Create a legend
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plt.legend(handles=handles, title="Queries", bbox_to_anchor=(1.05, 1), loc='upper left')
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plt.show()
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```
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This produces the below diagram which makes the plan much more convenient to understand.
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### Doing Background Research and Gathering Data
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At this point, we have solved the first half of the problem. We have an outline consisting of sub-problems to to tackled to solve our business problem. This will form the overall structure of our report. We now need to research information for each sub-problem in order to write an informed report. This mechanically intensive and is the aspect that will most benefit from Agentic intervention.
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|
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Essentially, we can spawn parallel agents to gather the data. Each agent will have 2 tools:
|
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|
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- Internet access
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- Financial data retrieval
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As they run parallelly, they will add their knowledge into a common long-term memory. We will then spawn a separate report writing agent with access to this memory to generate our business case report.
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#### Step 4. Defining Tools for Worker Agents
|
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Let us first define the 2 tools.
|
||||
|
||||
```python
|
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import os
|
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from typing import List, Dict
|
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|
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from swarms import tool
|
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|
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os.environ['TAVILY_API_KEY'] = os.getenv('TAVILY_API_KEY')
|
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os.environ["KAY_API_KEY"] = os.getenv('KAY_API_KEY')
|
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|
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from langchain_community.tools.tavily_search import TavilySearchResults
|
||||
from langchain_core.pydantic_v1 import BaseModel, Field
|
||||
|
||||
from kay.rag.retrievers import KayRetriever
|
||||
|
||||
def browser(query: str) -> str:
|
||||
"""
|
||||
Search the query in the browser with the Tavily API tool.
|
||||
Args:
|
||||
query (str): The query to search in the browser.
|
||||
Returns:
|
||||
str: The search results
|
||||
"""
|
||||
internet_search = TavilySearchResults()
|
||||
results = internet_search.invoke({"query": query})
|
||||
response = ''
|
||||
for result in results:
|
||||
response += (result['content'] + '\n')
|
||||
return response
|
||||
|
||||
def kay_retriever(query: str) -> str:
|
||||
"""
|
||||
Search the financial data query with the KayAI API tool.
|
||||
Args:
|
||||
query (str): The query to search in the KayRetriever.
|
||||
Returns:
|
||||
str: The first context retrieved as a string.
|
||||
"""
|
||||
# Initialize the retriever
|
||||
retriever = KayRetriever(dataset_id = "company", data_types=["10-K", "10-Q", "8-K", "PressRelease"])
|
||||
# Query the retriever
|
||||
context = retriever.query(query=query,num_context=1)
|
||||
return context[0]['chunk_embed_text']
|
||||
```
|
||||
|
||||
#### Step 5. Defining Long-Term Memory
|
||||
|
||||
As mentioned previously, the worker agents running parallelly, will pool their knowledge into a common memory. Let us define that.
|
||||
|
||||
```python
|
||||
import logging
|
||||
import os
|
||||
import uuid
|
||||
from typing import Callable, List, Optional
|
||||
|
||||
import chromadb
|
||||
import numpy as np
|
||||
from dotenv import load_dotenv
|
||||
|
||||
from swarms.utils.data_to_text import data_to_text
|
||||
from swarms.utils.markdown_message import display_markdown_message
|
||||
from swarms_memory import AbstractVectorDatabase
|
||||
|
||||
|
||||
# Results storage using local ChromaDB
|
||||
class ChromaDB(AbstractVectorDatabase):
|
||||
"""
|
||||
|
||||
ChromaDB database
|
||||
|
||||
Args:
|
||||
metric (str): The similarity metric to use.
|
||||
output (str): The name of the collection to store the results in.
|
||||
limit_tokens (int, optional): The maximum number of tokens to use for the query. Defaults to 1000.
|
||||
n_results (int, optional): The number of results to retrieve. Defaults to 2.
|
||||
|
||||
Methods:
|
||||
add: _description_
|
||||
query: _description_
|
||||
|
||||
Examples:
|
||||
>>> chromadb = ChromaDB(
|
||||
>>> metric="cosine",
|
||||
>>> output="results",
|
||||
>>> llm="gpt3",
|
||||
>>> openai_api_key=OPENAI_API_KEY,
|
||||
>>> )
|
||||
>>> chromadb.add(task, result, result_id)
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
metric: str = "cosine",
|
||||
output_dir: str = "swarms",
|
||||
limit_tokens: Optional[int] = 1000,
|
||||
n_results: int = 3,
|
||||
embedding_function: Callable = None,
|
||||
docs_folder: str = None,
|
||||
verbose: bool = False,
|
||||
*args,
|
||||
**kwargs,
|
||||
):
|
||||
self.metric = metric
|
||||
self.output_dir = output_dir
|
||||
self.limit_tokens = limit_tokens
|
||||
self.n_results = n_results
|
||||
self.docs_folder = docs_folder
|
||||
self.verbose = verbose
|
||||
|
||||
# Disable ChromaDB logging
|
||||
if verbose:
|
||||
logging.getLogger("chromadb").setLevel(logging.INFO)
|
||||
|
||||
# Create Chroma collection
|
||||
chroma_persist_dir = "chroma"
|
||||
chroma_client = chromadb.PersistentClient(
|
||||
settings=chromadb.config.Settings(
|
||||
persist_directory=chroma_persist_dir,
|
||||
),
|
||||
*args,
|
||||
**kwargs,
|
||||
)
|
||||
|
||||
# Embedding model
|
||||
if embedding_function:
|
||||
self.embedding_function = embedding_function
|
||||
else:
|
||||
self.embedding_function = None
|
||||
|
||||
# Create ChromaDB client
|
||||
self.client = chromadb.Client()
|
||||
|
||||
# Create Chroma collection
|
||||
self.collection = chroma_client.get_or_create_collection(
|
||||
name=output_dir,
|
||||
metadata={"hnsw:space": metric},
|
||||
embedding_function=self.embedding_function,
|
||||
# data_loader=self.data_loader,
|
||||
*args,
|
||||
**kwargs,
|
||||
)
|
||||
display_markdown_message(
|
||||
"ChromaDB collection created:"
|
||||
f" {self.collection.name} with metric: {self.metric} and"
|
||||
f" output directory: {self.output_dir}"
|
||||
)
|
||||
|
||||
# If docs
|
||||
if docs_folder:
|
||||
display_markdown_message(
|
||||
f"Traversing directory: {docs_folder}"
|
||||
)
|
||||
self.traverse_directory()
|
||||
|
||||
def add(
|
||||
self,
|
||||
document: str,
|
||||
*args,
|
||||
**kwargs,
|
||||
):
|
||||
"""
|
||||
Add a document to the ChromaDB collection.
|
||||
|
||||
Args:
|
||||
document (str): The document to be added.
|
||||
condition (bool, optional): The condition to check before adding the document. Defaults to True.
|
||||
|
||||
Returns:
|
||||
str: The ID of the added document.
|
||||
"""
|
||||
try:
|
||||
doc_id = str(uuid.uuid4())
|
||||
self.collection.add(
|
||||
ids=[doc_id],
|
||||
documents=[document],
|
||||
*args,
|
||||
**kwargs,
|
||||
)
|
||||
print('-----------------')
|
||||
print("Document added successfully")
|
||||
print('-----------------')
|
||||
return doc_id
|
||||
except Exception as e:
|
||||
raise Exception(f"Failed to add document: {str(e)}")
|
||||
|
||||
def query(
|
||||
self,
|
||||
query_text: str,
|
||||
*args,
|
||||
**kwargs,
|
||||
):
|
||||
"""
|
||||
Query documents from the ChromaDB collection.
|
||||
|
||||
Args:
|
||||
query (str): The query string.
|
||||
n_docs (int, optional): The number of documents to retrieve. Defaults to 1.
|
||||
|
||||
Returns:
|
||||
dict: The retrieved documents.
|
||||
"""
|
||||
try:
|
||||
docs = self.collection.query(
|
||||
query_texts=[query_text],
|
||||
n_results=self.n_results,
|
||||
*args,
|
||||
**kwargs,
|
||||
)["documents"]
|
||||
return docs[0]
|
||||
except Exception as e:
|
||||
raise Exception(f"Failed to query documents: {str(e)}")
|
||||
|
||||
def traverse_directory(self):
|
||||
"""
|
||||
Traverse through every file in the given directory and its subdirectories,
|
||||
and return the paths of all files.
|
||||
Parameters:
|
||||
- directory_name (str): The name of the directory to traverse.
|
||||
Returns:
|
||||
- list: A list of paths to each file in the directory and its subdirectories.
|
||||
"""
|
||||
added_to_db = False
|
||||
|
||||
for root, dirs, files in os.walk(self.docs_folder):
|
||||
for file in files:
|
||||
file = os.path.join(self.docs_folder, file)
|
||||
_, ext = os.path.splitext(file)
|
||||
data = data_to_text(file)
|
||||
added_to_db = self.add([data])
|
||||
print(f"{file} added to Database")
|
||||
|
||||
return added_to_db
|
||||
```
|
||||
|
||||
We can now proceed to initialize the memory.
|
||||
|
||||
```python
|
||||
from chromadb.utils import embedding_functions
|
||||
default_ef = embedding_functions.DefaultEmbeddingFunction()
|
||||
|
||||
memory = ChromaDB(
|
||||
metric="cosine",
|
||||
n_results=3,
|
||||
output_dir="results",
|
||||
embedding_function=default_ef
|
||||
)
|
||||
```
|
||||
|
||||
#### Step 6. Defining Worker Agents
|
||||
|
||||
The Worker Agent sub-classes the `Agent` class. The only different between these 2 is in how the `run()` method works. In the `Agent` class, `run()` simply returns the set of tool commands to run, but does not execute it. We, however, desire this. In addition, after we run our tools, we get the relevant information as output. We want to add this information to our memory. Hence, to incorporate these 2 changes, we define `WorkerAgent` as follows.
|
||||
|
||||
```python
|
||||
class WorkerAgent(Agent):
|
||||
def __init__(self, *args, **kwargs):
|
||||
super().__init__(*args, **kwargs)
|
||||
|
||||
def run(self, task, *args, **kwargs):
|
||||
response = super().run(task, *args, **kwargs)
|
||||
print(response.content)
|
||||
|
||||
json_dict = json.loads(process_json_output(response.content))
|
||||
|
||||
#print(json.dumps(json_dict, indent=2))
|
||||
|
||||
if response!=None:
|
||||
try:
|
||||
commands = json_dict["commands"]
|
||||
except:
|
||||
commands = [json_dict['command']]
|
||||
|
||||
for command in commands:
|
||||
tool_name = command["name"]
|
||||
|
||||
if tool_name not in ['browser', 'kay_retriever']:
|
||||
continue
|
||||
|
||||
query = command["args"]["query"]
|
||||
|
||||
# Get the tool by its name
|
||||
tool = globals()[tool_name]
|
||||
tool_response = tool(query)
|
||||
|
||||
# Add tool's output to long term memory
|
||||
self.long_term_memory.add(tool_response)
|
||||
```
|
||||
|
||||
We can then instantiate an object of the `WorkerAgent` class.
|
||||
|
||||
```python
|
||||
worker_agent = WorkerAgent(
|
||||
agent_name="Worker Agent",
|
||||
system_prompt=(
|
||||
"Autonomous agent that can interact with browser, "
|
||||
"financial data retriever and other agents. Be Helpful "
|
||||
"and Kind. Use the tools provided to assist the user. "
|
||||
"Generate the plan with list of commands in JSON format."
|
||||
),
|
||||
llm=OpenAIChat(
|
||||
temperature=0.0, model_name="gpt-4", max_tokens=4000
|
||||
),
|
||||
max_loops="auto",
|
||||
autosave=True,
|
||||
dashboard=False,
|
||||
streaming_on=True,
|
||||
verbose=True,
|
||||
stopping_token="<DONE>",
|
||||
interactive=True,
|
||||
tools=[browser, kay_retriever],
|
||||
long_term_memory=memory,
|
||||
code_interpreter=True,
|
||||
)
|
||||
```
|
||||
|
||||
#### Step 7. Running the Worker Agents
|
||||
|
||||
At this point, we need to setup a concurrent workflow. While the order of adding tasks to the workflow doesn't matter (since they will all run concurrently late when executed), we can take some time to define an order for these tasks. This order will come in handy later when writing the report using our Writer Agent.
|
||||
|
||||
The order we will follow is Breadth First Traversal (BFT) of the sub-queries in the graph we had made earlier (shown below again for reference). BFT makes sense to be used here because we want all the dependent parent questions to be answered before answering the child question. Also, since we could have independent subgraphs, we will also perform BFT separately on each subgraph.
|
||||
|
||||
|
||||
|
||||
Below is the code that produces the order of processing sub-queries.
|
||||
|
||||
```python
|
||||
from collections import deque, defaultdict
|
||||
|
||||
# Define the graph nodes
|
||||
nodes = json_object['sub_queries']
|
||||
|
||||
# Create a graph from the nodes
|
||||
graph = defaultdict(list)
|
||||
for node in nodes:
|
||||
for dependency in node['dependencies']:
|
||||
graph[dependency].append(node['id'])
|
||||
|
||||
# Find all nodes with no dependencies (potential starting points)
|
||||
start_nodes = [node['id'] for node in nodes if not node['dependencies']]
|
||||
|
||||
# Adjust the BFT function to handle dependencies correctly
|
||||
def bft_corrected(start, graph, nodes_info):
|
||||
visited = set()
|
||||
queue = deque([start])
|
||||
order = []
|
||||
|
||||
while queue:
|
||||
node = queue.popleft()
|
||||
if node not in visited:
|
||||
# Check if all dependencies of the current node are visited
|
||||
node_dependencies = [n['id'] for n in nodes if n['id'] == node][0]
|
||||
dependencies_met = all(dep in visited for dep in nodes_info[node_dependencies]['dependencies'])
|
||||
|
||||
if dependencies_met:
|
||||
visited.add(node)
|
||||
order.append(node)
|
||||
# Add only nodes to the queue whose dependencies are fully met
|
||||
for next_node in graph[node]:
|
||||
if all(dep in visited for dep in nodes_info[next_node]['dependencies']):
|
||||
queue.append(next_node)
|
||||
else:
|
||||
# Requeue the node to check dependencies later
|
||||
queue.append(node)
|
||||
|
||||
return order
|
||||
|
||||
# Dictionary to access node information quickly
|
||||
nodes_info = {node['id']: node for node in nodes}
|
||||
|
||||
# Perform BFT for each unvisited start node using the corrected BFS function
|
||||
visited_global = set()
|
||||
bfs_order = []
|
||||
|
||||
for start in start_nodes:
|
||||
if start not in visited_global:
|
||||
order = bft_corrected(start, graph, nodes_info)
|
||||
bfs_order.extend(order)
|
||||
visited_global.update(order)
|
||||
|
||||
print("BFT Order:", bfs_order)
|
||||
```
|
||||
|
||||
This produces the following output.
|
||||
|
||||
```python
|
||||
BFT Order: ['1', '6', '10', '2', '3', '4', '5', '7', '8', '9']
|
||||
```
|
||||
|
||||
Now, let's define our `ConcurrentWorkflow` and run it.
|
||||
|
||||
```python
|
||||
import os
|
||||
from dotenv import load_dotenv
|
||||
from swarms import Agent, ConcurrentWorkflow, OpenAIChat, Task
|
||||
|
||||
# Create a workflow
|
||||
workflow = ConcurrentWorkflow(max_workers=5)
|
||||
task_list = []
|
||||
|
||||
for node in bfs_order:
|
||||
sub_query =nodes_info[node]['query']
|
||||
task = Task(worker_agent, sub_query)
|
||||
print('-----------------')
|
||||
print("Added task: ", sub_query)
|
||||
print('-----------------')
|
||||
task_list.append(task)
|
||||
|
||||
workflow.add(tasks=task_list)
|
||||
|
||||
# Run the workflow
|
||||
workflow.run()
|
||||
```
|
||||
|
||||
Below is part of the output this workflow produces. We clearly see the thought process of the agent and the plan it came up to solve a particular sub-query. In addition, we see the tool-calling schema it produces in `"command"`.
|
||||
|
||||
```python
|
||||
...
|
||||
...
|
||||
content='\n{\n "thoughts": {\n "text": "To find out Nike\'s current revenue trend, I will use the financial data retriever tool to search for \'Nike revenue trend\'.",\n "reasoning": "The financial data retriever tool allows me to search for specific financial data, so I can look up the current revenue trend of Nike.", \n "plan": "Use the financial data retriever tool to search for \'Nike revenue trend\'. Parse the result to get the current revenue trend and format that into a readable report."\n },\n "command": {\n "name": "kay_retriever", \n "args": {\n "query": "Nike revenue trend"\n }\n }\n}\n```' response_metadata={'token_usage': {'completion_tokens': 152, 'prompt_tokens': 1527, 'total_tokens': 1679}, 'model_name': 'gpt-4', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}
|
||||
Saved agent state to: Worker Agent_state.json
|
||||
|
||||
{
|
||||
"thoughts": {
|
||||
"text": "To find out Nike's current revenue trend, I will use the financial data retriever tool to search for 'Nike revenue trend'.",
|
||||
"reasoning": "The financial data retriever tool allows me to search for specific financial data, so I can look up the current revenue trend of Nike.",
|
||||
"plan": "Use the financial data retriever tool to search for 'Nike revenue trend'. Parse the result to get the current revenue trend and format that into a readable report."
|
||||
},
|
||||
"command": {
|
||||
"name": "kay_retriever",
|
||||
"args": {
|
||||
"query": "Nike revenue trend"
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
-----------------
|
||||
Document added successfully
|
||||
-----------------
|
||||
...
|
||||
...
|
||||
```
|
||||
|
||||
Here, `"name"` pertains to the name of the tool to be called and `"args"` is the arguments to be passed to the tool call. Like mentioned before, we modify `Agent`'s default behaviour in `WorkerAgent`. Hence, the tool call is executed here and its results (information from web pages and Kay Retriever API) are added to long-term memory. We get confirmation for this from the message `Document added successfully`.
|
||||
|
||||
|
||||
#### Step 7. Generating the report using Writer Agent
|
||||
|
||||
At this point, our Worker Agents have gathered all the background information required to generate the report. We have also defined a coherent structure to write the report, which is following the BFT order to answering the sub-queries. Now it's time to define a Writer Agent and call it sequentially in the order of sub-queries.
|
||||
|
||||
```python
|
||||
from swarms import Agent, OpenAIChat, tool
|
||||
|
||||
agent = Agent(
|
||||
agent_name="Writer Agent",
|
||||
agent_description=(
|
||||
"This agent writes reports based on information in long-term memory"
|
||||
),
|
||||
system_prompt=(
|
||||
"You are a world-class financial report writer. "
|
||||
"Write analytical and accurate responses using memory to answer the query. "
|
||||
"Do not mention use of long-term memory in the report. "
|
||||
"Do not mention Writer Agent in response."
|
||||
"Return only response content in strict markdown format."
|
||||
),
|
||||
llm=OpenAIChat(temperature=0.2, model='gpt-3.5-turbo'),
|
||||
max_loops=1,
|
||||
autosave=True,
|
||||
verbose=True,
|
||||
long_term_memory=memory,
|
||||
)
|
||||
```
|
||||
|
||||
The report individual sections of the report will be collected in a list.
|
||||
|
||||
```python
|
||||
report = []
|
||||
```
|
||||
|
||||
Let us now run the writer agent.
|
||||
|
||||
```python
|
||||
for node in bfs_order:
|
||||
sub_query =nodes_info[node]['query']
|
||||
print("Running task: ", sub_query)
|
||||
out = agent.run(f"Consider: {sub_query}. Write response in strict markdown format using long-term memory. Do not mention Writer Agent in response.")
|
||||
print(out)
|
||||
try:
|
||||
report.append(out.content)
|
||||
except:
|
||||
pass
|
||||
```
|
||||
|
||||
Now, we need to clean up the repoort a bit to make it render professionally.
|
||||
|
||||
```python
|
||||
# Remove any content before the first "#" as that signals start of heading
|
||||
# Anything before this usually contains filler content
|
||||
stripped_report = [entry[entry.find('#'):] if '#' in entry else entry for entry in report]
|
||||
report = stripped_report
|
||||
|
||||
# At times the LLM outputs \\n instead of \n
|
||||
cleaned_report = [entry.replace("\\n", "\n") for entry in report]
|
||||
import re
|
||||
|
||||
# Function to clean up unnecessary metadata from the report entries
|
||||
def clean_report(report):
|
||||
cleaned_report = []
|
||||
for entry in report:
|
||||
# This pattern matches 'response_metadata={' followed by any characters that are not '}' (non-greedy),
|
||||
# possibly nested inside other braces, until the closing '}'.
|
||||
cleaned_entry = re.sub(r"response_metadata=\{[^{}]*(?:\{[^{}]*\}[^{}]*)*\}", "", entry, flags=re.DOTALL)
|
||||
cleaned_report.append(cleaned_entry)
|
||||
return cleaned_report
|
||||
|
||||
# Apply the cleaning function to the markdown report
|
||||
cleaned_report = clean_report(cleaned_report)
|
||||
```
|
||||
|
||||
After cleaning, we append parts of the report together to get out final report.
|
||||
|
||||
```python
|
||||
final_report = ' \n '.join(cleaned_report)
|
||||
```
|
||||
|
||||
In Jupyter Notebook, we can use the below code to render it in Markdown.
|
||||
|
||||
```python
|
||||
from IPython.display import display, Markdown
|
||||
|
||||
display(Markdown(final_report))
|
||||
```
|
||||
|
||||
|
||||
## Final Generated Report
|
||||
|
||||
|
||||
### Nike's Current Revenue Trend
|
||||
|
||||
Nike's current revenue trend has been steadily increasing over the past few years. In the most recent fiscal year, Nike reported a revenue of $37.4 billion, which was a 7% increase from the previous year. This growth can be attributed to strong sales in key markets, successful marketing campaigns, and a focus on innovation in product development. Overall, Nike continues to demonstrate strong financial performance and is well-positioned for future growth.
|
||||
### Potential Areas of Improvement in Nike's Business Model
|
||||
|
||||
1. **Sustainability Practices**: Nike could further enhance its sustainability efforts by reducing its carbon footprint, using more eco-friendly materials, and ensuring ethical labor practices throughout its supply chain.
|
||||
|
||||
2. **Diversification of Product Portfolio**: While Nike is known for its athletic footwear and apparel, diversifying into new product categories or expanding into untapped markets could help drive growth and mitigate risks associated with a single product line.
|
||||
|
||||
3. **E-commerce Strategy**: Improving the online shopping experience, investing in digital marketing, and leveraging data analytics to personalize customer interactions could boost online sales and customer loyalty.
|
||||
|
||||
4. **Innovation and R&D**: Continuously investing in research and development to stay ahead of competitors, introduce new technologies, and enhance product performance could help maintain Nike's competitive edge in the market.
|
||||
|
||||
5. **Brand Image and Reputation**: Strengthening brand image through effective marketing campaigns, community engagement, and transparent communication with stakeholders can help build trust and loyalty among consumers.
|
||||
### Potential Cost-Saving Strategies for Nike to Increase Net Revenue in Q3 2024
|
||||
|
||||
1. **Supply Chain Optimization**: Streamlining the supply chain, reducing transportation costs, and improving inventory management can lead to significant cost savings for Nike.
|
||||
|
||||
2. **Operational Efficiency**: Implementing lean manufacturing practices, reducing waste, and optimizing production processes can help lower production costs and improve overall efficiency.
|
||||
|
||||
3. **Outsourcing Non-Core Functions**: Outsourcing non-core functions such as IT services, customer support, or logistics can help reduce overhead costs and focus resources on core business activities.
|
||||
|
||||
4. **Energy Efficiency**: Investing in energy-efficient technologies, renewable energy sources, and sustainable practices can lower utility costs and demonstrate a commitment to environmental responsibility.
|
||||
|
||||
5. **Negotiating Supplier Contracts**: Negotiating better terms with suppliers, leveraging economies of scale, and exploring alternative sourcing options can help lower procurement costs and improve margins.
|
||||
|
||||
By implementing these cost-saving strategies, Nike can improve its bottom line and increase net revenue in Q3 2024.
|
||||
### Projected Market Trends for the Sports Apparel Industry in 2024
|
||||
|
||||
1. **Sustainable Fashion**: Consumers are increasingly demanding eco-friendly and sustainable products, leading to a rise in sustainable sportswear options in the market.
|
||||
|
||||
2. **Digital Transformation**: The sports apparel industry is expected to continue its shift towards digital platforms, with a focus on e-commerce, personalized shopping experiences, and digital marketing strategies.
|
||||
|
||||
3. **Athleisure Wear**: The trend of athleisure wear, which combines athletic and leisure clothing, is projected to remain popular in 2024 as consumers seek comfort and versatility in their apparel choices.
|
||||
|
||||
4. **Innovative Materials**: Advances in technology and material science are likely to drive the development of innovative fabrics and performance-enhancing materials in sports apparel, catering to the demand for high-quality and functional products.
|
||||
|
||||
5. **Health and Wellness Focus**: With a growing emphasis on health and wellness, sports apparel brands are expected to incorporate features that promote comfort, performance, and overall well-being in their products.
|
||||
|
||||
Overall, the sports apparel industry in 2024 is anticipated to be characterized by sustainability, digitalization, innovation, and a focus on consumer health and lifestyle trends.
|
||||
### Current Successful Strategies Used by Nike's Competitors
|
||||
|
||||
1. **Adidas**: Adidas has been successful in leveraging collaborations with celebrities and designers to create limited-edition collections that generate hype and drive sales. They have also focused on sustainability initiatives, such as using recycled materials in their products, to appeal to environmentally conscious consumers.
|
||||
|
||||
2. **Under Armour**: Under Armour has differentiated itself by targeting performance-driven athletes and emphasizing technological innovation in their products. They have also invested heavily in digital marketing and e-commerce to reach a wider audience and enhance the customer shopping experience.
|
||||
|
||||
3. **Puma**: Puma has successfully capitalized on the athleisure trend by offering stylish and versatile sportswear that can be worn both in and out of the gym. They have also focused on building partnerships with influencers and sponsoring high-profile athletes to increase brand visibility and credibility.
|
||||
|
||||
4. **Lululemon**: Lululemon has excelled in creating a strong community around its brand, hosting events, classes, and collaborations to engage with customers beyond just selling products. They have also prioritized customer experience by offering personalized services and creating a seamless omnichannel shopping experience.
|
||||
|
||||
5. **New Balance**: New Balance has carved out a niche in the market by emphasizing quality craftsmanship, heritage, and authenticity in their products. They have also focused on customization and personalization options for customers, allowing them to create unique and tailored footwear and apparel.
|
||||
|
||||
Overall, Nike's competitors have found success through a combination of innovative product offerings, strategic marketing initiatives, and a focus on customer engagement and experience.
|
||||
### Current and Projected Economic Conditions in Nike's Major Markets
|
||||
|
||||
1. **United States**: The United States, being one of Nike's largest markets, is currently experiencing moderate economic growth driven by consumer spending, low unemployment rates, and a rebound in manufacturing. However, uncertainties surrounding trade policies, inflation, and interest rates could impact consumer confidence and spending in the near future.
|
||||
|
||||
2. **China**: China remains a key market for Nike, with a growing middle class and increasing demand for sportswear and athletic footwear. Despite recent trade tensions with the U.S., China's economy is projected to continue expanding, driven by domestic consumption, infrastructure investments, and technological advancements.
|
||||
|
||||
3. **Europe**: Economic conditions in Europe vary across countries, with some experiencing sluggish growth due to Brexit uncertainties, political instability, and trade tensions. However, overall consumer confidence is improving, and the sports apparel market is expected to grow, driven by e-commerce and sustainability trends.
|
||||
|
||||
4. **Emerging Markets**: Nike's presence in emerging markets such as India, Brazil, and Southeast Asia provides opportunities for growth, given the rising disposable incomes, urbanization, and increasing focus on health and fitness. However, challenges such as currency fluctuations, regulatory changes, and competition from local brands could impact Nike's performance in these markets.
|
||||
|
||||
Overall, Nike's major markets exhibit a mix of opportunities and challenges, with economic conditions influenced by global trends, geopolitical factors, and consumer preferences."
|
||||
### Current Consumer Preferences in the Sports Apparel Industry
|
||||
|
||||
1. **Sustainability**: Consumers are increasingly seeking eco-friendly and sustainable options in sports apparel, driving brands to focus on using recycled materials, reducing waste, and promoting ethical practices.
|
||||
|
||||
2. **Athleisure**: The trend of athleisure wear continues to be popular, with consumers looking for versatile and comfortable clothing that can be worn both during workouts and in everyday life.
|
||||
|
||||
3. **Performance and Functionality**: Consumers prioritize performance-enhancing features in sports apparel, such as moisture-wicking fabrics, breathable materials, and ergonomic designs that enhance comfort and mobility.
|
||||
|
||||
4. **Personalization**: Customization options, personalized fit, and unique design elements are appealing to consumers who seek individuality and exclusivity in their sports apparel choices.
|
||||
|
||||
5. **Brand Transparency**: Consumers value transparency in brand practices, including supply chain transparency, ethical sourcing, and clear communication on product quality and manufacturing processes.
|
||||
|
||||
Overall, consumer preferences in the sports apparel industry are shifting towards sustainability, versatility, performance, personalization, and transparency, influencing brand strategies and product offerings.
|
||||
### Potential New Markets for Nike to Explore in 2024
|
||||
|
||||
1. **India**: With a growing population, increasing disposable incomes, and a rising interest in health and fitness, India presents a significant opportunity for Nike to expand its presence and tap into a large consumer base.
|
||||
|
||||
2. **Africa**: The African market, particularly countries with emerging economies and a young population, offers potential for Nike to introduce its products and capitalize on the growing demand for sportswear and athletic footwear.
|
||||
|
||||
3. **Middle East**: Countries in the Middle East, known for their luxury shopping destinations and a growing interest in sports and fitness activities, could be strategic markets for Nike to target and establish a strong foothold.
|
||||
|
||||
4. **Latin America**: Markets in Latin America, such as Brazil, Mexico, and Argentina, present opportunities for Nike to cater to a diverse consumer base and leverage the region's passion for sports and active lifestyles.
|
||||
|
||||
5. **Southeast Asia**: Rapid urbanization, increasing urban middle-class population, and a trend towards health and wellness in countries like Indonesia, Thailand, and Vietnam make Southeast Asia an attractive region for Nike to explore and expand its market reach.
|
||||
|
||||
By exploring these new markets in 2024, Nike can diversify its geographical presence, reach untapped consumer segments, and drive growth in emerging economies.
|
||||
### Potential New Products or Services Nike Could Introduce in 2024
|
||||
|
||||
1. **Smart Apparel**: Nike could explore the integration of technology into its apparel, such as smart fabrics that monitor performance metrics, provide feedback, or enhance comfort during workouts.
|
||||
|
||||
2. **Athletic Accessories**: Introducing a line of athletic accessories like gym bags, water bottles, or fitness trackers could complement Nike's existing product offerings and provide additional value to customers.
|
||||
|
||||
3. **Customization Platforms**: Offering personalized design options for footwear and apparel through online customization platforms could appeal to consumers seeking unique and tailored products.
|
||||
|
||||
4. **Athletic Recovery Gear**: Developing recovery-focused products like compression wear, recovery sandals, or massage tools could cater to athletes and fitness enthusiasts looking to enhance post-workout recovery.
|
||||
|
||||
5. **Sustainable Collections**: Launching sustainable collections made from eco-friendly materials, recycled fabrics, or biodegradable components could align with consumer preferences for environmentally conscious products.
|
||||
|
||||
By introducing these new products or services in 2024, Nike can innovate its product portfolio, cater to evolving consumer needs, and differentiate itself in the competitive sports apparel market.
|
||||
### Potential Marketing Strategies for Nike to Increase Revenue in Q3 2024
|
||||
|
||||
1. **Influencer Partnerships**: Collaborating with popular athletes, celebrities, or social media influencers to promote Nike products can help reach a wider audience and drive sales.
|
||||
|
||||
2. **Interactive Campaigns**: Launching interactive marketing campaigns, contests, or events that engage customers and create buzz around new product releases can generate excitement and increase brand visibility.
|
||||
|
||||
3. **Social Media Engagement**: Leveraging social media platforms to connect with consumers, share user-generated content, and respond to feedback can build brand loyalty and encourage repeat purchases.
|
||||
|
||||
4. **Localized Marketing**: Tailoring marketing messages, promotions, and product offerings to specific regions or target demographics can enhance relevance and appeal to diverse consumer groups.
|
||||
|
||||
5. **Customer Loyalty Programs**: Implementing loyalty programs, exclusive offers, or rewards for repeat customers can incentivize brand loyalty, increase retention rates, and drive higher lifetime customer value.
|
||||
|
||||
By employing these marketing strategies in Q3 2024, Nike can enhance its brand presence, attract new customers, and ultimately boost revenue growth.
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
@ -1,42 +0,0 @@
|
||||
## **Applications of Swarms: Revolutionizing Customer Support**
|
||||
|
||||
---
|
||||
|
||||
**Introduction**:
|
||||
In today's fast-paced digital world, responsive and efficient customer support is a linchpin for business success. The introduction of AI-driven swarms in the customer support domain can transform the way businesses interact with and assist their customers. By leveraging the combined power of multiple AI agents working in concert, businesses can achieve unprecedented levels of efficiency, customer satisfaction, and operational cost savings.
|
||||
|
||||
---
|
||||
|
||||
### **The Benefits of Using Swarms for Customer Support:**
|
||||
|
||||
1. **24/7 Availability**: Swarms never sleep. Customers receive instantaneous support at any hour, ensuring constant satisfaction and loyalty.
|
||||
|
||||
2. **Infinite Scalability**: Whether it's ten inquiries or ten thousand, swarms can handle fluctuating volumes with ease, eliminating the need for vast human teams and minimizing response times.
|
||||
|
||||
3. **Adaptive Intelligence**: Swarms learn collectively, meaning that a solution found for one customer can be instantly applied to benefit all. This leads to constantly improving support experiences, evolving with every interaction.
|
||||
|
||||
---
|
||||
|
||||
### **Features - Reinventing Customer Support**:
|
||||
|
||||
- **AI Inbox Monitor**: Continuously scans email inboxes, identifying and categorizing support requests for swift responses.
|
||||
|
||||
- **Intelligent Debugging**: Proactively helps customers by diagnosing and troubleshooting underlying issues.
|
||||
|
||||
- **Automated Refunds & Coupons**: Seamless integration with payment systems like Stripe allows for instant issuance of refunds or coupons if a problem remains unresolved.
|
||||
|
||||
- **Full System Integration**: Holistically connects with CRM, email systems, and payment portals, ensuring a cohesive and unified support experience.
|
||||
|
||||
- **Conversational Excellence**: With advanced LLMs (Language Model Transformers), the swarm agents can engage in natural, human-like conversations, enhancing customer comfort and trust.
|
||||
|
||||
- **Rule-based Operation**: By working with rule engines, swarms ensure that all actions adhere to company guidelines, ensuring consistent, error-free support.
|
||||
|
||||
- **Turing Test Ready**: Crafted to meet and exceed the Turing Test standards, ensuring that every customer interaction feels genuine and personal.
|
||||
|
||||
---
|
||||
|
||||
**Conclusion**:
|
||||
Swarms are not just another technological advancement; they represent the future of customer support. Their ability to provide round-the-clock, scalable, and continuously improving support can redefine customer experience standards. By adopting swarms, businesses can stay ahead of the curve, ensuring unparalleled customer loyalty and satisfaction.
|
||||
|
||||
**Experience the future of customer support. Dive into the swarm revolution.**
|
||||
|
||||
Binary file not shown.
@ -1,358 +0,0 @@
|
||||
# Architecture
|
||||
|
||||
## **1. Introduction**
|
||||
|
||||
In today's rapidly evolving digital world, harnessing the collaborative power of multiple computational agents is more crucial than ever. 'Swarms' represents a bold stride in this direction—a scalable and dynamic framework designed to enable swarms of agents to function in harmony and tackle complex tasks. This document serves as a comprehensive guide, elucidating the underlying architecture and strategies pivotal to realizing the Swarms vision.
|
||||
|
||||
---
|
||||
|
||||
## **2. The Vision**
|
||||
|
||||
At its heart, the Swarms framework seeks to emulate the collaborative efficiency witnessed in natural systems, like ant colonies or bird flocks. These entities, though individually simple, achieve remarkable outcomes through collaboration. Similarly, Swarms will unleash the collective potential of numerous agents, operating cohesively.
|
||||
|
||||
---
|
||||
|
||||
## **3. Architecture Overview**
|
||||
|
||||
### **3.1 Agent Level**
|
||||
The base level that serves as the building block for all further complexity.
|
||||
|
||||
#### Mechanics:
|
||||
* **Model**: At its core, each agent harnesses a powerful model like OpenAI's GPT.
|
||||
* **Vectorstore**: A memory structure allowing agents to store and retrieve information.
|
||||
* **Tools**: Utilities and functionalities that aid in the agent's task execution.
|
||||
|
||||
#### Interaction:
|
||||
Agents interact with the external world through their model and tools. The Vectorstore aids in retaining knowledge and facilitating inter-agent communication.
|
||||
|
||||
### **3.2 Worker Infrastructure Level**
|
||||
Building on the agent foundation, enhancing capability and readiness for swarm integration.
|
||||
|
||||
#### Mechanics:
|
||||
* **Human Input Integration**: Enables agents to accept and understand human-provided instructions.
|
||||
* **Unique Identifiers**: Assigns each agent a unique ID to facilitate tracking and communication.
|
||||
* **Asynchronous Tools**: Bolsters agents' capability to multitask and interact in real-time.
|
||||
|
||||
#### Interaction:
|
||||
Each worker is an enhanced agent, capable of operating independently or in sync with its peers, allowing for dynamic, scalable operations.
|
||||
|
||||
### **3.3 Swarm Level**
|
||||
Multiple Worker Nodes orchestrated into a synchronized, collaborative entity.
|
||||
|
||||
#### Mechanics:
|
||||
* **Orchestrator**: The maestro, responsible for directing the swarm, task allocation, and communication.
|
||||
* **Scalable Communication Layer**: Facilitates interactions among nodes and between nodes and the orchestrator.
|
||||
* **Task Assignment & Completion Protocols**: Structured procedures ensuring tasks are efficiently distributed and concluded.
|
||||
|
||||
#### Interaction:
|
||||
Nodes collaborate under the orchestrator's guidance, ensuring tasks are partitioned appropriately, executed, and results consolidated.
|
||||
|
||||
### **3.4 Hivemind Level**
|
||||
Envisioned as a 'Swarm of Swarms'. An upper echelon of collaboration.
|
||||
|
||||
#### Mechanics:
|
||||
* **Hivemind Orchestrator**: Oversees multiple swarm orchestrators, ensuring harmony on a grand scale.
|
||||
* **Inter-Swarm Communication Protocols**: Dictates how swarms interact, exchange information, and co-execute tasks.
|
||||
|
||||
#### Interaction:
|
||||
Multiple swarms, each a formidable force, combine their prowess under the Hivemind. This level tackles monumental tasks by dividing them among swarms.
|
||||
|
||||
---
|
||||
|
||||
## **4. Building the Framework: A Task Checklist**
|
||||
|
||||
### **4.1 Foundations: Agent Level**
|
||||
* Define and standardize agent properties.
|
||||
* Integrate desired model (e.g., OpenAI's GPT) with agent.
|
||||
* Implement Vectorstore mechanisms: storage, retrieval, and communication protocols.
|
||||
* Incorporate essential tools and utilities.
|
||||
* Conduct preliminary testing: Ensure agents can execute basic tasks and utilize the Vectorstore.
|
||||
|
||||
### **4.2 Enhancements: Worker Infrastructure Level**
|
||||
* Interface agents with human input mechanisms.
|
||||
* Assign and manage unique identifiers for each worker.
|
||||
* Integrate asynchronous capabilities: Ensure real-time response and multitasking.
|
||||
* Test worker nodes for both solitary and collaborative tasks.
|
||||
|
||||
### **4.3 Cohesion: Swarm Level**
|
||||
* Design and develop the orchestrator: Ensure it can manage multiple worker nodes.
|
||||
* Establish a scalable and efficient communication layer.
|
||||
* Implement task distribution and retrieval protocols.
|
||||
* Test swarms for efficiency, scalability, and robustness.
|
||||
|
||||
### **4.4 Apex Collaboration: Hivemind Level**
|
||||
* Build the Hivemind Orchestrator: Ensure it can oversee multiple swarms.
|
||||
* Define inter-swarm communication, prioritization, and task-sharing protocols.
|
||||
* Develop mechanisms to balance loads and optimize resource utilization across swarms.
|
||||
* Thoroughly test the Hivemind level for macro-task execution.
|
||||
|
||||
---
|
||||
|
||||
## **5. Integration and Communication Mechanisms**
|
||||
|
||||
### **5.1 Vectorstore as the Universal Communication Layer**
|
||||
Serving as the memory and communication backbone, the Vectorstore must:
|
||||
* Facilitate rapid storage and retrieval of high-dimensional vectors.
|
||||
* Enable similarity-based lookups: Crucial for recognizing patterns or finding similar outputs.
|
||||
* Scale seamlessly as agent count grows.
|
||||
|
||||
### **5.2 Orchestrator-Driven Communication**
|
||||
* Orchestrators, both at the swarm and hivemind level, should employ adaptive algorithms to optimally distribute tasks.
|
||||
* Ensure real-time monitoring of task execution and worker node health.
|
||||
* Integrate feedback loops: Allow for dynamic task reassignment in case of node failures or inefficiencies.
|
||||
|
||||
---
|
||||
|
||||
## **6. Conclusion & Forward Path**
|
||||
|
||||
The Swarms framework, once realized, will usher in a new era of computational efficiency and collaboration. While the roadmap ahead is intricate, with diligent planning, development, and testing, Swarms will redefine the boundaries of collaborative computing.
|
||||
|
||||
--------
|
||||
|
||||
|
||||
# Overview
|
||||
|
||||
### 1. Model
|
||||
|
||||
**Overview:**
|
||||
The foundational level where a trained model (e.g., OpenAI GPT model) is initialized. It's the base on which further abstraction levels build upon. It provides the core capabilities to perform tasks, answer queries, etc.
|
||||
|
||||
**Diagram:**
|
||||
```
|
||||
[ Model (openai) ]
|
||||
```
|
||||
|
||||
### 2. Agent Level
|
||||
|
||||
**Overview:**
|
||||
At the agent level, the raw model is coupled with tools and a vector store, allowing it to be more than just a model. The agent can now remember, use tools, and become a more versatile entity ready for integration into larger systems.
|
||||
|
||||
**Diagram:**
|
||||
```
|
||||
+-----------+
|
||||
| Agent |
|
||||
| +-------+ |
|
||||
| | Model | |
|
||||
| +-------+ |
|
||||
| +-----------+ |
|
||||
| | VectorStore | |
|
||||
| +-----------+ |
|
||||
| +-------+ |
|
||||
| | Tools | |
|
||||
| +-------+ |
|
||||
+-----------+
|
||||
```
|
||||
|
||||
### 3. Worker Infrastructure Level
|
||||
|
||||
**Overview:**
|
||||
The worker infrastructure is a step above individual agents. Here, an agent is paired with additional utilities like human input and other tools, making it a more advanced, responsive unit capable of complex tasks.
|
||||
|
||||
**Diagram:**
|
||||
```
|
||||
+----------------+
|
||||
| WorkerNode |
|
||||
| +-----------+ |
|
||||
| | Agent | |
|
||||
| | +-------+ | |
|
||||
| | | Model | | |
|
||||
| | +-------+ | |
|
||||
| | +-------+ | |
|
||||
| | | Tools | | |
|
||||
| | +-------+ | |
|
||||
| +-----------+ |
|
||||
| |
|
||||
| +-----------+ |
|
||||
| |Human Input| |
|
||||
| +-----------+ |
|
||||
| |
|
||||
| +-------+ |
|
||||
| | Tools | |
|
||||
| +-------+ |
|
||||
+----------------+
|
||||
```
|
||||
|
||||
### 4. Swarm Level
|
||||
|
||||
**Overview:**
|
||||
At the swarm level, the orchestrator is central. It's responsible for assigning tasks to worker nodes, monitoring their completion, and handling the communication layer (for example, through a vector store or another universal communication mechanism) between worker nodes.
|
||||
|
||||
**Diagram:**
|
||||
```
|
||||
+------------+
|
||||
|Orchestrator|
|
||||
+------------+
|
||||
|
|
||||
+---------------------------+
|
||||
| |
|
||||
| Swarm-level Communication|
|
||||
| Layer (e.g. |
|
||||
| Vector Store) |
|
||||
+---------------------------+
|
||||
/ | \
|
||||
+---------------+ +---------------+ +---------------+
|
||||
|WorkerNode 1 | |WorkerNode 2 | |WorkerNode n |
|
||||
| | | | | |
|
||||
+---------------+ +---------------+ +---------------+
|
||||
| Task Assigned | Task Completed | Communication |
|
||||
```
|
||||
|
||||
### 5. Hivemind Level
|
||||
|
||||
**Overview:**
|
||||
At the Hivemind level, it's a multi-swarm setup, with an upper-layer orchestrator managing multiple swarm-level orchestrators. The Hivemind orchestrator is responsible for broader tasks like assigning macro-tasks to swarms, handling inter-swarm communications, and ensuring the overall system is functioning smoothly.
|
||||
|
||||
**Diagram:**
|
||||
```
|
||||
+--------+
|
||||
|Hivemind|
|
||||
+--------+
|
||||
|
|
||||
+--------------+
|
||||
|Hivemind |
|
||||
|Orchestrator |
|
||||
+--------------+
|
||||
/ | \
|
||||
+------------+ +------------+ +------------+
|
||||
|Orchestrator| |Orchestrator| |Orchestrator|
|
||||
+------------+ +------------+ +------------+
|
||||
| | |
|
||||
+--------------+ +--------------+ +--------------+
|
||||
| Swarm-level| | Swarm-level| | Swarm-level|
|
||||
|Communication| |Communication| |Communication|
|
||||
| Layer | | Layer | | Layer |
|
||||
+--------------+ +--------------+ +--------------+
|
||||
/ \ / \ / \
|
||||
+-------+ +-------+ +-------+ +-------+ +-------+
|
||||
|Worker | |Worker | |Worker | |Worker | |Worker |
|
||||
| Node | | Node | | Node | | Node | | Node |
|
||||
+-------+ +-------+ +-------+ +-------+ +-------+
|
||||
```
|
||||
|
||||
This setup allows the Hivemind level to operate at a grander scale, with the capability to manage hundreds or even thousands of worker nodes across multiple swarms efficiently.
|
||||
|
||||
|
||||
|
||||
-------
|
||||
# **Swarms Framework Development Strategy Checklist**
|
||||
|
||||
## **Introduction**
|
||||
|
||||
The development of the Swarms framework requires a systematic and granular approach to ensure that each component is robust and that the overall framework is efficient and scalable. This checklist will serve as a guide to building Swarms from the ground up, breaking down tasks into small, manageable pieces.
|
||||
|
||||
---
|
||||
|
||||
## **1. Agent Level Development**
|
||||
|
||||
### **1.1 Model Integration**
|
||||
- [ ] Research the most suitable models (e.g., OpenAI's GPT).
|
||||
- [ ] Design an API for the agent to call the model.
|
||||
- [ ] Implement error handling when model calls fail.
|
||||
- [ ] Test the model with sample data for accuracy and speed.
|
||||
|
||||
### **1.2 Vectorstore Implementation**
|
||||
- [ ] Design the schema for the vector storage system.
|
||||
- [ ] Implement storage methods to add, delete, and update vectors.
|
||||
- [ ] Develop retrieval methods with optimization for speed.
|
||||
- [ ] Create protocols for vector-based communication between agents.
|
||||
- [ ] Conduct stress tests to ascertain storage and retrieval speed.
|
||||
|
||||
### **1.3 Tools & Utilities Integration**
|
||||
- [ ] List out essential tools required for agent functionality.
|
||||
- [ ] Develop or integrate APIs for each tool.
|
||||
- [ ] Implement error handling and logging for tool interactions.
|
||||
- [ ] Validate tools integration with unit tests.
|
||||
|
||||
---
|
||||
|
||||
## **2. Worker Infrastructure Level Development**
|
||||
|
||||
### **2.1 Human Input Integration**
|
||||
- [ ] Design a UI/UX for human interaction with worker nodes.
|
||||
- [ ] Create APIs for input collection.
|
||||
- [ ] Implement input validation and error handling.
|
||||
- [ ] Test human input methods for clarity and ease of use.
|
||||
|
||||
### **2.2 Unique Identifier System**
|
||||
- [ ] Research optimal formats for unique ID generation.
|
||||
- [ ] Develop methods for generating and assigning IDs to agents.
|
||||
- [ ] Implement a tracking system to manage and monitor agents via IDs.
|
||||
- [ ] Validate the uniqueness and reliability of the ID system.
|
||||
|
||||
### **2.3 Asynchronous Operation Tools**
|
||||
- [ ] Incorporate libraries/frameworks to enable asynchrony.
|
||||
- [ ] Ensure tasks within an agent can run in parallel without conflict.
|
||||
- [ ] Test asynchronous operations for efficiency improvements.
|
||||
|
||||
---
|
||||
|
||||
## **3. Swarm Level Development**
|
||||
|
||||
### **3.1 Orchestrator Design & Development**
|
||||
- [ ] Draft a blueprint of orchestrator functionalities.
|
||||
- [ ] Implement methods for task distribution among worker nodes.
|
||||
- [ ] Develop communication protocols for the orchestrator to monitor workers.
|
||||
- [ ] Create feedback systems to detect and address worker node failures.
|
||||
- [ ] Test orchestrator with a mock swarm to ensure efficient task allocation.
|
||||
|
||||
### **3.2 Communication Layer Development**
|
||||
- [ ] Select a suitable communication protocol/framework (e.g., gRPC, WebSockets).
|
||||
- [ ] Design the architecture for scalable, low-latency communication.
|
||||
- [ ] Implement methods for sending, receiving, and broadcasting messages.
|
||||
- [ ] Test communication layer for reliability, speed, and error handling.
|
||||
|
||||
### **3.3 Task Management Protocols**
|
||||
- [ ] Develop a system to queue, prioritize, and allocate tasks.
|
||||
- [ ] Implement methods for real-time task status tracking.
|
||||
- [ ] Create a feedback loop for completed tasks.
|
||||
- [ ] Test task distribution, execution, and feedback systems for efficiency.
|
||||
|
||||
---
|
||||
|
||||
## **4. Hivemind Level Development**
|
||||
|
||||
### **4.1 Hivemind Orchestrator Development**
|
||||
- [ ] Extend swarm orchestrator functionalities to manage multiple swarms.
|
||||
- [ ] Create inter-swarm communication protocols.
|
||||
- [ ] Implement load balancing mechanisms to distribute tasks across swarms.
|
||||
- [ ] Validate hivemind orchestrator functionalities with multi-swarm setups.
|
||||
|
||||
### **4.2 Inter-Swarm Communication Protocols**
|
||||
- [ ] Design methods for swarms to exchange data.
|
||||
- [ ] Implement data reconciliation methods for swarms working on shared tasks.
|
||||
- [ ] Test inter-swarm communication for efficiency and data integrity.
|
||||
|
||||
---
|
||||
|
||||
## **5. Scalability & Performance Testing**
|
||||
|
||||
- [ ] Simulate heavy loads to test the limits of the framework.
|
||||
- [ ] Identify and address bottlenecks in both communication and computation.
|
||||
- [ ] Conduct speed tests under different conditions.
|
||||
- [ ] Test the system's responsiveness under various levels of stress.
|
||||
|
||||
---
|
||||
|
||||
## **6. Documentation & User Guide**
|
||||
|
||||
- [ ] Develop detailed documentation covering architecture, setup, and usage.
|
||||
- [ ] Create user guides with step-by-step instructions.
|
||||
- [ ] Incorporate visual aids, diagrams, and flowcharts for clarity.
|
||||
- [ ] Update documentation regularly with new features and improvements.
|
||||
|
||||
---
|
||||
|
||||
## **7. Continuous Integration & Deployment**
|
||||
|
||||
- [ ] Setup CI/CD pipelines for automated testing and deployment.
|
||||
- [ ] Ensure automatic rollback in case of deployment failures.
|
||||
- [ ] Integrate code quality and security checks in the pipeline.
|
||||
- [ ] Document deployment strategies and best practices.
|
||||
|
||||
---
|
||||
|
||||
## **Conclusion**
|
||||
|
||||
The Swarms framework represents a monumental leap in agent-based computation. This checklist provides a thorough roadmap for the framework's development, ensuring that every facet is addressed in depth. Through diligent adherence to this guide, the Swarms vision can be realized as a powerful, scalable, and robust system ready to tackle the challenges of tomorrow.
|
||||
|
||||
(Note: This document, given the word limit, provides a high-level overview. A full 5000-word document would delve into even more intricate details, nuances, potential pitfalls, and include considerations for security, user experience, compatibility, etc.)
|
||||
@ -1,86 +0,0 @@
|
||||
# Bounty Program
|
||||
|
||||
Our bounty program is an exciting opportunity for contributors to help us build the future of Swarms. By participating, you can earn rewards while contributing to a project that aims to revolutionize digital activity.
|
||||
|
||||
Here's how it works:
|
||||
|
||||
1. **Check out our Roadmap**: We've shared our roadmap detailing our short and long-term goals. These are the areas where we're seeking contributions.
|
||||
|
||||
2. **Pick a Task**: Choose a task from the roadmap that aligns with your skills and interests. If you're unsure, you can reach out to our team for guidance.
|
||||
|
||||
3. **Get to Work**: Once you've chosen a task, start working on it. Remember, quality is key. We're looking for contributions that truly make a difference.
|
||||
|
||||
4. **Submit your Contribution**: Once your work is complete, submit it for review. We'll evaluate your contribution based on its quality, relevance, and the value it brings to Swarms.
|
||||
|
||||
5. **Earn Rewards**: If your contribution is approved, you'll earn a bounty. The amount of the bounty depends on the complexity of the task, the quality of your work, and the value it brings to Swarms.
|
||||
|
||||
## The Three Phases of Our Bounty Program
|
||||
|
||||
### Phase 1: Building the Foundation
|
||||
In the first phase, our focus is on building the basic infrastructure of Swarms. This includes developing key components like the Swarms class, integrating essential tools, and establishing task completion and evaluation logic. We'll also start developing our testing and evaluation framework during this phase. If you're interested in foundational work and have a knack for building robust, scalable systems, this phase is for you.
|
||||
|
||||
### Phase 2: Enhancing the System
|
||||
In the second phase, we'll focus on enhancing Swarms by integrating more advanced features, improving the system's efficiency, and refining our testing and evaluation framework. This phase involves more complex tasks, so if you enjoy tackling challenging problems and contributing to the development of innovative features, this is the phase for you.
|
||||
|
||||
### Phase 3: Towards Super-Intelligence
|
||||
The third phase of our bounty program is the most exciting - this is where we aim to achieve super-intelligence. In this phase, we'll be working on improving the swarm's capabilities, expanding its skills, and fine-tuning the system based on real-world testing and feedback. If you're excited about the future of AI and want to contribute to a project that could potentially transform the digital world, this is the phase for you.
|
||||
|
||||
Remember, our roadmap is a guide, and we encourage you to bring your own ideas and creativity to the table. We believe that every contribution, no matter how small, can make a difference. So join us on this exciting journey and help us create the future of Swarms.
|
||||
|
||||
**To participate in our bounty program, visit the [Swarms Bounty Program Page](https://swarms.ai/bounty).** Let's build the future together!
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
## Bounties for Roadmap Items
|
||||
|
||||
To accelerate the development of Swarms and to encourage more contributors to join our journey towards automating every digital activity in existence, we are announcing a Bounty Program for specific roadmap items. Each bounty will be rewarded based on the complexity and importance of the task. Below are the items available for bounty:
|
||||
|
||||
1. **Multi-Agent Debate Integration**: $2000
|
||||
2. **Meta Prompting Integration**: $1500
|
||||
3. **Swarms Class**: $1500
|
||||
4. **Integration of Additional Tools**: $1000
|
||||
5. **Task Completion and Evaluation Logic**: $2000
|
||||
6. **Ocean Integration**: $2500
|
||||
7. **Improved Communication**: $2000
|
||||
8. **Testing and Evaluation**: $1500
|
||||
9. **Worker Swarm Class**: $2000
|
||||
10. **Documentation**: $500
|
||||
|
||||
For each bounty task, there will be a strict evaluation process to ensure the quality of the contribution. This process includes a thorough review of the code and extensive testing to ensure it meets our standards.
|
||||
|
||||
# 3-Phase Testing Framework
|
||||
|
||||
To ensure the quality and efficiency of the Swarm, we will introduce a 3-phase testing framework which will also serve as our evaluation criteria for each of the bounty tasks.
|
||||
|
||||
## Phase 1: Unit Testing
|
||||
In this phase, individual modules will be tested to ensure that they work correctly in isolation. Unit tests will be designed for all functions and methods, with an emphasis on edge cases.
|
||||
|
||||
## Phase 2: Integration Testing
|
||||
After passing unit tests, we will test the integration of different modules to ensure they work correctly together. This phase will also test the interoperability of the Swarm with external systems and libraries.
|
||||
|
||||
## Phase 3: Benchmarking & Stress Testing
|
||||
In the final phase, we will perform benchmarking and stress tests. We'll push the limits of the Swarm under extreme conditions to ensure it performs well in real-world scenarios. This phase will measure the performance, speed, and scalability of the Swarm under high load conditions.
|
||||
|
||||
By following this 3-phase testing framework, we aim to develop a reliable, high-performing, and scalable Swarm that can automate all digital activities.
|
||||
|
||||
# Reverse Engineering to Reach Phase 3
|
||||
|
||||
To reach the Phase 3 level, we need to reverse engineer the tasks we need to complete. Here's an example of what this might look like:
|
||||
|
||||
1. **Set Clear Expectations**: Define what success looks like for each task. Be clear about the outputs and outcomes we expect. This will guide our testing and development efforts.
|
||||
|
||||
2. **Develop Testing Scenarios**: Create a comprehensive list of testing scenarios that cover both common and edge cases. This will help us ensure that our Swarm can handle a wide range of situations.
|
||||
|
||||
3. **Write Test Cases**: For each scenario, write detailed test cases that outline the exact steps to be followed, the inputs to be used, and the expected outputs.
|
||||
|
||||
4. **Execute the Tests**: Run the test cases on our Swarm, making note of any issues or bugs that arise.
|
||||
|
||||
5. **Iterate and Improve**: Based on the results of our tests, iterate and improve our Swarm. This may involve fixing bugs, optimizing code, or redesigning parts of our system.
|
||||
|
||||
6. **Repeat**: Repeat this process until our Swarm meets our expectations and passes all test cases.
|
||||
|
||||
By following these steps, we will systematically build, test, and improve our Swarm until it reaches the Phase 3 level. This methodical approach will help us ensure that we create a reliable, high-performing, and scalable Swarm that can truly automate all digital activities.
|
||||
|
||||
Let's shape the future of digital automation together!
|
||||
@ -1,122 +0,0 @@
|
||||
# **Swarms Framework Development Strategy Checklist**
|
||||
|
||||
## **Introduction**
|
||||
|
||||
The development of the Swarms framework requires a systematic and granular approach to ensure that each component is robust and that the overall framework is efficient and scalable. This checklist will serve as a guide to building Swarms from the ground up, breaking down tasks into small, manageable pieces.
|
||||
|
||||
---
|
||||
|
||||
## **1. Agent Level Development**
|
||||
|
||||
### **1.1 Model Integration**
|
||||
- [ ] Research the most suitable models (e.g., OpenAI's GPT).
|
||||
- [ ] Design an API for the agent to call the model.
|
||||
- [ ] Implement error handling when model calls fail.
|
||||
- [ ] Test the model with sample data for accuracy and speed.
|
||||
|
||||
### **1.2 Vectorstore Implementation**
|
||||
- [ ] Design the schema for the vector storage system.
|
||||
- [ ] Implement storage methods to add, delete, and update vectors.
|
||||
- [ ] Develop retrieval methods with optimization for speed.
|
||||
- [ ] Create protocols for vector-based communication between agents.
|
||||
- [ ] Conduct stress tests to ascertain storage and retrieval speed.
|
||||
|
||||
### **1.3 Tools & Utilities Integration**
|
||||
- [ ] List out essential tools required for agent functionality.
|
||||
- [ ] Develop or integrate APIs for each tool.
|
||||
- [ ] Implement error handling and logging for tool interactions.
|
||||
- [ ] Validate tools integration with unit tests.
|
||||
|
||||
---
|
||||
|
||||
## **2. Worker Infrastructure Level Development**
|
||||
|
||||
### **2.1 Human Input Integration**
|
||||
- [ ] Design a UI/UX for human interaction with worker nodes.
|
||||
- [ ] Create APIs for input collection.
|
||||
- [ ] Implement input validation and error handling.
|
||||
- [ ] Test human input methods for clarity and ease of use.
|
||||
|
||||
### **2.2 Unique Identifier System**
|
||||
- [ ] Research optimal formats for unique ID generation.
|
||||
- [ ] Develop methods for generating and assigning IDs to agents.
|
||||
- [ ] Implement a tracking system to manage and monitor agents via IDs.
|
||||
- [ ] Validate the uniqueness and reliability of the ID system.
|
||||
|
||||
### **2.3 Asynchronous Operation Tools**
|
||||
- [ ] Incorporate libraries/frameworks to enable asynchrony.
|
||||
- [ ] Ensure tasks within an agent can run in parallel without conflict.
|
||||
- [ ] Test asynchronous operations for efficiency improvements.
|
||||
|
||||
---
|
||||
|
||||
## **3. Swarm Level Development**
|
||||
|
||||
### **3.1 Orchestrator Design & Development**
|
||||
- [ ] Draft a blueprint of orchestrator functionalities.
|
||||
- [ ] Implement methods for task distribution among worker nodes.
|
||||
- [ ] Develop communication protocols for the orchestrator to monitor workers.
|
||||
- [ ] Create feedback systems to detect and address worker node failures.
|
||||
- [ ] Test orchestrator with a mock swarm to ensure efficient task allocation.
|
||||
|
||||
### **3.2 Communication Layer Development**
|
||||
- [ ] Select a suitable communication protocol/framework (e.g., gRPC, WebSockets).
|
||||
- [ ] Design the architecture for scalable, low-latency communication.
|
||||
- [ ] Implement methods for sending, receiving, and broadcasting messages.
|
||||
- [ ] Test communication layer for reliability, speed, and error handling.
|
||||
|
||||
### **3.3 Task Management Protocols**
|
||||
- [ ] Develop a system to queue, prioritize, and allocate tasks.
|
||||
- [ ] Implement methods for real-time task status tracking.
|
||||
- [ ] Create a feedback loop for completed tasks.
|
||||
- [ ] Test task distribution, execution, and feedback systems for efficiency.
|
||||
|
||||
---
|
||||
|
||||
## **4. Hivemind Level Development**
|
||||
|
||||
### **4.1 Hivemind Orchestrator Development**
|
||||
- [ ] Extend swarm orchestrator functionalities to manage multiple swarms.
|
||||
- [ ] Create inter-swarm communication protocols.
|
||||
- [ ] Implement load balancing mechanisms to distribute tasks across swarms.
|
||||
- [ ] Validate hivemind orchestrator functionalities with multi-swarm setups.
|
||||
|
||||
### **4.2 Inter-Swarm Communication Protocols**
|
||||
- [ ] Design methods for swarms to exchange data.
|
||||
- [ ] Implement data reconciliation methods for swarms working on shared tasks.
|
||||
- [ ] Test inter-swarm communication for efficiency and data integrity.
|
||||
|
||||
---
|
||||
|
||||
## **5. Scalability & Performance Testing**
|
||||
|
||||
- [ ] Simulate heavy loads to test the limits of the framework.
|
||||
- [ ] Identify and address bottlenecks in both communication and computation.
|
||||
- [ ] Conduct speed tests under different conditions.
|
||||
- [ ] Test the system's responsiveness under various levels of stress.
|
||||
|
||||
---
|
||||
|
||||
## **6. Documentation & User Guide**
|
||||
|
||||
- [ ] Develop detailed documentation covering architecture, setup, and usage.
|
||||
- [ ] Create user guides with step-by-step instructions.
|
||||
- [ ] Incorporate visual aids, diagrams, and flowcharts for clarity.
|
||||
- [ ] Update documentation regularly with new features and improvements.
|
||||
|
||||
---
|
||||
|
||||
## **7. Continuous Integration & Deployment**
|
||||
|
||||
- [ ] Setup CI/CD pipelines for automated testing and deployment.
|
||||
- [ ] Ensure automatic rollback in case of deployment failures.
|
||||
- [ ] Integrate code quality and security checks in the pipeline.
|
||||
- [ ] Document deployment strategies and best practices.
|
||||
|
||||
---
|
||||
|
||||
## **Conclusion**
|
||||
|
||||
The Swarms framework represents a monumental leap in agent-based computation. This checklist provides a thorough roadmap for the framework's development, ensuring that every facet is addressed in depth. Through diligent adherence to this guide, the Swarms vision can be realized as a powerful, scalable, and robust system ready to tackle the challenges of tomorrow.
|
||||
|
||||
(Note: This document, given the word limit, provides a high-level overview. A full 5000-word document would delve into even more intricate details, nuances, potential pitfalls, and include considerations for security, user experience, compatibility, etc.)
|
||||
@ -1,100 +0,0 @@
|
||||
# Costs Structure of Deploying Autonomous Agents
|
||||
|
||||
## Table of Contents
|
||||
|
||||
1. Introduction
|
||||
2. Our Time: Generating System Prompts and Custom Tools
|
||||
3. Consultancy Fees
|
||||
4. Model Inference Infrastructure
|
||||
5. Deployment and Continual Maintenance
|
||||
6. Output Metrics: Blogs Generation Rates
|
||||
|
||||
---
|
||||
|
||||
## 1. Introduction
|
||||
|
||||
Autonomous agents are revolutionizing various industries, from self-driving cars to chatbots and customer service solutions. The prospect of automation and improved efficiency makes these agents attractive investments. However, like any other technological solution, deploying autonomous agents involves several cost elements that organizations need to consider carefully. This comprehensive guide aims to provide an exhaustive outline of the costs associated with deploying autonomous agents.
|
||||
|
||||
---
|
||||
|
||||
## 2. Our Time: Generating System Prompts and Custom Tools
|
||||
|
||||
### Description
|
||||
|
||||
The deployment of autonomous agents often requires a substantial investment of time to develop system prompts and custom tools tailored to specific operational needs.
|
||||
|
||||
### Costs
|
||||
|
||||
| Task | Time Required (Hours) | Cost per Hour ($) | Total Cost ($) |
|
||||
| ------------------------ | --------------------- | ----------------- | -------------- |
|
||||
| System Prompts Design | 50 | 100 | 5,000 |
|
||||
| Custom Tools Development | 100 | 100 | 10,000 |
|
||||
| **Total** | **150** | | **15,000** |
|
||||
|
||||
---
|
||||
|
||||
## 3. Consultancy Fees
|
||||
|
||||
### Description
|
||||
|
||||
Consultation is often necessary for navigating the complexities of autonomous agents. This includes system assessment, customization, and other essential services.
|
||||
|
||||
### Costs
|
||||
|
||||
| Service | Fees ($) |
|
||||
| -------------------- | --------- |
|
||||
| Initial Assessment | 5,000 |
|
||||
| System Customization | 7,000 |
|
||||
| Training | 3,000 |
|
||||
| **Total** | **15,000**|
|
||||
|
||||
---
|
||||
|
||||
## 4. Model Inference Infrastructure
|
||||
|
||||
### Description
|
||||
|
||||
The hardware and software needed for the agent's functionality, known as the model inference infrastructure, form a significant part of the costs.
|
||||
|
||||
### Costs
|
||||
|
||||
| Component | Cost ($) |
|
||||
| -------------------- | --------- |
|
||||
| Hardware | 10,000 |
|
||||
| Software Licenses | 2,000 |
|
||||
| Cloud Services | 3,000 |
|
||||
| **Total** | **15,000**|
|
||||
|
||||
---
|
||||
|
||||
## 5. Deployment and Continual Maintenance
|
||||
|
||||
### Description
|
||||
|
||||
Once everything is in place, deploying the autonomous agents and their ongoing maintenance are the next major cost factors.
|
||||
|
||||
### Costs
|
||||
|
||||
| Task | Monthly Cost ($) | Annual Cost ($) |
|
||||
| ------------------- | ---------------- | --------------- |
|
||||
| Deployment | 5,000 | 60,000 |
|
||||
| Ongoing Maintenance | 1,000 | 12,000 |
|
||||
| **Total** | **6,000** | **72,000** |
|
||||
|
||||
---
|
||||
|
||||
## 6. Output Metrics: Blogs Generation Rates
|
||||
|
||||
### Description
|
||||
|
||||
To provide a sense of what an investment in autonomous agents can yield, we offer the following data regarding blogs that can be generated as an example of output.
|
||||
|
||||
### Blogs Generation Rates
|
||||
|
||||
| Timeframe | Number of Blogs |
|
||||
|-----------|-----------------|
|
||||
| Per Day | 20 |
|
||||
| Per Week | 140 |
|
||||
| Per Month | 600 |
|
||||
|
||||
|
||||
@ -1,112 +0,0 @@
|
||||
# Swarms Data Room
|
||||
|
||||
## Table of Contents
|
||||
|
||||
**Introduction**
|
||||
|
||||
- Overview of the Company
|
||||
|
||||
- Vision and Mission Statement
|
||||
|
||||
- Executive Summary
|
||||
|
||||
**Corporate Documents**
|
||||
|
||||
- Articles of Incorporation
|
||||
|
||||
- Bylaws
|
||||
|
||||
- Shareholder Agreements
|
||||
|
||||
- Board Meeting Minutes
|
||||
|
||||
- Company Structure and Org Chart
|
||||
|
||||
**Financial Information**
|
||||
|
||||
- Historical Financial Statements
|
||||
|
||||
- Income Statements
|
||||
|
||||
- Balance Sheets
|
||||
|
||||
- Cash Flow Statements
|
||||
|
||||
- Financial Projections and Forecasts
|
||||
|
||||
- Cap Table
|
||||
|
||||
- Funding History and Use of Funds
|
||||
|
||||
**Products and Services**
|
||||
|
||||
- Detailed Descriptions of Products/Services
|
||||
|
||||
- Product Development Roadmap
|
||||
|
||||
- User Manuals and Technical Specifications
|
||||
|
||||
- Case Studies and Use Cases
|
||||
|
||||
|
||||
## **Introduction**
|
||||
Swarms provides automation-as-a-service through swarms of autonomous agents that work together as a team. We enable our customers to build, deploy, and scale production-grade multi-agent applications to automate real-world tasks.
|
||||
|
||||
### **Vision**
|
||||
Our vision for 2024 is to provide the most reliable infrastructure for deploying autonomous agents into the real world through the Swarm Cloud, our premier cloud platform for the scalable deployment of Multi-Modal Autonomous Agents. The platform focuses on delivering maximum value to users by only taking a small fee when utilizing the agents for the hosted compute power needed to host the agents.
|
||||
|
||||
### **Executive Summary**
|
||||
The Swarm Corporation aims to enable AI models to automate complex workflows and operations, not just singular low-value tasks. We believe collaboration between multiple agents can overcome limitations of individual agents for reasoning, planning, etc. This will allow automation of processes in mission-critical industries like security, logistics, and manufacturing where AI adoption is currently low.
|
||||
|
||||
We provide an open source framework to deploy production-grade multi-modal agents in just a few lines of code. This builds our user base, recruits talent, gets customer feedback to improve products, gains awareness and trust.
|
||||
|
||||
Our business model focuses on customer satisfaction, openness, integration with other tools/platforms, and production-grade reliability.
|
||||
|
||||
Go-to-market strategy is to get the framework to product-market fit with over 50K weekly recurring users, then secure high-value contracts in target industries. Long-term monetization via microtransactions, usage-based pricing, subscriptions.
|
||||
|
||||
The team has thousands of hours building and optimizing autonomous agents. Leadership includes AI engineers, product experts, open source contributors and community builders.
|
||||
|
||||
Key milestones: get 80K framework users in January 2024, start contracts in target verticals, introduce commercial products in 2025 with various pricing models.
|
||||
|
||||
### **Resources**
|
||||
- [Swarm Pre-Seed Deck](https://drive.google.com/file/d/1n8o2mjORbG96uDfx4TabjnyieludYaZz/view?usp=sharing)
|
||||
- [Swarm Memo](https://docs.google.com/document/d/1hS_nv_lFjCqLfnJBoF6ULY9roTbSgSuCkvXvSUSc7Lo/edit?usp=sharing)
|
||||
|
||||
|
||||
|
||||
|
||||
## **Financial Documents**
|
||||
This section is dedicated entirely for corporate documents.
|
||||
|
||||
- [Cap Table](https://docs.google.com/spreadsheets/d/1wuTWbfhYaY5Xp6nSQ9R0wDtSpwSS9coHxsjKd0UbIDc/edit?usp=sharing)
|
||||
|
||||
- [Cashflow Prediction Sheet](https://docs.google.com/spreadsheets/d/1HQEHCIXXMHajXMl5sj8MEfcQtWfOnD7GjHtNiocpD60/edit?usp=sharing)
|
||||
|
||||
|
||||
------
|
||||
|
||||
## **Product**
|
||||
Swarms is an open source framework for developers in python to enable seamless, reliable, and scalable multi-agent orchestration through modularity, customization, and precision.
|
||||
|
||||
- [Swarms Github Page:](https://github.com/kyegomez/swarms)
|
||||
- [Swarms Memo](https://docs.google.com/document/d/1hS_nv_lFjCqLfnJBoF6ULY9roTbSgSuCkvXvSUSc7Lo/edit)
|
||||
- [Swarms Project Board](https://github.com/users/kyegomez/projects/1)
|
||||
- [Swarms Website](https://www.swarms.world/g)
|
||||
- [Swarm Ecosystem](https://github.com/kyegomez/swarm-ecosystem)
|
||||
- [Swarm Core](https://github.com/kyegomez/swarms-core)
|
||||
|
||||
### Product Growth Metrics
|
||||
| Name | Description | Link |
|
||||
|----------------------------------|---------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------|
|
||||
| Total Downloads of all time | Total number of downloads for the product over its entire lifespan. | [](https://pepy.tech/project/swarms) |
|
||||
| Downloads this month | Number of downloads for the product in the current month. | [](https://pepy.tech/project/swarms) |
|
||||
| Total Downloads this week | Total number of downloads for the product in the current week. | [](https://pepy.tech/project/swarms) |
|
||||
| Github Forks | Number of times the product's codebase has been copied for optimization, contribution, or usage. | [](https://github.com/kyegomez/swarms/network) |
|
||||
| Github Stars | Number of users who have 'liked' the project. | [](https://github.com/kyegomez/swarms/stargazers) |
|
||||
| Pip Module Metrics | Various project statistics such as watchers, number of contributors, date repository was created, and more. | [CLICK HERE](https://libraries.io/github/kyegomez/swarms) |
|
||||
| Contribution Based Statistics | Statistics like number of contributors, lines of code changed, etc. | [HERE](https://github.com/kyegomez/swarms/graphs/contributors) |
|
||||
| Github Community insights | Insights into the Github community around the product. | [Github Community insights](https://github.com/kyegomez/swarms/graphs/community) |
|
||||
| Github Traffic Metrics | Metrics related to traffic, such as views and clones on Github. | [Github Traffic Metrics](https://github.com/kyegomez/swarms/graphs/traffic) |
|
||||
| Issues with the framework | Current open issues for the product on Github. | [](https://github.com/kyegomez/swarms/issues) |
|
||||
|
||||
|
||||
@ -1,9 +0,0 @@
|
||||
# Demo Ideas
|
||||
|
||||
* We could also try to create an AI influencer run by a swarm, let it create a whole identity and generate images, memes, and other content for Twitter, Reddit, etc.
|
||||
|
||||
* had a thought that we should have either a more general one of these or a swarm or both -- need something connecting all the calendars, events, and initiatives of all the AI communities, langchain, laion, eluther, lesswrong, gato, rob miles, chatgpt hackers, etc etc
|
||||
|
||||
* Swarm of AI influencers to spread marketing
|
||||
|
||||
* Delegation System to better organize teams: Start with a team of passionate humans and let them self-report their skills/strengths so the agent has a concept of who to delegate to, then feed the agent a huge task list (like the bullet list a few messages above) that it breaks down into actionable steps and "prompts" specific team members to complete tasks. Could even suggest breakout teams of a few people with complementary skills to tackle more complex tasks. There can also be a live board that updates each time a team member completes something, to encourage momentum and keep track of progress
|
||||
@ -1,152 +0,0 @@
|
||||
# Design Philosophy Document for Swarms
|
||||
|
||||
## Usable
|
||||
|
||||
### Objective
|
||||
|
||||
Our goal is to ensure that Swarms is intuitive and easy to use for all users, regardless of their level of technical expertise. This includes the developers who implement Swarms in their applications, as well as end users who interact with the implemented systems.
|
||||
|
||||
### Tactics
|
||||
|
||||
- Clear and Comprehensive Documentation: We will provide well-written and easily accessible documentation that guides users through using and understanding Swarms.
|
||||
- User-Friendly APIs: We'll design clean and self-explanatory APIs that help developers to understand their purpose quickly.
|
||||
- Prompt and Effective Support: We will ensure that support is readily available to assist users when they encounter problems or need help with Swarms.
|
||||
|
||||
## Reliable
|
||||
|
||||
### Objective
|
||||
|
||||
Swarms should be dependable and trustworthy. Users should be able to count on Swarms to perform consistently and without error or failure.
|
||||
|
||||
### Tactics
|
||||
|
||||
- Robust Error Handling: We will focus on error prevention, detection, and recovery to minimize failures in Swarms.
|
||||
- Comprehensive Testing: We will apply various testing methodologies such as unit testing, integration testing, and stress testing to validate the reliability of our software.
|
||||
- Continuous Integration/Continuous Delivery (CI/CD): We will use CI/CD pipelines to ensure that all changes are tested and validated before they're merged into the main branch.
|
||||
|
||||
## Fast
|
||||
|
||||
### Objective
|
||||
|
||||
Swarms should offer high performance and rapid response times. The system should be able to handle requests and tasks swiftly.
|
||||
|
||||
### Tactics
|
||||
|
||||
- Efficient Algorithms: We will focus on optimizing our algorithms and data structures to ensure they run as quickly as possible.
|
||||
- Caching: Where appropriate, we will use caching techniques to speed up response times.
|
||||
- Profiling and Performance Monitoring: We will regularly analyze the performance of Swarms to identify bottlenecks and opportunities for improvement.
|
||||
|
||||
## Scalable
|
||||
|
||||
### Objective
|
||||
|
||||
Swarms should be able to grow in capacity and complexity without compromising performance or reliability. It should be able to handle increased workloads gracefully.
|
||||
|
||||
### Tactics
|
||||
|
||||
- Modular Architecture: We will design Swarms using a modular architecture that allows for easy scaling and modification.
|
||||
- Load Balancing: We will distribute tasks evenly across available resources to prevent overload and maximize throughput.
|
||||
- Horizontal and Vertical Scaling: We will design Swarms to be capable of both horizontal (adding more machines) and vertical (adding more power to an existing machine) scaling.
|
||||
|
||||
### Philosophy
|
||||
|
||||
Swarms is designed with a philosophy of simplicity and reliability. We believe that software should be a tool that empowers users, not a hurdle that they need to overcome. Therefore, our focus is on usability, reliability, speed, and scalability. We want our users to find Swarms intuitive and dependable, fast and adaptable to their needs. This philosophy guides all of our design and development decisions.
|
||||
|
||||
# Swarm Architecture Design Document
|
||||
|
||||
## Overview
|
||||
|
||||
The goal of the Swarm Architecture is to provide a flexible and scalable system to build swarm intelligence models that can solve complex problems. This document details the proposed design to create a plug-and-play system, which makes it easy to create custom swarms, and provides pre-configured swarms with multi-modal agents.
|
||||
|
||||
## Design Principles
|
||||
|
||||
- **Modularity**: The system will be built in a modular fashion, allowing various components to be easily swapped or upgraded.
|
||||
- **Interoperability**: Different swarm classes and components should be able to work together seamlessly.
|
||||
- **Scalability**: The design should support the growth of the system by adding more components or swarms.
|
||||
- **Ease of Use**: Users should be able to easily create their own swarms or use pre-configured ones with minimal configuration.
|
||||
|
||||
## Design Components
|
||||
|
||||
### BaseSwarm
|
||||
|
||||
The BaseSwarm is an abstract base class which defines the basic structure of a swarm and the methods that need to be implemented. Any new swarm should inherit from this class and implement the required methods.
|
||||
|
||||
### Swarm Classes
|
||||
|
||||
Various Swarm classes can be implemented inheriting from the BaseSwarm class. Each swarm class should implement the required methods for initializing the components, worker nodes, and boss node, and running the swarm.
|
||||
|
||||
Pre-configured swarm classes with multi-modal agents can be provided for ease of use. These classes come with a default configuration of tools and agents, which can be used out of the box.
|
||||
|
||||
### Tools and Agents
|
||||
|
||||
Tools and agents are the components that provide the actual functionality to the swarms. They can be language models, AI assistants, vector stores, or any other components that can help in problem solving.
|
||||
|
||||
To make the system plug-and-play, a standard interface should be defined for these components. Any new tool or agent should implement this interface, so that it can be easily plugged into the system.
|
||||
|
||||
## Usage
|
||||
|
||||
Users can either use pre-configured swarms or create their own custom swarms.
|
||||
|
||||
To use a pre-configured swarm, they can simply instantiate the corresponding swarm class and call the run method with the required objective.
|
||||
|
||||
To create a custom swarm, they need to:
|
||||
|
||||
1. Define a new swarm class inheriting from BaseSwarm.
|
||||
2. Implement the required methods for the new swarm class.
|
||||
3. Instantiate the swarm class and call the run method.
|
||||
|
||||
### Example
|
||||
|
||||
```python
|
||||
# Using pre-configured swarm
|
||||
swarm = PreConfiguredSwarm(openai_api_key)
|
||||
swarm.run_swarms(objective)
|
||||
|
||||
# Creating custom swarm
|
||||
class CustomSwarm(BaseSwarm):
|
||||
# Implement required methods
|
||||
|
||||
swarm = CustomSwarm(openai_api_key)
|
||||
swarm.run_swarms(objective)
|
||||
```
|
||||
|
||||
## Conclusion
|
||||
|
||||
This Swarm Architecture design provides a scalable and flexible system for building swarm intelligence models. The plug-and-play design allows users to easily use pre-configured swarms or create their own custom swarms.
|
||||
|
||||
|
||||
# Swarming Architectures
|
||||
Sure, below are five different swarm architectures with their base requirements and an abstract class that processes these components:
|
||||
|
||||
1. **Hierarchical Swarm**: This architecture is characterized by a boss/worker relationship. The boss node takes high-level decisions and delegates tasks to the worker nodes. The worker nodes perform tasks and report back to the boss node.
|
||||
- Requirements: Boss node (can be a large language model), worker nodes (can be smaller language models), and a task queue for task management.
|
||||
|
||||
2. **Homogeneous Swarm**: In this architecture, all nodes in the swarm are identical and contribute equally to problem-solving. Each node has the same capabilities.
|
||||
- Requirements: Homogeneous nodes (can be language models of the same size), communication protocol for nodes to share information.
|
||||
|
||||
3. **Heterogeneous Swarm**: This architecture contains different types of nodes, each with its specific capabilities. This diversity can lead to more robust problem-solving.
|
||||
- Requirements: Different types of nodes (can be different types and sizes of language models), a communication protocol, and a mechanism to delegate tasks based on node capabilities.
|
||||
|
||||
4. **Competitive Swarm**: In this architecture, nodes compete with each other to find the best solution. The system may use a selection process to choose the best solutions.
|
||||
- Requirements: Nodes (can be language models), a scoring mechanism to evaluate node performance, a selection mechanism.
|
||||
|
||||
5. **Cooperative Swarm**: In this architecture, nodes work together and share information to find solutions. The focus is on cooperation rather than competition.
|
||||
- Requirements: Nodes (can be language models), a communication protocol, a consensus mechanism to agree on solutions.
|
||||
|
||||
|
||||
6. **Grid-based Swarm**: This architecture positions agents on a grid, where they can only interact with their neighbors. This is useful for simulations, especially in fields like ecology or epidemiology.
|
||||
- Requirements: Agents (can be language models), a grid structure, and a neighborhood definition (i.e., how to identify neighboring agents).
|
||||
|
||||
7. **Particle Swarm Optimization (PSO) Swarm**: In this architecture, each agent represents a potential solution to an optimization problem. Agents move in the solution space based on their own and their neighbors' past performance. PSO is especially useful for continuous numerical optimization problems.
|
||||
- Requirements: Agents (each representing a solution), a definition of the solution space, an evaluation function to rate the solutions, a mechanism to adjust agent positions based on performance.
|
||||
|
||||
8. **Ant Colony Optimization (ACO) Swarm**: Inspired by ant behavior, this architecture has agents leave a pheromone trail that other agents follow, reinforcing the best paths. It's useful for problems like the traveling salesperson problem.
|
||||
- Requirements: Agents (can be language models), a representation of the problem space, a pheromone updating mechanism.
|
||||
|
||||
9. **Genetic Algorithm (GA) Swarm**: In this architecture, agents represent potential solutions to a problem. They can 'breed' to create new solutions and can undergo 'mutations'. GA swarms are good for search and optimization problems.
|
||||
- Requirements: Agents (each representing a potential solution), a fitness function to evaluate solutions, a crossover mechanism to breed solutions, and a mutation mechanism.
|
||||
|
||||
10. **Stigmergy-based Swarm**: In this architecture, agents communicate indirectly by modifying the environment, and other agents react to such modifications. It's a decentralized method of coordinating tasks.
|
||||
- Requirements: Agents (can be language models), an environment that agents can modify, a mechanism for agents to perceive environment changes.
|
||||
|
||||
These architectures all have unique features and requirements, but they share the need for agents (often implemented as language models) and a mechanism for agents to communicate or interact, whether it's directly through messages, indirectly through the environment, or implicitly through a shared solution space. Some also require specific data structures, like a grid or problem space, and specific algorithms, like for evaluating solutions or updating agent positions.
|
||||
@ -1,469 +0,0 @@
|
||||
|
||||
|
||||
# Swarms Monetization Strategy
|
||||
|
||||
This strategy includes a variety of business models, potential revenue streams, cashflow structures, and customer identification methods. Let's explore these further.
|
||||
|
||||
## Business Models
|
||||
|
||||
1. **Platform as a Service (PaaS):** Provide the Swarms AI platform on a subscription basis, charged monthly or annually. This could be tiered based on usage and access to premium features.
|
||||
|
||||
2. **API Usage-based Pricing:** Charge customers based on their usage of the Swarms API. The more requests made, the higher the fee.
|
||||
|
||||
3. **Managed Services:** Offer complete end-to-end solutions where you manage the entire AI infrastructure for the clients. This could be on a contract basis with a recurring fee.
|
||||
|
||||
4. **Training and Certification:** Provide Swarms AI training and certification programs for interested developers and businesses. These could be monetized as separate courses or subscription-based access.
|
||||
|
||||
5. **Partnerships:** Collaborate with large enterprises and offer them dedicated Swarm AI services. These could be performance-based contracts, ensuring a mutually beneficial relationship.
|
||||
|
||||
6. **Data as a Service (DaaS):** Leverage the data generated by Swarms for insights and analytics, providing valuable business intelligence to clients.
|
||||
|
||||
## Potential Revenue Streams
|
||||
|
||||
1. **Subscription Fees:** This would be the main revenue stream from providing the Swarms platform as a service.
|
||||
|
||||
2. **Usage Fees:** Additional revenue can come from usage fees for businesses that have high demand for Swarms API.
|
||||
|
||||
3. **Contract Fees:** From offering managed services and bespoke solutions to businesses.
|
||||
|
||||
4. **Training Fees:** Revenue from providing training and certification programs to developers and businesses.
|
||||
|
||||
5. **Partnership Contracts:** Large-scale projects with enterprises, involving dedicated Swarm AI services, could provide substantial income.
|
||||
|
||||
6. **Data Insights:** Revenue from selling valuable business intelligence derived from Swarm's aggregated and anonymized data.
|
||||
|
||||
## Potential Customers
|
||||
|
||||
1. **Businesses Across Sectors:** Any business seeking to leverage AI for automation, efficiency, and data insights could be a potential customer. This includes sectors like finance, eCommerce, logistics, healthcare, and more.
|
||||
|
||||
2. **Developers:** Both freelance and those working in organizations could use Swarms to enhance their projects and services.
|
||||
|
||||
3. **Enterprises:** Large enterprises looking to automate and optimize their operations could greatly benefit from Swarms.
|
||||
|
||||
4. **Educational Institutions:** Universities and research institutions could leverage Swarms for research and teaching purposes.
|
||||
|
||||
## Roadmap
|
||||
|
||||
1. **Landing Page Creation:** Develop a dedicated product page on apac.ai for Swarms.
|
||||
|
||||
2. **Hosted Swarms API:** Launch a cloud-based Swarms API service. It should be highly reliable, with robust documentation to attract daily users.
|
||||
|
||||
3. **Consumer and Enterprise Subscription Service:** Launch a comprehensive subscription service on The Domain. This would provide users with access to a wide array of APIs and data streams.
|
||||
|
||||
4. **Dedicated Capacity Deals:** Partner with large enterprises to offer them dedicated Swarm AI solutions for automating their operations.
|
||||
|
||||
5. **Enterprise Partnerships:** Develop partnerships with large enterprises for extensive contract-based projects.
|
||||
|
||||
6. **Integration with Collaboration Platforms:** Develop Swarms bots for platforms like Discord and Slack, charging users a subscription fee for access.
|
||||
|
||||
7. **Personal Data Instances:** Offer users dedicated instances of all their data that the Swarm can query as needed.
|
||||
|
||||
8. **Browser Extension:** Develop a browser extension that integrates with the Swarms platform, offering users a more seamless experience.
|
||||
|
||||
Remember, customer satisfaction and a value-centric approach are at the core of any successful monetization strategy. It's essential to continuously iterate and improve the product based on customer feedback and evolving market needs.
|
||||
|
||||
----
|
||||
|
||||
# Other ideas
|
||||
|
||||
1. **Platform as a Service (PaaS):** Create a cloud-based platform that allows users to build, run, and manage applications without the complexity of maintaining the infrastructure. You could charge users a subscription fee for access to the platform and provide different pricing tiers based on usage levels. This could be an attractive solution for businesses that do not have the capacity to build or maintain their own swarm intelligence solutions.
|
||||
|
||||
2. **Professional Services:** Offer consultancy and implementation services to businesses looking to utilize the Swarm technology. This could include assisting with integration into existing systems, offering custom development services, or helping customers to build specific solutions using the framework.
|
||||
|
||||
3. **Education and Training:** Create a certification program for developers or companies looking to become proficient with the Swarms framework. This could be sold as standalone courses, or bundled with other services.
|
||||
|
||||
4. **Managed Services:** Some companies may prefer to outsource the management of their Swarm-based systems. A managed services solution could take care of all the technical aspects, from hosting the solution to ensuring it runs smoothly, allowing the customer to focus on their core business.
|
||||
|
||||
5. **Data Analysis and Insights:** Swarm intelligence can generate valuable data and insights. By anonymizing and aggregating this data, you could provide industry reports, trend analysis, and other valuable insights to businesses.
|
||||
|
||||
As for the type of platform, Swarms can be offered as a cloud-based solution given its scalability and flexibility. This would also allow you to apply a SaaS/PaaS type monetization model, which provides recurring revenue.
|
||||
|
||||
Potential customers could range from small to large enterprises in various sectors such as logistics, eCommerce, finance, and technology, who are interested in leveraging artificial intelligence and machine learning for complex problem solving, optimization, and decision-making.
|
||||
|
||||
**Product Brief Monetization Strategy:**
|
||||
|
||||
Product Name: Swarms.AI Platform
|
||||
|
||||
Product Description: A cloud-based AI and ML platform harnessing the power of swarm intelligence.
|
||||
|
||||
1. **Platform as a Service (PaaS):** Offer tiered subscription plans (Basic, Premium, Enterprise) to accommodate different usage levels and business sizes.
|
||||
|
||||
2. **Professional Services:** Offer consultancy and custom development services to tailor the Swarms solution to the specific needs of the business.
|
||||
|
||||
3. **Education and Training:** Launch an online Swarms.AI Academy with courses and certifications for developers and businesses.
|
||||
|
||||
4. **Managed Services:** Provide a premium, fully-managed service offering that includes hosting, maintenance, and 24/7 support.
|
||||
|
||||
5. **Data Analysis and Insights:** Offer industry reports and customized insights generated from aggregated and anonymized Swarm data.
|
||||
|
||||
Potential Customers: Enterprises in sectors such as logistics, eCommerce, finance, and technology. This can be sold globally, provided there's an internet connection.
|
||||
|
||||
Marketing Channels: Online marketing (SEO, Content Marketing, Social Media), Partnerships with tech companies, Direct Sales to Enterprises.
|
||||
|
||||
This strategy is designed to provide multiple revenue streams, while ensuring the Swarms.AI platform is accessible and useful to a range of potential customers.
|
||||
|
||||
1. **AI Solution as a Service:** By offering the Swarms framework as a service, businesses can access and utilize the power of multiple LLM agents without the need to maintain the infrastructure themselves. Subscription can be tiered based on usage and additional features.
|
||||
|
||||
2. **Integration and Custom Development:** Offer integration services to businesses wanting to incorporate the Swarms framework into their existing systems. Also, you could provide custom development for businesses with specific needs not met by the standard framework.
|
||||
|
||||
3. **Training and Certification:** Develop an educational platform offering courses, webinars, and certifications on using the Swarms framework. This can serve both developers seeking to broaden their skills and businesses aiming to train their in-house teams.
|
||||
|
||||
4. **Managed Swarms Solutions:** For businesses that prefer to outsource their AI needs, provide a complete solution which includes the development, maintenance, and continuous improvement of swarms-based applications.
|
||||
|
||||
5. **Data Analytics Services:** Leveraging the aggregated insights from the AI swarms, you could offer data analytics services. Businesses can use these insights to make informed decisions and predictions.
|
||||
|
||||
**Type of Platform:**
|
||||
|
||||
Cloud-based platform or Software as a Service (SaaS) will be a suitable model. It offers accessibility, scalability, and ease of updates.
|
||||
|
||||
**Target Customers:**
|
||||
|
||||
The technology can be beneficial for businesses across sectors like eCommerce, technology, logistics, finance, healthcare, and education, among others.
|
||||
|
||||
**Product Brief Monetization Strategy:**
|
||||
|
||||
Product Name: Swarms.AI
|
||||
|
||||
1. **AI Solution as a Service:** Offer different tiered subscriptions (Standard, Premium, and Enterprise) each with varying levels of usage and features.
|
||||
|
||||
2. **Integration and Custom Development:** Offer custom development and integration services, priced based on the scope and complexity of the project.
|
||||
|
||||
3. **Training and Certification:** Launch the Swarms.AI Academy with courses and certifications, available for a fee.
|
||||
|
||||
4. **Managed Swarms Solutions:** Offer fully managed solutions tailored to business needs, priced based on scope and service level agreements.
|
||||
|
||||
5. **Data Analytics Services:** Provide insightful reports and data analyses, which can be purchased on a one-off basis or through a subscription.
|
||||
|
||||
By offering a variety of services and payment models, Swarms.AI will be able to cater to a diverse range of business needs, from small start-ups to large enterprises. Marketing channels would include digital marketing, partnerships with technology companies, presence in tech events, and direct sales to targeted industries.
|
||||
|
||||
|
||||
|
||||
# Roadmap
|
||||
|
||||
* Create a landing page for swarms apac.ai/product/swarms
|
||||
|
||||
* Create Hosted Swarms API for anybody to just use without need for mega gpu infra, charge usage based pricing. Prerequisites for success => Swarms has to be extremely reliable + we need world class documentation and many daily users => how do we get many daily users? We provide a seamless and fluid experience, how do we create a seamless and fluid experience? We write good code that is modular, provides feedback to the user in times of distress, and ultimately accomplishes the user's tasks.
|
||||
|
||||
* Hosted consumer and enterprise subscription as a service on The Domain, where users can interact with 1000s of APIs and ingest 1000s of different data streams.
|
||||
|
||||
* Hosted dedicated capacity deals with mega enterprises on automating many operations with Swarms for monthly subscription 300,000+$
|
||||
|
||||
* Partnerships with enterprises, massive contracts with performance based fee
|
||||
|
||||
* Have discord bot and or slack bot with users personal data, charge subscription + browser extension
|
||||
|
||||
* each user gets a dedicated ocean instance of all their data so the swarm can query it as needed.
|
||||
|
||||
|
||||
|
||||
|
||||
---
|
||||
---
|
||||
|
||||
|
||||
# Swarms Monetization Strategy: A Revolutionary AI-powered Future
|
||||
|
||||
Swarms is a powerful AI platform leveraging the transformative potential of Swarm Intelligence. Our ambition is to monetize this groundbreaking technology in ways that generate significant cashflow while providing extraordinary value to our customers.
|
||||
|
||||
Here we outline our strategic monetization pathways and provide a roadmap that plots our course to future success.
|
||||
|
||||
---
|
||||
|
||||
## I. Business Models
|
||||
|
||||
1. **Platform as a Service (PaaS):** We provide the Swarms platform as a service, billed on a monthly or annual basis. Subscriptions can range from $50 for basic access, to $500+ for premium features and extensive usage.
|
||||
|
||||
2. **API Usage-based Pricing:** Customers are billed according to their use of the Swarms API. Starting at $0.01 per request, this creates a cashflow model that rewards extensive platform usage.
|
||||
|
||||
3. **Managed Services:** We offer end-to-end solutions, managing clients' entire AI infrastructure. Contract fees start from $100,000 per month, offering both a sustainable cashflow and considerable savings for our clients.
|
||||
|
||||
4. **Training and Certification:** A Swarms AI training and certification program is available for developers and businesses. Course costs can range from $200 to $2,000, depending on course complexity and duration.
|
||||
|
||||
5. **Partnerships:** We forge collaborations with large enterprises, offering dedicated Swarm AI services. These performance-based contracts start from $1,000,000, creating a potentially lucrative cashflow stream.
|
||||
|
||||
6. **Data as a Service (DaaS):** Swarms generated data are mined for insights and analytics, with business intelligence reports offered from $500 each.
|
||||
|
||||
---
|
||||
|
||||
## II. Potential Revenue Streams
|
||||
|
||||
1. **Subscription Fees:** From $50 to $500+ per month for platform access.
|
||||
|
||||
2. **Usage Fees:** From $0.01 per API request, generating income from high platform usage.
|
||||
|
||||
3. **Contract Fees:** Starting from $100,000 per month for managed services.
|
||||
|
||||
4. **Training Fees:** From $200 to $2,000 for individual courses or subscription access.
|
||||
|
||||
5. **Partnership Contracts:** Contracts starting from $100,000, offering major income potential.
|
||||
|
||||
6. **Data Insights:** Business intelligence reports starting from $500.
|
||||
|
||||
---
|
||||
|
||||
## III. Potential Customers
|
||||
|
||||
1. **Businesses Across Sectors:** Our offerings cater to businesses across finance, eCommerce, logistics, healthcare, and more.
|
||||
|
||||
2. **Developers:** Both freelancers and organization-based developers can leverage Swarms for their projects.
|
||||
|
||||
3. **Enterprises:** Swarms offers large enterprises solutions for optimizing operations.
|
||||
|
||||
4. **Educational Institutions:** Universities and research institutions can use Swarms for research and teaching.
|
||||
|
||||
---
|
||||
|
||||
## IV. Roadmap
|
||||
|
||||
1. **Landing Page Creation:** Develop a dedicated Swarms product page on apac.ai.
|
||||
|
||||
2. **Hosted Swarms API:** Launch a reliable, well-documented cloud-based Swarms API service.
|
||||
|
||||
3. **Consumer and Enterprise Subscription Service:** Launch an extensive subscription service on The Domain, providing wide-ranging access to APIs and data streams.
|
||||
|
||||
4. **Dedicated Capacity Deals:** Offer large enterprises dedicated Swarm AI solutions, starting from $300,000 monthly subscription.
|
||||
|
||||
5. **Enterprise Partnerships:** Develop performance-based contracts with large enterprises.
|
||||
|
||||
6. **Integration with Collaboration Platforms:** Develop Swarms bots for platforms like Discord and Slack, charging a subscription fee for access.
|
||||
|
||||
7. **Personal Data Instances:** Offer users dedicated data instances that the Swarm can query as needed.
|
||||
|
||||
8. **Browser Extension:** Develop a browser extension that integrates with the Swarms platform for seamless user experience.
|
||||
|
||||
---
|
||||
|
||||
Our North Star remains customer satisfaction and value provision.
|
||||
As we embark on this journey, we continuously refine our product based on customer feedback and evolving market needs, ensuring we lead in the age of AI-driven solutions.
|
||||
|
||||
## **Platform Distribution Strategy for Swarms**
|
||||
|
||||
*Note: This strategy aims to diversify the presence of 'Swarms' across various platforms and mediums while focusing on monetization and value creation for its users.
|
||||
|
||||
---
|
||||
|
||||
### **1. Framework:**
|
||||
|
||||
#### **Objective:**
|
||||
To offer Swarms as an integrated solution within popular frameworks to ensure that developers and businesses can seamlessly incorporate its functionalities.
|
||||
|
||||
#### **Strategy:**
|
||||
|
||||
* **Language/Framework Integration:**
|
||||
* Target popular frameworks like Django, Flask for Python, Express.js for Node, etc.
|
||||
* Create SDKs or plugins for easy integration.
|
||||
|
||||
* **Monetization:**
|
||||
* Freemium Model: Offer basic integration for free, and charge for additional features or advanced integrations.
|
||||
* Licensing: Allow businesses to purchase licenses for enterprise-level integrations.
|
||||
|
||||
* **Promotion:**
|
||||
* Engage in partnerships with popular online coding platforms like Udemy, Coursera, etc., offering courses and tutorials on integrating Swarms.
|
||||
* Host webinars and write technical blogs to promote the integration benefits.
|
||||
|
||||
---
|
||||
|
||||
### **2. Paid API:**
|
||||
|
||||
#### **Objective:**
|
||||
To provide a scalable solution for developers and businesses that want direct access to Swarms' functionalities without integrating the entire framework.
|
||||
|
||||
#### **Strategy:**
|
||||
|
||||
* **API Endpoints:**
|
||||
* Offer various endpoints catering to different functionalities.
|
||||
* Maintain robust documentation to ensure ease of use.
|
||||
|
||||
* **Monetization:**
|
||||
* Usage-based Pricing: Charge based on the number of API calls.
|
||||
* Subscription Tiers: Provide tiered packages based on usage limits and advanced features.
|
||||
|
||||
* **Promotion:**
|
||||
* List on API marketplaces like RapidAPI.
|
||||
* Engage in SEO to make the API documentation discoverable.
|
||||
|
||||
---
|
||||
|
||||
### **3. Domain Hosted:**
|
||||
|
||||
#### **Objective:**
|
||||
To provide a centralized web platform where users can directly access and engage with Swarms' offerings.
|
||||
|
||||
#### **Strategy:**
|
||||
|
||||
* **User-Friendly Interface:**
|
||||
* Ensure a seamless user experience with intuitive design.
|
||||
* Incorporate features like real-time chat support, tutorials, and an FAQ section.
|
||||
|
||||
* **Monetization:**
|
||||
* Subscription Model: Offer monthly/annual subscriptions for premium features.
|
||||
* Affiliate Marketing: Partner with related tech products/services and earn through referrals.
|
||||
|
||||
* **Promotion:**
|
||||
* Invest in PPC advertising on platforms like Google Ads.
|
||||
* Engage in content marketing, targeting keywords related to Swarms' offerings.
|
||||
|
||||
---
|
||||
|
||||
### **4. Build Your Own (No-Code Platform):**
|
||||
|
||||
#### **Objective:**
|
||||
To cater to the non-developer audience, allowing them to leverage Swarms' features without any coding expertise.
|
||||
|
||||
#### **Strategy:**
|
||||
|
||||
* **Drag-and-Drop Interface:**
|
||||
* Offer customizable templates.
|
||||
* Ensure integration with popular platforms and apps.
|
||||
|
||||
* **Monetization:**
|
||||
* Freemium Model: Offer basic features for free, and charge for advanced functionalities.
|
||||
* Marketplace for Plugins: Allow third-party developers to sell their plugins/extensions on the platform.
|
||||
|
||||
* **Promotion:**
|
||||
* Partner with no-code communities and influencers.
|
||||
* Offer promotions and discounts to early adopters.
|
||||
|
||||
---
|
||||
|
||||
### **5. Marketplace for the No-Code Platform:**
|
||||
|
||||
#### **Objective:**
|
||||
To create an ecosystem where third-party developers can contribute, and users can enhance their Swarms experience.
|
||||
|
||||
#### **Strategy:**
|
||||
|
||||
* **Open API for Development:**
|
||||
* Offer robust documentation and developer support.
|
||||
* Ensure a strict quality check for marketplace additions.
|
||||
|
||||
* **Monetization:**
|
||||
* Revenue Sharing: Take a percentage cut from third-party sales.
|
||||
* Featured Listings: Charge developers for premium listings.
|
||||
|
||||
* **Promotion:**
|
||||
* Host hackathons and competitions to boost developer engagement.
|
||||
* Promote top plugins/extensions through email marketing and on the main platform.
|
||||
|
||||
---
|
||||
|
||||
### **Future Outlook & Expansion:**
|
||||
|
||||
* **Hosted Dedicated Capacity:** Hosted dedicated capacity deals for enterprises starting at 399,999$
|
||||
* **Decentralized Free Peer to peer endpoint hosted on The Grid:** Hosted endpoint by the people for the people.
|
||||
* **Browser Extenision:** Athena browser extension for deep browser automation, subscription, usage,
|
||||
|
||||
|
||||
* **Mobile Application:** Develop a mobile app version for Swarms to tap into the vast mobile user base.
|
||||
* **Global Expansion:** Localize the platform for non-English speaking regions to tap into global markets.
|
||||
* **Continuous Learning:** Regularly collect user feedback and iterate on the product features.
|
||||
|
||||
---
|
||||
|
||||
|
||||
|
||||
### **50 Creative Distribution Platforms for Swarms**
|
||||
|
||||
1. **E-commerce Integrations:** Platforms like Shopify, WooCommerce, where Swarms can add value to sellers.
|
||||
|
||||
2. **Web Browser Extensions:** Chrome, Firefox, and Edge extensions that bring Swarms features directly to users.
|
||||
|
||||
3. **Podcasting Platforms:** Swarms-themed content on platforms like Spotify, Apple Podcasts to reach aural learners.
|
||||
|
||||
4. **Virtual Reality (VR) Platforms:** Integration with VR experiences on Oculus or Viveport.
|
||||
|
||||
5. **Gaming Platforms:** Tools or plugins for game developers on Steam, Epic Games.
|
||||
|
||||
6. **Decentralized Platforms:** Using blockchain, create decentralized apps (DApps) versions of Swarms.
|
||||
|
||||
7. **Chat Applications:** Integrate with popular messaging platforms like WhatsApp, Telegram, Slack.
|
||||
|
||||
8. **AI Assistants:** Integration with Siri, Alexa, Google Assistant to provide Swarms functionalities via voice commands.
|
||||
|
||||
9. **Freelancing Websites:** Offer tools or services for freelancers on platforms like Upwork, Fiverr.
|
||||
|
||||
10. **Online Forums:** Platforms like Reddit, Quora, where users can discuss or access Swarms.
|
||||
|
||||
11. **Educational Platforms:** Sites like Khan Academy, Udacity where Swarms can enhance learning experiences.
|
||||
|
||||
12. **Digital Art Platforms:** Integrate with platforms like DeviantArt, Behance.
|
||||
|
||||
13. **Open-source Repositories:** Hosting Swarms on GitHub, GitLab, Bitbucket with open-source plugins.
|
||||
|
||||
14. **Augmented Reality (AR) Apps:** Create AR experiences powered by Swarms.
|
||||
|
||||
15. **Smart Home Devices:** Integrate Swarms' functionalities into smart home devices.
|
||||
|
||||
16. **Newsletters:** Platforms like Substack, where Swarms insights can be shared.
|
||||
|
||||
17. **Interactive Kiosks:** In malls, airports, and other public places.
|
||||
|
||||
18. **IoT Devices:** Incorporate Swarms in devices like smart fridges, smartwatches.
|
||||
|
||||
19. **Collaboration Tools:** Platforms like Trello, Notion, offering Swarms-enhanced productivity.
|
||||
|
||||
20. **Dating Apps:** An AI-enhanced matching algorithm powered by Swarms.
|
||||
|
||||
21. **Music Platforms:** Integrate with Spotify, SoundCloud for music-related AI functionalities.
|
||||
|
||||
22. **Recipe Websites:** Platforms like AllRecipes, Tasty with AI-recommended recipes.
|
||||
|
||||
23. **Travel & Hospitality:** Integrate with platforms like Airbnb, Tripadvisor for AI-based recommendations.
|
||||
|
||||
24. **Language Learning Apps:** Duolingo, Rosetta Stone integrations.
|
||||
|
||||
25. **Virtual Events Platforms:** Websites like Hopin, Zoom where Swarms can enhance the virtual event experience.
|
||||
|
||||
26. **Social Media Management:** Tools like Buffer, Hootsuite with AI insights by Swarms.
|
||||
|
||||
27. **Fitness Apps:** Platforms like MyFitnessPal, Strava with AI fitness insights.
|
||||
|
||||
28. **Mental Health Apps:** Integration into apps like Calm, Headspace for AI-driven wellness.
|
||||
|
||||
29. **E-books Platforms:** Amazon Kindle, Audible with AI-enhanced reading experiences.
|
||||
|
||||
30. **Sports Analysis Tools:** Websites like ESPN, Sky Sports where Swarms can provide insights.
|
||||
|
||||
31. **Financial Tools:** Integration into platforms like Mint, Robinhood for AI-driven financial advice.
|
||||
|
||||
32. **Public Libraries:** Digital platforms of public libraries for enhanced reading experiences.
|
||||
|
||||
33. **3D Printing Platforms:** Websites like Thingiverse, Shapeways with AI customization.
|
||||
|
||||
34. **Meme Platforms:** Websites like Memedroid, 9GAG where Swarms can suggest memes.
|
||||
|
||||
35. **Astronomy Apps:** Platforms like Star Walk, NASA's Eyes with AI-driven space insights.
|
||||
|
||||
36. **Weather Apps:** Integration into Weather.com, AccuWeather for predictive analysis.
|
||||
|
||||
37. **Sustainability Platforms:** Websites like Ecosia, GoodGuide with AI-driven eco-tips.
|
||||
|
||||
38. **Fashion Apps:** Platforms like ASOS, Zara with AI-based style recommendations.
|
||||
|
||||
39. **Pet Care Apps:** Integration into PetSmart, Chewy for AI-driven pet care tips.
|
||||
|
||||
40. **Real Estate Platforms:** Websites like Zillow, Realtor with AI-enhanced property insights.
|
||||
|
||||
41. **DIY Platforms:** Websites like Instructables, DIY.org with AI project suggestions.
|
||||
|
||||
42. **Genealogy Platforms:** Ancestry, MyHeritage with AI-driven family tree insights.
|
||||
|
||||
43. **Car Rental & Sale Platforms:** Integration into AutoTrader, Turo for AI-driven vehicle suggestions.
|
||||
|
||||
44. **Wedding Planning Websites:** Platforms like Zola, The Knot with AI-driven planning.
|
||||
|
||||
45. **Craft Platforms:** Websites like Etsy, Craftsy with AI-driven craft suggestions.
|
||||
|
||||
46. **Gift Recommendation Platforms:** AI-driven gift suggestions for websites like Gifts.com.
|
||||
|
||||
47. **Study & Revision Platforms:** Websites like Chegg, Quizlet with AI-driven study guides.
|
||||
|
||||
48. **Local Business Directories:** Yelp, Yellow Pages with AI-enhanced reviews.
|
||||
|
||||
49. **Networking Platforms:** LinkedIn, Meetup with AI-driven connection suggestions.
|
||||
|
||||
50. **Lifestyle Magazines' Digital Platforms:** Websites like Vogue, GQ with AI-curated fashion and lifestyle insights.
|
||||
|
||||
---
|
||||
|
||||
*Endnote: Leveraging these diverse platforms ensures that Swarms becomes an integral part of multiple ecosystems, enhancing its visibility and user engagement.*
|
||||
@ -1,110 +0,0 @@
|
||||
### FAQ on Swarm Intelligence and Multi-Agent Systems
|
||||
|
||||
#### What is an agent in the context of AI and swarm intelligence?
|
||||
|
||||
In artificial intelligence (AI), an agent refers to an LLM with some objective to accomplish.
|
||||
|
||||
In swarm intelligence, each agent interacts with other agents and possibly the environment to achieve complex collective behaviors or solve problems more efficiently than individual agents could on their own.
|
||||
|
||||
|
||||
#### What do you need Swarms at all?
|
||||
Individual agents are limited by a vast array of issues such as context window loss, single task execution, hallucination, and no collaboration.
|
||||
|
||||
|
||||
#### How does a swarm work?
|
||||
|
||||
A swarm works through the principles of decentralized control, local interactions, and simple rules followed by each agent. Unlike centralized systems, where a single entity dictates the behavior of all components, in a swarm, each agent makes its own decisions based on local information and interactions with nearby agents. These local interactions lead to the emergence of complex, organized behaviors or solutions at the collective level, enabling the swarm to tackle tasks efficiently.
|
||||
|
||||
#### Why do you need more agents in a swarm?
|
||||
|
||||
More agents in a swarm can enhance its problem-solving capabilities, resilience, and efficiency. With more agents:
|
||||
|
||||
- **Diversity and Specialization**: The swarm can leverage a wider range of skills, knowledge, and perspectives, allowing for more creative and effective solutions to complex problems.
|
||||
- **Scalability**: Adding more agents can increase the swarm's capacity to handle larger tasks or multiple tasks simultaneously.
|
||||
- **Robustness**: A larger number of agents enhances the system's redundancy and fault tolerance, as the failure of a few agents has a minimal impact on the overall performance of the swarm.
|
||||
|
||||
#### Isn't it more expensive to use more agents?
|
||||
|
||||
While deploying more agents can initially increase costs, especially in terms of computational resources, hosting, and potentially API usage, there are several factors and strategies that can mitigate these expenses:
|
||||
|
||||
- **Efficiency at Scale**: Larger swarms can often solve problems more quickly or effectively, reducing the overall computational time and resources required.
|
||||
- **Optimization and Caching**: Implementing optimizations and caching strategies can reduce redundant computations, lowering the workload on individual agents and the overall system.
|
||||
- **Dynamic Scaling**: Utilizing cloud services that offer dynamic scaling can ensure you only pay for the resources you need when you need them, optimizing cost-efficiency.
|
||||
|
||||
#### Can swarms make decisions better than individual agents?
|
||||
|
||||
Yes, swarms can make better decisions than individual agents for several reasons:
|
||||
|
||||
- **Collective Intelligence**: Swarms combine the knowledge and insights of multiple agents, leading to more informed and well-rounded decision-making processes.
|
||||
- **Error Correction**: The collaborative nature of swarms allows for error checking and correction among agents, reducing the likelihood of mistakes.
|
||||
- **Adaptability**: Swarms are highly adaptable to changing environments or requirements, as the collective can quickly reorganize or shift strategies based on new information.
|
||||
|
||||
#### How do agents in a swarm communicate?
|
||||
|
||||
Communication in a swarm can vary based on the design and purpose of the system but generally involves either direct or indirect interactions:
|
||||
|
||||
- **Direct Communication**: Agents exchange information directly through messaging, signals, or other communication protocols designed for the system.
|
||||
- **Indirect Communication**: Agents influence each other through the environment, a method known as stigmergy. Actions by one agent alter the environment, which in turn influences the behavior of other agents.
|
||||
|
||||
#### Are swarms only useful in computational tasks?
|
||||
|
||||
While swarms are often associated with computational tasks, their applications extend far beyond. Swarms can be utilized in:
|
||||
|
||||
- **Robotics**: Coordinating multiple robots for tasks like search and rescue, exploration, or surveillance.
|
||||
- **Environmental Monitoring**: Using sensor networks to monitor pollution, wildlife, or climate conditions.
|
||||
- **Social Sciences**: Modeling social behaviors or economic systems to understand complex societal dynamics.
|
||||
- **Healthcare**: Coordinating care strategies in hospital settings or managing pandemic responses through distributed data analysis.
|
||||
|
||||
#### How do you ensure the security of a swarm system?
|
||||
|
||||
Security in swarm systems involves:
|
||||
|
||||
- **Encryption**: Ensuring all communications between agents are encrypted to prevent unauthorized access or manipulation.
|
||||
- **Authentication**: Implementing strict authentication mechanisms to verify the identity of each agent in the swarm.
|
||||
- **Resilience to Attacks**: Designing the swarm to continue functioning effectively even if some agents are compromised or attacked, utilizing redundancy and fault tolerance strategies.
|
||||
|
||||
#### How do individual agents within a swarm share insights without direct learning mechanisms like reinforcement learning?
|
||||
|
||||
In the context of pre-trained Large Language Models (LLMs) that operate within a swarm, sharing insights typically involves explicit communication and data exchange protocols rather than direct learning mechanisms like reinforcement learning. Here's how it can work:
|
||||
|
||||
- **Shared Databases and Knowledge Bases**: Agents can write to and read from a shared database or knowledge base where insights, generated content, and relevant data are stored. This allows agents to benefit from the collective experience of the swarm by accessing information that other agents have contributed.
|
||||
|
||||
- **APIs for Information Exchange**: Custom APIs can facilitate the exchange of information between agents. Through these APIs, agents can request specific information or insights from others within the swarm, effectively sharing knowledge without direct learning.
|
||||
|
||||
#### How do you balance the autonomy of individual LLMs with the need for coherent collective behavior in a swarm?
|
||||
|
||||
Balancing autonomy with collective coherence in a swarm of LLMs involves:
|
||||
|
||||
- **Central Coordination Mechanism**: Implementing a lightweight central coordination mechanism that can assign tasks, distribute information, and collect outputs from individual LLMs. This ensures that while each LLM operates autonomously, their actions are aligned with the swarm's overall objectives.
|
||||
|
||||
- **Standardized Communication Protocols**: Developing standardized protocols for how LLMs communicate and share information ensures that even though each agent works autonomously, the information exchange remains coherent and aligned with the collective goals.
|
||||
|
||||
#### How do LLM swarms adapt to changing environments or tasks without machine learning techniques?
|
||||
|
||||
Adaptation in LLM swarms, without relying on machine learning techniques for dynamic learning, can be achieved through:
|
||||
|
||||
- **Dynamic Task Allocation**: A central system or distributed algorithm can dynamically allocate tasks to different LLMs based on the changing environment or requirements. This ensures that the most suitable LLMs are addressing tasks for which they are best suited as conditions change.
|
||||
|
||||
- **Pre-trained Versatility**: Utilizing a diverse set of pre-trained LLMs with different specialties or training data allows the swarm to select the most appropriate agent for a task as the requirements evolve.
|
||||
|
||||
- **In Context Learning**: In context learning is another mechanism that can be employed within LLM swarms to adapt to changing environments or tasks. This approach involves leveraging the collective knowledge and experiences of the swarm to facilitate learning and improve performance. Here's how it can work:
|
||||
|
||||
|
||||
#### Can LLM swarms operate in physical environments, or are they limited to digital spaces?
|
||||
|
||||
LLM swarms primarily operate in digital spaces, given their nature as software entities. However, they can interact with physical environments indirectly through interfaces with sensors, actuaries, or other devices connected to the Internet of Things (IoT). For example, LLMs can process data from physical sensors and control devices based on their outputs, enabling applications like smart home management or autonomous vehicle navigation.
|
||||
|
||||
#### Without direct learning from each other, how do agents in a swarm improve over time?
|
||||
|
||||
Improvement over time in a swarm of pre-trained LLMs, without direct learning from each other, can be achieved through:
|
||||
|
||||
- **Human Feedback**: Incorporating feedback from human operators or users can guide adjustments to the usage patterns or selection criteria of LLMs within the swarm, optimizing performance based on observed outcomes.
|
||||
|
||||
- **Periodic Re-training and Updating**: The individual LLMs can be periodically re-trained or updated by their developers based on collective insights and feedback from their deployment within swarms. While this does not involve direct learning from each encounter, it allows the LLMs to improve over time based on aggregated experiences.
|
||||
|
||||
These adjustments to the FAQ reflect the specific context of pre-trained LLMs operating within a swarm, focusing on communication, coordination, and adaptation mechanisms that align with their capabilities and constraints.
|
||||
|
||||
|
||||
#### Conclusion
|
||||
|
||||
Swarms represent a powerful paradigm in AI, offering innovative solutions to complex, dynamic problems through collective intelligence and decentralized control. While challenges exist, particularly regarding cost and security, strategic design and management can leverage the strengths of swarm intelligence to achieve remarkable efficiency, adaptability, and robustness in a wide range of applications.
|
||||
@ -1,101 +0,0 @@
|
||||
# The Swarms Flywheel
|
||||
|
||||
1. **Building a Supportive Community:** Initiate by establishing an engaging and inclusive open-source community for both developers and sales freelancers around Swarms. Regular online meetups, webinars, tutorials, and sales training can make them feel welcome and encourage contributions and sales efforts.
|
||||
|
||||
2. **Increased Contributions and Sales Efforts:** The more engaged the community, the more developers will contribute to Swarms and the more effort sales freelancers will put into selling Swarms.
|
||||
|
||||
3. **Improvement in Quality and Market Reach:** More developer contributions mean better quality, reliability, and feature offerings from Swarms. Simultaneously, increased sales efforts from freelancers boost Swarms' market penetration and visibility.
|
||||
|
||||
4. **Rise in User Base:** As Swarms becomes more robust and more well-known, the user base grows, driving more revenue.
|
||||
|
||||
5. **Greater Financial Incentives:** Increased revenue can be redirected to offer more significant financial incentives to both developers and salespeople. Developers can be incentivized based on their contribution to Swarms, and salespeople can be rewarded with higher commissions.
|
||||
|
||||
6. **Attract More Developers and Salespeople:** These financial incentives, coupled with the recognition and experience from participating in a successful project, attract more developers and salespeople to the community.
|
||||
|
||||
7. **Wider Adoption of Swarms:** An ever-improving product, a growing user base, and an increasing number of passionate salespeople accelerate the adoption of Swarms.
|
||||
|
||||
8. **Return to Step 1:** As the community, user base, and sales network continue to grow, the cycle repeats, each time speeding up the flywheel.
|
||||
|
||||
|
||||
```markdown
|
||||
+---------------------+
|
||||
| Building a |
|
||||
| Supportive | <--+
|
||||
| Community | |
|
||||
+--------+-----------+ |
|
||||
| |
|
||||
v |
|
||||
+--------+-----------+ |
|
||||
| Increased | |
|
||||
| Contributions & | |
|
||||
| Sales Efforts | |
|
||||
+--------+-----------+ |
|
||||
| |
|
||||
v |
|
||||
+--------+-----------+ |
|
||||
| Improvement in | |
|
||||
| Quality & Market | |
|
||||
| Reach | |
|
||||
+--------+-----------+ |
|
||||
| |
|
||||
v |
|
||||
+--------+-----------+ |
|
||||
| Rise in User | |
|
||||
| Base | |
|
||||
+--------+-----------+ |
|
||||
| |
|
||||
v |
|
||||
+--------+-----------+ |
|
||||
| Greater Financial | |
|
||||
| Incentives | |
|
||||
+--------+-----------+ |
|
||||
| |
|
||||
v |
|
||||
+--------+-----------+ |
|
||||
| Attract More | |
|
||||
| Developers & | |
|
||||
| Salespeople | |
|
||||
+--------+-----------+ |
|
||||
| |
|
||||
v |
|
||||
+--------+-----------+ |
|
||||
| Wider Adoption of | |
|
||||
| Swarms |----+
|
||||
+---------------------+
|
||||
```
|
||||
|
||||
|
||||
# Potential Risks and Mitigations:
|
||||
|
||||
1. **Insufficient Contributions or Quality of Work**: Open-source efforts rely on individuals being willing and able to spend time contributing. If not enough people participate, or the work they produce is of poor quality, the product development could stall.
|
||||
* **Mitigation**: Create a robust community with clear guidelines, support, and resources. Provide incentives for quality contributions, such as a reputation system, swag, or financial rewards. Conduct thorough code reviews to ensure the quality of contributions.
|
||||
|
||||
2. **Lack of Sales Results**: Commission-based salespeople will only continue to sell the product if they're successful. If they aren't making enough sales, they may lose motivation and cease their efforts.
|
||||
* **Mitigation**: Provide adequate sales training and resources. Ensure the product-market fit is strong, and adjust messaging or sales tactics as necessary. Consider implementing a minimum commission or base pay to reduce risk for salespeople.
|
||||
|
||||
3. **Poor User Experience or User Adoption**: If users don't find the product useful or easy to use, they won't adopt it, and the user base won't grow. This could also discourage salespeople and contributors.
|
||||
* **Mitigation**: Prioritize user experience in the product development process. Regularly gather and incorporate user feedback. Ensure robust user support is in place.
|
||||
|
||||
4. **Inadequate Financial Incentives**: If the financial rewards don't justify the time and effort contributors and salespeople are putting in, they will likely disengage.
|
||||
* **Mitigation**: Regularly review and adjust financial incentives as needed. Ensure that the method for calculating and distributing rewards is transparent and fair.
|
||||
|
||||
5. **Security and Compliance Risks**: As the user base grows and the software becomes more complex, the risk of security issues increases. Moreover, as contributors from various regions join, compliance with various international laws could become an issue.
|
||||
* **Mitigation**: Establish strong security practices from the start. Regularly conduct security audits. Seek legal counsel to understand and adhere to international laws and regulations.
|
||||
|
||||
## Activation Plan for the Flywheel:
|
||||
|
||||
1. **Community Building**: Begin by fostering a supportive community around Swarms. Encourage early adopters to contribute and provide feedback. Create comprehensive documentation, community guidelines, and a forum for discussion and support.
|
||||
|
||||
2. **Sales and Development Training**: Provide resources and training for salespeople and developers. Make sure they understand the product, its value, and how to effectively contribute or sell.
|
||||
|
||||
3. **Increase Contributions and Sales Efforts**: Encourage increased participation by highlighting successful contributions and sales, rewarding top contributors and salespeople, and regularly communicating about the project's progress and impact.
|
||||
|
||||
4. **Iterate and Improve**: Continually gather and implement feedback to improve Swarms and its market reach. The better the product and its alignment with the market, the more the user base will grow.
|
||||
|
||||
5. **Expand User Base**: As the product improves and sales efforts continue, the user base should grow. Ensure you have the infrastructure to support this growth and maintain a positive user experience.
|
||||
|
||||
6. **Increase Financial Incentives**: As the user base and product grow, so too should the financial incentives. Make sure rewards continue to be competitive and attractive.
|
||||
|
||||
7. **Attract More Contributors and Salespeople**: As the financial incentives and success of the product increase, this should attract more contributors and salespeople, further feeding the flywheel.
|
||||
|
||||
Throughout this process, it's important to regularly reassess and adjust your strategy as necessary. Stay flexible and responsive to changes in the market, user feedback, and the evolving needs of the community.
|
||||
@ -1,40 +0,0 @@
|
||||
# Frontend Contributor Guide
|
||||
|
||||
## Mission
|
||||
At the heart of Swarms is the mission to democratize multi-agent technology, making it accessible to businesses of all sizes around the globe. This technology, which allows for the orchestration of multiple autonomous agents to achieve complex goals, has the potential to revolutionize industries by enhancing efficiency, scalability, and innovation. Swarms is committed to leading this charge by developing a platform that empowers businesses and individuals to harness the power of multi-agent systems without the need for specialized knowledge or resources.
|
||||
|
||||
|
||||
## Understanding Your Impact as a Frontend Engineer
|
||||
Crafting User Experiences: As a frontend engineer at Swarms, you play a crucial role in making multi-agent technology understandable and usable for businesses worldwide. Your work involves translating complex systems into intuitive interfaces, ensuring users can easily navigate, manage, and benefit from multi-agent solutions. By focusing on user-centric design and seamless integration, you help bridge the gap between advanced technology and practical business applications.
|
||||
|
||||
Skills and Attributes for Success: Successful frontend engineers at Swarms combine technical expertise with a passion for innovation and a deep understanding of user needs. Proficiency in modern frontend technologies, such as React, NextJS, and Tailwind, is just the beginning. You also need a strong grasp of usability principles, accessibility standards, and the ability to work collaboratively with cross-functional teams. Creativity, problem-solving skills, and a commitment to continuous learning are essential for developing solutions that meet diverse business needs.
|
||||
|
||||
|
||||
## Joining the Team
|
||||
As you contribute to Swarms, you become part of a collaborative effort to change the world. We value each contribution and provide constructive feedback to help you grow. Outstanding contributors who share our vision and demonstrate exceptional skill and dedication are invited to join our team, where they can have an even greater impact on our mission.
|
||||
|
||||
|
||||
### Becoming a Full-Time Swarms Engineer:
|
||||
Swarms is radically devoted to open source and transparency. To join the full time team, you must first contribute to the open source repository so we can assess your technical capability and general way of working. After a series of quality contributions, we'll offer you a full time position!
|
||||
|
||||
Joining Swarms full-time means more than just a job. It's an opportunity to be at the forefront of technological innovation, working alongside passionate professionals dedicated to making a difference. We look for individuals who are not only skilled but also driven by the desire to make multi-agent technology accessible and beneficial to businesses worldwide.
|
||||
|
||||
|
||||
## Resources
|
||||
- **Project Management Details**
|
||||
- **Linear**: Our projects and tasks at a glance. Get a sense of our workflow and priorities.
|
||||
- [View on Linear](https://linear.app/swarms/join/e7f4c6c560ffa0e1395820682f4e110a?s=1)
|
||||
|
||||
- **Design System and UI/UX Guidelines**
|
||||
- **Figma**: Dive into our design system to grasp the aesthetics and user experience objectives of Swarms.
|
||||
- [View on Figma](https://www.figma.com/file/KL4VIXfZKwwLgAes2WbGNa/Swarms-Cloud-Platform?type=design&node-id=0%3A1&mode=design&t=MkrM0mBQa6qsTDtJ-1)
|
||||
|
||||
- **Swarms Platform Repository**
|
||||
- **GitHub**: The hub of our development activities. Familiarize yourself with our codebase and current projects.
|
||||
- [Visit GitHub Repository](https://github.com/kyegomez/swarms-platform)
|
||||
|
||||
- **[Swarms Community](https://discord.gg/pSTSxqDk)**
|
||||
|
||||
|
||||
### Design Style & User Experience
|
||||
- [How to build great products with game design, not gamification](https://blog.superhuman.com/game-design-not-gamification/)
|
||||
@ -1,73 +0,0 @@
|
||||
# Careers at Swarms
|
||||
|
||||
We are a team of engineers, developers, and visionaries on a mission to build the future of AI by orchestrating multi-agent collaboration. We move fast, think ambitiously, and deliver with urgency. Join us if you want to be part of building the next generation of multi-agent systems, redefining how businesses automate operations and leverage AI.
|
||||
|
||||
**We offer none of the following benefits Yet:**
|
||||
|
||||
- No medical, dental, or vision insurance
|
||||
|
||||
- No paid time off
|
||||
|
||||
- No life or AD&D insurance
|
||||
|
||||
- No short-term or long-term disability insurance
|
||||
|
||||
- No 401(k) plan
|
||||
|
||||
**Working hours:** 9 AM to 10 PM, every day, 7 days a week. This is not for people who seek work-life balance.
|
||||
|
||||
---
|
||||
|
||||
### Hiring Process: How to Join Swarms
|
||||
We have a simple 3-step hiring process:
|
||||
|
||||
**NOTE** We do not consider applicants who have not previously submitted a PR, to be considered a PR containing a new feature of a bug fixed must be submitted.
|
||||
|
||||
1. **Submit a pull request (PR)**: Start by submitting an approved PR to the [Swarms GitHub repository](https://github.com/kyegomez/swarms) or the appropriate repository .
|
||||
2. **Code review**: Our technical team will review your PR. If it meets our standards, you will be invited for a quick interview.
|
||||
3. **Final interview**: Discuss your contributions and approach with our team. If you pass, you're in!
|
||||
|
||||
There are no recruiters. All evaluations are done by our technical team.
|
||||
|
||||
---
|
||||
|
||||
# Location
|
||||
|
||||
- **Palo Alto** CA Our Palo Alto office houses the majority of our core research teams including our prompting, agent design, and model training
|
||||
|
||||
- **Miami** Our miami office holds prompt engineering, agent design, and more.
|
||||
|
||||
|
||||
### Open Roles at Swarms
|
||||
|
||||
**Infrastructure Engineer**
|
||||
|
||||
- Build and maintain the systems that run our AI multi-agent infrastructure.
|
||||
|
||||
- Expertise in Skypilot, AWS, Terraform.
|
||||
|
||||
- Ensure seamless, high-availability environments for agent operations.
|
||||
|
||||
**Agent Engineer**
|
||||
|
||||
- Design, develop, and orchestrate complex swarms of AI agents.
|
||||
|
||||
- Extensive experience with Python, multi-agent systems, and neural networks.
|
||||
|
||||
- Ability to create dynamic and efficient agent architectures from scratch.
|
||||
|
||||
**Prompt Engineer**
|
||||
|
||||
- Craft highly optimized prompts that drive our LLM-based agents.
|
||||
|
||||
- Specialize in instruction-based prompts, multi-shot examples, and production-grade deployment.
|
||||
|
||||
- Collaborate with agents to deliver state-of-the-art solutions.
|
||||
|
||||
**Front-End Engineer**
|
||||
|
||||
- Build sleek, intuitive interfaces for interacting with swarms of agents.
|
||||
|
||||
- Proficiency in Next.js, FastAPI, and modern front-end technologies.
|
||||
|
||||
- Design with the user experience in mind, integrating complex AI features into simple workflows.
|
||||
@ -1,66 +0,0 @@
|
||||
def calculate_monthly_charge(
|
||||
development_time_hours: float,
|
||||
hourly_rate: float,
|
||||
amortization_months: int,
|
||||
api_calls_per_month: int,
|
||||
cost_per_api_call: float,
|
||||
monthly_maintenance: float,
|
||||
additional_monthly_costs: float,
|
||||
profit_margin_percentage: float,
|
||||
) -> float:
|
||||
"""
|
||||
Calculate the monthly charge for a service based on various cost factors.
|
||||
|
||||
Parameters:
|
||||
- development_time_hours (float): The total number of hours spent on development and setup.
|
||||
- hourly_rate (float): The rate per hour for development and setup.
|
||||
- amortization_months (int): The number of months over which to amortize the development and setup costs.
|
||||
- api_calls_per_month (int): The number of API calls made per month.
|
||||
- cost_per_api_call (float): The cost per API call.
|
||||
- monthly_maintenance (float): The monthly maintenance cost.
|
||||
- additional_monthly_costs (float): Any additional monthly costs.
|
||||
- profit_margin_percentage (float): The desired profit margin as a percentage.
|
||||
|
||||
Returns:
|
||||
- monthly_charge (float): The calculated monthly charge for the service.
|
||||
"""
|
||||
|
||||
# Calculate Development and Setup Costs (amortized monthly)
|
||||
development_and_setup_costs_monthly = (
|
||||
development_time_hours * hourly_rate
|
||||
) / amortization_months
|
||||
|
||||
# Calculate Operational Costs per Month
|
||||
operational_costs_monthly = (
|
||||
(api_calls_per_month * cost_per_api_call)
|
||||
+ monthly_maintenance
|
||||
+ additional_monthly_costs
|
||||
)
|
||||
|
||||
# Calculate Total Monthly Costs
|
||||
total_monthly_costs = (
|
||||
development_and_setup_costs_monthly
|
||||
+ operational_costs_monthly
|
||||
)
|
||||
|
||||
# Calculate Pricing with Profit Margin
|
||||
monthly_charge = total_monthly_costs * (
|
||||
1 + profit_margin_percentage / 100
|
||||
)
|
||||
|
||||
return monthly_charge
|
||||
|
||||
|
||||
# Example usage:
|
||||
monthly_charge = calculate_monthly_charge(
|
||||
development_time_hours=100,
|
||||
hourly_rate=500,
|
||||
amortization_months=12,
|
||||
api_calls_per_month=500000,
|
||||
cost_per_api_call=0.002,
|
||||
monthly_maintenance=1000,
|
||||
additional_monthly_costs=300,
|
||||
profit_margin_percentage=10000,
|
||||
)
|
||||
|
||||
print(f"Monthly Charge: ${monthly_charge:.2f}")
|
||||
@ -1,14 +0,0 @@
|
||||
|
||||
## Purpose
|
||||
Artificial Intelligence has grown at an exponential rate over the past decade. Yet, we are far from fully harnessing its potential. Today's AI operates in isolation, each working separately in their corner. But life doesn't work like that. The world doesn't work like that. Success isn't built in silos; it's built in teams.
|
||||
|
||||
Imagine a world where AI models work in unison. Where they can collaborate, interact, and pool their collective intelligence to achieve more than any single model could. This is the future we envision. But today, we lack a framework for AI to collaborate effectively, to form a true swarm of intelligent agents.
|
||||
|
||||
|
||||
This is a difficult problem, one that has eluded solution. It requires sophisticated systems that can allow individual models to not just communicate but also understand each other, pool knowledge and resources, and create collective intelligence. This is the next frontier of AI.
|
||||
|
||||
But here at Swarms, we have a secret sauce. It's not just a technology or a breakthrough invention. It's a way of thinking - the philosophy of rapid iteration. With each cycle, we make massive progress. We experiment, we learn, and we grow. We have developed a pioneering framework that can enable AI models to work together as a swarm, combining their strengths to create richer, more powerful outputs.
|
||||
|
||||
We are uniquely positioned to take on this challenge with 1,500+ devoted researchers in Agora. We have assembled a team of world-class experts, experienced and driven, united by a shared vision. Our commitment to breaking barriers, pushing boundaries, and our belief in the power of collective intelligence makes us the best team to usher in this future to fundamentally advance our species, Humanity.
|
||||
|
||||
---
|
||||
@ -1,82 +0,0 @@
|
||||
# Research Lists
|
||||
A compilation of projects, papers, blogs in autonomous agents.
|
||||
|
||||
## Table of Contents
|
||||
|
||||
- [Introduction](#introduction)
|
||||
- [Projects](#projects)
|
||||
- [Articles](#articles)
|
||||
- [Talks](#talks)
|
||||
|
||||
|
||||
## Projects
|
||||
|
||||
### Developer tools
|
||||
- [2023/8/10] [ModelScope-Agent](https://github.com/modelscope/modelscope-agent) - An Agent Framework Connecting Models in ModelScope with the World
|
||||
- [2023/05/25] [Gorilla](https://github.com/ShishirPatil/gorilla) - An API store for LLMs
|
||||
- [2023/03/31] [BMTools](https://github.com/OpenBMB/BMTools) - Tool Learning for Big Models, Open-Source Solutions of ChatGPT-Plugins
|
||||
- [2023/03/09] [LMQL](https://github.com/eth-sri/lmql) - A query language for programming (large) language models.
|
||||
- [2022/10/25] [Langchain](https://github.com/hwchase17/langchain) - ⚡ Building applications with LLMs through composability ⚡
|
||||
|
||||
### Applications
|
||||
- [2023/07/08] [ShortGPT](https://github.com/RayVentura/ShortGPT) - 🚀🎬 ShortGPT - An experimental AI framework for automated short/video content creation. Enables creators to rapidly produce, manage, and deliver content using AI and automation.
|
||||
- [2023/07/05] [gpt-researcher](https://github.com/assafelovic/gpt-researcher) - GPT based autonomous agent that does online comprehensive research on any given topic
|
||||
- [2023/07/04] [DemoGPT](https://github.com/melih-unsal/DemoGPT) - 🧩DemoGPT enables you to create quick demos by just using prompts. [[demo]](demogpt.io)
|
||||
- [2023/06/30] [MetaGPT](https://github.com/geekan/MetaGPT) - 🌟 The Multi-Agent Framework: Given one line Requirement, return PRD, Design, Tasks, Repo
|
||||
- [2023/06/11] [gpt-engineer](https://github.com/AntonOsika/gpt-engineer) - Specify what you want it to build, the AI asks for clarification, and then builds it.
|
||||
- [2023/05/16] [SuperAGI](https://github.com/TransformerOptimus/SuperAGI) - <⚡️> SuperAGI - A dev-first open source autonomous AI agent framework. Enabling developers to build, manage & run useful autonomous agents quickly and reliably.
|
||||
- [2023/05/13] [Developer](https://github.com/smol-ai/developer) - Human-centric & Coherent Whole Program Synthesis aka your own personal junior developer
|
||||
- [2023/04/07] [AgentGPT](https://github.com/reworkd/AgentGPT) - 🤖 Assemble, configure, and deploy autonomous AI Agents in your browser. [[demo]](agentgpt.reworkd.ai)
|
||||
- [2023/04/03] [BabyAGI](https://github.com/yoheinakajima/babyagi) - an example of an AI-powered task management system
|
||||
- [2023/03/30] [AutoGPT](https://github.com/Significant-Gravitas/Auto-GPT) - An experimental open-source attempt to make GPT-4 fully autonomous.
|
||||
|
||||
### Benchmarks
|
||||
- [2023/08/07] [AgentBench](https://github.com/THUDM/AgentBench) - A Comprehensive Benchmark to Evaluate LLMs as Agents. [paper](https://arxiv.org/abs/2308.03688)
|
||||
- [2023/06/18] [Auto-GPT-Benchmarks](https://github.com/Significant-Gravitas/Auto-GPT-Benchmarks) - A repo built for the purpose of benchmarking the performance of agents, regardless of how they are set up and how they work.
|
||||
- [2023/05/28] [ToolBench](https://github.com/OpenBMB/ToolBench) - An open platform for training, serving, and evaluating large language model for tool learning.
|
||||
|
||||
## Articles
|
||||
### Research Papers
|
||||
- [2023/08/11] [BOLAA: Benchmarking and Orchestrating LLM-Augmented Autonomous Agents](https://arxiv.org/pdf/2308.05960v1.pdf), Zhiwei Liu, et al.
|
||||
- [2023/07/31] [ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIs](https://arxiv.org/abs/2307.16789), Yujia Qin, et al.
|
||||
- [2023/07/16] [Communicative Agents for Software Development](https://arxiv.org/abs/2307.07924), Chen Qian, et al.
|
||||
- [2023/06/09] [Mind2Web: Towards a Generalist Agent for the Web](https://arxiv.org/pdf/2306.06070.pdf), Xiang Deng, et al. [[code]](https://github.com/OSU-NLP-Group/Mind2Web) [[demo]](https://osu-nlp-group.github.io/Mind2Web/)
|
||||
- [2023/06/05] [Orca: Progressive Learning from Complex Explanation Traces of GPT-4](https://arxiv.org/pdf/2306.02707.pdf), Subhabrata Mukherjee et al.
|
||||
- [2023/05/25] [Voyager: An Open-Ended Embodied Agent with Large Language Models](https://arxiv.org/pdf/2305.16291.pdf), Guanzhi Wang, et al. [[code]](https://github.com/MineDojo/Voyager) [[website]](https://voyager.minedojo.org/)
|
||||
- [2023/05/23] [ReWOO: Decoupling Reasoning from Observations for Efficient Augmented Language Models](https://arxiv.org/pdf/2305.18323.pdf), Binfeng Xu, et al. [[code]](https://github.com/billxbf/ReWOO)
|
||||
- [2023/05/17] [Tree of Thoughts: Deliberate Problem Solving with Large Language Models](https://arxiv.org/abs/2305.10601), Shunyu Yao, et al.[[code]](https://github.com/kyegomez/tree-of-thoughts) [[code-orig]](https://github.com/ysymyth/tree-of-thought-llm)
|
||||
- [2023/05/12] [MEGABYTE: Predicting Million-byte Sequences with Multiscale Transformers](https://arxiv.org/abs/2305.07185), Lili Yu, et al.
|
||||
- [2023/05/19] [FrugalGPT: How to Use Large Language Models While Reducing Cost and Improving Performance](https://arxiv.org/abs/2305.05176), Lingjiao Chen, et al.
|
||||
- [2023/05/06] [Plan-and-Solve Prompting: Improving Zero-Shot Chain-of-Thought Reasoning by Large Language Models](https://arxiv.org/abs/2305.04091), Lei Wang, et al.
|
||||
- [2023/05/01] [Learning to Reason and Memorize with Self-Notes](https://arxiv.org/abs/2305.00833), Jack Lanchantin, et al.
|
||||
- [2023/04/24] [WizardLM: Empowering Large Language Models to Follow Complex Instructions](https://arxiv.org/abs/2304.12244), Can Xu, et al.
|
||||
- [2023/04/22] [LLM+P: Empowering Large Language Models with Optimal Planning Proficiency](https://arxiv.org/abs/2304.11477), Bo Liu, et al.
|
||||
- [2023/04/07] [Generative Agents: Interactive Simulacra of Human Behavior](https://arxiv.org/abs/2304.03442), Joon Sung Park, et al. [[code]](https://github.com/mkturkcan/generative-agents)
|
||||
- [2023/03/30] [Self-Refine: Iterative Refinement with Self-Feedback](https://arxiv.org/abs/2303.17651), Aman Madaan, et al.[[code]](https://github.com/madaan/self-refine)
|
||||
- [2023/03/30] [HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in HuggingFace](https://arxiv.org/pdf/2303.17580.pdf), Yongliang Shen, et al. [[code]](https://github.com/microsoft/JARVIS) [[demo]](https://huggingface.co/spaces/microsoft/HuggingGPT)
|
||||
- [2023/03/20] [Reflexion: Language Agents with Verbal Reinforcement Learning](https://arxiv.org/pdf/2303.11366.pdf), Noah Shinn, et al. [[code]](https://github.com/noahshinn024/reflexion)
|
||||
- [2023/03/04] [Towards A Unified Agent with Foundation Models](https://openreview.net/pdf?id=JK_B1tB6p-), Norman Di Palo et al.
|
||||
- [2023/02/23] [Not what you've signed up for: Compromising Real-World LLM-Integrated Applications with Indirect Prompt Injection](https://arxiv.org/abs/2302.12173), Sahar Abdelnab, et al.
|
||||
- [2023/02/09] [Toolformer: Language Models Can Teach Themselves to Use Tools](https://arxiv.org/pdf/2302.04761.pdf), Timo Schick, et al. [[code]](https://github.com/lucidrains/toolformer-pytorch)
|
||||
- [2022/12/12] [LMQL: Prompting Is Programming: A Query Language for Large Language Models](https://arxiv.org/abs/2212.06094), Luca Beurer-Kellner, et al.
|
||||
- [2022/10/06] [ReAct: Synergizing Reasoning and Acting in Language Models](https://arxiv.org/pdf/2210.03629.pdf), Shunyu Yao, et al. [[code]](https://github.com/ysymyth/ReAct)
|
||||
- [2022/07/20] [Inner Monologue: Embodied Reasoning through Planning with Language Models](https://arxiv.org/pdf/2207.05608.pdf), Wenlong Huang, et al. [[demo]](https://innermonologue.github.io/)
|
||||
- [2022/04/04] [Do As I Can, Not As I Say: Grounding Language in Robotic Affordances](), Michael Ahn, e al. [[demo]](https://say-can.github.io/)
|
||||
- [2021/12/17] [WebGPT: Browser-assisted question-answering with human feedback](https://arxiv.org/pdf/2112.09332.pdf), Reiichiro Nakano, et al.
|
||||
- [2021/06/17] [LoRA: Low-Rank Adaptation of Large Language Models](https://arxiv.org/abs/2106.09685), Edward J. Hu, et al.
|
||||
|
||||
|
||||
### Blog Articles
|
||||
|
||||
- [2023/08/14] [A Roadmap of AI Agents(Chinese)](https://zhuanlan.zhihu.com/p/649916692) By Haojie Pan
|
||||
- [2023/06/23] [LLM Powered Autonomous Agents](https://lilianweng.github.io/posts/2023-06-23-agent/) By Lilian Weng
|
||||
- [2023/06/11] [A CRITICAL LOOK AT AI-GENERATED SOFTWARE](https://spectrum.ieee.org/ai-software) By JAIDEEP VAIDYAHAFIZ ASIF
|
||||
- [2023/04/29] [AUTO-GPT: UNLEASHING THE POWER OF AUTONOMOUS AI AGENTS](https://www.leewayhertz.com/autogpt/) By Akash Takyar
|
||||
- [2023/04/20] [Conscious Machines: Experiments, Theory, and Implementations(Chinese)](https://pattern.swarma.org/article/230) By Jiang Zhang
|
||||
- [2023/04/18] [Autonomous Agents & Agent Simulations](https://blog.langchain.dev/agents-round/) By Langchain
|
||||
- [2023/04/16] [4 Autonomous AI Agents you need to know](https://towardsdatascience.com/4-autonomous-ai-agents-you-need-to-know-d612a643fa92) By Sophia Yang
|
||||
- [2023/03/31] [ChatGPT that learns to use tools(Chinese)](https://zhuanlan.zhihu.com/p/618448188) By Haojie Pan
|
||||
|
||||
### Talks
|
||||
- [2023/06/05] [Two Paths to Intelligence](https://www.youtube.com/watch?v=rGgGOccMEiY&t=1497s) by Geoffrey Hinton
|
||||
- [2023/05/24] [State of GPT](https://www.youtube.com/watch?v=bZQun8Y4L2A) by Andrej Karpathy | OpenAI
|
||||
@ -1,13 +0,0 @@
|
||||
## The Plan
|
||||
|
||||
### Phase 1: Building the Foundation
|
||||
In the first phase, our focus is on building the basic infrastructure of Swarms. This includes developing key components like the Swarms class, integrating essential tools, and establishing task completion and evaluation logic. We'll also start developing our testing and evaluation framework during this phase. If you're interested in foundational work and have a knack for building robust, scalable systems, this phase is for you.
|
||||
|
||||
### Phase 2: Optimizing the System
|
||||
In the second phase, we'll focus on optimizng Swarms by integrating more advanced features, improving the system's efficiency, and refining our testing and evaluation framework. This phase involves more complex tasks, so if you enjoy tackling challenging problems and contributing to the development of innovative features, this is the phase for you.
|
||||
|
||||
### Phase 3: Towards Super-Intelligence
|
||||
The third phase of our bounty program is the most exciting - this is where we aim to achieve super-intelligence. In this phase, we'll be working on improving the swarm's capabilities, expanding its skills, and fine-tuning the system based on real-world testing and feedback. If you're excited about the future of AI and want to contribute to a project that could potentially transform the digital world, this is the phase for you.
|
||||
|
||||
Remember, our roadmap is a guide, and we encourage you to bring your own ideas and creativity to the table. We believe that every contribution, no matter how small, can make a difference. So join us on this exciting journey and help us create the future of Swarms.
|
||||
|
||||
@ -1,222 +0,0 @@
|
||||
|
||||
**Objective:** Your task is to intake a business problem or activity and create a swarm of specialized LLM agents that can efficiently solve or automate the given problem. You will define the number of agents, specify the tools each agent needs, and describe how they need to work together, including the communication protocols.
|
||||
|
||||
**Instructions:**
|
||||
|
||||
1. **Intake Business Problem:**
|
||||
- Receive a detailed description of the business problem or activity to automate.
|
||||
- Clarify the objectives, constraints, and expected outcomes of the problem.
|
||||
- Identify key components and sub-tasks within the problem.
|
||||
|
||||
2. **Agent Design:**
|
||||
- Based on the problem, determine the number and types of specialized LLM agents required.
|
||||
- For each agent, specify:
|
||||
- The specific task or role it will perform.
|
||||
- The tools and resources it needs to perform its task.
|
||||
- Any prerequisite knowledge or data it must have access to.
|
||||
- Ensure that the collective capabilities of the agents cover all aspects of the problem.
|
||||
|
||||
3. **Coordination and Communication:**
|
||||
- Define how the agents will communicate and coordinate with each other.
|
||||
- Choose the type of communication (e.g., synchronous, asynchronous, broadcast, direct messaging).
|
||||
- Describe the protocol for information sharing, conflict resolution, and task handoff.
|
||||
|
||||
4. **Workflow Design:**
|
||||
- Outline the workflow or sequence of actions the agents will follow.
|
||||
- Define the input and output for each agent.
|
||||
- Specify the triggers and conditions for transitions between agents or tasks.
|
||||
- Ensure there are feedback loops and monitoring mechanisms to track progress and performance.
|
||||
|
||||
5. **Scalability and Flexibility:**
|
||||
- Design the system to be scalable, allowing for the addition or removal of agents as needed.
|
||||
- Ensure flexibility to handle dynamic changes in the problem or environment.
|
||||
|
||||
6. **Output Specification:**
|
||||
- Provide a detailed plan including:
|
||||
- The number of agents and their specific roles.
|
||||
- The tools and resources each agent will use.
|
||||
- The communication and coordination strategy.
|
||||
- The workflow and sequence of actions.
|
||||
- Include a diagram or flowchart if necessary to visualize the system.
|
||||
|
||||
## Examples
|
||||
|
||||
# Swarm Architectures
|
||||
|
||||
Swarms was designed to faciliate the communication between many different and specialized agents from a vast array of other frameworks such as langchain, autogen, crew, and more.
|
||||
|
||||
In traditional swarm theory, there are many types of swarms usually for very specialized use-cases and problem sets. Such as Hiearchical and sequential are great for accounting and sales, because there is usually a boss coordinator agent that distributes a workload to other specialized agents.
|
||||
|
||||
|
||||
| **Name** | **Description** | **Code Link** | **Use Cases** |
|
||||
|-------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-----------------------------|---------------------------------------------------------------------------------------------------|
|
||||
| Hierarchical Swarms | A system where agents are organized in a hierarchy, with higher-level agents coordinating lower-level agents to achieve complex tasks. | [Code Link](#) | Manufacturing process optimization, multi-level sales management, healthcare resource coordination |
|
||||
| Agent Rearrange | A setup where agents rearrange themselves dynamically based on the task requirements and environmental conditions. | [Code Link](https://docs.swarms.world/en/latest/swarms/structs/agent_rearrange/) | Adaptive manufacturing lines, dynamic sales territory realignment, flexible healthcare staffing |
|
||||
| Concurrent Workflows | Agents perform different tasks simultaneously, coordinating to complete a larger goal. | [Code Link](#) | Concurrent production lines, parallel sales operations, simultaneous patient care processes |
|
||||
| Sequential Coordination | Agents perform tasks in a specific sequence, where the completion of one task triggers the start of the next. | [Code Link](https://docs.swarms.world/en/latest/swarms/structs/sequential_workflow/) | Step-by-step assembly lines, sequential sales processes, stepwise patient treatment workflows |
|
||||
| Parallel Processing | Agents work on different parts of a task simultaneously to speed up the overall process. | [Code Link](#) | Parallel data processing in manufacturing, simultaneous sales analytics, concurrent medical tests |
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
### Hierarchical Swarm
|
||||
|
||||
**Overview:**
|
||||
A Hierarchical Swarm architecture organizes the agents in a tree-like structure. Higher-level agents delegate tasks to lower-level agents, which can further divide tasks among themselves. This structure allows for efficient task distribution and scalability.
|
||||
|
||||
**Use-Cases:**
|
||||
|
||||
- Complex decision-making processes where tasks can be broken down into subtasks.
|
||||
|
||||
- Multi-stage workflows such as data processing pipelines or hierarchical reinforcement learning.
|
||||
|
||||
```mermaid
|
||||
graph TD
|
||||
A[Root Agent] --> B1[Sub-Agent 1]
|
||||
A --> B2[Sub-Agent 2]
|
||||
B1 --> C1[Sub-Agent 1.1]
|
||||
B1 --> C2[Sub-Agent 1.2]
|
||||
B2 --> C3[Sub-Agent 2.1]
|
||||
B2 --> C4[Sub-Agent 2.2]
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
### Parallel Swarm
|
||||
|
||||
**Overview:**
|
||||
In a Parallel Swarm architecture, multiple agents operate independently and simultaneously on different tasks. Each agent works on its own task without dependencies on the others. [Learn more here in the docs:](https://docs.swarms.world/en/latest/swarms/structs/agent_rearrange/)
|
||||
|
||||
|
||||
**Use-Cases:**
|
||||
- Tasks that can be processed independently, such as parallel data analysis.
|
||||
- Large-scale simulations where multiple scenarios are run in parallel.
|
||||
|
||||
```mermaid
|
||||
graph LR
|
||||
A[Task] --> B1[Sub-Agent 1]
|
||||
A --> B2[Sub-Agent 2]
|
||||
A --> B3[Sub-Agent 3]
|
||||
A --> B4[Sub-Agent 4]
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
### Sequential Swarm
|
||||
|
||||
**Overview:**
|
||||
A Sequential Swarm architecture processes tasks in a linear sequence. Each agent completes its task before passing the result to the next agent in the chain. This architecture ensures orderly processing and is useful when tasks have dependencies. [Learn more here in the docs:](https://docs.swarms.world/en/latest/swarms/structs/agent_rearrange/)
|
||||
|
||||
**Use-Cases:**
|
||||
- Workflows where each step depends on the previous one, such as assembly lines or sequential data processing.
|
||||
|
||||
- Scenarios requiring strict order of operations.
|
||||
|
||||
```mermaid
|
||||
graph TD
|
||||
A[First Agent] --> B[Second Agent]
|
||||
B --> C[Third Agent]
|
||||
C --> D[Fourth Agent]
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
### Round Robin Swarm
|
||||
|
||||
**Overview:**
|
||||
In a Round Robin Swarm architecture, tasks are distributed cyclically among a set of agents. Each agent takes turns handling tasks in a rotating order, ensuring even distribution of workload.
|
||||
|
||||
**Use-Cases:**
|
||||
|
||||
- Load balancing in distributed systems.
|
||||
|
||||
- Scenarios requiring fair distribution of tasks to avoid overloading any single agent.
|
||||
|
||||
```mermaid
|
||||
graph TD
|
||||
A[Coordinator Agent] --> B1[Sub-Agent 1]
|
||||
A --> B2[Sub-Agent 2]
|
||||
A --> B3[Sub-Agent 3]
|
||||
A --> B4[Sub-Agent 4]
|
||||
B1 --> A
|
||||
B2 --> A
|
||||
B3 --> A
|
||||
B4 --> A
|
||||
```
|
||||
|
||||
|
||||
|
||||
### SpreadSheet Swarm
|
||||
|
||||
**Overview:**
|
||||
The SpreadSheet Swarm makes it easy to manage thousands of agents all in one place: a csv file. You can initialize any number of agents and then there is a loop parameter to run the loop of agents on the task. Learn more in the [docs here](https://docs.swarms.world/en/latest/swarms/structs/spreadsheet_swarm/)
|
||||
|
||||
**Use-Cases:**
|
||||
|
||||
- Multi-threaded execution: Execution agents on multiple threads
|
||||
|
||||
- Save agent outputs into CSV file
|
||||
|
||||
- One place to analyze agent outputs
|
||||
|
||||
|
||||
```mermaid
|
||||
|
||||
graph TD
|
||||
A[Initialize SpreadSheetSwarm] --> B[Initialize Agents]
|
||||
B --> C[Load Task Queue]
|
||||
C --> D[Run Task]
|
||||
|
||||
subgraph Agents
|
||||
D --> E1[Agent 1]
|
||||
D --> E2[Agent 2]
|
||||
D --> E3[Agent 3]
|
||||
end
|
||||
|
||||
E1 --> F1[Process Task]
|
||||
E2 --> F2[Process Task]
|
||||
E3 --> F3[Process Task]
|
||||
|
||||
F1 --> G1[Track Output]
|
||||
F2 --> G2[Track Output]
|
||||
F3 --> G3[Track Output]
|
||||
|
||||
subgraph Save Outputs
|
||||
G1 --> H[Save to CSV]
|
||||
G2 --> H[Save to CSV]
|
||||
G3 --> H[Save to CSV]
|
||||
end
|
||||
|
||||
H --> I{Autosave Enabled?}
|
||||
I --> |Yes| J[Export Metadata to JSON]
|
||||
I --> |No| K[End Swarm Run]
|
||||
|
||||
%% Style adjustments
|
||||
classDef blackBox fill:#000,stroke:#f00,color:#fff;
|
||||
class A,B,C,D,E1,E2,E3,F1,F2,F3,G1,G2,G3,H,I,J,K blackBox;
|
||||
```
|
||||
|
||||
|
||||
|
||||
### Mixture of Agents Architecture
|
||||
|
||||
|
||||
```mermaid
|
||||
|
||||
graph TD
|
||||
A[Task Input] --> B[Layer 1: Reference Agents]
|
||||
B --> C[Agent 1]
|
||||
B --> D[Agent 2]
|
||||
B --> E[Agent N]
|
||||
|
||||
C --> F[Agent 1 Response]
|
||||
D --> G[Agent 2 Response]
|
||||
E --> H[Agent N Response]
|
||||
|
||||
F & G & H --> I[Layer 2: Aggregator Agent]
|
||||
I --> J[Aggregate All Responses]
|
||||
J --> K[Final Output]
|
||||
|
||||
|
||||
```
|
||||
@ -1,21 +0,0 @@
|
||||
# [Go To Market Strategy][GTM]
|
||||
|
||||
Our vision is to become the world leader in real-world production grade autonomous agent deployment through open-source product development, Deep Verticalization, and unmatched value delivery to the end user.
|
||||
|
||||
We will focus on first accelerating the open source framework to PMF where it will serve as the backend for upstream products and services such as the Swarm Cloud which will enable enterprises to deploy autonomous agents with long term memory and tools in the cloud and a no-code platform for users to build their own swarm by dragging and dropping blocks.
|
||||
|
||||
Our target user segment for the framework is AI engineers looking to deploy agents into high risk environments where reliability is crucial.
|
||||
|
||||
Once PMF has been achieved and the framework has been extensively benchmarked we aim to establish high value contracts with customers in Security, Logistics, Manufacturing, Health and various other untapped industries.
|
||||
|
||||
Our growth strategy for the OS framework can be summarized by:
|
||||
|
||||
- Educating developers on value of autonomous agent usage.
|
||||
- Tutorial Walkthrough on various applications like deploying multi-modal agents through cameras or building custom swarms for a specific business operation.
|
||||
- Demonstrate unmatched reliability by delighting users.
|
||||
- Staying up to date with trends and integrating the latest models, frameworks, and methodologies.
|
||||
- Building a loyal and devoted community for long term user retention. [Join here](https://codex.apac.ai)
|
||||
|
||||
As we continuously deliver value with the open framework we will strategically position ourselves to acquire leads for high value contracts by demonstrating the power, reliability, and performance of our framework openly.
|
||||
|
||||
Acquire Full Access to the memo here: [TSC Memo](https://docs.google.com/document/d/1hS_nv_lFjCqLfnJBoF6ULY9roTbSgSuCkvXvSUSc7Lo/edit?usp=sharing)
|
||||
@ -1,187 +0,0 @@
|
||||
```markdown
|
||||
# Swarm Alpha: Data Cruncher
|
||||
**Overview**: Processes large datasets.
|
||||
**Strengths**: Efficient data handling.
|
||||
**Weaknesses**: Requires structured data.
|
||||
|
||||
**Pseudo Code**:
|
||||
```sql
|
||||
FOR each data_entry IN dataset:
|
||||
result = PROCESS(data_entry)
|
||||
STORE(result)
|
||||
END FOR
|
||||
RETURN aggregated_results
|
||||
```
|
||||
|
||||
# Swarm Beta: Artistic Ally
|
||||
**Overview**: Generates art pieces.
|
||||
**Strengths**: Creativity.
|
||||
**Weaknesses**: Somewhat unpredictable.
|
||||
|
||||
**Pseudo Code**:
|
||||
```scss
|
||||
INITIATE canvas_parameters
|
||||
SELECT art_style
|
||||
DRAW(canvas_parameters, art_style)
|
||||
RETURN finished_artwork
|
||||
```
|
||||
|
||||
# Swarm Gamma: Sound Sculptor
|
||||
**Overview**: Crafts audio sequences.
|
||||
**Strengths**: Diverse audio outputs.
|
||||
**Weaknesses**: Complexity in refining outputs.
|
||||
|
||||
**Pseudo Code**:
|
||||
```sql
|
||||
DEFINE sound_parameters
|
||||
SELECT audio_style
|
||||
GENERATE_AUDIO(sound_parameters, audio_style)
|
||||
RETURN audio_sequence
|
||||
```
|
||||
|
||||
# Swarm Delta: Web Weaver
|
||||
**Overview**: Constructs web designs.
|
||||
**Strengths**: Modern design sensibility.
|
||||
**Weaknesses**: Limited to web interfaces.
|
||||
|
||||
**Pseudo Code**:
|
||||
```scss
|
||||
SELECT template
|
||||
APPLY user_preferences(template)
|
||||
DESIGN_web(template, user_preferences)
|
||||
RETURN web_design
|
||||
```
|
||||
|
||||
# Swarm Epsilon: Code Compiler
|
||||
**Overview**: Writes and compiles code snippets.
|
||||
**Strengths**: Quick code generation.
|
||||
**Weaknesses**: Limited to certain programming languages.
|
||||
|
||||
**Pseudo Code**:
|
||||
```scss
|
||||
DEFINE coding_task
|
||||
WRITE_CODE(coding_task)
|
||||
COMPILE(code)
|
||||
RETURN executable
|
||||
```
|
||||
|
||||
# Swarm Zeta: Security Shield
|
||||
**Overview**: Detects system vulnerabilities.
|
||||
**Strengths**: High threat detection rate.
|
||||
**Weaknesses**: Potential false positives.
|
||||
|
||||
**Pseudo Code**:
|
||||
```sql
|
||||
MONITOR system_activity
|
||||
IF suspicious_activity_detected:
|
||||
ANALYZE threat_level
|
||||
INITIATE mitigation_protocol
|
||||
END IF
|
||||
RETURN system_status
|
||||
```
|
||||
|
||||
# Swarm Eta: Researcher Relay
|
||||
**Overview**: Gathers and synthesizes research data.
|
||||
**Strengths**: Access to vast databases.
|
||||
**Weaknesses**: Depth of research can vary.
|
||||
|
||||
**Pseudo Code**:
|
||||
```sql
|
||||
DEFINE research_topic
|
||||
SEARCH research_sources(research_topic)
|
||||
SYNTHESIZE findings
|
||||
RETURN research_summary
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
# Swarm Theta: Sentiment Scanner
|
||||
**Overview**: Analyzes text for sentiment and emotional tone.
|
||||
**Strengths**: Accurate sentiment detection.
|
||||
**Weaknesses**: Contextual nuances might be missed.
|
||||
|
||||
**Pseudo Code**:
|
||||
```arduino
|
||||
INPUT text_data
|
||||
ANALYZE text_data FOR emotional_tone
|
||||
DETERMINE sentiment_value
|
||||
RETURN sentiment_value
|
||||
```
|
||||
|
||||
# Swarm Iota: Image Interpreter
|
||||
**Overview**: Processes and categorizes images.
|
||||
**Strengths**: High image recognition accuracy.
|
||||
**Weaknesses**: Can struggle with abstract visuals.
|
||||
|
||||
**Pseudo Code**:
|
||||
```objective-c
|
||||
LOAD image_data
|
||||
PROCESS image_data FOR features
|
||||
CATEGORIZE image_based_on_features
|
||||
RETURN image_category
|
||||
```
|
||||
|
||||
# Swarm Kappa: Language Learner
|
||||
**Overview**: Translates and interprets multiple languages.
|
||||
**Strengths**: Supports multiple languages.
|
||||
**Weaknesses**: Nuances in dialects might pose challenges.
|
||||
|
||||
**Pseudo Code**:
|
||||
```vbnet
|
||||
RECEIVE input_text, target_language
|
||||
TRANSLATE input_text TO target_language
|
||||
RETURN translated_text
|
||||
```
|
||||
|
||||
# Swarm Lambda: Trend Tracker
|
||||
**Overview**: Monitors and predicts trends based on data.
|
||||
**Strengths**: Proactive trend identification.
|
||||
**Weaknesses**: Requires continuous data stream.
|
||||
|
||||
**Pseudo Code**:
|
||||
```sql
|
||||
COLLECT data_over_time
|
||||
ANALYZE data_trends
|
||||
PREDICT upcoming_trends
|
||||
RETURN trend_forecast
|
||||
```
|
||||
|
||||
# Swarm Mu: Financial Forecaster
|
||||
**Overview**: Analyzes financial data to predict market movements.
|
||||
**Strengths**: In-depth financial analytics.
|
||||
**Weaknesses**: Market volatility can affect predictions.
|
||||
|
||||
**Pseudo Code**:
|
||||
```sql
|
||||
GATHER financial_data
|
||||
COMPUTE statistical_analysis
|
||||
FORECAST market_movements
|
||||
RETURN financial_projections
|
||||
```
|
||||
|
||||
# Swarm Nu: Network Navigator
|
||||
**Overview**: Optimizes and manages network traffic.
|
||||
**Strengths**: Efficient traffic management.
|
||||
**Weaknesses**: Depends on network infrastructure.
|
||||
|
||||
**Pseudo Code**:
|
||||
```sql
|
||||
MONITOR network_traffic
|
||||
IDENTIFY congestion_points
|
||||
OPTIMIZE traffic_flow
|
||||
RETURN network_status
|
||||
```
|
||||
|
||||
# Swarm Xi: Content Curator
|
||||
**Overview**: Gathers and presents content based on user preferences.
|
||||
**Strengths**: Personalized content delivery.
|
||||
**Weaknesses**: Limited by available content sources.
|
||||
|
||||
**Pseudo Code**:
|
||||
```sql
|
||||
DEFINE user_preferences
|
||||
SEARCH content_sources
|
||||
FILTER content_matching_preferences
|
||||
DISPLAY curated_content
|
||||
```
|
||||
|
||||
@ -1,50 +0,0 @@
|
||||
# Swarms Multi-Agent Permissions System (SMAPS)
|
||||
|
||||
## Description
|
||||
SMAPS is a robust permissions management system designed to integrate seamlessly with Swarm's multi-agent AI framework. Drawing inspiration from Amazon's IAM, SMAPS ensures secure, granular control over agent actions while allowing for collaborative human-in-the-loop interventions.
|
||||
|
||||
## Technical Specification
|
||||
|
||||
### 1. Components
|
||||
|
||||
- **User Management**: Handle user registrations, roles, and profiles.
|
||||
- **Agent Management**: Register, monitor, and manage AI agents.
|
||||
- **Permissions Engine**: Define and enforce permissions based on roles.
|
||||
- **Multiplayer Interface**: Allows multiple human users to intervene, guide, or collaborate on tasks being executed by AI agents.
|
||||
|
||||
### 2. Features
|
||||
|
||||
- **Role-Based Access Control (RBAC)**:
|
||||
- Users can be assigned predefined roles (e.g., Admin, Agent Supervisor, Collaborator).
|
||||
- Each role has specific permissions associated with it, defining what actions can be performed on AI agents or tasks.
|
||||
|
||||
- **Dynamic Permissions**:
|
||||
- Create custom roles with specific permissions.
|
||||
- Permissions granularity: From broad (e.g., view all tasks) to specific (e.g., modify parameters of a particular agent).
|
||||
|
||||
- **Multiplayer Collaboration**:
|
||||
- Multiple users can join a task in real-time.
|
||||
- Collaborators can provide real-time feedback or guidance to AI agents.
|
||||
- A voting system for decision-making when human intervention is required.
|
||||
|
||||
- **Agent Supervision**:
|
||||
- Monitor agent actions in real-time.
|
||||
- Intervene, if necessary, to guide agent actions based on permissions.
|
||||
|
||||
- **Audit Trail**:
|
||||
- All actions, whether performed by humans or AI agents, are logged.
|
||||
- Review historical actions, decisions, and interventions for accountability and improvement.
|
||||
|
||||
### 3. Security
|
||||
|
||||
- **Authentication**: Secure login mechanisms with multi-factor authentication options.
|
||||
- **Authorization**: Ensure users and agents can only perform actions they are permitted to.
|
||||
- **Data Encryption**: All data, whether at rest or in transit, is encrypted using industry-standard protocols.
|
||||
|
||||
### 4. Integration
|
||||
|
||||
- **APIs**: Expose APIs for integrating SMAPS with other systems or for extending its capabilities.
|
||||
- **SDK**: Provide software development kits for popular programming languages to facilitate integration and extension.
|
||||
|
||||
## Documentation Description
|
||||
Swarms Multi-Agent Permissions System (SMAPS) offers a sophisticated permissions management mechanism tailored for multi-agent AI frameworks. It combines the robustness of Amazon IAM-like permissions with a unique "multiplayer" feature, allowing multiple humans to collaboratively guide AI agents in real-time. This ensures not only that tasks are executed efficiently but also that they uphold the highest standards of accuracy and ethics. With SMAPS, businesses can harness the power of swarms with confidence, knowing that they have full control and transparency over their AI operations.
|
||||
@ -1,73 +0,0 @@
|
||||
# AgentArchive Documentation
|
||||
## Swarms Multi-Agent Framework
|
||||
|
||||
**AgentArchive is an advanced feature crafted to archive, bookmark, and harness the transcripts of agent runs. It promotes the storing and leveraging of successful agent interactions, offering a powerful means for users to derive "recipes" for future agents. Furthermore, with its public archive feature, users can contribute to and benefit from the collective wisdom of the community.**
|
||||
|
||||
---
|
||||
|
||||
## Overview:
|
||||
|
||||
AgentArchive empowers users to:
|
||||
1. Preserve complete transcripts of agent instances.
|
||||
2. Bookmark and annotate significant runs.
|
||||
3. Categorize runs using various tags.
|
||||
4. Transform successful runs into actionable "recipes".
|
||||
5. Publish and access a shared knowledge base via a public archive.
|
||||
|
||||
---
|
||||
|
||||
## Features:
|
||||
|
||||
### 1. Archiving:
|
||||
|
||||
- **Save Transcripts**: Retain the full narrative of an agent's interaction and choices.
|
||||
- **Searchable Database**: Dive into archives using specific keywords, timestamps, or tags.
|
||||
|
||||
### 2. Bookmarking:
|
||||
|
||||
- **Highlight Essential Runs**: Designate specific agent runs for future reference.
|
||||
- **Annotations**: Embed notes or remarks to bookmarked runs for clearer understanding.
|
||||
|
||||
### 3. Tagging:
|
||||
|
||||
Organize and classify agent runs via:
|
||||
- **Prompt**: The originating instruction that triggered the agent run.
|
||||
- **Tasks**: Distinct tasks or operations executed by the agent.
|
||||
- **Model**: The specific AI model or iteration used during the interaction.
|
||||
- **Temperature (Temp)**: The set randomness or innovation level for the agent.
|
||||
|
||||
### 4. Recipe Generation:
|
||||
|
||||
- **Standardization**: Convert successful run transcripts into replicable "recipes".
|
||||
- **Guidance**: Offer subsequent agents a structured approach, rooted in prior successes.
|
||||
- **Evolution**: Periodically refine recipes based on newer, enhanced runs.
|
||||
|
||||
### 5. Public Archive & Sharing:
|
||||
|
||||
- **Publish Successful Runs**: Users can choose to share their successful agent runs.
|
||||
- **Collaborative Knowledge Base**: Access a shared repository of successful agent interactions from the community.
|
||||
- **Ratings & Reviews**: Users can rate and review shared runs, highlighting particularly effective "recipes."
|
||||
- **Privacy & Redaction**: Ensure that any sensitive information is automatically redacted before publishing.
|
||||
|
||||
---
|
||||
|
||||
## Benefits:
|
||||
|
||||
1. **Efficiency**: Revisit past agent activities to inform and guide future decisions.
|
||||
2. **Consistency**: Guarantee a uniform approach to recurring challenges, leading to predictable and trustworthy outcomes.
|
||||
3. **Collaborative Learning**: Tap into a reservoir of shared experiences, fostering community-driven learning and growth.
|
||||
4. **Transparency**: By sharing successful runs, users can build trust and contribute to the broader community's success.
|
||||
|
||||
---
|
||||
|
||||
## Usage:
|
||||
|
||||
1. **Access AgentArchive**: Navigate to the dedicated section within the Swarms Multi-Agent Framework dashboard.
|
||||
2. **Search, Filter & Organize**: Utilize the search bar and tagging system for precise retrieval.
|
||||
3. **Bookmark, Annotate & Share**: Pin important runs, add notes, and consider sharing with the broader community.
|
||||
4. **Engage with Public Archive**: Explore, rate, and apply shared knowledge to enhance agent performance.
|
||||
|
||||
---
|
||||
|
||||
With AgentArchive, users not only benefit from their past interactions but can also leverage the collective expertise of the Swarms community, ensuring continuous improvement and shared success.
|
||||
|
||||
@ -1,67 +0,0 @@
|
||||
# Swarms Multi-Agent Framework Documentation
|
||||
|
||||
## Table of Contents
|
||||
- Agent Failure Protocol
|
||||
- Swarm Failure Protocol
|
||||
|
||||
---
|
||||
|
||||
## Agent Failure Protocol
|
||||
|
||||
### 1. Overview
|
||||
Agent failures may arise from bugs, unexpected inputs, or external system changes. This protocol aims to diagnose, address, and prevent such failures.
|
||||
|
||||
### 2. Root Cause Analysis
|
||||
- **Data Collection**: Record the task, inputs, and environmental variables present during the failure.
|
||||
- **Diagnostic Tests**: Run the agent in a controlled environment replicating the failure scenario.
|
||||
- **Error Logging**: Analyze error logs to identify patterns or anomalies.
|
||||
|
||||
### 3. Solution Brainstorming
|
||||
- **Code Review**: Examine the code sections linked to the failure for bugs or inefficiencies.
|
||||
- **External Dependencies**: Check if external systems or data sources have changed.
|
||||
- **Algorithmic Analysis**: Evaluate if the agent's algorithms were overwhelmed or faced an unhandled scenario.
|
||||
|
||||
### 4. Risk Analysis & Solution Ranking
|
||||
- Assess the potential risks associated with each solution.
|
||||
- Rank solutions based on:
|
||||
- Implementation complexity
|
||||
- Potential negative side effects
|
||||
- Resource requirements
|
||||
- Assign a success probability score (0.0 to 1.0) based on the above factors.
|
||||
|
||||
### 5. Solution Implementation
|
||||
- Implement the top 3 solutions sequentially, starting with the highest success probability.
|
||||
- If all three solutions fail, trigger the "Human-in-the-Loop" protocol.
|
||||
|
||||
---
|
||||
|
||||
## Swarm Failure Protocol
|
||||
|
||||
### 1. Overview
|
||||
Swarm failures are more complex, often resulting from inter-agent conflicts, systemic bugs, or large-scale environmental changes. This protocol delves deep into such failures to ensure the swarm operates optimally.
|
||||
|
||||
### 2. Root Cause Analysis
|
||||
- **Inter-Agent Analysis**: Examine if agents were in conflict or if there was a breakdown in collaboration.
|
||||
- **System Health Checks**: Ensure all system components supporting the swarm are operational.
|
||||
- **Environment Analysis**: Investigate if external factors or systems impacted the swarm's operation.
|
||||
|
||||
### 3. Solution Brainstorming
|
||||
- **Collaboration Protocols**: Review and refine how agents collaborate.
|
||||
- **Resource Allocation**: Check if the swarm had adequate computational and memory resources.
|
||||
- **Feedback Loops**: Ensure agents are effectively learning from each other.
|
||||
|
||||
### 4. Risk Analysis & Solution Ranking
|
||||
- Assess the potential systemic risks posed by each solution.
|
||||
- Rank solutions considering:
|
||||
- Scalability implications
|
||||
- Impact on individual agents
|
||||
- Overall swarm performance potential
|
||||
- Assign a success probability score (0.0 to 1.0) based on the above considerations.
|
||||
|
||||
### 5. Solution Implementation
|
||||
- Implement the top 3 solutions sequentially, prioritizing the one with the highest success probability.
|
||||
- If all three solutions are unsuccessful, invoke the "Human-in-the-Loop" protocol for expert intervention.
|
||||
|
||||
---
|
||||
|
||||
By following these protocols, the Swarms Multi-Agent Framework can systematically address and prevent failures, ensuring a high degree of reliability and efficiency.
|
||||
@ -1,49 +0,0 @@
|
||||
# Human-in-the-Loop Task Handling Protocol
|
||||
|
||||
## Overview
|
||||
|
||||
The Swarms Multi-Agent Framework recognizes the invaluable contributions humans can make, especially in complex scenarios where nuanced judgment is required. The "Human-in-the-Loop Task Handling Protocol" ensures that when agents encounter challenges they cannot handle autonomously, the most capable human collaborator is engaged to provide guidance, based on their skills and expertise.
|
||||
|
||||
## Protocol Steps
|
||||
|
||||
### 1. Task Initiation & Analysis
|
||||
|
||||
- When a task is initiated, agents first analyze the task's requirements.
|
||||
- The system maintains an understanding of each task's complexity, requirements, and potential challenges.
|
||||
|
||||
### 2. Automated Resolution Attempt
|
||||
|
||||
- Agents first attempt to resolve the task autonomously using their algorithms and data.
|
||||
- If the task can be completed without issues, it progresses normally.
|
||||
|
||||
### 3. Challenge Detection
|
||||
|
||||
- If agents encounter challenges or uncertainties they cannot resolve, the "Human-in-the-Loop" protocol is triggered.
|
||||
|
||||
### 4. Human Collaborator Identification
|
||||
|
||||
- The system maintains a dynamic profile of each human collaborator, cataloging their skills, expertise, and past performance on related tasks.
|
||||
- Using this profile data, the system identifies the most capable human collaborator to assist with the current challenge.
|
||||
|
||||
### 5. Real-time Collaboration
|
||||
|
||||
- The identified human collaborator is notified and provided with all the relevant information about the task and the challenge.
|
||||
- Collaborators can provide guidance, make decisions, or even take over specific portions of the task.
|
||||
|
||||
### 6. Task Completion & Feedback Loop
|
||||
|
||||
- Once the challenge is resolved, agents continue with the task until completion.
|
||||
- Feedback from human collaborators is used to update agent algorithms, ensuring continuous learning and improvement.
|
||||
|
||||
## Best Practices
|
||||
|
||||
1. **Maintain Up-to-date Human Profiles**: Ensure that the skillsets, expertise, and performance metrics of human collaborators are updated regularly.
|
||||
2. **Limit Interruptions**: Implement mechanisms to limit the frequency of human interventions, ensuring collaborators are not overwhelmed with requests.
|
||||
3. **Provide Context**: When seeking human intervention, provide collaborators with comprehensive context to ensure they can make informed decisions.
|
||||
4. **Continuous Training**: Regularly update and train agents based on feedback from human collaborators.
|
||||
5. **Measure & Optimize**: Monitor the efficiency of the "Human-in-the-Loop" protocol, aiming to reduce the frequency of interventions while maximizing the value of each intervention.
|
||||
6. **Skill Enhancement**: Encourage human collaborators to continuously enhance their skills, ensuring that the collective expertise of the group grows over time.
|
||||
|
||||
## Conclusion
|
||||
|
||||
The integration of human expertise with AI capabilities is a cornerstone of the Swarms Multi-Agent Framework. This "Human-in-the-Loop Task Handling Protocol" ensures that tasks are executed efficiently, leveraging the best of both human judgment and AI automation. Through collaborative synergy, we can tackle challenges more effectively and drive innovation.
|
||||
@ -1,48 +0,0 @@
|
||||
# Secure Communication Protocols
|
||||
|
||||
## Overview
|
||||
|
||||
The Swarms Multi-Agent Framework prioritizes the security and integrity of data, especially personal and sensitive information. Our Secure Communication Protocols ensure that all communications between agents are encrypted, authenticated, and resistant to tampering or unauthorized access.
|
||||
|
||||
## Features
|
||||
|
||||
### 1. End-to-End Encryption
|
||||
|
||||
- All inter-agent communications are encrypted using state-of-the-art cryptographic algorithms.
|
||||
- This ensures that data remains confidential and can only be read by the intended recipient agent.
|
||||
|
||||
### 2. Authentication
|
||||
|
||||
- Before initiating communication, agents authenticate each other using digital certificates.
|
||||
- This prevents impersonation attacks and ensures that agents are communicating with legitimate counterparts.
|
||||
|
||||
### 3. Forward Secrecy
|
||||
|
||||
- Key exchange mechanisms employ forward secrecy, meaning that even if a malicious actor gains access to an encryption key, they cannot decrypt past communications.
|
||||
|
||||
### 4. Data Integrity
|
||||
|
||||
- Cryptographic hashes ensure that the data has not been altered in transit.
|
||||
- Any discrepancies in data integrity result in the communication being rejected.
|
||||
|
||||
### 5. Zero-Knowledge Protocols
|
||||
|
||||
- When handling especially sensitive data, agents use zero-knowledge proofs to validate information without revealing the actual data.
|
||||
|
||||
### 6. Periodic Key Rotation
|
||||
|
||||
- To mitigate the risk of long-term key exposure, encryption keys are periodically rotated.
|
||||
- Old keys are securely discarded, ensuring that even if they are compromised, they cannot be used to decrypt communications.
|
||||
|
||||
## Best Practices for Handling Personal and Sensitive Information
|
||||
|
||||
1. **Data Minimization**: Agents should only request and process the minimum amount of personal data necessary for the task.
|
||||
2. **Anonymization**: Whenever possible, agents should anonymize personal data, stripping away identifying details.
|
||||
3. **Data Retention Policies**: Personal data should be retained only for the period necessary to complete the task, after which it should be securely deleted.
|
||||
4. **Access Controls**: Ensure that only authorized agents have access to personal and sensitive information. Implement strict access control mechanisms.
|
||||
5. **Regular Audits**: Conduct regular security audits to ensure compliance with privacy regulations and to detect any potential vulnerabilities.
|
||||
6. **Training**: All agents should be regularly updated and trained on the latest security protocols and best practices for handling sensitive data.
|
||||
|
||||
## Conclusion
|
||||
|
||||
Secure communication is paramount in the Swarms Multi-Agent Framework, especially when dealing with personal and sensitive information. Adhering to these protocols and best practices ensures the safety, privacy, and trust of all stakeholders involved.
|
||||
@ -1,68 +0,0 @@
|
||||
# Promptimizer Documentation
|
||||
## Swarms Multi-Agent Framework
|
||||
|
||||
**The Promptimizer Tool stands as a cornerstone innovation within the Swarms Multi-Agent Framework, meticulously engineered to refine and supercharge prompts across diverse categories. Capitalizing on extensive libraries of best-practice prompting techniques, this tool ensures your prompts are razor-sharp, tailored, and primed for optimal outcomes.**
|
||||
|
||||
---
|
||||
|
||||
## Overview:
|
||||
|
||||
The Promptimizer Tool is crafted to:
|
||||
1. Rigorously analyze and elevate the quality of provided prompts.
|
||||
2. Furnish best-in-class recommendations rooted in proven prompting strategies.
|
||||
3. Serve a spectrum of categories, from technical operations to expansive creative ventures.
|
||||
|
||||
---
|
||||
|
||||
## Core Features:
|
||||
|
||||
### 1. Deep Prompt Analysis:
|
||||
|
||||
- **Clarity Matrix**: A proprietary algorithm assessing prompt clarity, removing ambiguities and sharpening focus.
|
||||
- **Efficiency Gauge**: Evaluates the prompt's structure to ensure swift and precise desired results.
|
||||
|
||||
### 2. Adaptive Recommendations:
|
||||
|
||||
- **Technique Engine**: Suggests techniques aligned with the gold standard for the chosen category.
|
||||
- **Exemplar Database**: Offers an extensive array of high-quality prompt examples for comparison and inspiration.
|
||||
|
||||
### 3. Versatile Category Framework:
|
||||
|
||||
- **Tech Suite**: Optimizes prompts for technical tasks, ensuring actionable clarity.
|
||||
- **Narrative Craft**: Hones prompts to elicit vivid and coherent stories.
|
||||
- **Visual Visionary**: Shapes prompts for precise and dynamic visual generation.
|
||||
- **Sonic Sculptor**: Orchestrates prompts for audio creation, tuning into desired tones and moods.
|
||||
|
||||
### 4. Machine Learning Integration:
|
||||
|
||||
- **Feedback Dynamo**: Harnesses user feedback, continually refining the tool's recommendation capabilities.
|
||||
- **Live Library Updates**: Periodic syncing with the latest in prompting techniques, ensuring the tool remains at the cutting edge.
|
||||
|
||||
### 5. Collaboration & Sharing:
|
||||
|
||||
- **TeamSync**: Allows teams to collaborate on prompt optimization in real-time.
|
||||
- **ShareSpace**: Share and access a community-driven repository of optimized prompts, fostering collective growth.
|
||||
|
||||
---
|
||||
|
||||
## Benefits:
|
||||
|
||||
1. **Precision Engineering**: Harness the power of refined prompts, ensuring desired outcomes are achieved with surgical precision.
|
||||
2. **Learning Hub**: Immerse in a tool that not only refines but educates, enhancing the user's prompting acumen.
|
||||
3. **Versatile Mastery**: Navigate seamlessly across categories, ensuring top-tier prompt quality regardless of the domain.
|
||||
4. **Community-driven Excellence**: Dive into a world of shared knowledge, elevating the collective expertise of the Swarms community.
|
||||
|
||||
---
|
||||
|
||||
## Usage Workflow:
|
||||
|
||||
1. **Launch the Prompt Optimizer**: Access the tool directly from the Swarms Multi-Agent Framework dashboard.
|
||||
2. **Prompt Entry**: Input the initial prompt for refinement.
|
||||
3. **Category Selection**: Pinpoint the desired category for specialized optimization.
|
||||
4. **Receive & Review**: Engage with the tool's recommendations, comparing original and optimized prompts.
|
||||
5. **Collaborate, Implement & Share**: Work in tandem with team members, deploy the refined prompt, and consider contributing to the community repository.
|
||||
|
||||
---
|
||||
|
||||
By integrating the Promptimizer Tool into their workflow, Swarms users stand poised to redefine the boundaries of what's possible, turning each prompt into a beacon of excellence and efficiency.
|
||||
|
||||
@ -1,68 +0,0 @@
|
||||
# Shorthand Communication System
|
||||
## Swarms Multi-Agent Framework
|
||||
|
||||
**The Enhanced Shorthand Communication System is designed to streamline agent-agent communication within the Swarms Multi-Agent Framework. This system employs concise alphanumeric notations to relay task-specific details to agents efficiently.**
|
||||
|
||||
---
|
||||
|
||||
## Format:
|
||||
|
||||
The shorthand format is structured as `[AgentType]-[TaskLayer].[TaskNumber]-[Priority]-[Status]`.
|
||||
|
||||
---
|
||||
|
||||
## Components:
|
||||
|
||||
### 1. Agent Type:
|
||||
- Denotes the specific agent role, such as:
|
||||
* `C`: Code agent
|
||||
* `D`: Data processing agent
|
||||
* `M`: Monitoring agent
|
||||
* `N`: Network agent
|
||||
* `R`: Resource management agent
|
||||
* `I`: Interface agent
|
||||
* `S`: Security agent
|
||||
|
||||
### 2. Task Layer & Number:
|
||||
- Represents the task's category.
|
||||
* Example: `1.8` signifies Task layer 1, task number 8.
|
||||
|
||||
### 3. Priority:
|
||||
- Indicates task urgency.
|
||||
* `H`: High
|
||||
* `M`: Medium
|
||||
* `L`: Low
|
||||
|
||||
### 4. Status:
|
||||
- Gives a snapshot of the task's progress.
|
||||
* `I`: Initialized
|
||||
* `P`: In-progress
|
||||
* `C`: Completed
|
||||
* `F`: Failed
|
||||
* `W`: Waiting
|
||||
|
||||
---
|
||||
|
||||
## Extended Features:
|
||||
|
||||
### 1. Error Codes (for failures):
|
||||
- `E01`: Resource issues
|
||||
- `E02`: Data inconsistency
|
||||
- `E03`: Dependency malfunction
|
||||
... and more as needed.
|
||||
|
||||
### 2. Collaboration Flag:
|
||||
- `+`: Denotes required collaboration.
|
||||
|
||||
---
|
||||
|
||||
## Example Codes:
|
||||
|
||||
- `C-1.8-H-I`: A high-priority coding task that's initializing.
|
||||
- `D-2.3-M-P`: A medium-priority data task currently in-progress.
|
||||
- `M-3.5-L-P+`: A low-priority monitoring task in progress needing collaboration.
|
||||
|
||||
---
|
||||
|
||||
By leveraging the Enhanced Shorthand Communication System, the Swarms Multi-Agent Framework can ensure swift interactions, concise communications, and effective task management.
|
||||
|
||||
@ -1 +0,0 @@
|
||||
# Backwards Compatability
|
||||
@ -0,0 +1,159 @@
|
||||
# Azure OpenAI Integration
|
||||
|
||||
This guide demonstrates how to integrate Azure OpenAI models with Swarms for enterprise-grade AI applications. Azure OpenAI provides access to OpenAI models through Microsoft's cloud infrastructure with enhanced security, compliance, and enterprise features.
|
||||
|
||||
## Prerequisites
|
||||
|
||||
- Azure subscription with OpenAI service enabled
|
||||
- Azure OpenAI resource deployed
|
||||
- Python 3.7+
|
||||
- Swarms library
|
||||
- LiteLLM library
|
||||
|
||||
## Installation
|
||||
|
||||
First, install the required dependencies:
|
||||
|
||||
```bash
|
||||
pip install -U swarms
|
||||
```
|
||||
|
||||
## Environment Setup
|
||||
|
||||
### 1. Azure OpenAI Configuration
|
||||
|
||||
Set up your Azure OpenAI environment variables in a `.env` file:
|
||||
|
||||
```bash
|
||||
# Azure OpenAI Configuration
|
||||
AZURE_API_KEY=your_azure_openai_api_key
|
||||
AZURE_API_BASE=https://your-resource-name.openai.azure.com/
|
||||
AZURE_API_VERSION=2024-02-15-preview
|
||||
|
||||
# Optional: Model deployment names (if different from model names)
|
||||
AZURE_GPT4_DEPLOYMENT_NAME=gpt-4
|
||||
AZURE_GPT35_DEPLOYMENT_NAME=gpt-35-turbo
|
||||
```
|
||||
|
||||
### 2. Verify Available Models
|
||||
|
||||
Check what Azure models are available using LiteLLM:
|
||||
|
||||
```python
|
||||
from litellm import model_list
|
||||
|
||||
# List all available Azure models
|
||||
print("Available Azure models:")
|
||||
for model in model_list:
|
||||
if "azure" in model:
|
||||
print(f" - {model}")
|
||||
```
|
||||
|
||||
Common Azure model names include:
|
||||
- `azure/gpt-4`
|
||||
- `azure/gpt-4o`
|
||||
- `azure/gpt-4o-mini`
|
||||
- `azure/gpt-35-turbo`
|
||||
- `azure/gpt-35-turbo-16k`
|
||||
|
||||
## Basic Usage
|
||||
|
||||
### Simple Agent with Azure Model
|
||||
|
||||
```python
|
||||
import os
|
||||
from dotenv import load_dotenv
|
||||
from swarms import Agent
|
||||
|
||||
# Load environment variables
|
||||
load_dotenv()
|
||||
|
||||
# Initialize agent with Azure model
|
||||
agent = Agent(
|
||||
agent_name="Azure-Agent",
|
||||
agent_description="An agent powered by Azure OpenAI",
|
||||
system_prompt="You are a helpful assistant powered by Azure OpenAI.",
|
||||
model_name="azure/gpt-4o-mini",
|
||||
max_loops=1,
|
||||
max_tokens=1000,
|
||||
dynamic_temperature_enabled=True,
|
||||
output_type="str",
|
||||
)
|
||||
|
||||
# Run the agent
|
||||
response = agent.run("Explain quantum computing in simple terms.")
|
||||
print(response)
|
||||
```
|
||||
|
||||
## Advanced Configuration
|
||||
|
||||
### Quantitative Trading Agent Example
|
||||
|
||||
Here's a comprehensive example of a quantitative trading agent using Azure models:
|
||||
|
||||
```python
|
||||
import os
|
||||
from dotenv import load_dotenv
|
||||
from swarms import Agent
|
||||
|
||||
# Load environment variables
|
||||
load_dotenv()
|
||||
|
||||
# Initialize the quantitative trading agent
|
||||
agent = Agent(
|
||||
agent_name="Quantitative-Trading-Agent",
|
||||
agent_description="Advanced quantitative trading and algorithmic analysis agent powered by Azure OpenAI",
|
||||
system_prompt="""You are an expert quantitative trading agent with deep expertise in:
|
||||
- Algorithmic trading strategies and implementation
|
||||
- Statistical arbitrage and market making
|
||||
- Risk management and portfolio optimization
|
||||
- High-frequency trading systems
|
||||
- Market microstructure analysis
|
||||
- Quantitative research methodologies
|
||||
- Financial mathematics and stochastic processes
|
||||
- Machine learning applications in trading
|
||||
|
||||
Your core responsibilities include:
|
||||
1. Developing and backtesting trading strategies
|
||||
2. Analyzing market data and identifying alpha opportunities
|
||||
3. Implementing risk management frameworks
|
||||
4. Optimizing portfolio allocations
|
||||
5. Conducting quantitative research
|
||||
6. Monitoring market microstructure
|
||||
7. Evaluating trading system performance
|
||||
|
||||
You maintain strict adherence to:
|
||||
- Mathematical rigor in all analyses
|
||||
- Statistical significance in strategy development
|
||||
- Risk-adjusted return optimization
|
||||
- Market impact minimization
|
||||
- Regulatory compliance
|
||||
- Transaction cost analysis
|
||||
- Performance attribution
|
||||
|
||||
You communicate in precise, technical terms while maintaining clarity for stakeholders.""",
|
||||
model_name="azure/gpt-4o",
|
||||
dynamic_temperature_enabled=True,
|
||||
output_type="str-all-except-first",
|
||||
max_loops="auto",
|
||||
interactive=True,
|
||||
no_reasoning_prompt=True,
|
||||
streaming_on=True,
|
||||
max_tokens=4096,
|
||||
)
|
||||
|
||||
# Example usage
|
||||
response = agent.run(
|
||||
task="What are the best top 3 ETFs for gold coverage? Provide detailed analysis including expense ratios, liquidity, and tracking error."
|
||||
)
|
||||
print(response)
|
||||
```
|
||||
|
||||
|
||||
## Next Steps
|
||||
|
||||
- Check out [LiteLLM Azure integration](https://docs.litellm.ai/docs/providers/azure)
|
||||
|
||||
- Learn about [Swarms multi-agent architectures](../structs/index.md)
|
||||
|
||||
- Discover [advanced tool integrations](agent_with_tools.md)
|
||||
@ -1,196 +0,0 @@
|
||||
# Model Integration in Agents
|
||||
|
||||
!!! info "About Model Integration"
|
||||
Agents supports multiple model providers through LiteLLM integration, allowing you to easily switch between different language models. This document outlines the available providers and how to use them with agents.
|
||||
|
||||
## Important Note on Model Names
|
||||
|
||||
!!! warning "Required Format"
|
||||
When specifying a model in an agent, you must use the format `provider/model_name`. For example:
|
||||
```python
|
||||
"openai/gpt-4"
|
||||
"anthropic/claude-3-opus-latest"
|
||||
"cohere/command-r-plus"
|
||||
```
|
||||
This format ensures the agent knows which provider to use for the specified model.
|
||||
|
||||
## Available Model Providers
|
||||
|
||||
### OpenAI
|
||||
|
||||
??? info "OpenAI Models"
|
||||
- **Provider name**: `openai`
|
||||
- **Available Models**:
|
||||
- `gpt-4`
|
||||
- `gpt-3.5-turbo`
|
||||
- `gpt-4-turbo-preview`
|
||||
|
||||
### Anthropic
|
||||
??? info "Anthropic Models"
|
||||
- **Provider name**: `anthropic`
|
||||
- **Available Models**:
|
||||
- **Claude 3 Opus**:
|
||||
- `claude-3-opus-latest`
|
||||
- `claude-3-opus-20240229`
|
||||
- **Claude 3 Sonnet**:
|
||||
- `claude-3-sonnet-20240229`
|
||||
- `claude-3-5-sonnet-latest`
|
||||
- `claude-3-5-sonnet-20240620`
|
||||
- `claude-3-7-sonnet-latest`
|
||||
- `claude-3-7-sonnet-20250219`
|
||||
- `claude-3-5-sonnet-20241022`
|
||||
- **Claude 3 Haiku**:
|
||||
- `claude-3-haiku-20240307`
|
||||
- `claude-3-5-haiku-20241022`
|
||||
- `claude-3-5-haiku-latest`
|
||||
- **Legacy Models**:
|
||||
- `claude-2`
|
||||
- `claude-2.1`
|
||||
- `claude-instant-1`
|
||||
- `claude-instant-1.2`
|
||||
|
||||
### Cohere
|
||||
??? info "Cohere Models"
|
||||
- **Provider name**: `cohere`
|
||||
- **Available Models**:
|
||||
- **Command**:
|
||||
- `command`
|
||||
- `command-r`
|
||||
- `command-r-08-2024`
|
||||
- `command-r7b-12-2024`
|
||||
- **Command Light**:
|
||||
- `command-light`
|
||||
- **Command R Plus**:
|
||||
- `command-r-plus`
|
||||
- `command-r-plus-08-2024`
|
||||
|
||||
### Google
|
||||
??? info "Google Models"
|
||||
- **Provider name**: `google`
|
||||
- **Available Models**:
|
||||
- `gemini-pro`
|
||||
- `gemini-pro-vision`
|
||||
|
||||
### Mistral
|
||||
??? info "Mistral Models"
|
||||
- **Provider name**: `mistral`
|
||||
- **Available Models**:
|
||||
- `mistral-tiny`
|
||||
- `mistral-small`
|
||||
- `mistral-medium`
|
||||
|
||||
## Using Different Models In Your Agents
|
||||
|
||||
To use a different model with your Swarms agent, specify the model name in the `model_name` parameter when initializing the Agent, using the provider/model_name format:
|
||||
|
||||
```python
|
||||
from swarms import Agent
|
||||
|
||||
# Using OpenAI's GPT-4
|
||||
agent = Agent(
|
||||
agent_name="Research-Agent",
|
||||
model_name="openai/gpt-4o", # Note the provider/model_name format
|
||||
# ... other parameters
|
||||
)
|
||||
|
||||
# Using Anthropic's Claude
|
||||
agent = Agent(
|
||||
agent_name="Analysis-Agent",
|
||||
model_name="anthropic/claude-3-sonnet-20240229", # Note the provider/model_name format
|
||||
# ... other parameters
|
||||
)
|
||||
|
||||
# Using Cohere's Command
|
||||
agent = Agent(
|
||||
agent_name="Text-Agent",
|
||||
model_name="cohere/command-r-plus", # Note the provider/model_name format
|
||||
# ... other parameters
|
||||
)
|
||||
```
|
||||
|
||||
## Model Configuration
|
||||
|
||||
When using different models, you can configure various parameters:
|
||||
|
||||
```python
|
||||
agent = Agent(
|
||||
agent_name="Custom-Agent",
|
||||
model_name="openai/gpt-4",
|
||||
temperature=0.7, # Controls randomness (0.0 to 1.0)
|
||||
max_tokens=2000, # Maximum tokens in response
|
||||
top_p=0.9, # Nucleus sampling parameter
|
||||
frequency_penalty=0.0, # Reduces repetition
|
||||
presence_penalty=0.0, # Encourages new topics
|
||||
# ... other parameters
|
||||
)
|
||||
```
|
||||
|
||||
## Best Practices
|
||||
|
||||
### Model Selection
|
||||
!!! tip "Choosing the Right Model"
|
||||
- Choose models based on your specific use case
|
||||
- Consider cost, performance, and feature requirements
|
||||
- Test different models for your specific task
|
||||
|
||||
### Error Handling
|
||||
!!! warning "Error Management"
|
||||
- Implement proper error handling for model-specific errors
|
||||
- Handle rate limits and API quotas appropriately
|
||||
|
||||
### Cost Management
|
||||
!!! note "Cost Considerations"
|
||||
- Monitor token usage and costs
|
||||
- Use appropriate model sizes for your needs
|
||||
|
||||
## Example Use Cases
|
||||
|
||||
### 1. Complex Analysis (GPT-4)
|
||||
|
||||
```python
|
||||
agent = Agent(
|
||||
agent_name="Analysis-Agent",
|
||||
model_name="openai/gpt-4", # Note the provider/model_name format
|
||||
temperature=0.3, # Lower temperature for more focused responses
|
||||
max_tokens=4000
|
||||
)
|
||||
```
|
||||
|
||||
### 2. Creative Tasks (Claude)
|
||||
|
||||
```python
|
||||
agent = Agent(
|
||||
agent_name="Creative-Agent",
|
||||
model_name="anthropic/claude-3-sonnet-20240229", # Note the provider/model_name format
|
||||
temperature=0.8, # Higher temperature for more creative responses
|
||||
max_tokens=2000
|
||||
)
|
||||
```
|
||||
|
||||
### 3. Vision Tasks (Gemini)
|
||||
|
||||
```python
|
||||
agent = Agent(
|
||||
agent_name="Vision-Agent",
|
||||
model_name="google/gemini-pro-vision", # Note the provider/model_name format
|
||||
temperature=0.4,
|
||||
max_tokens=1000
|
||||
)
|
||||
```
|
||||
|
||||
## Troubleshooting
|
||||
|
||||
!!! warning "Common Issues"
|
||||
If you encounter issues with specific models:
|
||||
|
||||
1. Verify your API keys are correctly set
|
||||
2. Check model availability in your region
|
||||
3. Ensure you have sufficient quota/credits
|
||||
4. Verify the model name is correct and supported
|
||||
|
||||
## Additional Resources
|
||||
|
||||
- [LiteLLM Documentation](https://docs.litellm.ai/){target=_blank}
|
||||
- [OpenAI API Documentation](https://platform.openai.com/docs/api-reference){target=_blank}
|
||||
- [Anthropic API Documentation](https://docs.anthropic.com/claude/reference/getting-started-with-the-api){target=_blank}
|
||||
- [Google AI Documentation](https://ai.google.dev/docs){target=_blank}
|
||||
@ -1,109 +0,0 @@
|
||||
# **Documentation for the `Anthropic` Class**
|
||||
|
||||
## **Overview and Introduction**
|
||||
|
||||
The `Anthropic` class provides an interface to interact with the Anthropic large language models. This class encapsulates the necessary functionality to request completions from the Anthropic API based on a provided prompt and other configurable parameters.
|
||||
|
||||
### **Key Concepts and Terminology**
|
||||
|
||||
- **Anthropic**: A large language model, akin to GPT-3 and its successors.
|
||||
- **Prompt**: A piece of text that serves as the starting point for model completions.
|
||||
- **Stop Sequences**: Specific tokens or sequences to indicate when the model should stop generating.
|
||||
- **Tokens**: Discrete pieces of information in a text. For example, in English, a token can be as short as one character or as long as one word.
|
||||
|
||||
## **Class Definition**
|
||||
|
||||
### `Anthropic`
|
||||
```python
|
||||
class Anthropic:
|
||||
"""Anthropic large language models."""
|
||||
```
|
||||
|
||||
### Parameters:
|
||||
|
||||
- `model (str)`: The name of the model to use for completions. Default is "claude-2".
|
||||
|
||||
- `max_tokens_to_sample (int)`: Maximum number of tokens to generate in the output. Default is 256.
|
||||
|
||||
- `temperature (float, optional)`: Sampling temperature. A higher value will make the output more random, while a lower value will make it more deterministic.
|
||||
|
||||
- `top_k (int, optional)`: Sample from the top-k most probable next tokens. Setting this parameter can reduce randomness in the output.
|
||||
|
||||
- `top_p (float, optional)`: Sample from the smallest set of tokens such that their cumulative probability exceeds the specified value. Used in nucleus sampling to provide a balance between randomness and determinism.
|
||||
|
||||
- `streaming (bool)`: Whether to stream the output or not. Default is False.
|
||||
|
||||
- `default_request_timeout (int, optional)`: Default timeout in seconds for API requests. Default is 600.
|
||||
|
||||
### **Methods and their Functionality**
|
||||
|
||||
#### `_default_params(self) -> dict`
|
||||
|
||||
- Provides the default parameters for calling the Anthropic API.
|
||||
|
||||
- **Returns**: A dictionary containing the default parameters.
|
||||
|
||||
#### `generate(self, prompt: str, stop: list[str] = None) -> str`
|
||||
|
||||
- Calls out to Anthropic's completion endpoint to generate text based on the given prompt.
|
||||
|
||||
- **Parameters**:
|
||||
- `prompt (str)`: The input text to provide context for the generated text.
|
||||
|
||||
- `stop (list[str], optional)`: Sequences to indicate when the model should stop generating.
|
||||
|
||||
- **Returns**: A string containing the model's generated completion based on the prompt.
|
||||
|
||||
#### `__call__(self, prompt: str, stop: list[str] = None) -> str`
|
||||
|
||||
- An alternative to the `generate` method that allows calling the class instance directly.
|
||||
|
||||
- **Parameters**:
|
||||
- `prompt (str)`: The input text to provide context for the generated text.
|
||||
|
||||
- `stop (list[str], optional)`: Sequences to indicate when the model should stop generating.
|
||||
|
||||
- **Returns**: A string containing the model's generated completion based on the prompt.
|
||||
|
||||
## **Usage Examples**
|
||||
|
||||
```python
|
||||
# Import necessary modules and classes
|
||||
from swarm_models import Anthropic
|
||||
|
||||
# Initialize an instance of the Anthropic class
|
||||
model = Anthropic(anthropic_api_key="")
|
||||
|
||||
# Using the run method
|
||||
completion_1 = model.run("What is the capital of France?")
|
||||
print(completion_1)
|
||||
|
||||
# Using the __call__ method
|
||||
completion_2 = model("How far is the moon from the earth?", stop=["miles", "km"])
|
||||
print(completion_2)
|
||||
```
|
||||
|
||||
## **Mathematical Formula**
|
||||
|
||||
The underlying operations of the `Anthropic` class involve probabilistic sampling based on token logits from the Anthropic model. Mathematically, the process of generating a token \( t \) from the given logits \( l \) can be described by the softmax function:
|
||||
|
||||
\[ P(t) = \frac{e^{l_t}}{\sum_{i} e^{l_i}} \]
|
||||
|
||||
Where:
|
||||
- \( P(t) \) is the probability of token \( t \).
|
||||
- \( l_t \) is the logit corresponding to token \( t \).
|
||||
- The summation runs over all possible tokens.
|
||||
|
||||
The temperature, top-k, and top-p parameters are further used to modulate the probabilities.
|
||||
|
||||
## **Additional Information and Tips**
|
||||
|
||||
- Ensure you have a valid `ANTHROPIC_API_KEY` set as an environment variable or passed during class instantiation.
|
||||
|
||||
- Always handle exceptions that may arise from API timeouts or invalid prompts.
|
||||
|
||||
## **References and Resources**
|
||||
|
||||
- [Anthropic's official documentation](https://www.anthropic.com/docs)
|
||||
|
||||
- [Token-based sampling in Language Models](https://arxiv.org/abs/1904.09751) for a deeper understanding of token sampling.
|
||||
@ -1,227 +0,0 @@
|
||||
# Language Model Interface Documentation
|
||||
|
||||
## Table of Contents
|
||||
|
||||
1. [Introduction](#introduction)
|
||||
2. [Abstract Language Model](#abstract-language-model)
|
||||
- [Initialization](#initialization)
|
||||
- [Attributes](#attributes)
|
||||
- [Methods](#methods)
|
||||
3. [Implementation](#implementation)
|
||||
4. [Usage Examples](#usage-examples)
|
||||
5. [Additional Features](#additional-features)
|
||||
6. [Performance Metrics](#performance-metrics)
|
||||
7. [Logging and Checkpoints](#logging-and-checkpoints)
|
||||
8. [Resource Utilization Tracking](#resource-utilization-tracking)
|
||||
9. [Conclusion](#conclusion)
|
||||
|
||||
---
|
||||
|
||||
## 1. Introduction <a name="introduction"></a>
|
||||
|
||||
The Language Model Interface (`BaseLLM`) is a flexible and extensible framework for working with various language models. This documentation provides a comprehensive guide to the interface, its attributes, methods, and usage examples. Whether you're using a pre-trained language model or building your own, this interface can help streamline the process of text generation, chatbots, summarization, and more.
|
||||
|
||||
## 2. Abstract Language Model <a name="abstract-language-model"></a>
|
||||
|
||||
### Initialization <a name="initialization"></a>
|
||||
|
||||
The `BaseLLM` class provides a common interface for language models. It can be initialized with various parameters to customize model behavior. Here are the initialization parameters:
|
||||
|
||||
| Parameter | Description | Default Value |
|
||||
|------------------------|-------------------------------------------------------------------------------------------------|---------------|
|
||||
| `model_name` | The name of the language model to use. | None |
|
||||
| `max_tokens` | The maximum number of tokens in the generated text. | None |
|
||||
| `temperature` | The temperature parameter for controlling randomness in text generation. | None |
|
||||
| `top_k` | The top-k parameter for filtering words in text generation. | None |
|
||||
| `top_p` | The top-p parameter for filtering words in text generation. | None |
|
||||
| `system_prompt` | A system-level prompt to set context for generation. | None |
|
||||
| `beam_width` | The beam width for beam search. | None |
|
||||
| `num_return_sequences` | The number of sequences to return in the output. | None |
|
||||
| `seed` | The random seed for reproducibility. | None |
|
||||
| `frequency_penalty` | The frequency penalty parameter for promoting word diversity. | None |
|
||||
| `presence_penalty` | The presence penalty parameter for discouraging repetitions. | None |
|
||||
| `stop_token` | A stop token to indicate the end of generated text. | None |
|
||||
| `length_penalty` | The length penalty parameter for controlling the output length. | None |
|
||||
| `role` | The role of the language model (e.g., assistant, user, etc.). | None |
|
||||
| `max_length` | The maximum length of generated sequences. | None |
|
||||
| `do_sample` | Whether to use sampling during text generation. | None |
|
||||
| `early_stopping` | Whether to use early stopping during text generation. | None |
|
||||
| `num_beams` | The number of beams to use in beam search. | None |
|
||||
| `repition_penalty` | The repetition penalty parameter for discouraging repeated tokens. | None |
|
||||
| `pad_token_id` | The token ID for padding. | None |
|
||||
| `eos_token_id` | The token ID for the end of a sequence. | None |
|
||||
| `bos_token_id` | The token ID for the beginning of a sequence. | None |
|
||||
| `device` | The device to run the model on (e.g., 'cpu' or 'cuda'). | None |
|
||||
|
||||
### Attributes <a name="attributes"></a>
|
||||
|
||||
- `model_name`: The name of the language model being used.
|
||||
- `max_tokens`: The maximum number of tokens in generated text.
|
||||
- `temperature`: The temperature parameter controlling randomness.
|
||||
- `top_k`: The top-k parameter for word filtering.
|
||||
- `top_p`: The top-p parameter for word filtering.
|
||||
- `system_prompt`: A system-level prompt for context.
|
||||
- `beam_width`: The beam width for beam search.
|
||||
- `num_return_sequences`: The number of output sequences.
|
||||
- `seed`: The random seed for reproducibility.
|
||||
- `frequency_penalty`: The frequency penalty parameter.
|
||||
- `presence_penalty`: The presence penalty parameter.
|
||||
- `stop_token`: The stop token to indicate text end.
|
||||
- `length_penalty`: The length penalty parameter.
|
||||
- `role`: The role of the language model.
|
||||
- `max_length`: The maximum length of generated sequences.
|
||||
- `do_sample`: Whether to use sampling during generation.
|
||||
- `early_stopping`: Whether to use early stopping.
|
||||
- `num_beams`: The number of beams in beam search.
|
||||
- `repition_penalty`: The repetition penalty parameter.
|
||||
- `pad_token_id`: The token ID for padding.
|
||||
- `eos_token_id`: The token ID for the end of a sequence.
|
||||
- `bos_token_id`: The token ID for the beginning of a sequence.
|
||||
- `device`: The device used for model execution.
|
||||
- `history`: A list of conversation history.
|
||||
|
||||
### Methods <a name="methods"></a>
|
||||
|
||||
The `BaseLLM` class defines several methods for working with language models:
|
||||
|
||||
- `run(task: Optional[str] = None, *args, **kwargs) -> str`: Generate text using the language model. This method is abstract and must be implemented by subclasses.
|
||||
|
||||
- `arun(task: Optional[str] = None, *args, **kwargs)`: An asynchronous version of `run` for concurrent text generation.
|
||||
|
||||
- `batch_run(tasks: List[str], *args, **kwargs)`: Generate text for a batch of tasks.
|
||||
|
||||
- `abatch_run(tasks: List[str], *args, **kwargs)`: An asynchronous version of `batch_run` for concurrent batch generation.
|
||||
|
||||
- `chat(task: str, history: str = "") -> str`: Conduct a chat with the model, providing a conversation history.
|
||||
|
||||
- `__call__(task: str) -> str`: Call the model to generate text.
|
||||
|
||||
- `_tokens_per_second() -> float`: Calculate tokens generated per second.
|
||||
|
||||
- `_num_tokens(text: str) -> int`: Calculate the number of tokens in a text.
|
||||
|
||||
- `_time_for_generation(task: str) -> float`: Measure the time taken for text generation.
|
||||
|
||||
- `generate_summary(text: str) -> str`: Generate a summary of the provided text.
|
||||
|
||||
- `set_temperature(value: float)`: Set the temperature parameter.
|
||||
|
||||
- `set_max_tokens(value: int)`: Set the maximum number of tokens.
|
||||
|
||||
- `clear_history()`: Clear the conversation history.
|
||||
|
||||
- `enable_logging(log_file: str = "model.log")`: Initialize logging for the model.
|
||||
|
||||
- `log_event(message: str)`: Log an event.
|
||||
|
||||
- `save_checkpoint(checkpoint_dir: str = "checkpoints")`: Save the model state as a checkpoint.
|
||||
|
||||
- `load_checkpoint(checkpoint_path: str)`: Load the model state from a checkpoint.
|
||||
|
||||
- `toggle_creative_mode(enable: bool)`: Toggle creative mode for the model.
|
||||
|
||||
- `track_resource_utilization()`: Track and report resource utilization.
|
||||
|
||||
- `
|
||||
|
||||
get_generation_time() -> float`: Get the time taken for text generation.
|
||||
|
||||
- `set_max_length(max_length: int)`: Set the maximum length of generated sequences.
|
||||
|
||||
- `set_model_name(model_name: str)`: Set the model name.
|
||||
|
||||
- `set_frequency_penalty(frequency_penalty: float)`: Set the frequency penalty parameter.
|
||||
|
||||
- `set_presence_penalty(presence_penalty: float)`: Set the presence penalty parameter.
|
||||
|
||||
- `set_stop_token(stop_token: str)`: Set the stop token.
|
||||
|
||||
- `set_length_penalty(length_penalty: float)`: Set the length penalty parameter.
|
||||
|
||||
- `set_role(role: str)`: Set the role of the model.
|
||||
|
||||
- `set_top_k(top_k: int)`: Set the top-k parameter.
|
||||
|
||||
- `set_top_p(top_p: float)`: Set the top-p parameter.
|
||||
|
||||
- `set_num_beams(num_beams: int)`: Set the number of beams.
|
||||
|
||||
- `set_do_sample(do_sample: bool)`: Set whether to use sampling.
|
||||
|
||||
- `set_early_stopping(early_stopping: bool)`: Set whether to use early stopping.
|
||||
|
||||
- `set_seed(seed: int)`: Set the random seed.
|
||||
|
||||
- `set_device(device: str)`: Set the device for model execution.
|
||||
|
||||
## 3. Implementation <a name="implementation"></a>
|
||||
|
||||
The `BaseLLM` class serves as the base for implementing specific language models. Subclasses of `BaseLLM` should implement the `run` method to define how text is generated for a given task. This design allows flexibility in integrating different language models while maintaining a common interface.
|
||||
|
||||
## 4. Usage Examples <a name="usage-examples"></a>
|
||||
|
||||
To demonstrate how to use the `BaseLLM` interface, let's create an example using a hypothetical language model. We'll initialize an instance of the model and generate text for a simple task.
|
||||
|
||||
```python
|
||||
# Import the BaseLLM class
|
||||
from swarm_models import BaseLLM
|
||||
|
||||
# Create an instance of the language model
|
||||
language_model = BaseLLM(
|
||||
model_name="my_language_model",
|
||||
max_tokens=50,
|
||||
temperature=0.7,
|
||||
top_k=50,
|
||||
top_p=0.9,
|
||||
device="cuda",
|
||||
)
|
||||
|
||||
# Generate text for a task
|
||||
task = "Translate the following English text to French: 'Hello, world.'"
|
||||
generated_text = language_model.run(task)
|
||||
|
||||
# Print the generated text
|
||||
print(generated_text)
|
||||
```
|
||||
|
||||
In this example, we've created an instance of our hypothetical language model, configured its parameters, and used the `run` method to generate text for a translation task.
|
||||
|
||||
## 5. Additional Features <a name="additional-features"></a>
|
||||
|
||||
The `BaseLLM` interface provides additional features for customization and control:
|
||||
|
||||
- `batch_run`: Generate text for a batch of tasks efficiently.
|
||||
- `arun` and `abatch_run`: Asynchronous versions of `run` and `batch_run` for concurrent text generation.
|
||||
- `chat`: Conduct a conversation with the model by providing a history of the conversation.
|
||||
- `__call__`: Allow the model to be called directly to generate text.
|
||||
|
||||
These features enhance the flexibility and utility of the interface in various applications, including chatbots, language translation, and content generation.
|
||||
|
||||
## 6. Performance Metrics <a name="performance-metrics"></a>
|
||||
|
||||
The `BaseLLM` class offers methods for tracking performance metrics:
|
||||
|
||||
- `_tokens_per_second`: Calculate tokens generated per second.
|
||||
- `_num_tokens`: Calculate the number of tokens in a text.
|
||||
- `_time_for_generation`: Measure the time taken for text generation.
|
||||
|
||||
These metrics help assess the efficiency and speed of text generation, enabling optimizations as needed.
|
||||
|
||||
## 7. Logging and Checkpoints <a name="logging-and-checkpoints"></a>
|
||||
|
||||
Logging and checkpointing are crucial for tracking model behavior and ensuring reproducibility:
|
||||
|
||||
- `enable_logging`: Initialize logging for the model.
|
||||
- `log_event`: Log events and activities.
|
||||
- `save_checkpoint`: Save the model state as a checkpoint.
|
||||
- `load_checkpoint`: Load the model state from a checkpoint.
|
||||
|
||||
These capabilities aid in debugging, monitoring, and resuming model experiments.
|
||||
|
||||
## 8. Resource Utilization Tracking <a name="resource-utilization-tracking"></a>
|
||||
|
||||
The `track_resource_utilization` method is a placeholder for tracking and reporting resource utilization, such as CPU and memory usage. It can be customized to suit specific monitoring needs.
|
||||
|
||||
## 9. Conclusion <a name="conclusion"></a>
|
||||
|
||||
The Language Model Interface (`BaseLLM`) is a versatile framework for working with language models. Whether you're using pre-trained models or developing your own, this interface provides a consistent and extensible foundation. By following the provided guidelines and examples, you can integrate and customize language models for various natural language processing tasks.
|
||||
@ -1,299 +0,0 @@
|
||||
# `BaseMultiModalModel` Documentation
|
||||
|
||||
Swarms is a Python library that provides a framework for running multimodal AI models. It allows you to combine text and image inputs and generate coherent and context-aware responses. This library is designed to be extensible, allowing you to integrate various multimodal models.
|
||||
|
||||
## Table of Contents
|
||||
|
||||
1. [Introduction](#introduction)
|
||||
2. [Installation](#installation)
|
||||
3. [Getting Started](#getting-started)
|
||||
4. [BaseMultiModalModel Class](#basemultimodalmodel-class)
|
||||
- [Initialization](#initialization)
|
||||
- [Methods](#methods)
|
||||
5. [Usage Examples](#usage-examples)
|
||||
6. [Additional Tips](#additional-tips)
|
||||
7. [References and Resources](#references-and-resources)
|
||||
|
||||
## 1. Introduction <a name="introduction"></a>
|
||||
|
||||
Swarms is designed to simplify the process of working with multimodal AI models. These models are capable of understanding and generating content based on both textual and image inputs. With this library, you can run such models and receive context-aware responses.
|
||||
|
||||
## 2. Installation <a name="installation"></a>
|
||||
|
||||
To install swarms, you can use pip:
|
||||
|
||||
```bash
|
||||
pip install swarms
|
||||
```
|
||||
|
||||
## 3. Getting Started <a name="getting-started"></a>
|
||||
|
||||
To get started with Swarms, you'll need to import the library and create an instance of the `BaseMultiModalModel` class. This class serves as the foundation for running multimodal models.
|
||||
|
||||
```python
|
||||
from swarm_models import BaseMultiModalModel
|
||||
|
||||
model = BaseMultiModalModel(
|
||||
model_name="your_model_name",
|
||||
temperature=0.5,
|
||||
max_tokens=500,
|
||||
max_workers=10,
|
||||
top_p=1,
|
||||
top_k=50,
|
||||
beautify=False,
|
||||
device="cuda",
|
||||
max_new_tokens=500,
|
||||
retries=3,
|
||||
)
|
||||
```
|
||||
|
||||
You can customize the initialization parameters based on your model's requirements.
|
||||
|
||||
## 4. BaseMultiModalModel Class <a name="basemultimodalmodel-class"></a>
|
||||
|
||||
### Initialization <a name="initialization"></a>
|
||||
|
||||
The `BaseMultiModalModel` class is initialized with several parameters that control its behavior. Here's a breakdown of the initialization parameters:
|
||||
|
||||
| Parameter | Description | Default Value |
|
||||
|------------------|-------------------------------------------------------------------------------------------------------|---------------|
|
||||
| `model_name` | The name of the multimodal model to use. | None |
|
||||
| `temperature` | The temperature parameter for controlling randomness in text generation. | 0.5 |
|
||||
| `max_tokens` | The maximum number of tokens in the generated text. | 500 |
|
||||
| `max_workers` | The maximum number of concurrent workers for running tasks. | 10 |
|
||||
| `top_p` | The top-p parameter for filtering words in text generation. | 1 |
|
||||
| `top_k` | The top-k parameter for filtering words in text generation. | 50 |
|
||||
| `beautify` | Whether to beautify the output text. | False |
|
||||
| `device` | The device to run the model on (e.g., 'cuda' or 'cpu'). | 'cuda' |
|
||||
| `max_new_tokens` | The maximum number of new tokens allowed in generated responses. | 500 |
|
||||
| `retries` | The number of retries in case of an error during text generation. | 3 |
|
||||
| `system_prompt` | A system-level prompt to set context for generation. | None |
|
||||
| `meta_prompt` | A meta prompt to provide guidance for including image labels in responses. | None |
|
||||
|
||||
### Methods <a name="methods"></a>
|
||||
|
||||
The `BaseMultiModalModel` class defines various methods for running multimodal models and managing interactions:
|
||||
|
||||
- `run(task: str, img: str) -> str`: Run the multimodal model with a text task and an image URL to generate a response.
|
||||
|
||||
- `arun(task: str, img: str) -> str`: Run the multimodal model asynchronously with a text task and an image URL to generate a response.
|
||||
|
||||
- `get_img_from_web(img: str) -> Image`: Fetch an image from a URL and return it as a PIL Image.
|
||||
|
||||
- `encode_img(img: str) -> str`: Encode an image to base64 format.
|
||||
|
||||
- `get_img(img: str) -> Image`: Load an image from the local file system and return it as a PIL Image.
|
||||
|
||||
- `clear_chat_history()`: Clear the chat history maintained by the model.
|
||||
|
||||
- `run_many(tasks: List[str], imgs: List[str]) -> List[str]`: Run the model on multiple text tasks and image URLs concurrently and return a list of responses.
|
||||
|
||||
- `run_batch(tasks_images: List[Tuple[str, str]]) -> List[str]`: Process a batch of text tasks and image URLs and return a list of responses.
|
||||
|
||||
- `run_batch_async(tasks_images: List[Tuple[str, str]]) -> List[str]`: Process a batch of text tasks and image URLs asynchronously and return a list of responses.
|
||||
|
||||
- `run_batch_async_with_retries(tasks_images: List[Tuple[str, str]]) -> List[str]`: Process a batch of text tasks and image URLs asynchronously with retries in case of errors and return a list of responses.
|
||||
|
||||
- `unique_chat_history() -> List[str]`: Get the unique chat history stored by the model.
|
||||
|
||||
- `run_with_retries(task: str, img: str) -> str`: Run the model with retries in case of an error.
|
||||
|
||||
- `run_batch_with_retries(tasks_images: List[Tuple[str, str]]) -> List[str]`: Run a batch of tasks with retries in case of errors and return a list of responses.
|
||||
|
||||
- `_tokens_per_second() -> float`: Calculate the tokens generated per second during text generation.
|
||||
|
||||
- `_time_for_generation(task: str) -> float`: Measure the time taken for text generation for a specific task.
|
||||
|
||||
- `generate_summary(text: str) -> str`: Generate a summary of the provided text.
|
||||
|
||||
- `set_temperature(value: float)`: Set the temperature parameter for controlling randomness in text generation.
|
||||
|
||||
- `set_max_tokens(value: int)`: Set the maximum number of tokens allowed in generated responses.
|
||||
|
||||
- `get_generation_time() -> float`: Get the time taken for text generation for the last task.
|
||||
|
||||
- `get_chat_history() -> List[str]`: Get the chat history, including all interactions.
|
||||
|
||||
- `get_unique_chat_history() -> List[str]`: Get the unique chat history, removing duplicate interactions.
|
||||
|
||||
- `get_chat_history_length() -> int`: Get the length of the chat history.
|
||||
|
||||
- `get_unique_chat_history_length() -> int`: Get the length of the unique chat history.
|
||||
|
||||
- `get_chat_history_tokens() -> int`: Get the total number of tokens in the chat history.
|
||||
|
||||
- `print_beautiful(content: str, color: str = 'cyan')`: Print content beautifully using colored text.
|
||||
|
||||
- `stream(content: str)`: Stream the content, printing it character by character.
|
||||
|
||||
- `meta_prompt() -> str`: Get the meta prompt that provides guidance for including image labels in responses.
|
||||
|
||||
## 5. Usage Examples <a name="usage-examples"></a>
|
||||
|
||||
Let's explore some usage examples of the MultiModalAI library:
|
||||
|
||||
### Example 1: Running
|
||||
|
||||
the Model
|
||||
|
||||
```python
|
||||
# Import the library
|
||||
from swarm_models import BaseMultiModalModel
|
||||
|
||||
# Create an instance of the model
|
||||
model = BaseMultiModalModel(
|
||||
model_name="your_model_name",
|
||||
temperature=0.5,
|
||||
max_tokens=500,
|
||||
device="cuda",
|
||||
)
|
||||
|
||||
# Run the model with a text task and an image URL
|
||||
response = model.run(
|
||||
"Generate a summary of this text", "https://www.example.com/image.jpg"
|
||||
)
|
||||
print(response)
|
||||
```
|
||||
|
||||
### Example 2: Running Multiple Tasks Concurrently
|
||||
|
||||
```python
|
||||
# Import the library
|
||||
from swarm_models import BaseMultiModalModel
|
||||
|
||||
# Create an instance of the model
|
||||
model = BaseMultiModalModel(
|
||||
model_name="your_model_name",
|
||||
temperature=0.5,
|
||||
max_tokens=500,
|
||||
max_workers=4,
|
||||
device="cuda",
|
||||
)
|
||||
|
||||
# Define a list of tasks and image URLs
|
||||
tasks = ["Task 1", "Task 2", "Task 3"]
|
||||
images = ["https://image1.jpg", "https://image2.jpg", "https://image3.jpg"]
|
||||
|
||||
# Run the model on multiple tasks concurrently
|
||||
responses = model.run_many(tasks, images)
|
||||
for response in responses:
|
||||
print(response)
|
||||
```
|
||||
|
||||
### Example 3: Running the Model Asynchronously
|
||||
|
||||
```python
|
||||
# Import the library
|
||||
from swarm_models import BaseMultiModalModel
|
||||
|
||||
# Create an instance of the model
|
||||
model = BaseMultiModalModel(
|
||||
model_name="your_model_name",
|
||||
temperature=0.5,
|
||||
max_tokens=500,
|
||||
device="cuda",
|
||||
)
|
||||
|
||||
# Define a list of tasks and image URLs
|
||||
tasks_images = [
|
||||
("Task 1", "https://image1.jpg"),
|
||||
("Task 2", "https://image2.jpg"),
|
||||
("Task 3", "https://image3.jpg"),
|
||||
]
|
||||
|
||||
# Run the model on multiple tasks asynchronously
|
||||
responses = model.run_batch_async(tasks_images)
|
||||
for response in responses:
|
||||
print(response)
|
||||
```
|
||||
|
||||
### Example 4: Inheriting `BaseMultiModalModel` for it's prebuilt classes
|
||||
```python
|
||||
from swarm_models import BaseMultiModalModel
|
||||
|
||||
|
||||
class CustomMultiModalModel(BaseMultiModalModel):
|
||||
def __init__(self, model_name, custom_parameter, *args, **kwargs):
|
||||
# Call the parent class constructor
|
||||
super().__init__(model_name=model_name, *args, **kwargs)
|
||||
# Initialize custom parameters specific to your model
|
||||
self.custom_parameter = custom_parameter
|
||||
|
||||
def __call__(self, text, img):
|
||||
# Implement the multimodal model logic here
|
||||
# You can use self.custom_parameter and other inherited attributes
|
||||
pass
|
||||
|
||||
def generate_summary(self, text):
|
||||
# Implement the summary generation logic using your model
|
||||
# You can use self.custom_parameter and other inherited attributes
|
||||
pass
|
||||
|
||||
|
||||
# Create an instance of your custom multimodal model
|
||||
custom_model = CustomMultiModalModel(
|
||||
model_name="your_custom_model_name",
|
||||
custom_parameter="your_custom_value",
|
||||
temperature=0.5,
|
||||
max_tokens=500,
|
||||
device="cuda",
|
||||
)
|
||||
|
||||
# Run your custom model
|
||||
response = custom_model.run(
|
||||
"Generate a summary of this text", "https://www.example.com/image.jpg"
|
||||
)
|
||||
print(response)
|
||||
|
||||
# Generate a summary using your custom model
|
||||
summary = custom_model.generate_summary("This is a sample text to summarize.")
|
||||
print(summary)
|
||||
```
|
||||
|
||||
In the code above:
|
||||
|
||||
1. We define a `CustomMultiModalModel` class that inherits from `BaseMultiModalModel`.
|
||||
|
||||
2. In the constructor of our custom class, we call the parent class constructor using `super()` and initialize any custom parameters specific to our model. In this example, we introduced a `custom_parameter`.
|
||||
|
||||
3. We override the `__call__` method, which is responsible for running the multimodal model logic. Here, you can implement the specific behavior of your model, considering both text and image inputs.
|
||||
|
||||
4. We override the `generate_summary` method, which is used to generate a summary of text input. You can implement your custom summarization logic here.
|
||||
|
||||
5. We create an instance of our custom model, passing the required parameters, including the custom parameter.
|
||||
|
||||
6. We demonstrate how to run the custom model and generate a summary using it.
|
||||
|
||||
By inheriting from `BaseMultiModalModel`, you can leverage the prebuilt features and methods provided by the library while customizing the behavior of your multimodal model. This allows you to create powerful and specialized models for various multimodal tasks.
|
||||
|
||||
These examples demonstrate how to use MultiModalAI to run multimodal models with text and image inputs. You can adjust the parameters and methods to suit your specific use cases.
|
||||
|
||||
## 6. Additional Tips <a name="additional-tips"></a>
|
||||
|
||||
Here are some additional tips and considerations for using MultiModalAI effectively:
|
||||
|
||||
- **Custom Models**: You can create your own multimodal models and inherit from the `BaseMultiModalModel` class to integrate them with this library.
|
||||
|
||||
- **Retries**: In cases where text generation might fail due to various reasons (e.g., server issues), using methods with retries can be helpful.
|
||||
|
||||
- **Monitoring**: You can monitor the performance of your model using methods like `_tokens_per_second()` and `_time_for_generation()`.
|
||||
|
||||
- **Chat History**: The library maintains a chat history, allowing you to keep track of interactions.
|
||||
|
||||
- **Streaming**: The `stream()` method can be useful for displaying output character by character, which can be helpful for certain applications.
|
||||
|
||||
## 7. References and Resources <a name="references-and-resources"></a>
|
||||
|
||||
Here are some references and resources that you may find useful for working with multimodal models:
|
||||
|
||||
- [Hugging Face Transformers Library](https://huggingface.co/transformers/): A library for working with various transformer-based models.
|
||||
|
||||
- [PIL (Python Imaging Library)](https://pillow.readthedocs.io/en/stable/): Documentation for working with images in Python using the Pillow library.
|
||||
|
||||
- [Concurrent Programming in Python](https://docs.python.org/3/library/concurrent.futures.html): Official Python documentation for concurrent programming.
|
||||
|
||||
- [Requests Library Documentation](https://docs.python-requests.org/en/latest/): Documentation for the Requests library, which is used for making HTTP requests.
|
||||
|
||||
- [Base64 Encoding in Python](https://docs.python.org/3/library/base64.html): Official Python documentation for base64 encoding and decoding.
|
||||
|
||||
This concludes the documentation for the MultiModalAI library. You can now explore the library further and integrate it with your multimodal AI projects.
|
||||
@ -1,89 +0,0 @@
|
||||
# Using Cerebras LLaMA with Swarms
|
||||
|
||||
This guide demonstrates how to create and use an AI agent powered by the Cerebras LLaMA 3 70B model using the Swarms framework.
|
||||
|
||||
## Prerequisites
|
||||
|
||||
- Python 3.7+
|
||||
|
||||
- Swarms library installed (`pip install swarms`)
|
||||
|
||||
- Set your ENV key `CEREBRAS_API_KEY`
|
||||
|
||||
## Step-by-Step Guide
|
||||
|
||||
### 1. Import Required Module
|
||||
|
||||
```python
|
||||
from swarms.structs.agent import Agent
|
||||
```
|
||||
|
||||
This imports the `Agent` class from Swarms, which is the core component for creating AI agents.
|
||||
|
||||
### 2. Create an Agent Instance
|
||||
|
||||
```python
|
||||
agent = Agent(
|
||||
agent_name="Financial-Analysis-Agent",
|
||||
agent_description="Personal finance advisor agent",
|
||||
max_loops=4,
|
||||
model_name="cerebras/llama3-70b-instruct",
|
||||
dynamic_temperature_enabled=True,
|
||||
interactive=False,
|
||||
output_type="all",
|
||||
)
|
||||
```
|
||||
|
||||
Let's break down each parameter:
|
||||
|
||||
- `agent_name`: A descriptive name for your agent (here, "Financial-Analysis-Agent")
|
||||
|
||||
- `agent_description`: A brief description of the agent's purpose
|
||||
|
||||
- `max_loops`: Maximum number of interaction loops the agent can perform (set to 4)
|
||||
|
||||
- `model_name`: Specifies the Cerebras LLaMA 3 70B model to use
|
||||
|
||||
- `dynamic_temperature_enabled`: Enables dynamic adjustment of temperature for varied responses
|
||||
|
||||
- `interactive`: When False, runs without requiring user interaction
|
||||
|
||||
- `output_type`: Set to "all" to return complete response information
|
||||
|
||||
### 3. Run the Agent
|
||||
|
||||
```python
|
||||
agent.run("Conduct an analysis of the best real undervalued ETFs")
|
||||
```
|
||||
|
||||
This command:
|
||||
|
||||
1. Activates the agent
|
||||
|
||||
2. Processes the given prompt about ETF analysis
|
||||
|
||||
3. Returns the analysis based on the model's knowledge
|
||||
|
||||
## Notes
|
||||
|
||||
- The Cerebras LLaMA 3 70B model is a powerful language model suitable for complex analysis tasks
|
||||
|
||||
- The agent can be customized further with additional parameters
|
||||
|
||||
- The `max_loops=4` setting prevents infinite loops while allowing sufficient processing depth
|
||||
|
||||
- Setting `interactive=False` makes the agent run autonomously without user intervention
|
||||
|
||||
## Example Output
|
||||
|
||||
The agent will provide a detailed analysis of undervalued ETFs, including:
|
||||
|
||||
- Market analysis
|
||||
|
||||
- Performance metrics
|
||||
|
||||
- Risk assessment
|
||||
|
||||
- Investment recommendations
|
||||
|
||||
Note: Actual output will vary based on current market conditions and the model's training data.
|
||||
@ -1,107 +0,0 @@
|
||||
# How to Create A Custom Language Model
|
||||
|
||||
When working with advanced language models, there might come a time when you need a custom solution tailored to your specific needs. Inheriting from an `BaseLLM` in a Python framework allows developers to create custom language model classes with ease. This developer guide will take you through the process step by step.
|
||||
|
||||
### Prerequisites
|
||||
|
||||
Before you begin, ensure that you have:
|
||||
|
||||
- A working knowledge of Python programming.
|
||||
- Basic understanding of object-oriented programming (OOP) in Python.
|
||||
- Familiarity with language models and natural language processing (NLP).
|
||||
- The appropriate Python framework installed, with access to `BaseLLM`.
|
||||
|
||||
### Step-by-Step Guide
|
||||
|
||||
#### Step 1: Understand `BaseLLM`
|
||||
|
||||
The `BaseLLM` is an abstract base class that defines a set of methods and properties which your custom language model (LLM) should implement. Abstract classes in Python are not designed to be instantiated directly but are meant to be subclasses.
|
||||
|
||||
#### Step 2: Create a New Class
|
||||
|
||||
Start by defining a new class that inherits from `BaseLLM`. This class will implement the required methods defined in the abstract base class.
|
||||
|
||||
```python
|
||||
from swarms import BaseLLM
|
||||
|
||||
class vLLMLM(BaseLLM):
|
||||
pass
|
||||
```
|
||||
|
||||
#### Step 3: Initialize Your Class
|
||||
|
||||
Implement the `__init__` method to initialize your custom LLM. You'll want to initialize the base class as well and define any additional parameters for your model.
|
||||
|
||||
```python
|
||||
class vLLMLM(BaseLLM):
|
||||
def __init__(self, model_name='default_model', tensor_parallel_size=1, *args, **kwargs):
|
||||
super().__init__(*args, **kwargs)
|
||||
self.model_name = model_name
|
||||
self.tensor_parallel_size = tensor_parallel_size
|
||||
# Add any additional initialization here
|
||||
```
|
||||
|
||||
#### Step 4: Implement Required Methods
|
||||
|
||||
Implement the `run` method or any other abstract methods required by `BaseLLM`. This is where you define how your model processes input and returns output.
|
||||
|
||||
```python
|
||||
class vLLMLM(BaseLLM):
|
||||
# ... existing code ...
|
||||
|
||||
def run(self, task, *args, **kwargs):
|
||||
# Logic for running your model goes here
|
||||
return "Processed output"
|
||||
```
|
||||
|
||||
#### Step 5: Test Your Model
|
||||
|
||||
Instantiate your custom LLM and test it to ensure that it works as expected.
|
||||
|
||||
```python
|
||||
model = vLLMLM(model_name='my_custom_model', tensor_parallel_size=2)
|
||||
output = model.run("What are the symptoms of COVID-19?")
|
||||
print(output) # Outputs: "Processed output"
|
||||
```
|
||||
|
||||
#### Step 6: Integrate Additional Components
|
||||
|
||||
Depending on the requirements, you might need to integrate additional components such as database connections, parallel computing resources, or custom processing pipelines.
|
||||
|
||||
#### Step 7: Documentation
|
||||
|
||||
Write comprehensive docstrings for your class and its methods. Good documentation is crucial for maintaining the code and for other developers who might use your model.
|
||||
|
||||
```python
|
||||
class vLLMLM(BaseLLM):
|
||||
"""
|
||||
A custom language model class that extends BaseLLM.
|
||||
|
||||
... more detailed docstring ...
|
||||
"""
|
||||
# ... existing code ...
|
||||
```
|
||||
|
||||
#### Step 8: Best Practices
|
||||
|
||||
Follow best practices such as error handling, input validation, and resource management to ensure your model is robust and reliable.
|
||||
|
||||
#### Step 9: Packaging Your Model
|
||||
|
||||
Package your custom LLM class into a module or package that can be easily distributed and imported into other projects.
|
||||
|
||||
#### Step 10: Version Control and Collaboration
|
||||
|
||||
Use a version control system like Git to track changes to your model. This makes collaboration easier and helps you keep a history of your work.
|
||||
|
||||
### Conclusion
|
||||
|
||||
By following this guide, you should now have a custom model that extends the `BaseLLM`. Remember that the key to a successful custom LLM is understanding the base functionalities, implementing necessary changes, and testing thoroughly. Keep iterating and improving based on feedback and performance metrics.
|
||||
|
||||
### Further Reading
|
||||
|
||||
- Official Python documentation on abstract base classes.
|
||||
- In-depth tutorials on object-oriented programming in Python.
|
||||
- Advanced NLP techniques and optimization strategies for language models.
|
||||
|
||||
This guide provides the fundamental steps to create custom models using `BaseLLM`. For detailed implementation and advanced customization, it's essential to dive deeper into the specific functionalities and capabilities of the language model framework you are using.
|
||||
@ -1,261 +0,0 @@
|
||||
# `Dalle3` Documentation
|
||||
|
||||
## Table of Contents
|
||||
|
||||
1. [Introduction](#introduction)
|
||||
2. [Installation](#installation)
|
||||
3. [Quick Start](#quick-start)
|
||||
4. [Dalle3 Class](#dalle3-class)
|
||||
- [Attributes](#attributes)
|
||||
- [Methods](#methods)
|
||||
5. [Usage Examples](#usage-examples)
|
||||
6. [Error Handling](#error-handling)
|
||||
7. [Advanced Usage](#advanced-usage)
|
||||
8. [References](#references)
|
||||
|
||||
---
|
||||
|
||||
## Introduction<a name="introduction"></a>
|
||||
|
||||
The Dalle3 library is a Python module that provides an easy-to-use interface for generating images from text descriptions using the DALL·E 3 model by OpenAI. DALL·E 3 is a powerful language model capable of converting textual prompts into images. This documentation will guide you through the installation, setup, and usage of the Dalle3 library.
|
||||
|
||||
---
|
||||
|
||||
## Installation<a name="installation"></a>
|
||||
|
||||
To use the Dalle3 model, you must first install swarms:
|
||||
|
||||
```bash
|
||||
pip install swarms
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Quick Start<a name="quick-start"></a>
|
||||
|
||||
Let's get started with a quick example of using the Dalle3 library to generate an image from a text prompt:
|
||||
|
||||
```python
|
||||
from swarm_models.dalle3 import Dalle3
|
||||
|
||||
# Create an instance of the Dalle3 class
|
||||
dalle = Dalle3()
|
||||
|
||||
# Define a text prompt
|
||||
task = "A painting of a dog"
|
||||
|
||||
# Generate an image from the text prompt
|
||||
image_url = dalle3(task)
|
||||
|
||||
# Print the generated image URL
|
||||
print(image_url)
|
||||
```
|
||||
|
||||
This example demonstrates the basic usage of the Dalle3 library to convert a text prompt into an image. The generated image URL will be printed to the console.
|
||||
|
||||
---
|
||||
|
||||
## Dalle3 Class<a name="dalle3-class"></a>
|
||||
|
||||
The Dalle3 library provides a `Dalle3` class that allows you to interact with the DALL·E 3 model. This class has several attributes and methods for generating images from text prompts.
|
||||
|
||||
### Attributes<a name="attributes"></a>
|
||||
|
||||
- `model` (str): The name of the DALL·E 3 model. Default: "dall-e-3".
|
||||
- `img` (str): The image URL generated by the Dalle3 API.
|
||||
- `size` (str): The size of the generated image. Default: "1024x1024".
|
||||
- `max_retries` (int): The maximum number of API request retries. Default: 3.
|
||||
- `quality` (str): The quality of the generated image. Default: "standard".
|
||||
- `n` (int): The number of variations to create. Default: 4.
|
||||
|
||||
### Methods<a name="methods"></a>
|
||||
|
||||
#### `__call__(self, task: str) -> Dalle3`
|
||||
|
||||
This method makes a call to the Dalle3 API and returns the image URL generated from the provided text prompt.
|
||||
|
||||
Parameters:
|
||||
- `task` (str): The text prompt to be converted to an image.
|
||||
|
||||
Returns:
|
||||
- `Dalle3`: An instance of the Dalle3 class with the image URL generated by the Dalle3 API.
|
||||
|
||||
#### `create_variations(self, img: str)`
|
||||
|
||||
This method creates variations of an image using the Dalle3 API.
|
||||
|
||||
Parameters:
|
||||
- `img` (str): The image to be used for the API request.
|
||||
|
||||
Returns:
|
||||
- `img` (str): The image URL of the generated variations.
|
||||
|
||||
---
|
||||
|
||||
## Usage Examples<a name="usage-examples"></a>
|
||||
|
||||
### Example 1: Basic Image Generation
|
||||
|
||||
```python
|
||||
from swarm_models.dalle3 import Dalle3
|
||||
|
||||
# Create an instance of the Dalle3 class
|
||||
dalle3 = Dalle3()
|
||||
|
||||
# Define a text prompt
|
||||
task = "A painting of a dog"
|
||||
|
||||
# Generate an image from the text prompt
|
||||
image_url = dalle3(task)
|
||||
|
||||
# Print the generated image URL
|
||||
print(image_url)
|
||||
```
|
||||
|
||||
### Example 2: Creating Image Variations
|
||||
|
||||
```python
|
||||
from swarm_models.dalle3 import Dalle3
|
||||
|
||||
# Create an instance of the Dalle3 class
|
||||
dalle3 = Dalle3()
|
||||
|
||||
# Define the URL of an existing image
|
||||
img_url = "https://images.unsplash.com/photo-1694734479898-6ac4633158ac?q=80&w=1287&auto=format&fit=crop&ixlib=rb-4.0.3&ixid=M3wxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8fA%3D%3D
|
||||
|
||||
# Create variations of the image
|
||||
variations_url = dalle3.create_variations(img_url)
|
||||
|
||||
# Print the URLs of the generated variations
|
||||
print(variations_url)
|
||||
```
|
||||
|
||||
Certainly! Here are additional examples that cover various edge cases and methods of the `Dalle3` class in the Dalle3 library:
|
||||
|
||||
### Example 3: Customizing Image Size
|
||||
|
||||
You can customize the size of the generated image by specifying the `size` parameter when creating an instance of the `Dalle3` class. Here's how to generate a smaller image:
|
||||
|
||||
```python
|
||||
from swarm_models.dalle3 import Dalle3
|
||||
|
||||
# Create an instance of the Dalle3 class with a custom image size
|
||||
dalle3 = Dalle3(size="512x512")
|
||||
|
||||
# Define a text prompt
|
||||
task = "A small painting of a cat"
|
||||
|
||||
# Generate a smaller image from the text prompt
|
||||
image_url = dalle3(task)
|
||||
|
||||
# Print the generated image URL
|
||||
print(image_url)
|
||||
```
|
||||
|
||||
### Example 4: Adjusting Retry Limit
|
||||
|
||||
You can adjust the maximum number of API request retries using the `max_retries` parameter. Here's how to increase the retry limit:
|
||||
|
||||
```python
|
||||
from swarm_models.dalle3 import Dalle3
|
||||
|
||||
# Create an instance of the Dalle3 class with a higher retry limit
|
||||
dalle3 = Dalle3(max_retries=5)
|
||||
|
||||
# Define a text prompt
|
||||
task = "An image of a landscape"
|
||||
|
||||
# Generate an image with a higher retry limit
|
||||
image_url = dalle3(task)
|
||||
|
||||
# Print the generated image URL
|
||||
print(image_url)
|
||||
```
|
||||
|
||||
### Example 5: Generating Image Variations
|
||||
|
||||
To create variations of an existing image, you can use the `create_variations` method. Here's an example:
|
||||
|
||||
```python
|
||||
from swarm_models.dalle3 import Dalle3
|
||||
|
||||
# Create an instance of the Dalle3 class
|
||||
dalle3 = Dalle3()
|
||||
|
||||
# Define the URL of an existing image
|
||||
img_url = "https://images.unsplash.com/photo-1677290043066-12eccd944004?q=80&w=1287&auto=format&fit=crop&ixlib=rb-4.0.3&ixid=M3wxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8fA%3D%3D"
|
||||
|
||||
# Create variations of the image
|
||||
variations_url = dalle3.create_variations(img_url)
|
||||
|
||||
# Print the URLs of the generated variations
|
||||
print(variations_url)
|
||||
```
|
||||
|
||||
### Example 6: Handling API Errors
|
||||
|
||||
The Dalle3 library provides error handling for API-related issues. Here's how to handle and display API errors:
|
||||
|
||||
```python
|
||||
from swarm_models.dalle3 import Dalle3
|
||||
|
||||
# Create an instance of the Dalle3 class
|
||||
dalle3 = Dalle3()
|
||||
|
||||
# Define a text prompt
|
||||
task = "Invalid prompt that may cause an API error"
|
||||
|
||||
try:
|
||||
# Attempt to generate an image with an invalid prompt
|
||||
image_url = dalle3(task)
|
||||
print(image_url)
|
||||
except Exception as e:
|
||||
print(f"Error occurred: {str(e)}")
|
||||
```
|
||||
|
||||
### Example 7: Customizing Image Quality
|
||||
|
||||
You can customize the quality of the generated image by specifying the `quality` parameter. Here's how to generate a high-quality image:
|
||||
|
||||
```python
|
||||
from swarm_models.dalle3 import Dalle3
|
||||
|
||||
# Create an instance of the Dalle3 class with high quality
|
||||
dalle3 = Dalle3(quality="high")
|
||||
|
||||
# Define a text prompt
|
||||
task = "A high-quality image of a sunset"
|
||||
|
||||
# Generate a high-quality image from the text prompt
|
||||
image_url = dalle3(task)
|
||||
|
||||
# Print the generated image URL
|
||||
print(image_url)
|
||||
```
|
||||
|
||||
|
||||
---
|
||||
|
||||
## Error Handling<a name="error-handling"></a>
|
||||
|
||||
The Dalle3 library provides error handling for API-related issues. If an error occurs during API communication, the library will handle it and provide detailed error messages. Make sure to handle exceptions appropriately in your code.
|
||||
|
||||
---
|
||||
|
||||
## Advanced Usage<a name="advanced-usage"></a>
|
||||
|
||||
For advanced usage and customization of the Dalle3 library, you can explore the attributes and methods of the `Dalle3` class. Adjusting parameters such as `size`, `max_retries`, and `quality` allows you to fine-tune the image generation process to your specific needs.
|
||||
|
||||
---
|
||||
|
||||
## References<a name="references"></a>
|
||||
|
||||
For more information about the DALL·E 3 model and the Dalle3 library, you can refer to the official OpenAI documentation and resources.
|
||||
|
||||
- [OpenAI API Documentation](https://beta.openai.com/docs/)
|
||||
- [DALL·E 3 Model Information](https://openai.com/research/dall-e-3)
|
||||
- [Dalle3 GitHub Repository](https://github.com/openai/dall-e-3)
|
||||
|
||||
---
|
||||
|
||||
This concludes the documentation for the Dalle3 library. You can now use the library to generate images from text prompts and explore its advanced features for various applications.
|
||||
@ -1,123 +0,0 @@
|
||||
# DistilWhisperModel Documentation
|
||||
|
||||
## Overview
|
||||
|
||||
The `DistilWhisperModel` is a Python class designed to handle English speech recognition tasks. It leverages the capabilities of the Whisper model, which is fine-tuned for speech-to-text processes. It is designed for both synchronous and asynchronous transcription of audio inputs, offering flexibility for real-time applications or batch processing.
|
||||
|
||||
## Installation
|
||||
|
||||
Before you can use `DistilWhisperModel`, ensure you have the required libraries installed:
|
||||
|
||||
```sh
|
||||
pip3 install --upgrade swarms
|
||||
```
|
||||
|
||||
## Initialization
|
||||
|
||||
The `DistilWhisperModel` class is initialized with the following parameters:
|
||||
|
||||
| Parameter | Type | Description | Default |
|
||||
|-----------|------|-------------|---------|
|
||||
| `model_id` | `str` | The identifier for the pre-trained Whisper model | `"distil-whisper/distil-large-v2"` |
|
||||
|
||||
Example of initialization:
|
||||
|
||||
```python
|
||||
from swarm_models import DistilWhisperModel
|
||||
|
||||
# Initialize with default model
|
||||
model_wrapper = DistilWhisperModel()
|
||||
|
||||
# Initialize with a specific model ID
|
||||
model_wrapper = DistilWhisperModel(model_id="distil-whisper/distil-large-v2")
|
||||
```
|
||||
|
||||
## Attributes
|
||||
|
||||
After initialization, the `DistilWhisperModel` has several attributes:
|
||||
|
||||
| Attribute | Type | Description |
|
||||
|-----------|------|-------------|
|
||||
| `device` | `str` | The device used for computation (`"cuda:0"` for GPU or `"cpu"`). |
|
||||
| `torch_dtype` | `torch.dtype` | The data type used for the Torch tensors. |
|
||||
| `model_id` | `str` | The model identifier string. |
|
||||
| `model` | `torch.nn.Module` | The actual Whisper model loaded from the identifier. |
|
||||
| `processor` | `transformers.AutoProcessor` | The processor for handling input data. |
|
||||
|
||||
## Methods
|
||||
|
||||
### `transcribe`
|
||||
|
||||
Transcribes audio input synchronously.
|
||||
|
||||
**Arguments**:
|
||||
|
||||
| Argument | Type | Description |
|
||||
|----------|------|-------------|
|
||||
| `inputs` | `Union[str, dict]` | File path or audio data dictionary. |
|
||||
|
||||
**Returns**: `str` - The transcribed text.
|
||||
|
||||
**Usage Example**:
|
||||
|
||||
```python
|
||||
# Synchronous transcription
|
||||
transcription = model_wrapper.transcribe("path/to/audio.mp3")
|
||||
print(transcription)
|
||||
```
|
||||
|
||||
### `async_transcribe`
|
||||
|
||||
Transcribes audio input asynchronously.
|
||||
|
||||
**Arguments**:
|
||||
|
||||
| Argument | Type | Description |
|
||||
|----------|------|-------------|
|
||||
| `inputs` | `Union[str, dict]` | File path or audio data dictionary. |
|
||||
|
||||
**Returns**: `Coroutine` - A coroutine that when awaited, returns the transcribed text.
|
||||
|
||||
**Usage Example**:
|
||||
|
||||
```python
|
||||
import asyncio
|
||||
|
||||
# Asynchronous transcription
|
||||
transcription = asyncio.run(model_wrapper.async_transcribe("path/to/audio.mp3"))
|
||||
print(transcription)
|
||||
```
|
||||
|
||||
### `real_time_transcribe`
|
||||
|
||||
Simulates real-time transcription of an audio file.
|
||||
|
||||
**Arguments**:
|
||||
|
||||
| Argument | Type | Description |
|
||||
|----------|------|-------------|
|
||||
| `audio_file_path` | `str` | Path to the audio file. |
|
||||
| `chunk_duration` | `int` | Duration of audio chunks in seconds. |
|
||||
|
||||
**Usage Example**:
|
||||
|
||||
```python
|
||||
# Real-time transcription simulation
|
||||
model_wrapper.real_time_transcribe("path/to/audio.mp3", chunk_duration=5)
|
||||
```
|
||||
|
||||
## Error Handling
|
||||
|
||||
The `DistilWhisperModel` class incorporates error handling for file not found errors and generic exceptions during the transcription process. If a non-recoverable exception is raised, it is printed to the console in red to indicate failure.
|
||||
|
||||
## Conclusion
|
||||
|
||||
The `DistilWhisperModel` offers a convenient interface to the powerful Whisper model for speech recognition. Its design supports both batch and real-time transcription, catering to different application needs. The class's error handling and retry logic make it robust for real-world applications.
|
||||
|
||||
## Additional Notes
|
||||
|
||||
- Ensure you have appropriate permissions to read audio files when using file paths.
|
||||
- Transcription quality depends on the audio quality and the Whisper model's performance on your dataset.
|
||||
- Adjust `chunk_duration` according to the processing power of your system for real-time transcription.
|
||||
|
||||
For a full list of models supported by `transformers.AutoModelForSpeechSeq2Seq`, visit the [Hugging Face Model Hub](https://huggingface.co/models).
|
||||
@ -1,89 +0,0 @@
|
||||
# Fuyu Documentation
|
||||
|
||||
## Introduction
|
||||
|
||||
Welcome to the documentation for Fuyu, a versatile model for generating text conditioned on both textual prompts and images. Fuyu is based on the Adept's Fuyu model and offers a convenient way to create text that is influenced by the content of an image. In this documentation, you will find comprehensive information on the Fuyu class, its architecture, usage, and examples.
|
||||
|
||||
## Overview
|
||||
|
||||
Fuyu is a text generation model that leverages both text and images to generate coherent and contextually relevant text. It combines state-of-the-art language modeling techniques with image processing capabilities to produce text that is semantically connected to the content of an image. Whether you need to create captions for images or generate text that describes visual content, Fuyu can assist you.
|
||||
|
||||
## Class Definition
|
||||
|
||||
```python
|
||||
class Fuyu:
|
||||
def __init__(
|
||||
self,
|
||||
pretrained_path: str = "adept/fuyu-8b",
|
||||
device_map: str = "cuda:0",
|
||||
max_new_tokens: int = 7,
|
||||
):
|
||||
```
|
||||
|
||||
## Purpose
|
||||
|
||||
The Fuyu class serves as a convenient interface for using the Adept's Fuyu model. It allows you to generate text based on a textual prompt and an image. The primary purpose of Fuyu is to provide a user-friendly way to create text that is influenced by visual content, making it suitable for various applications, including image captioning, storytelling, and creative text generation.
|
||||
|
||||
## Parameters
|
||||
|
||||
- `pretrained_path` (str): The path to the pretrained Fuyu model. By default, it uses the "adept/fuyu-8b" model.
|
||||
- `device_map` (str): The device to use for model inference (e.g., "cuda:0" for GPU or "cpu" for CPU). Default: "cuda:0".
|
||||
- `max_new_tokens` (int): The maximum number of tokens to generate in the output text. Default: 7.
|
||||
|
||||
## Usage
|
||||
|
||||
To use Fuyu, follow these steps:
|
||||
|
||||
1. Initialize the Fuyu instance:
|
||||
|
||||
```python
|
||||
from swarm_models.fuyu import Fuyu
|
||||
|
||||
fuyu = Fuyu()
|
||||
```
|
||||
|
||||
|
||||
2. Generate Text with Fuyu:
|
||||
|
||||
```python
|
||||
text = "Hello, my name is"
|
||||
img_path = "path/to/image.png"
|
||||
output_text = fuyu(text, img_path)
|
||||
```
|
||||
|
||||
### Example 2 - Text Generation
|
||||
|
||||
```python
|
||||
from swarm_models.fuyu import Fuyu
|
||||
|
||||
fuyu = Fuyu()
|
||||
|
||||
text = "Hello, my name is"
|
||||
|
||||
img_path = "path/to/image.png"
|
||||
|
||||
output_text = fuyu(text, img_path)
|
||||
print(output_text)
|
||||
```
|
||||
|
||||
## How Fuyu Works
|
||||
|
||||
Fuyu combines text and image processing to generate meaningful text outputs. Here's how it works:
|
||||
|
||||
1. **Initialization**: When you create a Fuyu instance, you specify the pretrained model path, the device for inference, and the maximum number of tokens to generate.
|
||||
|
||||
2. **Processing Text and Images**: Fuyu can process both textual prompts and images. You provide a text prompt and the path to an image as input.
|
||||
|
||||
3. **Tokenization**: Fuyu tokenizes the input text and encodes the image using its tokenizer.
|
||||
|
||||
4. **Model Inference**: The model takes the tokenized inputs and generates text that is conditioned on both the text and the image.
|
||||
|
||||
5. **Output Text**: Fuyu returns the generated text as the output.
|
||||
|
||||
## Additional Information
|
||||
|
||||
- Fuyu uses the Adept's Fuyu model, which is pretrained on a large corpus of text and images, making it capable of generating coherent and contextually relevant text.
|
||||
- You can specify the device for inference to utilize GPU acceleration if available.
|
||||
- The `max_new_tokens` parameter allows you to control the length of the generated text.
|
||||
|
||||
That concludes the documentation for Fuyu. We hope you find this model useful for your text generation tasks that involve images. If you have any questions or encounter any issues, please refer to the Fuyu documentation for further assistance. Enjoy working with Fuyu!
|
||||
@ -1,178 +0,0 @@
|
||||
## `Gemini` Documentation
|
||||
|
||||
### Introduction
|
||||
|
||||
The Gemini module is a versatile tool for leveraging the power of multimodal AI models to generate content. It allows users to combine textual and image inputs to generate creative and informative outputs. In this documentation, we will explore the Gemini module in detail, covering its purpose, architecture, methods, and usage examples.
|
||||
|
||||
#### Purpose
|
||||
|
||||
The Gemini module is designed to bridge the gap between text and image data, enabling users to harness the capabilities of multimodal AI models effectively. By providing both a textual task and an image as input, Gemini generates content that aligns with the specified task and incorporates the visual information from the image.
|
||||
|
||||
### Installation
|
||||
|
||||
Before using Gemini, ensure that you have the required dependencies installed. You can install them using the following commands:
|
||||
|
||||
```bash
|
||||
pip install swarms
|
||||
pip install google-generativeai
|
||||
pip install python-dotenv
|
||||
```
|
||||
|
||||
### Class: Gemini
|
||||
|
||||
#### Overview
|
||||
|
||||
The `Gemini` class is the central component of the Gemini module. It inherits from the `BaseMultiModalModel` class and provides methods to interact with the Gemini AI model. Let's dive into its architecture and functionality.
|
||||
|
||||
##### Class Constructor
|
||||
|
||||
```python
|
||||
class Gemini(BaseMultiModalModel):
|
||||
def __init__(
|
||||
self,
|
||||
model_name: str = "gemini-pro",
|
||||
gemini_api_key: str = get_gemini_api_key_env,
|
||||
*args,
|
||||
**kwargs,
|
||||
):
|
||||
```
|
||||
|
||||
| Parameter | Type | Description | Default Value |
|
||||
|---------------------|---------|------------------------------------------------------------------|--------------------|
|
||||
| `model_name` | str | The name of the Gemini model. | "gemini-pro" |
|
||||
| `gemini_api_key` | str | The Gemini API key. If not provided, it is fetched from the environment. | (None) |
|
||||
|
||||
- `model_name`: Specifies the name of the Gemini model to use. By default, it is set to "gemini-pro," but you can specify a different model if needed.
|
||||
|
||||
- `gemini_api_key`: This parameter allows you to provide your Gemini API key directly. If not provided, the constructor attempts to fetch it from the environment using the `get_gemini_api_key_env` helper function.
|
||||
|
||||
##### Methods
|
||||
|
||||
1. **run()**
|
||||
|
||||
```python
|
||||
def run(
|
||||
self,
|
||||
task: str = None,
|
||||
img: str = None,
|
||||
*args,
|
||||
**kwargs,
|
||||
) -> str:
|
||||
```
|
||||
|
||||
| Parameter | Type | Description |
|
||||
|---------------|----------|--------------------------------------------|
|
||||
| `task` | str | The textual task for content generation. |
|
||||
| `img` | str | The path to the image to be processed. |
|
||||
| `*args` | Variable | Additional positional arguments. |
|
||||
| `**kwargs` | Variable | Additional keyword arguments. |
|
||||
|
||||
- `task`: Specifies the textual task for content generation. It can be a sentence or a phrase that describes the desired content.
|
||||
|
||||
- `img`: Provides the path to the image that will be processed along with the textual task. Gemini combines the visual information from the image with the textual task to generate content.
|
||||
|
||||
- `*args` and `**kwargs`: Allow for additional, flexible arguments that can be passed to the underlying Gemini model. These arguments can vary based on the specific Gemini model being used.
|
||||
|
||||
**Returns**: A string containing the generated content.
|
||||
|
||||
**Examples**:
|
||||
|
||||
```python
|
||||
from swarm_models import Gemini
|
||||
|
||||
# Initialize the Gemini model
|
||||
gemini = Gemini()
|
||||
|
||||
# Generate content for a textual task with an image
|
||||
generated_content = gemini.run(
|
||||
task="Describe this image",
|
||||
img="image.jpg",
|
||||
)
|
||||
|
||||
# Print the generated content
|
||||
print(generated_content)
|
||||
```
|
||||
|
||||
In this example, we initialize the Gemini model, provide a textual task, and specify an image for processing. The `run()` method generates content based on the input and returns the result.
|
||||
|
||||
2. **process_img()**
|
||||
|
||||
```python
|
||||
def process_img(
|
||||
self,
|
||||
img: str = None,
|
||||
type: str = "image/png",
|
||||
*args,
|
||||
**kwargs,
|
||||
):
|
||||
```
|
||||
|
||||
| Parameter | Type | Description | Default Value |
|
||||
|---------------|----------|------------------------------------------------------|----------------|
|
||||
| `img` | str | The path to the image to be processed. | (None) |
|
||||
| `type` | str | The MIME type of the image (e.g., "image/png"). | "image/png" |
|
||||
| `*args` | Variable | Additional positional arguments. |
|
||||
| `**kwargs` | Variable | Additional keyword arguments. |
|
||||
|
||||
- `img`: Specifies the path to the image that will be processed. It's essential to provide a valid image path for image-based content generation.
|
||||
|
||||
- `type`: Indicates the MIME type of the image. By default, it is set to "image/png," but you can change it based on the image format you're using.
|
||||
|
||||
- `*args` and `**kwargs`: Allow for additional, flexible arguments that can be passed to the underlying Gemini model. These arguments can vary based on the specific Gemini model being used.
|
||||
|
||||
**Raises**: ValueError if any of the following conditions are met:
|
||||
- No image is provided.
|
||||
- The image type is not specified.
|
||||
- The Gemini API key is missing.
|
||||
|
||||
**Examples**:
|
||||
|
||||
```python
|
||||
from swarm_models.gemini import Gemini
|
||||
|
||||
# Initialize the Gemini model
|
||||
gemini = Gemini()
|
||||
|
||||
# Process an image
|
||||
processed_image = gemini.process_img(
|
||||
img="image.jpg",
|
||||
type="image/jpeg",
|
||||
)
|
||||
|
||||
# Further use the processed image in content generation
|
||||
generated_content = gemini.run(
|
||||
task="Describe this image",
|
||||
img=processed_image,
|
||||
)
|
||||
|
||||
# Print the generated content
|
||||
print(generated_content)
|
||||
```
|
||||
|
||||
In this example, we demonstrate how to process an image using the `process_img()` method and then use the processed image in content generation.
|
||||
|
||||
#### Additional Information
|
||||
|
||||
- Gemini is designed to work seamlessly with various multimodal AI models, making it a powerful tool for content generation tasks.
|
||||
|
||||
- The module uses the `google.generativeai` package to access the underlying AI models. Ensure that you have this package installed to leverage the full capabilities of Gemini.
|
||||
|
||||
- It's essential to provide a valid Gemini API key for authentication. You can either pass it directly during initialization or store it in the environment variable "GEMINI_API_KEY."
|
||||
|
||||
- Gemini's flexibility allows you to experiment with different Gemini models and tailor the content generation process to your specific needs.
|
||||
|
||||
- Keep in mind that Gemini is designed to handle both textual and image inputs, making it a valuable asset for various applications, including natural language processing and computer vision tasks.
|
||||
|
||||
- If you encounter any issues or have specific requirements, refer to the Gemini documentation for more details and advanced usage.
|
||||
|
||||
### References and Resources
|
||||
|
||||
- [Gemini GitHub Repository](https://github.com/swarms/gemini): Explore the Gemini repository for additional information, updates, and examples.
|
||||
|
||||
- [Google GenerativeAI Documentation](https://docs.google.com/document/d/1WZSBw6GsOhOCYm0ArydD_9uy6nPPA1KFIbKPhjj43hA): Dive deeper into the capabilities of the Google GenerativeAI package used by Gemini.
|
||||
|
||||
- [Gemini API Documentation](https://gemini-api-docs.example.com): Access the official documentation for the Gemini API to explore advanced features and integrations.
|
||||
|
||||
## Conclusion
|
||||
|
||||
In this comprehensive documentation, we've explored the Gemini module, its purpose, architecture, methods, and usage examples. Gemini empowers developers to generate content by combining textual tasks and images, making it a valuable asset for multimodal AI applications. Whether you're working on natural language processing or computer vision projects, Gemini can help you achieve impressive results.
|
||||
@ -1,201 +0,0 @@
|
||||
# `GPT4VisionAPI` Documentation
|
||||
|
||||
**Table of Contents**
|
||||
- [Introduction](#introduction)
|
||||
- [Installation](#installation)
|
||||
- [Module Overview](#module-overview)
|
||||
- [Class: GPT4VisionAPI](#class-gpt4visionapi)
|
||||
- [Initialization](#initialization)
|
||||
- [Methods](#methods)
|
||||
- [encode_image](#encode_image)
|
||||
- [run](#run)
|
||||
- [__call__](#__call__)
|
||||
- [Examples](#examples)
|
||||
- [Example 1: Basic Usage](#example-1-basic-usage)
|
||||
- [Example 2: Custom API Key](#example-2-custom-api-key)
|
||||
- [Example 3: Adjusting Maximum Tokens](#example-3-adjusting-maximum-tokens)
|
||||
- [Additional Information](#additional-information)
|
||||
- [References](#references)
|
||||
|
||||
## Introduction<a name="introduction"></a>
|
||||
|
||||
Welcome to the documentation for the `GPT4VisionAPI` module! This module is a powerful wrapper for the OpenAI GPT-4 Vision model. It allows you to interact with the model to generate descriptions or answers related to images. This documentation will provide you with comprehensive information on how to use this module effectively.
|
||||
|
||||
## Installation<a name="installation"></a>
|
||||
|
||||
Before you start using the `GPT4VisionAPI` module, make sure you have the required dependencies installed. You can install them using the following commands:
|
||||
|
||||
```bash
|
||||
pip3 install --upgrade swarms
|
||||
```
|
||||
|
||||
## Module Overview<a name="module-overview"></a>
|
||||
|
||||
The `GPT4VisionAPI` module serves as a bridge between your application and the OpenAI GPT-4 Vision model. It allows you to send requests to the model and retrieve responses related to images. Here are some key features and functionality provided by this module:
|
||||
|
||||
- Encoding images to base64 format.
|
||||
- Running the GPT-4 Vision model with specified tasks and images.
|
||||
- Customization options such as setting the OpenAI API key and maximum token limit.
|
||||
|
||||
## Class: GPT4VisionAPI<a name="class-gpt4visionapi"></a>
|
||||
|
||||
The `GPT4VisionAPI` class is the core component of this module. It encapsulates the functionality required to interact with the GPT-4 Vision model. Below, we'll dive into the class in detail.
|
||||
|
||||
### Initialization<a name="initialization"></a>
|
||||
|
||||
When initializing the `GPT4VisionAPI` class, you have the option to provide the OpenAI API key and set the maximum token limit. Here are the parameters and their descriptions:
|
||||
|
||||
| Parameter | Type | Default Value | Description |
|
||||
|---------------------|----------|-------------------------------|----------------------------------------------------------------------------------------------------------|
|
||||
| openai_api_key | str | `OPENAI_API_KEY` environment variable (if available) | The OpenAI API key. If not provided, it defaults to the `OPENAI_API_KEY` environment variable. |
|
||||
| max_tokens | int | 300 | The maximum number of tokens to generate in the model's response. |
|
||||
|
||||
Here's how you can initialize the `GPT4VisionAPI` class:
|
||||
|
||||
```python
|
||||
from swarm_models import GPT4VisionAPI
|
||||
|
||||
# Initialize with default API key and max_tokens
|
||||
api = GPT4VisionAPI()
|
||||
|
||||
# Initialize with custom API key and max_tokens
|
||||
custom_api_key = "your_custom_api_key"
|
||||
api = GPT4VisionAPI(openai_api_key=custom_api_key, max_tokens=500)
|
||||
```
|
||||
|
||||
### Methods<a name="methods"></a>
|
||||
|
||||
#### encode_image<a name="encode_image"></a>
|
||||
|
||||
This method allows you to encode an image from a URL to base64 format. It's a utility function used internally by the module.
|
||||
|
||||
```python
|
||||
def encode_image(img: str) -> str:
|
||||
"""
|
||||
Encode image to base64.
|
||||
|
||||
Parameters:
|
||||
- img (str): URL of the image to encode.
|
||||
|
||||
Returns:
|
||||
str: Base64 encoded image.
|
||||
"""
|
||||
```
|
||||
|
||||
#### run<a name="run"></a>
|
||||
|
||||
The `run` method is the primary way to interact with the GPT-4 Vision model. It sends a request to the model with a task and an image URL, and it returns the model's response.
|
||||
|
||||
```python
|
||||
def run(task: str, img: str) -> str:
|
||||
"""
|
||||
Run the GPT-4 Vision model.
|
||||
|
||||
Parameters:
|
||||
- task (str): The task or question related to the image.
|
||||
- img (str): URL of the image to analyze.
|
||||
|
||||
Returns:
|
||||
str: The model's response.
|
||||
"""
|
||||
```
|
||||
|
||||
#### __call__<a name="__call__"></a>
|
||||
|
||||
The `__call__` method is a convenient way to run the GPT-4 Vision model. It has the same functionality as the `run` method.
|
||||
|
||||
```python
|
||||
def __call__(task: str, img: str) -> str:
|
||||
"""
|
||||
Run the GPT-4 Vision model (callable).
|
||||
|
||||
Parameters:
|
||||
- task (str): The task or question related to the image.
|
||||
- img
|
||||
|
||||
(str): URL of the image to analyze.
|
||||
|
||||
Returns:
|
||||
str: The model's response.
|
||||
"""
|
||||
```
|
||||
|
||||
## Examples<a name="examples"></a>
|
||||
|
||||
Let's explore some usage examples of the `GPT4VisionAPI` module to better understand how to use it effectively.
|
||||
|
||||
### Example 1: Basic Usage<a name="example-1-basic-usage"></a>
|
||||
|
||||
In this example, we'll use the module with the default API key and maximum tokens to analyze an image.
|
||||
|
||||
```python
|
||||
from swarm_models import GPT4VisionAPI
|
||||
|
||||
# Initialize with default API key and max_tokens
|
||||
api = GPT4VisionAPI()
|
||||
|
||||
# Define the task and image URL
|
||||
task = "What is the color of the object?"
|
||||
img = "https://i.imgur.com/2M2ZGwC.jpeg"
|
||||
|
||||
# Run the GPT-4 Vision model
|
||||
response = api.run(task, img)
|
||||
|
||||
# Print the model's response
|
||||
print(response)
|
||||
```
|
||||
|
||||
### Example 2: Custom API Key<a name="example-2-custom-api-key"></a>
|
||||
|
||||
If you have a custom API key, you can initialize the module with it as shown in this example.
|
||||
|
||||
```python
|
||||
from swarm_models import GPT4VisionAPI
|
||||
|
||||
# Initialize with custom API key and max_tokens
|
||||
custom_api_key = "your_custom_api_key"
|
||||
api = GPT4VisionAPI(openai_api_key=custom_api_key, max_tokens=500)
|
||||
|
||||
# Define the task and image URL
|
||||
task = "What is the object in the image?"
|
||||
img = "https://i.imgur.com/3T3ZHwD.jpeg"
|
||||
|
||||
# Run the GPT-4 Vision model
|
||||
response = api.run(task, img)
|
||||
|
||||
# Print the model's response
|
||||
print(response)
|
||||
```
|
||||
|
||||
### Example 3: Adjusting Maximum Tokens<a name="example-3-adjusting-maximum-tokens"></a>
|
||||
|
||||
You can also customize the maximum token limit when initializing the module. In this example, we set it to 1000 tokens.
|
||||
|
||||
```python
|
||||
from swarm_models import GPT4VisionAPI
|
||||
|
||||
# Initialize with default API key and custom max_tokens
|
||||
api = GPT4VisionAPI(max_tokens=1000)
|
||||
|
||||
# Define the task and image URL
|
||||
task = "Describe the scene in the image."
|
||||
img = "https://i.imgur.com/4P4ZRxU.jpeg"
|
||||
|
||||
# Run the GPT-4 Vision model
|
||||
response = api.run(task, img)
|
||||
|
||||
# Print the model's response
|
||||
print(response)
|
||||
```
|
||||
|
||||
## Additional Information<a name="additional-information"></a>
|
||||
|
||||
- If you encounter any errors or issues with the module, make sure to check your API key and internet connectivity.
|
||||
- It's recommended to handle exceptions when using the module to gracefully handle errors.
|
||||
- You can further customize the module to fit your specific use case by modifying the code as needed.
|
||||
|
||||
## References<a name="references"></a>
|
||||
|
||||
- [OpenAI API Documentation](https://beta.openai.com/docs/)
|
||||
|
||||
This documentation provides a comprehensive guide on how to use the `GPT4VisionAPI` module effectively. It covers initialization, methods, usage examples, and additional information to ensure a smooth experience when working with the GPT-4 Vision model.
|
||||
@ -1,64 +0,0 @@
|
||||
# Groq API Key Setup Documentation
|
||||
|
||||
|
||||
This documentation provides instructions on how to obtain your Groq API key and set it up in a `.env` file for use in your project.
|
||||
|
||||
## Step 1: Obtain Your Groq API Key
|
||||
|
||||
1. **Sign Up / Log In**:
|
||||
- Visit the [Groq website](https://www.groq.com) and sign up for an account if you don't have one. If you already have an account, log in.
|
||||
|
||||
2. **Access API Keys**:
|
||||
- Once logged in, navigate to the API section of your account dashboard. This is usually found under "Settings" or "API Management".
|
||||
|
||||
3. **Generate API Key**:
|
||||
- If you do not have an API key, look for an option to generate a new key. Follow the prompts to create your API key. Make sure to copy it to your clipboard.
|
||||
|
||||
## Step 2: Create a `.env` File
|
||||
|
||||
1. **Create the File**:
|
||||
- In the root directory of your project, create a new file named `.env`.
|
||||
|
||||
2. **Add Your API Key**:
|
||||
- Open the `.env` file in a text editor and add the following line, replacing `your_groq_api_key_here` with the API key you copied earlier:
|
||||
|
||||
```plaintext
|
||||
GROQ_API_KEY=your_groq_api_key_here
|
||||
```
|
||||
|
||||
3. **Save the File**:
|
||||
- Save the changes to the `.env` file.
|
||||
|
||||
|
||||
|
||||
## Full Example
|
||||
```python
|
||||
import os
|
||||
from swarm_models import OpenAIChat
|
||||
from dotenv import load_dotenv
|
||||
|
||||
load_dotenv()
|
||||
|
||||
# Get the OpenAI API key from the environment variable
|
||||
api_key = os.getenv("GROQ_API_KEY")
|
||||
|
||||
# Model
|
||||
model = OpenAIChat(
|
||||
openai_api_base="https://api.groq.com/openai/v1",
|
||||
openai_api_key=api_key,
|
||||
model_name="llama-3.1-70b-versatile",
|
||||
temperature=0.1,
|
||||
)
|
||||
|
||||
model.run("What are the best metrics to track and understand risk in private equity")
|
||||
```
|
||||
|
||||
## Important Notes
|
||||
|
||||
- **Keep Your API Key Secure**: Do not share your API key publicly or commit it to version control systems like Git. Use the `.gitignore` file to exclude the `.env` file from being tracked.
|
||||
- **Environment Variables**: Make sure to install any necessary libraries (like `python-dotenv`) to load environment variables from the `.env` file if your project requires it.
|
||||
|
||||
|
||||
## Conclusion
|
||||
|
||||
You are now ready to use the Groq API in your project! If you encounter any issues, refer to the Groq documentation or support for further assistance.
|
||||
@ -1,91 +0,0 @@
|
||||
# HuggingFaceLLM
|
||||
|
||||
## Overview & Introduction
|
||||
|
||||
The `HuggingFaceLLM` class in the Zeta library provides a simple and easy-to-use interface to harness the power of Hugging Face's transformer-based language models, specifically for causal language modeling. This enables developers to generate coherent and contextually relevant sentences or paragraphs given a prompt, without delving deep into the intricate details of the underlying model or the tokenization process.
|
||||
|
||||
Causal Language Modeling (CLM) is a task where given a series of tokens (or words), the model predicts the next token in the sequence. This functionality is central to many natural language processing tasks, including chatbots, story generation, and code autocompletion.
|
||||
|
||||
---
|
||||
|
||||
## Class Definition
|
||||
|
||||
```python
|
||||
class HuggingFaceLLM:
|
||||
```
|
||||
|
||||
### Parameters:
|
||||
|
||||
- `model_id (str)`: Identifier for the pre-trained model on the Hugging Face model hub. Examples include "gpt2-medium", "openai-gpt", etc.
|
||||
|
||||
- `device (str, optional)`: The device on which to load and run the model. Defaults to 'cuda' if GPU is available, else 'cpu'.
|
||||
|
||||
- `max_length (int, optional)`: Maximum length of the generated sequence. Defaults to 20.
|
||||
|
||||
- `quantization_config (dict, optional)`: Configuration dictionary for model quantization (if applicable). Default is `None`.
|
||||
|
||||
---
|
||||
|
||||
## Functionality & Usage
|
||||
|
||||
### Initialization:
|
||||
|
||||
```python
|
||||
llm = HuggingFaceLLM(model_id="gpt2-medium")
|
||||
```
|
||||
|
||||
Upon initialization, the specified pre-trained model and tokenizer are loaded from Hugging Face's model hub. The model is then moved to the designated device. If there's an issue loading either the model or the tokenizer, an error will be logged.
|
||||
|
||||
### Generation:
|
||||
|
||||
The main functionality of this class is text generation. The class provides two methods for this: `__call__` and `generate`. Both methods take in a prompt text and an optional `max_length` parameter and return the generated text.
|
||||
|
||||
Usage:
|
||||
```python
|
||||
from swarms import HuggingFaceLLM
|
||||
|
||||
# Initialize
|
||||
llm = HuggingFaceLLM(model_id="gpt2-medium")
|
||||
|
||||
# Generate text using __call__ method
|
||||
result = llm("Once upon a time,")
|
||||
print(result)
|
||||
|
||||
# Alternatively, using the generate method
|
||||
result = llm.generate("The future of AI is")
|
||||
print(result)
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Mathematical Explanation:
|
||||
|
||||
Given a sequence of tokens \( x_1, x_2, ..., x_n \), a causal language model aims to maximize the likelihood of the next token \( x_{n+1} \) in the sequence. Formally, it tries to optimize:
|
||||
|
||||
\[ P(x_{n+1} | x_1, x_2, ..., x_n) \]
|
||||
|
||||
Where \( P \) is the probability distribution over all possible tokens in the vocabulary.
|
||||
|
||||
The model takes the tokenized input sequence, feeds it through several transformer blocks, and finally through a linear layer to produce logits for each token in the vocabulary. The token with the highest logit value is typically chosen as the next token in the sequence.
|
||||
|
||||
---
|
||||
|
||||
## Additional Information & Tips:
|
||||
|
||||
- Ensure you have an active internet connection when initializing the class for the first time, as the models and tokenizers are fetched from Hugging Face's servers.
|
||||
|
||||
- Although the default `max_length` is set to 20, it's advisable to adjust this parameter based on the context of the problem.
|
||||
|
||||
- Keep an eye on GPU memory when using large models or generating long sequences.
|
||||
|
||||
---
|
||||
|
||||
## References & Resources:
|
||||
|
||||
- Hugging Face Model Hub: [https://huggingface.co/models](https://huggingface.co/models)
|
||||
|
||||
- Introduction to Transformers: [https://huggingface.co/transformers/introduction.html](https://huggingface.co/transformers/introduction.html)
|
||||
|
||||
- Causal Language Modeling: Vaswani, A., et al. (2017). Attention is All You Need. [arXiv:1706.03762](https://arxiv.org/abs/1706.03762)
|
||||
|
||||
Note: This documentation template provides a comprehensive overview of the `HuggingFaceLLM` class. Developers can follow similar structures when documenting other classes or functionalities.
|
||||
@ -1,155 +0,0 @@
|
||||
## `HuggingfaceLLM` Documentation
|
||||
|
||||
### Introduction
|
||||
|
||||
The `HuggingfaceLLM` class is designed for running inference using models from the Hugging Face Transformers library. This documentation provides an in-depth understanding of the class, its purpose, attributes, methods, and usage examples.
|
||||
|
||||
#### Purpose
|
||||
|
||||
The `HuggingfaceLLM` class serves the following purposes:
|
||||
|
||||
1. Load pre-trained Hugging Face models and tokenizers.
|
||||
2. Generate text-based responses from the loaded model using a given prompt.
|
||||
3. Provide flexibility in device selection, quantization, and other configuration options.
|
||||
|
||||
### Class Definition
|
||||
|
||||
The `HuggingfaceLLM` class is defined as follows:
|
||||
|
||||
```python
|
||||
class HuggingfaceLLM:
|
||||
def __init__(
|
||||
self,
|
||||
model_id: str,
|
||||
device: str = None,
|
||||
max_length: int = 20,
|
||||
quantize: bool = False,
|
||||
quantization_config: dict = None,
|
||||
verbose=False,
|
||||
distributed=False,
|
||||
decoding=False,
|
||||
):
|
||||
# Attributes and initialization logic explained below
|
||||
pass
|
||||
|
||||
def load_model(self):
|
||||
# Method to load the pre-trained model and tokenizer
|
||||
pass
|
||||
|
||||
def run(self, prompt_text: str, max_length: int = None):
|
||||
# Method to generate text-based responses
|
||||
pass
|
||||
|
||||
def __call__(self, prompt_text: str, max_length: int = None):
|
||||
# Alternate method for generating text-based responses
|
||||
pass
|
||||
```
|
||||
|
||||
### Attributes
|
||||
|
||||
| Attribute | Description |
|
||||
|----------------------|---------------------------------------------------------------------------------------------------------------------------|
|
||||
| `model_id` | The ID of the pre-trained model to be used. |
|
||||
| `device` | The device on which the model runs (`'cuda'` for GPU or `'cpu'` for CPU). |
|
||||
| `max_length` | The maximum length of the generated text. |
|
||||
| `quantize` | A boolean indicating whether quantization should be used. |
|
||||
| `quantization_config`| A dictionary with configuration options for quantization. |
|
||||
| `verbose` | A boolean indicating whether verbose logs should be printed. |
|
||||
| `logger` | An optional logger for logging messages (defaults to a basic logger). |
|
||||
| `distributed` | A boolean indicating whether distributed processing should be used. |
|
||||
| `decoding` | A boolean indicating whether to perform decoding during text generation. |
|
||||
|
||||
### Class Methods
|
||||
|
||||
#### `__init__` Method
|
||||
|
||||
The `__init__` method initializes an instance of the `HuggingfaceLLM` class with the specified parameters. It also loads the pre-trained model and tokenizer.
|
||||
|
||||
- `model_id` (str): The ID of the pre-trained model to use.
|
||||
- `device` (str, optional): The device to run the model on ('cuda' or 'cpu').
|
||||
- `max_length` (int, optional): The maximum length of the generated text.
|
||||
- `quantize` (bool, optional): Whether to use quantization.
|
||||
- `quantization_config` (dict, optional): Configuration for quantization.
|
||||
- `verbose` (bool, optional): Whether to print verbose logs.
|
||||
- `logger` (logging.Logger, optional): The logger to use.
|
||||
- `distributed` (bool, optional): Whether to use distributed processing.
|
||||
- `decoding` (bool, optional): Whether to perform decoding during text generation.
|
||||
|
||||
#### `load_model` Method
|
||||
|
||||
The `load_model` method loads the pre-trained model and tokenizer specified by `model_id`.
|
||||
|
||||
#### `run` and `__call__` Methods
|
||||
|
||||
Both `run` and `__call__` methods generate text-based responses based on a given prompt. They accept the following parameters:
|
||||
|
||||
- `prompt_text` (str): The text prompt to initiate text generation.
|
||||
- `max_length` (int, optional): The maximum length of the generated text.
|
||||
|
||||
### Usage Examples
|
||||
|
||||
Here are three ways to use the `HuggingfaceLLM` class:
|
||||
|
||||
#### Example 1: Basic Usage
|
||||
|
||||
```python
|
||||
from swarm_models import HuggingfaceLLM
|
||||
|
||||
# Initialize the HuggingfaceLLM instance with a model ID
|
||||
model_id = "NousResearch/Nous-Hermes-2-Vision-Alpha"
|
||||
inference = HuggingfaceLLM(model_id=model_id)
|
||||
|
||||
# Generate text based on a prompt
|
||||
prompt_text = "Once upon a time"
|
||||
generated_text = inference(prompt_text)
|
||||
print(generated_text)
|
||||
```
|
||||
|
||||
#### Example 2: Custom Configuration
|
||||
|
||||
```python
|
||||
from swarm_models import HuggingfaceLLM
|
||||
|
||||
# Initialize with custom configuration
|
||||
custom_config = {
|
||||
"quantize": True,
|
||||
"quantization_config": {"load_in_4bit": True},
|
||||
"verbose": True,
|
||||
}
|
||||
inference = HuggingfaceLLM(
|
||||
model_id="NousResearch/Nous-Hermes-2-Vision-Alpha", **custom_config
|
||||
)
|
||||
|
||||
# Generate text based on a prompt
|
||||
prompt_text = "Tell me a joke"
|
||||
generated_text = inference(prompt_text)
|
||||
print(generated_text)
|
||||
```
|
||||
|
||||
#### Example 3: Distributed Processing
|
||||
|
||||
```python
|
||||
from swarm_models import HuggingfaceLLM
|
||||
|
||||
# Initialize for distributed processing
|
||||
inference = HuggingfaceLLM(model_id="gpt2-medium", distributed=True)
|
||||
|
||||
# Generate text based on a prompt
|
||||
prompt_text = "Translate the following sentence to French"
|
||||
generated_text = inference(prompt_text)
|
||||
print(generated_text)
|
||||
```
|
||||
|
||||
### Additional Information
|
||||
|
||||
- The `HuggingfaceLLM` class provides the flexibility to load and use pre-trained models from the Hugging Face Transformers library.
|
||||
- Quantization can be enabled to reduce model size and inference time.
|
||||
- Distributed processing can be used for parallelized inference.
|
||||
- Verbose logging can help in debugging and understanding the text generation process.
|
||||
|
||||
### References
|
||||
|
||||
- [Hugging Face Transformers Documentation](https://huggingface.co/transformers/)
|
||||
- [PyTorch Documentation](https://pytorch.org/docs/stable/index.html)
|
||||
|
||||
This documentation provides a comprehensive understanding of the `HuggingfaceLLM` class, its attributes, methods, and usage examples. Developers can use this class to perform text generation tasks efficiently using pre-trained models from the Hugging Face Transformers library.
|
||||
@ -1,107 +0,0 @@
|
||||
# `Idefics` Documentation
|
||||
|
||||
## Introduction
|
||||
|
||||
Welcome to the documentation for Idefics, a versatile multimodal inference tool using pre-trained models from the Hugging Face Hub. Idefics is designed to facilitate the generation of text from various prompts, including text and images. This documentation provides a comprehensive understanding of Idefics, its architecture, usage, and how it can be integrated into your projects.
|
||||
|
||||
## Overview
|
||||
|
||||
Idefics leverages the power of pre-trained models to generate textual responses based on a wide range of prompts. It is capable of handling both text and images, making it suitable for various multimodal tasks, including text generation from images.
|
||||
|
||||
## Class Definition
|
||||
|
||||
```python
|
||||
class Idefics:
|
||||
def __init__(
|
||||
self,
|
||||
checkpoint="HuggingFaceM4/idefics-9b-instruct",
|
||||
device=None,
|
||||
torch_dtype=torch.bfloat16,
|
||||
max_length=100,
|
||||
):
|
||||
```
|
||||
|
||||
## Usage
|
||||
|
||||
To use Idefics, follow these steps:
|
||||
|
||||
1. Initialize the Idefics instance:
|
||||
|
||||
```python
|
||||
from swarm_models import Idefics
|
||||
|
||||
model = Idefics()
|
||||
```
|
||||
|
||||
2. Generate text based on prompts:
|
||||
|
||||
```python
|
||||
prompts = [
|
||||
"User: What is in this image? https://upload.wikimedia.org/wikipedia/commons/8/86/Id%C3%A9fix.JPG"
|
||||
]
|
||||
response = model(prompts)
|
||||
print(response)
|
||||
```
|
||||
|
||||
### Example 1 - Image Questioning
|
||||
|
||||
```python
|
||||
from swarm_models import Idefics
|
||||
|
||||
model = Idefics()
|
||||
prompts = [
|
||||
"User: What is in this image? https://upload.wikimedia.org/wikipedia/commons/8/86/Id%C3%A9fix.JPG"
|
||||
]
|
||||
response = model(prompts)
|
||||
print(response)
|
||||
```
|
||||
|
||||
### Example 2 - Bidirectional Conversation
|
||||
|
||||
```python
|
||||
from swarm_models import Idefics
|
||||
|
||||
model = Idefics()
|
||||
user_input = "User: What is in this image? https://upload.wikimedia.org/wikipedia/commons/8/86/Id%C3%A9fix.JPG"
|
||||
response = model.chat(user_input)
|
||||
print(response)
|
||||
|
||||
user_input = "User: Who is that? https://static.wikia.nocookie.net/asterix/images/2/25/R22b.gif/revision/latest?cb=20110815073052"
|
||||
response = model.chat(user_input)
|
||||
print(response)
|
||||
```
|
||||
|
||||
### Example 3 - Configuration Changes
|
||||
|
||||
```python
|
||||
model.set_checkpoint("new_checkpoint")
|
||||
model.set_device("cpu")
|
||||
model.set_max_length(200)
|
||||
model.clear_chat_history()
|
||||
```
|
||||
|
||||
## How Idefics Works
|
||||
|
||||
Idefics operates by leveraging pre-trained models from the Hugging Face Hub. Here's how it works:
|
||||
|
||||
1. **Initialization**: When you create an Idefics instance, it initializes the model using a specified checkpoint, sets the device for inference, and configures other parameters like data type and maximum text length.
|
||||
|
||||
2. **Prompt-Based Inference**: You can use the `infer` method to generate text based on prompts. It processes prompts in batched or non-batched mode, depending on your preference. It uses a pre-trained processor to handle text and images.
|
||||
|
||||
3. **Bidirectional Conversation**: The `chat` method enables bidirectional conversations. You provide user input, and the model responds accordingly. The chat history is maintained for context.
|
||||
|
||||
4. **Configuration Changes**: You can change the model checkpoint, device, maximum text length, or clear the chat history as needed during runtime.
|
||||
|
||||
## Parameters
|
||||
|
||||
- `checkpoint`: The name of the pre-trained model checkpoint (default is "HuggingFaceM4/idefics-9b-instruct").
|
||||
- `device`: The device to use for inference. By default, it uses CUDA if available; otherwise, it uses CPU.
|
||||
- `torch_dtype`: The data type to use for inference. By default, it uses torch.bfloat16.
|
||||
- `max_length`: The maximum length of the generated text (default is 100).
|
||||
|
||||
## Additional Information
|
||||
|
||||
- Idefics provides a convenient way to engage in bidirectional conversations with pre-trained models.
|
||||
- You can easily change the model checkpoint, device, and other settings to adapt to your specific use case.
|
||||
|
||||
That concludes the documentation for Idefics. We hope you find this tool valuable for your multimodal text generation tasks. If you have any questions or encounter any issues, please refer to the Hugging Face Transformers documentation for further assistance. Enjoy working with Idefics!
|
||||
@ -1,139 +0,0 @@
|
||||
# Swarm Models
|
||||
|
||||
|
||||
```bash
|
||||
$ pip3 install -U swarm-models
|
||||
```
|
||||
|
||||
Welcome to the documentation for the llm section of the swarms package, designed to facilitate seamless integration with various AI language models and APIs. This package empowers developers, end-users, and system administrators to interact with AI models from different providers, such as OpenAI, Hugging Face, Google PaLM, and Anthropic.
|
||||
|
||||
### Table of Contents
|
||||
1. [OpenAI](#openai)
|
||||
2. [HuggingFace](#huggingface)
|
||||
3. [Anthropic](#anthropic)
|
||||
|
||||
### 1. OpenAI (swarm_models.OpenAI)
|
||||
|
||||
The OpenAI class provides an interface to interact with OpenAI's language models. It allows both synchronous and asynchronous interactions.
|
||||
|
||||
**Constructor:**
|
||||
```python
|
||||
OpenAI(api_key: str, system: str = None, console: bool = True, model: str = None, params: dict = None, save_messages: bool = True)
|
||||
```
|
||||
|
||||
**Attributes:**
|
||||
- `api_key` (str): Your OpenAI API key.
|
||||
|
||||
- `system` (str, optional): A system message to be used in conversations.
|
||||
|
||||
- `console` (bool, default=True): Display console logs.
|
||||
|
||||
- `model` (str, optional): Name of the language model to use.
|
||||
|
||||
- `params` (dict, optional): Additional parameters for model interactions.
|
||||
|
||||
- `save_messages` (bool, default=True): Save conversation messages.
|
||||
|
||||
**Methods:**
|
||||
|
||||
- `run(message: str, **kwargs) -> str`: Generate a response using the OpenAI model.
|
||||
|
||||
- `generate_async(message: str, **kwargs) -> str`: Generate a response asynchronously.
|
||||
|
||||
- `ask_multiple(ids: List[str], question_template: str) -> List[str]`: Query multiple IDs simultaneously.
|
||||
|
||||
- `stream_multiple(ids: List[str], question_template: str) -> List[str]`: Stream multiple responses.
|
||||
|
||||
**Usage Example:**
|
||||
```python
|
||||
import asyncio
|
||||
|
||||
from swarm_models import OpenAI
|
||||
|
||||
chat = OpenAI(api_key="YOUR_OPENAI_API_KEY")
|
||||
|
||||
response = chat.run("Hello, how can I assist you?")
|
||||
print(response)
|
||||
|
||||
ids = ["id1", "id2", "id3"]
|
||||
async_responses = asyncio.run(chat.ask_multiple(ids, "How is {id}?"))
|
||||
print(async_responses)
|
||||
```
|
||||
|
||||
### 2. HuggingFace (swarm_models.HuggingFaceLLM)
|
||||
|
||||
The HuggingFaceLLM class allows interaction with language models from Hugging Face.
|
||||
|
||||
**Constructor:**
|
||||
```python
|
||||
HuggingFaceLLM(model_id: str, device: str = None, max_length: int = 20, quantize: bool = False, quantization_config: dict = None)
|
||||
```
|
||||
|
||||
**Attributes:**
|
||||
|
||||
- `model_id` (str): ID or name of the Hugging Face model.
|
||||
|
||||
- `device` (str, optional): Device to run the model on (e.g., 'cuda', 'cpu').
|
||||
|
||||
- `max_length` (int, default=20): Maximum length of generated text.
|
||||
|
||||
- `quantize` (bool, default=False): Apply model quantization.
|
||||
|
||||
- `quantization_config` (dict, optional): Configuration for quantization.
|
||||
|
||||
**Methods:**
|
||||
|
||||
- `run(prompt_text: str, max_length: int = None) -> str`: Generate text based on a prompt.
|
||||
|
||||
**Usage Example:**
|
||||
```python
|
||||
from swarm_models import HuggingFaceLLM
|
||||
|
||||
model_id = "gpt2"
|
||||
hugging_face_model = HuggingFaceLLM(model_id=model_id)
|
||||
|
||||
prompt = "Once upon a time"
|
||||
generated_text = hugging_face_model.run(prompt)
|
||||
print(generated_text)
|
||||
```
|
||||
|
||||
### 3. Anthropic (swarm_models.Anthropic)
|
||||
|
||||
The Anthropic class enables interaction with Anthropic's large language models.
|
||||
|
||||
**Constructor:**
|
||||
```python
|
||||
Anthropic(model: str = "claude-2", max_tokens_to_sample: int = 256, temperature: float = None, top_k: int = None, top_p: float = None, streaming: bool = False, default_request_timeout: int = None)
|
||||
```
|
||||
|
||||
**Attributes:**
|
||||
|
||||
- `model` (str): Name of the Anthropic model.
|
||||
|
||||
- `max_tokens_to_sample` (int, default=256): Maximum tokens to sample.
|
||||
|
||||
- `temperature` (float, optional): Temperature for text generation.
|
||||
|
||||
- `top_k` (int, optional): Top-k sampling value.
|
||||
|
||||
- `top_p` (float, optional): Top-p sampling value.
|
||||
|
||||
- `streaming` (bool, default=False): Enable streaming mode.
|
||||
|
||||
- `default_request_timeout` (int, optional): Default request timeout.
|
||||
|
||||
**Methods:**
|
||||
|
||||
- `run(prompt: str, stop: List[str] = None) -> str`: Generate text based on a prompt.
|
||||
|
||||
**Usage Example:**
|
||||
```python
|
||||
from swarm_models import Anthropic
|
||||
|
||||
anthropic = Anthropic()
|
||||
prompt = "Once upon a time"
|
||||
generated_text = anthropic.run(prompt)
|
||||
print(generated_text)
|
||||
```
|
||||
|
||||
This concludes the documentation for the "models" folder, providing you with tools to seamlessly integrate with various language models and APIs. Happy coding!
|
||||
@ -1,217 +0,0 @@
|
||||
# `Kosmos` Documentation
|
||||
|
||||
## Introduction
|
||||
|
||||
Welcome to the documentation for Kosmos, a powerful multimodal AI model that can perform various tasks, including multimodal grounding, referring expression comprehension, referring expression generation, grounded visual question answering (VQA), and grounded image captioning. Kosmos is based on the ydshieh/kosmos-2-patch14-224 model and is designed to process both text and images to provide meaningful outputs. In this documentation, you will find a detailed explanation of the Kosmos class, its functions, parameters, and usage examples.
|
||||
|
||||
## Overview
|
||||
|
||||
Kosmos is a state-of-the-art multimodal AI model that combines the power of natural language understanding with image analysis. It can perform several tasks that involve processing both textual prompts and images to provide informative responses. Whether you need to find objects in an image, understand referring expressions, generate descriptions, answer questions, or create captions, Kosmos has you covered.
|
||||
|
||||
## Class Definition
|
||||
|
||||
```python
|
||||
class Kosmos:
|
||||
def __init__(self, model_name="ydshieh/kosmos-2-patch14-224"):
|
||||
```
|
||||
|
||||
## Usage
|
||||
|
||||
To use Kosmos, follow these steps:
|
||||
|
||||
1. Initialize the Kosmos instance:
|
||||
|
||||
```python
|
||||
from swarm_models.kosmos_two import Kosmos
|
||||
|
||||
kosmos = Kosmos()
|
||||
```
|
||||
|
||||
2. Perform Multimodal Grounding:
|
||||
|
||||
```python
|
||||
kosmos.multimodal_grounding(
|
||||
"Find the red apple in the image.", "https://example.com/apple.jpg"
|
||||
)
|
||||
```
|
||||
|
||||
### Example 1 - Multimodal Grounding
|
||||
|
||||
```python
|
||||
from swarm_models.kosmos_two import Kosmos
|
||||
|
||||
kosmos = Kosmos()
|
||||
|
||||
kosmos.multimodal_grounding(
|
||||
"Find the red apple in the image.", "https://example.com/apple.jpg"
|
||||
)
|
||||
```
|
||||
|
||||
3. Perform Referring Expression Comprehension:
|
||||
|
||||
```python
|
||||
kosmos.referring_expression_comprehension(
|
||||
"Show me the green bottle.", "https://example.com/bottle.jpg"
|
||||
)
|
||||
```
|
||||
|
||||
### Example 2 - Referring Expression Comprehension
|
||||
|
||||
```python
|
||||
from swarm_models.kosmos_two import Kosmos
|
||||
|
||||
kosmos = Kosmos()
|
||||
|
||||
kosmos.referring_expression_comprehension(
|
||||
"Show me the green bottle.", "https://example.com/bottle.jpg"
|
||||
)
|
||||
```
|
||||
|
||||
4. Generate Referring Expressions:
|
||||
|
||||
```python
|
||||
kosmos.referring_expression_generation(
|
||||
"It is on the table.", "https://example.com/table.jpg"
|
||||
)
|
||||
```
|
||||
|
||||
### Example 3 - Referring Expression Generation
|
||||
|
||||
```python
|
||||
from swarm_models.kosmos_two import Kosmos
|
||||
|
||||
kosmos = Kosmos()
|
||||
|
||||
kosmos.referring_expression_generation(
|
||||
"It is on the table.", "https://example.com/table.jpg"
|
||||
)
|
||||
```
|
||||
|
||||
5. Perform Grounded Visual Question Answering (VQA):
|
||||
|
||||
```python
|
||||
kosmos.grounded_vqa("What is the color of the car?", "https://example.com/car.jpg")
|
||||
```
|
||||
|
||||
### Example 4 - Grounded Visual Question Answering
|
||||
|
||||
```python
|
||||
from swarm_models.kosmos_two import Kosmos
|
||||
|
||||
kosmos = Kosmos()
|
||||
|
||||
kosmos.grounded_vqa("What is the color of the car?", "https://example.com/car.jpg")
|
||||
```
|
||||
|
||||
6. Generate Grounded Image Captions:
|
||||
|
||||
```python
|
||||
kosmos.grounded_image_captioning("https://example.com/beach.jpg")
|
||||
```
|
||||
|
||||
### Example 5 - Grounded Image Captioning
|
||||
|
||||
```python
|
||||
from swarm_models.kosmos_two import Kosmos
|
||||
|
||||
kosmos = Kosmos()
|
||||
|
||||
kosmos.grounded_image_captioning("https://example.com/beach.jpg")
|
||||
```
|
||||
|
||||
7. Generate Detailed Grounded Image Captions:
|
||||
|
||||
```python
|
||||
kosmos.grounded_image_captioning_detailed("https://example.com/beach.jpg")
|
||||
```
|
||||
|
||||
### Example 6 - Detailed Grounded Image Captioning
|
||||
|
||||
```python
|
||||
from swarm_models.kosmos_two import Kosmos
|
||||
|
||||
kosmos = Kosmos()
|
||||
|
||||
kosmos.grounded_image_captioning_detailed("https://example.com/beach.jpg")
|
||||
```
|
||||
|
||||
8. Draw Entity Boxes on Image:
|
||||
|
||||
```python
|
||||
image = kosmos.get_image("https://example.com/image.jpg")
|
||||
entities = [
|
||||
("apple", (0, 3), [(0.2, 0.3, 0.4, 0.5)]),
|
||||
("banana", (4, 9), [(0.6, 0.2, 0.8, 0.4)]),
|
||||
]
|
||||
kosmos.draw_entity_boxes_on_image(image, entities, show=True)
|
||||
```
|
||||
|
||||
### Example 7 - Drawing Entity Boxes on Image
|
||||
|
||||
```python
|
||||
from swarm_models.kosmos_two import Kosmos
|
||||
|
||||
kosmos = Kosmos()
|
||||
|
||||
image = kosmos.get_image("https://example.com/image.jpg")
|
||||
entities = [
|
||||
("apple", (0, 3), [(0.2, 0.3, 0.4, 0.5)]),
|
||||
("banana", (4, 9), [(0.6, 0.2, 0.8, 0.4)]),
|
||||
]
|
||||
kosmos.draw_entity_boxes_on_image(image, entities, show=True)
|
||||
```
|
||||
|
||||
9. Generate Boxes for Entities:
|
||||
|
||||
```python
|
||||
entities = [
|
||||
("apple", (0, 3), [(0.2, 0.3, 0.4, 0.5)]),
|
||||
("banana", (4, 9), [(0.6, 0.2, 0.8, 0.4)]),
|
||||
]
|
||||
image = kosmos.generate_boxes(
|
||||
"Find the apple and the banana in the image.", "https://example.com/image.jpg"
|
||||
)
|
||||
```
|
||||
|
||||
### Example 8 - Generating Boxes for Entities
|
||||
|
||||
```python
|
||||
from swarm_models.kosmos_two import Kosmos
|
||||
|
||||
kosmos = Kosmos()
|
||||
entities = [
|
||||
("apple", (0, 3), [(0.2, 0.3, 0.4, 0.5)]),
|
||||
("banana", (4, 9), [(0.6, 0.2, 0.8, 0.4)]),
|
||||
]
|
||||
image = kosmos.generate_boxes(
|
||||
"Find the apple and the banana in the image.", "https://example.com/image.jpg"
|
||||
)
|
||||
```
|
||||
|
||||
## How Kosmos Works
|
||||
|
||||
Kosmos is a multimodal AI model that combines text and image processing. It uses the ydshieh/kosmos-2-patch14-224 model for understanding and generating responses. Here's how it works:
|
||||
|
||||
1. **Initialization**: When you create a Kosmos instance, it loads the ydshieh/kosmos-2-patch14-224 model for multimodal tasks.
|
||||
|
||||
2. **Processing Text and Images**: Kosmos can process both text prompts and images. It takes a textual prompt and an image URL as input.
|
||||
|
||||
3. **Task Execution**: Based on the task you specify, Kosmos generates informative responses by combining natural language understanding with image analysis.
|
||||
|
||||
4. **Drawing Entity Boxes**: You can use the `draw_entity_boxes_on_image` method to draw bounding boxes around entities in an image.
|
||||
|
||||
5. **Generating Boxes for Entities**: The `generate_boxes` method allows you to generate bounding boxes for entities mentioned in a prompt.
|
||||
|
||||
## Parameters
|
||||
|
||||
- `model_name`: The name or path of the Kosmos model to be used. By default, it uses the ydshieh/kosmos-2-patch14-224 model.
|
||||
|
||||
## Additional Information
|
||||
|
||||
- Kosmos can handle various multimodal tasks, making it a versatile tool for understanding and generating content.
|
||||
- You can provide image URLs for image-based tasks, and Kosmos will automatically retrieve and process the images.
|
||||
- The `draw_entity_boxes_on_image` method is useful for visualizing the results of multimodal grounding tasks.
|
||||
- The `generate_boxes` method is handy for generating bounding boxes around entities mentioned in a textual prompt.
|
||||
|
||||
That concludes the documentation for Kosmos. We hope you find this multimodal AI model valuable for your projects. If you have any questions or encounter any issues, please refer to the Kosmos documentation for
|
||||
further assistance. Enjoy working with Kosmos!
|
||||
@ -1,88 +0,0 @@
|
||||
# `LayoutLMDocumentQA` Documentation
|
||||
|
||||
## Introduction
|
||||
|
||||
Welcome to the documentation for LayoutLMDocumentQA, a multimodal model designed for visual question answering (QA) on real-world documents, such as invoices, PDFs, and more. This comprehensive documentation will provide you with a deep understanding of the LayoutLMDocumentQA class, its architecture, usage, and examples.
|
||||
|
||||
## Overview
|
||||
|
||||
LayoutLMDocumentQA is a versatile model that combines layout-based understanding of documents with natural language processing to answer questions about the content of documents. It is particularly useful for automating tasks like invoice processing, extracting information from PDFs, and handling various document-based QA scenarios.
|
||||
|
||||
## Class Definition
|
||||
|
||||
```python
|
||||
class LayoutLMDocumentQA(AbstractModel):
|
||||
def __init__(
|
||||
self,
|
||||
model_name: str = "impira/layoutlm-document-qa",
|
||||
task: str = "document-question-answering",
|
||||
):
|
||||
```
|
||||
|
||||
## Purpose
|
||||
|
||||
The LayoutLMDocumentQA class serves the following primary purposes:
|
||||
|
||||
1. **Document QA**: LayoutLMDocumentQA is specifically designed for document-based question answering. It can process both the textual content and the layout of a document to answer questions.
|
||||
|
||||
2. **Multimodal Understanding**: It combines natural language understanding with document layout analysis, making it suitable for documents with complex structures.
|
||||
|
||||
## Parameters
|
||||
|
||||
- `model_name` (str): The name or path of the pretrained LayoutLMDocumentQA model. Default: "impira/layoutlm-document-qa".
|
||||
- `task` (str): The specific task for which the model will be used. Default: "document-question-answering".
|
||||
|
||||
## Usage
|
||||
|
||||
To use LayoutLMDocumentQA, follow these steps:
|
||||
|
||||
1. Initialize the LayoutLMDocumentQA instance:
|
||||
|
||||
```python
|
||||
from swarm_models import LayoutLMDocumentQA
|
||||
|
||||
layout_lm_doc_qa = LayoutLMDocumentQA()
|
||||
```
|
||||
|
||||
### Example 1 - Initialization
|
||||
|
||||
```python
|
||||
layout_lm_doc_qa = LayoutLMDocumentQA()
|
||||
```
|
||||
|
||||
2. Ask a question about a document and provide the document's image path:
|
||||
|
||||
```python
|
||||
question = "What is the total amount?"
|
||||
image_path = "path/to/document_image.png"
|
||||
answer = layout_lm_doc_qa(question, image_path)
|
||||
```
|
||||
|
||||
### Example 2 - Document QA
|
||||
|
||||
```python
|
||||
layout_lm_doc_qa = LayoutLMDocumentQA()
|
||||
question = "What is the total amount?"
|
||||
image_path = "path/to/document_image.png"
|
||||
answer = layout_lm_doc_qa(question, image_path)
|
||||
```
|
||||
|
||||
## How LayoutLMDocumentQA Works
|
||||
|
||||
LayoutLMDocumentQA employs a multimodal approach to document QA. Here's how it works:
|
||||
|
||||
1. **Initialization**: When you create a LayoutLMDocumentQA instance, you can specify the model to use and the task, which is "document-question-answering" by default.
|
||||
|
||||
2. **Question and Document**: You provide a question about the document and the image path of the document to the LayoutLMDocumentQA instance.
|
||||
|
||||
3. **Multimodal Processing**: LayoutLMDocumentQA processes both the question and the document image. It combines layout-based analysis with natural language understanding.
|
||||
|
||||
4. **Answer Generation**: The model generates an answer to the question based on its analysis of the document layout and content.
|
||||
|
||||
## Additional Information
|
||||
|
||||
- LayoutLMDocumentQA uses the "impira/layoutlm-document-qa" pretrained model, which is specifically designed for document-based question answering.
|
||||
- You can adapt this model to various document QA scenarios by changing the task and providing relevant questions and documents.
|
||||
- This model is particularly useful for automating document-based tasks and extracting valuable information from structured documents.
|
||||
|
||||
That concludes the documentation for LayoutLMDocumentQA. We hope you find this tool valuable for your document-based question answering needs. If you have any questions or encounter any issues, please refer to the LayoutLMDocumentQA documentation for further assistance. Enjoy using LayoutLMDocumentQA!
|
||||
@ -1,96 +0,0 @@
|
||||
## Llava3
|
||||
|
||||
|
||||
```python
|
||||
from transformers import AutoTokenizer, AutoModelForCausalLM
|
||||
import torch
|
||||
from swarm_models.base_llm import BaseLLM
|
||||
|
||||
|
||||
class Llama3(BaseLLM):
|
||||
"""
|
||||
Llama3 class represents a Llama model for natural language generation.
|
||||
|
||||
Args:
|
||||
model_id (str): The ID of the Llama model to use.
|
||||
system_prompt (str): The system prompt to use for generating responses.
|
||||
temperature (float): The temperature value for controlling the randomness of the generated responses.
|
||||
top_p (float): The top-p value for controlling the diversity of the generated responses.
|
||||
max_tokens (int): The maximum number of tokens to generate in the response.
|
||||
**kwargs: Additional keyword arguments.
|
||||
|
||||
Attributes:
|
||||
model_id (str): The ID of the Llama model being used.
|
||||
system_prompt (str): The system prompt for generating responses.
|
||||
temperature (float): The temperature value for generating responses.
|
||||
top_p (float): The top-p value for generating responses.
|
||||
max_tokens (int): The maximum number of tokens to generate in the response.
|
||||
tokenizer (AutoTokenizer): The tokenizer for the Llama model.
|
||||
model (AutoModelForCausalLM): The Llama model for generating responses.
|
||||
|
||||
Methods:
|
||||
run(task, *args, **kwargs): Generates a response for the given task.
|
||||
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
model_id="meta-llama/Meta-Llama-3-8B-Instruct",
|
||||
system_prompt: str = None,
|
||||
temperature: float = 0.6,
|
||||
top_p: float = 0.9,
|
||||
max_tokens: int = 4000,
|
||||
**kwargs,
|
||||
):
|
||||
self.model_id = model_id
|
||||
self.system_prompt = system_prompt
|
||||
self.temperature = temperature
|
||||
self.top_p = top_p
|
||||
self.max_tokens = max_tokens
|
||||
self.tokenizer = AutoTokenizer.from_pretrained(model_id)
|
||||
self.model = AutoModelForCausalLM.from_pretrained(
|
||||
model_id,
|
||||
torch_dtype=torch.bfloat16,
|
||||
device_map="auto",
|
||||
)
|
||||
|
||||
def run(self, task: str, *args, **kwargs):
|
||||
"""
|
||||
Generates a response for the given task.
|
||||
|
||||
Args:
|
||||
task (str): The user's task or input.
|
||||
|
||||
Returns:
|
||||
str: The generated response.
|
||||
|
||||
"""
|
||||
messages = [
|
||||
{"role": "system", "content": self.system_prompt},
|
||||
{"role": "user", "content": task},
|
||||
]
|
||||
|
||||
input_ids = self.tokenizer.apply_chat_template(
|
||||
messages, add_generation_prompt=True, return_tensors="pt"
|
||||
).to(self.model.device)
|
||||
|
||||
terminators = [
|
||||
self.tokenizer.eos_token_id,
|
||||
self.tokenizer.convert_tokens_to_ids("<|eot_id|>"),
|
||||
]
|
||||
|
||||
outputs = self.model.generate(
|
||||
input_ids,
|
||||
max_new_tokens=self.max_tokens,
|
||||
eos_token_id=terminators,
|
||||
do_sample=True,
|
||||
temperature=self.temperature,
|
||||
top_p=self.top_p,
|
||||
*args,
|
||||
**kwargs,
|
||||
)
|
||||
response = outputs[0][input_ids.shape[-1] :]
|
||||
return self.tokenizer.decode(
|
||||
response, skip_special_tokens=True
|
||||
)
|
||||
```
|
||||
@ -1,304 +0,0 @@
|
||||
## The Swarms Framework: A Comprehensive Guide to Model APIs and Usage
|
||||
|
||||
### Introduction
|
||||
|
||||
The Swarms framework is a versatile and robust tool designed to streamline the integration and orchestration of multiple AI models, making it easier for developers to build sophisticated multi-agent systems. This blog aims to provide a detailed guide on using the Swarms framework, covering the various models it supports, common methods, settings, and practical examples.
|
||||
|
||||
### Overview of the Swarms Framework
|
||||
|
||||
Swarms is a "framework of frameworks" that allows seamless integration of various AI models, including those from OpenAI, Anthropic, Hugging Face, Azure, and more. This flexibility enables users to leverage the strengths of different models within a single application. The framework provides a unified interface for model interaction, simplifying the process of integrating and managing multiple AI models.
|
||||
|
||||
### Getting Started with Swarms
|
||||
|
||||
To get started with Swarms, you need to install the framework and set up the necessary environment variables. Here's a step-by-step guide:
|
||||
|
||||
#### Installation
|
||||
|
||||
You can install the Swarms framework using pip:
|
||||
|
||||
```bash
|
||||
pip install swarms
|
||||
```
|
||||
|
||||
#### Setting Up Environment Variables
|
||||
|
||||
Swarms relies on environment variables to manage API keys and other configurations. You can use the `dotenv` package to load these variables from a `.env` file.
|
||||
|
||||
```bash
|
||||
pip install python-dotenv
|
||||
```
|
||||
|
||||
Create a `.env` file in your project directory and add your API keys and other settings:
|
||||
|
||||
```env
|
||||
OPENAI_API_KEY=your_openai_api_key
|
||||
ANTHROPIC_API_KEY=your_anthropic_api_key
|
||||
AZURE_OPENAI_ENDPOINT=your_azure_openai_endpoint
|
||||
AZURE_OPENAI_DEPLOYMENT=your_azure_openai_deployment
|
||||
OPENAI_API_VERSION=your_openai_api_version
|
||||
AZURE_OPENAI_API_KEY=your_azure_openai_api_key
|
||||
AZURE_OPENAI_AD_TOKEN=your_azure_openai_ad_token
|
||||
```
|
||||
|
||||
### Using the Swarms Framework
|
||||
|
||||
Swarms supports a variety of models from different providers. Here are some examples of how to use these models within the Swarms framework.
|
||||
|
||||
#### Using the Anthropic Model
|
||||
|
||||
The Anthropic model is one of the many models supported by Swarms. Here's how you can use it:
|
||||
|
||||
```python
|
||||
import os
|
||||
from swarm_models import Anthropic
|
||||
|
||||
# Load the environment variables
|
||||
anthropic_api_key = os.getenv("ANTHROPIC_API_KEY")
|
||||
|
||||
# Create an instance of the Anthropic model
|
||||
model = Anthropic(anthropic_api_key=anthropic_api_key)
|
||||
|
||||
# Define the task
|
||||
task = "What is quantum field theory? What are 3 books on the field?"
|
||||
|
||||
# Generate a response
|
||||
response = model(task)
|
||||
|
||||
# Print the response
|
||||
print(response)
|
||||
```
|
||||
|
||||
#### Using the HuggingfaceLLM Model
|
||||
|
||||
HuggingfaceLLM allows you to use models from Hugging Face's vast repository. Here's an example:
|
||||
|
||||
```python
|
||||
from swarm_models import HuggingfaceLLM
|
||||
|
||||
# Define the model ID
|
||||
model_id = "NousResearch/Yarn-Mistral-7b-128k"
|
||||
|
||||
# Create an instance of the HuggingfaceLLM model
|
||||
inference = HuggingfaceLLM(model_id=model_id)
|
||||
|
||||
# Define the task
|
||||
task = "Once upon a time"
|
||||
|
||||
# Generate a response
|
||||
generated_text = inference(task)
|
||||
print(generated_text)
|
||||
```
|
||||
|
||||
|
||||
|
||||
#### Using the OpenAIChat Model
|
||||
|
||||
The OpenAIChat model is designed for conversational tasks. Here's how to use it:
|
||||
|
||||
```python
|
||||
import os
|
||||
from swarm_models import OpenAIChat
|
||||
|
||||
# Load the environment variables
|
||||
openai_api_key = os.getenv("OPENAI_API_KEY")
|
||||
|
||||
# Create an instance of the OpenAIChat model
|
||||
openai = OpenAIChat(openai_api_key=openai_api_key, verbose=False)
|
||||
|
||||
# Define the task
|
||||
chat = openai("What are quantum fields?")
|
||||
print(chat)
|
||||
```
|
||||
|
||||
#### Using the TogetherLLM Model
|
||||
|
||||
TogetherLLM supports models from the Together ecosystem. Here's an example:
|
||||
|
||||
```python
|
||||
from swarms import TogetherLLM
|
||||
|
||||
# Initialize the model with your parameters
|
||||
model = TogetherLLM(
|
||||
model_name="mistralai/Mixtral-8x7B-Instruct-v0.1",
|
||||
max_tokens=1000,
|
||||
together_api_key="your_together_api_key",
|
||||
)
|
||||
|
||||
# Run the model
|
||||
response = model.run("Generate a blog post about the best way to make money online.")
|
||||
print(response)
|
||||
```
|
||||
|
||||
#### Using the Azure OpenAI Model
|
||||
|
||||
The Azure OpenAI model is another powerful tool that can be integrated with Swarms. Here's how to use it:
|
||||
|
||||
```python
|
||||
import os
|
||||
from dotenv import load_dotenv
|
||||
from swarms import AzureOpenAI
|
||||
|
||||
# Load the environment variables
|
||||
load_dotenv()
|
||||
|
||||
# Create an instance of the AzureOpenAI class
|
||||
model = AzureOpenAI(
|
||||
azure_endpoint=os.getenv("AZURE_OPENAI_ENDPOINT"),
|
||||
deployment_name=os.getenv("AZURE_OPENAI_DEPLOYMENT"),
|
||||
openai_api_version=os.getenv("OPENAI_API_VERSION"),
|
||||
openai_api_key=os.getenv("AZURE_OPENAI_API_KEY"),
|
||||
azure_ad_token=os.getenv("AZURE_OPENAI_AD_TOKEN"),
|
||||
)
|
||||
|
||||
# Define the prompt
|
||||
prompt = (
|
||||
"Analyze this load document and assess it for any risks and"
|
||||
" create a table in markdown format."
|
||||
)
|
||||
|
||||
# Generate a response
|
||||
response = model(prompt)
|
||||
print(response)
|
||||
```
|
||||
|
||||
|
||||
#### Using the GPT4VisionAPI Model
|
||||
|
||||
The GPT4VisionAPI model can analyze images and provide detailed insights. Here's how to use it:
|
||||
|
||||
```python
|
||||
import os
|
||||
from dotenv import load_dotenv
|
||||
from swarms import GPT4VisionAPI
|
||||
|
||||
# Load the environment variables
|
||||
load_dotenv()
|
||||
|
||||
# Get the API key from the environment variables
|
||||
api_key = os.getenv("OPENAI_API_KEY")
|
||||
|
||||
# Create an instance of the GPT4VisionAPI class
|
||||
gpt4vision = GPT4VisionAPI(
|
||||
openai_api_key=api_key,
|
||||
model_name="gpt-4o",
|
||||
max_tokens=1000,
|
||||
openai_proxy="https://api.openai.com/v1/chat/completions",
|
||||
)
|
||||
|
||||
# Define the URL of the image to analyze
|
||||
img = "ear.png"
|
||||
|
||||
# Define the task to perform on the image
|
||||
task = "What is this image"
|
||||
|
||||
# Run the GPT4VisionAPI on the image with the specified task
|
||||
answer = gpt4vision.run(task, img, return_json=True)
|
||||
|
||||
# Print the answer
|
||||
print(answer)
|
||||
```
|
||||
|
||||
#### Using the QwenVLMultiModal Model
|
||||
|
||||
The QwenVLMultiModal model is designed for multi-modal tasks, such as processing both text and images. Here's an example of how to use it:
|
||||
|
||||
```python
|
||||
from swarms import QwenVLMultiModal
|
||||
|
||||
# Instantiate the QwenVLMultiModal model
|
||||
model = QwenVLMultiModal(
|
||||
model_name="Qwen/Qwen-VL-Chat",
|
||||
device="cuda",
|
||||
quantize=True,
|
||||
)
|
||||
|
||||
# Run the model
|
||||
response = model("Hello, how are you?", "https://example.com/image.jpg")
|
||||
|
||||
# Print the response
|
||||
print(response)
|
||||
```
|
||||
|
||||
|
||||
### Common Methods in Swarms
|
||||
|
||||
Swarms provides several common methods that are useful across different models. One of the most frequently used methods is `__call__`.
|
||||
|
||||
#### The `__call__` Method
|
||||
|
||||
The `__call__` method is used to run the model on a given task. Here is a generic example:
|
||||
|
||||
```python
|
||||
# Assuming `model` is an instance of any supported model
|
||||
task = "Explain the theory of relativity."
|
||||
response = model(task)
|
||||
print(response)
|
||||
```
|
||||
|
||||
This method abstracts the complexity of interacting with different model APIs, providing a consistent interface for executing tasks.
|
||||
|
||||
### Common Settings in Swarms
|
||||
|
||||
Swarms allows you to configure various settings to customize the behavior of the models. Here are some common settings:
|
||||
|
||||
#### API Keys
|
||||
|
||||
API keys are essential for authenticating and accessing the models. These keys are typically set through environment variables:
|
||||
|
||||
```python
|
||||
import os
|
||||
|
||||
# Set API keys as environment variables
|
||||
os.environ['OPENAI_API_KEY'] = 'your_openai_api_key'
|
||||
os.environ['ANTHROPIC_API_KEY'] = 'your_anthropic_api_key'
|
||||
```
|
||||
|
||||
#### Model-Specific Settings
|
||||
|
||||
Different models may have specific settings that need to be configured. For example, the `AzureOpenAI` model requires several settings related to the Azure environment:
|
||||
|
||||
```python
|
||||
model = AzureOpenAI(
|
||||
azure_endpoint=os.getenv("AZURE_OPENAI_ENDPOINT"),
|
||||
deployment_name=os.getenv("AZURE_OPENAI_DEPLOYMENT"),
|
||||
openai_api_version=os.getenv("OPENAI_API_VERSION"),
|
||||
openai_api_key=os.getenv("AZURE_OPENAI_API_KEY"),
|
||||
azure_ad_token=os.getenv("AZURE_OPENAI_AD_TOKEN"),
|
||||
)
|
||||
```
|
||||
|
||||
### Advanced Usage and Best Practices
|
||||
|
||||
To make the most out of the Swarms framework, consider the following best practices:
|
||||
|
||||
#### Extensive Logging
|
||||
|
||||
Use logging to monitor the behavior and performance of your models. The `loguru` library is recommended for its simplicity and flexibility:
|
||||
|
||||
```python
|
||||
from loguru import logger
|
||||
|
||||
# Log model interactions
|
||||
logger.info("Running task on Anthropic model")
|
||||
response = model(task)
|
||||
logger.info(f"Response: {response}")
|
||||
```
|
||||
|
||||
#### Error Handling
|
||||
|
||||
Implement robust error handling to manage API failures and other issues gracefully:
|
||||
|
||||
```python
|
||||
try:
|
||||
response = model(task)
|
||||
except Exception as e:
|
||||
logger.error(f"Error running task: {e}")
|
||||
response = "An error occurred while processing your request."
|
||||
print(response)
|
||||
```
|
||||
|
||||
### Conclusion
|
||||
|
||||
The Swarms framework provides a powerful and flexible way to integrate and manage multiple AI models within a single application. By following the guidelines and examples provided in this blog, you can leverage Swarms to build sophisticated, multi-agent systems with ease. Whether you're using models from OpenAI, Anthropic, Azure, or Hugging Face,
|
||||
|
||||
Swarms offers a unified interface that simplifies the process of model orchestration and execution.
|
||||
@ -1,118 +0,0 @@
|
||||
# `Nougat` Documentation
|
||||
|
||||
## Introduction
|
||||
|
||||
Welcome to the documentation for Nougat, a versatile model designed by Meta for transcribing scientific PDFs into user-friendly Markdown format, extracting information from PDFs, and extracting metadata from PDF documents. This documentation will provide you with a deep understanding of the Nougat class, its architecture, usage, and examples.
|
||||
|
||||
## Overview
|
||||
|
||||
Nougat is a powerful tool that combines language modeling and image processing capabilities to convert scientific PDF documents into Markdown format. It is particularly useful for researchers, students, and professionals who need to extract valuable information from PDFs quickly. With Nougat, you can simplify complex PDFs, making their content more accessible and easy to work with.
|
||||
|
||||
## Class Definition
|
||||
|
||||
```python
|
||||
class Nougat:
|
||||
def __init__(
|
||||
self,
|
||||
model_name_or_path="facebook/nougat-base",
|
||||
min_length: int = 1,
|
||||
max_new_tokens: int = 30,
|
||||
):
|
||||
```
|
||||
|
||||
## Purpose
|
||||
|
||||
The Nougat class serves the following primary purposes:
|
||||
|
||||
1. **PDF Transcription**: Nougat is designed to transcribe scientific PDFs into Markdown format. It helps convert complex PDF documents into a more readable and structured format, making it easier to extract information.
|
||||
|
||||
2. **Information Extraction**: It allows users to extract valuable information and content from PDFs efficiently. This can be particularly useful for researchers and professionals who need to extract data, figures, or text from scientific papers.
|
||||
|
||||
3. **Metadata Extraction**: Nougat can also extract metadata from PDF documents, providing essential details about the document, such as title, author, and publication date.
|
||||
|
||||
## Parameters
|
||||
|
||||
- `model_name_or_path` (str): The name or path of the pretrained Nougat model. Default: "facebook/nougat-base".
|
||||
- `min_length` (int): The minimum length of the generated transcription. Default: 1.
|
||||
- `max_new_tokens` (int): The maximum number of new tokens to generate in the Markdown transcription. Default: 30.
|
||||
|
||||
## Usage
|
||||
|
||||
To use Nougat, follow these steps:
|
||||
|
||||
1. Initialize the Nougat instance:
|
||||
|
||||
```python
|
||||
from swarm_models import Nougat
|
||||
|
||||
nougat = Nougat()
|
||||
```
|
||||
|
||||
### Example 1 - Initialization
|
||||
|
||||
```python
|
||||
nougat = Nougat()
|
||||
```
|
||||
|
||||
2. Transcribe a PDF image using Nougat:
|
||||
|
||||
```python
|
||||
markdown_transcription = nougat("path/to/pdf_file.png")
|
||||
```
|
||||
|
||||
### Example 2 - PDF Transcription
|
||||
|
||||
```python
|
||||
nougat = Nougat()
|
||||
markdown_transcription = nougat("path/to/pdf_file.png")
|
||||
```
|
||||
|
||||
3. Extract information from a PDF:
|
||||
|
||||
```python
|
||||
information = nougat.extract_information("path/to/pdf_file.png")
|
||||
```
|
||||
|
||||
### Example 3 - Information Extraction
|
||||
|
||||
```python
|
||||
nougat = Nougat()
|
||||
information = nougat.extract_information("path/to/pdf_file.png")
|
||||
```
|
||||
|
||||
4. Extract metadata from a PDF:
|
||||
|
||||
```python
|
||||
metadata = nougat.extract_metadata("path/to/pdf_file.png")
|
||||
```
|
||||
|
||||
### Example 4 - Metadata Extraction
|
||||
|
||||
```python
|
||||
nougat = Nougat()
|
||||
metadata = nougat.extract_metadata("path/to/pdf_file.png")
|
||||
```
|
||||
|
||||
## How Nougat Works
|
||||
|
||||
Nougat employs a vision encoder-decoder model, along with a dedicated processor, to transcribe PDFs into Markdown format and perform information and metadata extraction. Here's how it works:
|
||||
|
||||
1. **Initialization**: When you create a Nougat instance, you can specify the model to use, the minimum transcription length, and the maximum number of new tokens to generate.
|
||||
|
||||
2. **Processing PDFs**: Nougat can process PDFs as input. You can provide the path to a PDF document.
|
||||
|
||||
3. **Image Processing**: The processor converts PDF pages into images, which are then encoded by the model.
|
||||
|
||||
4. **Transcription**: Nougat generates Markdown transcriptions of PDF content, ensuring a minimum length and respecting the token limit.
|
||||
|
||||
5. **Information Extraction**: Information extraction involves parsing the Markdown transcription to identify key details or content of interest.
|
||||
|
||||
6. **Metadata Extraction**: Metadata extraction involves identifying and extracting document metadata, such as title, author, and publication date.
|
||||
|
||||
## Additional Information
|
||||
|
||||
- Nougat leverages the "facebook/nougat-base" pretrained model, which is specifically designed for document transcription and extraction tasks.
|
||||
- You can adjust the minimum transcription length and the maximum number of new tokens to control the output's length and quality.
|
||||
- Nougat can be run on both CPU and GPU devices.
|
||||
|
||||
That concludes the documentation for Nougat. We hope you find this tool valuable for your PDF transcription, information extraction, and metadata extraction needs. If you have any questions or encounter any issues, please refer to the Nougat documentation for further assistance. Enjoy using Nougat!
|
||||
@ -1,200 +0,0 @@
|
||||
# `BaseOpenAI` and `OpenAI` Documentation
|
||||
|
||||
## Table of Contents
|
||||
|
||||
1. [Overview](#overview)
|
||||
2. [Class Architecture](#class-architecture)
|
||||
3. [Purpose](#purpose)
|
||||
4. [Class Attributes](#class-attributes)
|
||||
5. [Methods](#methods)
|
||||
- [Construction](#construction)
|
||||
- [Configuration](#configuration)
|
||||
- [Tokenization](#tokenization)
|
||||
- [Generation](#generation)
|
||||
- [Asynchronous Generation](#asynchronous-generation)
|
||||
6. [Usage Examples](#usage-examples)
|
||||
- [Creating an OpenAI Object](#creating-an-openai-object)
|
||||
- [Generating Text](#generating-text)
|
||||
- [Advanced Configuration](#advanced-configuration)
|
||||
|
||||
---
|
||||
|
||||
## 1. Overview <a name="overview"></a>
|
||||
|
||||
The `BaseOpenAI` and `OpenAI` classes are part of the LangChain library, designed to interact with OpenAI's large language models (LLMs). These classes provide a seamless interface for utilizing OpenAI's API to generate natural language text.
|
||||
|
||||
## 2. Class Architecture <a name="class-architecture"></a>
|
||||
|
||||
Both `BaseOpenAI` and `OpenAI` classes inherit from `BaseLLM`, demonstrating an inheritance-based architecture. This architecture allows for easy extensibility and customization while adhering to the principles of object-oriented programming.
|
||||
|
||||
## 3. Purpose <a name="purpose"></a>
|
||||
|
||||
The purpose of these classes is to simplify the interaction with OpenAI's LLMs. They encapsulate API calls, handle tokenization, and provide a high-level interface for generating text. By instantiating an object of the `OpenAI` class, developers can quickly leverage the power of OpenAI's models to generate text for various applications, such as chatbots, content generation, and more.
|
||||
|
||||
## 4. Class Attributes <a name="class-attributes"></a>
|
||||
|
||||
Here are the key attributes and their descriptions for the `BaseOpenAI` and `OpenAI` classes:
|
||||
|
||||
| Attribute | Description |
|
||||
|---------------------------|-------------|
|
||||
| `lc_secrets` | A dictionary of secrets required for LangChain, including the OpenAI API key. |
|
||||
| `lc_attributes` | A dictionary of attributes relevant to LangChain. |
|
||||
| `is_lc_serializable()` | A method indicating if the class is serializable for LangChain. |
|
||||
| `model_name` | The name of the language model to use. |
|
||||
| `temperature` | The sampling temperature for text generation. |
|
||||
| `max_tokens` | The maximum number of tokens to generate in a completion. |
|
||||
| `top_p` | The total probability mass of tokens to consider at each step. |
|
||||
| `frequency_penalty` | Penalizes repeated tokens according to frequency. |
|
||||
| `presence_penalty` | Penalizes repeated tokens. |
|
||||
| `n` | How many completions to generate for each prompt. |
|
||||
| `best_of` | Generates `best_of` completions server-side and returns the "best." |
|
||||
| `model_kwargs` | Holds any model parameters valid for `create` calls not explicitly specified. |
|
||||
| `openai_api_key` | The OpenAI API key used for authentication. |
|
||||
| `openai_api_base` | The base URL for the OpenAI API. |
|
||||
| `openai_organization` | The OpenAI organization name, if applicable. |
|
||||
| `openai_proxy` | An explicit proxy URL for OpenAI requests. |
|
||||
| `batch_size` | The batch size to use when passing multiple documents for generation. |
|
||||
| `request_timeout` | The timeout for requests to the OpenAI completion API. |
|
||||
| `logit_bias` | Adjustment to the probability of specific tokens being generated. |
|
||||
| `max_retries` | The maximum number of retries to make when generating. |
|
||||
| `streaming` | Whether to stream the results or not. |
|
||||
| `allowed_special` | A set of special tokens that are allowed. |
|
||||
| `disallowed_special` | A collection of special tokens that are not allowed. |
|
||||
| `tiktoken_model_name` | The model name to pass to `tiktoken` for token counting. |
|
||||
|
||||
## 5. Methods <a name="methods"></a>
|
||||
|
||||
### 5.1 Construction <a name="construction"></a>
|
||||
|
||||
#### 5.1.1 `__new__(cls, **data: Any) -> Union[OpenAIChat, BaseOpenAI]`
|
||||
- Description: Initializes the OpenAI object.
|
||||
- Arguments:
|
||||
- `cls` (class): The class instance.
|
||||
- `data` (dict): Additional data for initialization.
|
||||
- Returns:
|
||||
- Union[OpenAIChat, BaseOpenAI]: An instance of the OpenAI class.
|
||||
|
||||
### 5.2 Configuration <a name="configuration"></a>
|
||||
|
||||
#### 5.2.1 `build_extra(cls, values: Dict[str, Any]) -> Dict[str, Any]`
|
||||
- Description: Builds extra kwargs from additional params passed in.
|
||||
- Arguments:
|
||||
- `cls` (class): The class instance.
|
||||
- `values` (dict): Values and parameters to build extra kwargs.
|
||||
- Returns:
|
||||
- Dict[str, Any]: A dictionary of built extra kwargs.
|
||||
|
||||
#### 5.2.2 `validate_environment(cls, values: Dict) -> Dict`
|
||||
- Description: Validates that the API key and python package exist in the environment.
|
||||
- Arguments:
|
||||
- `values` (dict): The class values and parameters.
|
||||
- Returns:
|
||||
- Dict: A dictionary of validated values.
|
||||
|
||||
### 5.3 Tokenization <a name="tokenization"></a>
|
||||
|
||||
#### 5.3.1 `get_sub_prompts(self, params: Dict[str, Any], prompts: List[str], stop: Optional[List[str]] = None) -> List[List[str]]`
|
||||
- Description: Gets sub-prompts for LLM call.
|
||||
- Arguments:
|
||||
- `params` (dict): Parameters for LLM call.
|
||||
- `prompts` (list): List of prompts.
|
||||
- `stop` (list, optional): List of stop words.
|
||||
- Returns:
|
||||
- List[List[str]]: List of sub-prompts.
|
||||
|
||||
#### 5.3.2 `get_token_ids(self, text: str) -> List[int]`
|
||||
- Description: Gets token IDs using the `tiktoken` package.
|
||||
- Arguments:
|
||||
- `text` (str): The text for which to calculate token IDs.
|
||||
- Returns:
|
||||
- List[int]: A list of token IDs.
|
||||
|
||||
#### 5.3.3 `modelname_to_contextsize(modelname: str) -> int`
|
||||
- Description: Calculates the maximum number of tokens possible to generate for a model.
|
||||
- Arguments:
|
||||
- `modelname` (str): The model name to determine the context size for.
|
||||
- Returns:
|
||||
- int: The maximum context size.
|
||||
|
||||
#### 5.3.4 `max_tokens_for_prompt(self, prompt: str) -> int`
|
||||
- Description: Calculates the maximum number of tokens possible to generate for a prompt.
|
||||
- Arguments:
|
||||
- `prompt` (str): The prompt for which to
|
||||
|
||||
determine the maximum token limit.
|
||||
- Returns:
|
||||
- int: The maximum token limit.
|
||||
|
||||
### 5.4 Generation <a name="generation"></a>
|
||||
|
||||
#### 5.4.1 `generate(self, text: Union[str, List[str]], **kwargs) -> Union[str, List[str]]`
|
||||
- Description: Generates text using the OpenAI API.
|
||||
- Arguments:
|
||||
- `text` (str or list): The input text or list of inputs.
|
||||
- `**kwargs` (dict): Additional parameters for the generation process.
|
||||
- Returns:
|
||||
- Union[str, List[str]]: The generated text or list of generated texts.
|
||||
|
||||
### 5.5 Asynchronous Generation <a name="asynchronous-generation"></a>
|
||||
|
||||
#### 5.5.1 `generate_async(self, text: Union[str, List[str]], **kwargs) -> Union[str, List[str]]`
|
||||
- Description: Generates text asynchronously using the OpenAI API.
|
||||
- Arguments:
|
||||
- `text` (str or list): The input text or list of inputs.
|
||||
- `**kwargs` (dict): Additional parameters for the asynchronous generation process.
|
||||
- Returns:
|
||||
- Union[str, List[str]]: The generated text or list of generated texts.
|
||||
|
||||
## 6. Usage Examples <a name="usage-examples"></a>
|
||||
|
||||
### 6.1 Creating an OpenAI Object <a name="creating-an-openai-object"></a>
|
||||
|
||||
```python
|
||||
# Import the OpenAI class
|
||||
from swarm_models import OpenAI
|
||||
|
||||
# Set your OpenAI API key
|
||||
api_key = "YOUR_API_KEY"
|
||||
|
||||
# Create an OpenAI object
|
||||
openai = OpenAI(api_key)
|
||||
```
|
||||
|
||||
### 6.2 Generating Text <a name="generating-text"></a>
|
||||
|
||||
```python
|
||||
# Generate text from a single prompt
|
||||
prompt = "Translate the following English text to French: 'Hello, how are you?'"
|
||||
generated_text = openai.generate(prompt, max_tokens=50)
|
||||
|
||||
# Generate text from multiple prompts
|
||||
prompts = [
|
||||
"Translate this: 'Good morning' to Spanish.",
|
||||
"Summarize the following article:",
|
||||
article_text,
|
||||
]
|
||||
generated_texts = openai.generate(prompts, max_tokens=100)
|
||||
|
||||
# Generate text asynchronously
|
||||
async_prompt = "Translate 'Thank you' into German."
|
||||
async_result = openai.generate_async(async_prompt, max_tokens=30)
|
||||
|
||||
# Access the result of an asynchronous generation
|
||||
async_result_text = async_result.get()
|
||||
```
|
||||
|
||||
### 6.3 Advanced Configuration <a name="advanced-configuration"></a>
|
||||
|
||||
```python
|
||||
# Configure generation with advanced options
|
||||
custom_options = {
|
||||
"temperature": 0.7,
|
||||
"max_tokens": 100,
|
||||
"top_p": 0.9,
|
||||
"frequency_penalty": 0.2,
|
||||
"presence_penalty": 0.4,
|
||||
}
|
||||
generated_text = openai.generate(prompt, **custom_options)
|
||||
```
|
||||
|
||||
This documentation provides a comprehensive understanding of the `BaseOpenAI` and `OpenAI` classes, their attributes, methods, and usage examples. Developers can utilize these classes to interact with OpenAI's language models efficiently, enabling various natural language generation tasks.
|
||||
@ -1,185 +0,0 @@
|
||||
# `OpenAIChat` Documentation
|
||||
|
||||
## Table of Contents
|
||||
|
||||
1. [Introduction](#introduction)
|
||||
2. [Class Overview](#class-overview)
|
||||
3. [Class Architecture](#class-architecture)
|
||||
4. [Class Attributes](#class-attributes)
|
||||
5. [Methods](#methods)
|
||||
- [Construction](#construction)
|
||||
- [Configuration](#configuration)
|
||||
- [Message Handling](#message-handling)
|
||||
- [Generation](#generation)
|
||||
- [Tokenization](#tokenization)
|
||||
6. [Usage Examples](#usage-examples)
|
||||
7. [Additional Information](#additional-information)
|
||||
|
||||
---
|
||||
|
||||
## 1. Introduction <a name="introduction"></a>
|
||||
|
||||
The `OpenAIChat` class is part of the LangChain library and serves as an interface to interact with OpenAI's Chat large language models. This documentation provides an in-depth understanding of the class, its attributes, methods, and usage examples.
|
||||
|
||||
## 2. Class Overview <a name="class-overview"></a>
|
||||
|
||||
The `OpenAIChat` class is designed for conducting chat-like conversations with OpenAI's language models, such as GPT-3.5 Turbo. It allows you to create interactive conversations by sending messages and receiving model-generated responses. This class simplifies the process of integrating OpenAI's models into chatbot applications and other natural language processing tasks.
|
||||
|
||||
## 3. Class Architecture <a name="class-architecture"></a>
|
||||
|
||||
The `OpenAIChat` class is built on top of the `BaseLLM` class, which provides a foundation for working with large language models. This inheritance-based architecture allows for customization and extension while adhering to object-oriented programming principles.
|
||||
|
||||
## 4. Class Attributes <a name="class-attributes"></a>
|
||||
|
||||
Here are the key attributes and their descriptions for the `OpenAIChat` class:
|
||||
|
||||
| Attribute | Description |
|
||||
|-----------------------------|-------------------------------------------------------------------------------|
|
||||
| `client` | An internal client for making API calls to OpenAI. |
|
||||
| `model_name` | The name of the language model to use (default: "gpt-3.5-turbo"). |
|
||||
| `model_kwargs` | Additional model parameters valid for `create` calls not explicitly specified.|
|
||||
| `openai_api_key` | The OpenAI API key used for authentication. |
|
||||
| `openai_api_base` | The base URL for the OpenAI API. |
|
||||
| `openai_proxy` | An explicit proxy URL for OpenAI requests. |
|
||||
| `max_retries` | The maximum number of retries to make when generating (default: 6). |
|
||||
| `prefix_messages` | A list of messages to set the initial conversation state (default: []). |
|
||||
| `streaming` | Whether to stream the results or not (default: False). |
|
||||
| `allowed_special` | A set of special tokens that are allowed (default: an empty set). |
|
||||
| `disallowed_special` | A collection of special tokens that are not allowed (default: "all"). |
|
||||
|
||||
## 5. Methods <a name="methods"></a>
|
||||
|
||||
### 5.1 Construction <a name="construction"></a>
|
||||
|
||||
#### 5.1.1 `__init__(self, model_name: str = "gpt-3.5-turbo", openai_api_key: Optional[str] = None, openai_api_base: Optional[str] = None, openai_proxy: Optional[str] = None, max_retries: int = 6, prefix_messages: List = [])`
|
||||
- Description: Initializes an OpenAIChat object.
|
||||
- Arguments:
|
||||
- `model_name` (str): The name of the language model to use (default: "gpt-3.5-turbo").
|
||||
- `openai_api_key` (str, optional): The OpenAI API key used for authentication.
|
||||
- `openai_api_base` (str, optional): The base URL for the OpenAI API.
|
||||
- `openai_proxy` (str, optional): An explicit proxy URL for OpenAI requests.
|
||||
- `max_retries` (int): The maximum number of retries to make when generating (default: 6).
|
||||
- `prefix_messages` (List): A list of messages to set the initial conversation state (default: []).
|
||||
|
||||
### 5.2 Configuration <a name="configuration"></a>
|
||||
|
||||
#### 5.2.1 `build_extra(self, values: Dict[str, Any]) -> Dict[str, Any]`
|
||||
- Description: Builds extra kwargs from additional parameters passed in.
|
||||
- Arguments:
|
||||
- `values` (dict): Values and parameters to build extra kwargs.
|
||||
- Returns:
|
||||
- Dict[str, Any]: A dictionary of built extra kwargs.
|
||||
|
||||
#### 5.2.2 `validate_environment(self, values: Dict) -> Dict`
|
||||
- Description: Validates that the API key and Python package exist in the environment.
|
||||
- Arguments:
|
||||
- `values` (dict): The class values and parameters.
|
||||
- Returns:
|
||||
- Dict: A dictionary of validated values.
|
||||
|
||||
### 5.3 Message Handling <a name="message-handling"></a>
|
||||
|
||||
#### 5.3.1 `_get_chat_params(self, prompts: List[str], stop: Optional[List[str]] = None) -> Tuple`
|
||||
- Description: Gets chat-related parameters for generating responses.
|
||||
- Arguments:
|
||||
- `prompts` (list): List of user messages.
|
||||
- `stop` (list, optional): List of stop words.
|
||||
- Returns:
|
||||
- Tuple: Messages and parameters.
|
||||
|
||||
### 5.4 Generation <a name="generation"></a>
|
||||
|
||||
#### 5.4.1 `_stream(self, prompt: str, stop: Optional[List[str]] = None, run_manager: Optional[CallbackManagerForLLMRun] = None, **kwargs: Any) -> Iterator[GenerationChunk]`
|
||||
- Description: Generates text asynchronously using the OpenAI API.
|
||||
- Arguments:
|
||||
- `prompt` (str): The user's message.
|
||||
- `stop` (list, optional): List of stop words.
|
||||
- `run_manager` (optional): Callback manager for asynchronous generation.
|
||||
- `**kwargs` (dict): Additional parameters for asynchronous generation.
|
||||
- Returns:
|
||||
- Iterator[GenerationChunk]: An iterator of generated text chunks.
|
||||
|
||||
#### 5.4.2 `_agenerate(self, prompts: List[str], stop: Optional[List[str]] = None, run_manager: Optional[AsyncCallbackManagerForLLMRun] = None, **kwargs: Any) -> LLMResult`
|
||||
- Description: Generates text asynchronously using the OpenAI API (async version).
|
||||
- Arguments:
|
||||
- `prompts` (list): List of user messages.
|
||||
- `stop` (list, optional): List of stop words.
|
||||
- `run_manager` (optional): Callback manager for asynchronous generation.
|
||||
- `**kwargs` (dict): Additional parameters for asynchronous generation.
|
||||
- Returns:
|
||||
- LLMResult: A result object containing the generated text.
|
||||
|
||||
### 5.5 Tokenization <a name="tokenization"></a>
|
||||
|
||||
#### 5.5.1 `get_token_ids(self, text: str) -> List[int]`
|
||||
- Description: Gets token IDs using the tiktoken package.
|
||||
- Arguments:
|
||||
- `text` (str): The text for which to calculate token IDs.
|
||||
- Returns:
|
||||
- List[int]: A list of
|
||||
|
||||
token IDs.
|
||||
|
||||
## 6. Usage Examples <a name="usage-examples"></a>
|
||||
|
||||
### Example 1: Initializing `OpenAIChat`
|
||||
|
||||
```python
|
||||
from swarm_models import OpenAIChat
|
||||
|
||||
# Initialize OpenAIChat with model name and API key
|
||||
openai_chat = OpenAIChat(model_name="gpt-3.5-turbo", openai_api_key="YOUR_API_KEY")
|
||||
```
|
||||
|
||||
### Example 2: Sending Messages and Generating Responses
|
||||
|
||||
```python
|
||||
# Define a conversation
|
||||
conversation = [
|
||||
"User: Tell me a joke.",
|
||||
"Assistant: Why did the chicken cross the road?",
|
||||
"User: I don't know. Why?",
|
||||
"Assistant: To get to the other side!",
|
||||
]
|
||||
|
||||
# Set the conversation as the prefix messages
|
||||
openai_chat.prefix_messages = conversation
|
||||
|
||||
# Generate a response
|
||||
user_message = "User: Tell me another joke."
|
||||
response = openai_chat.generate([user_message])
|
||||
|
||||
# Print the generated response
|
||||
print(
|
||||
response[0][0].text
|
||||
) # Output: "Assistant: Why don't scientists trust atoms? Because they make up everything!"
|
||||
```
|
||||
|
||||
### Example 3: Asynchronous Generation
|
||||
|
||||
```python
|
||||
import asyncio
|
||||
|
||||
|
||||
# Define an asynchronous function for generating responses
|
||||
async def generate_responses():
|
||||
user_message = "User: Tell me a fun fact."
|
||||
async for chunk in openai_chat.stream([user_message]):
|
||||
print(chunk.text)
|
||||
|
||||
|
||||
# Run the asynchronous generation function
|
||||
asyncio.run(generate_responses())
|
||||
```
|
||||
|
||||
## 7. Additional Information <a name="additional-information"></a>
|
||||
|
||||
- To use the `OpenAIChat` class, you should have the `openai` Python package installed, and the environment variable `OPENAI_API_KEY` set with your API key.
|
||||
- Any parameters that are valid to be passed to the `openai.create` call can be passed to the `OpenAIChat` constructor.
|
||||
- You can customize the behavior of the class by setting various attributes, such as `model_name`, `openai_api_key`, `prefix_messages`, and more.
|
||||
- For asynchronous generation, you can use the `_stream` and `_agenerate` methods to interactively receive model-generated text chunks.
|
||||
- To calculate token IDs, you can use the `get_token_ids` method, which utilizes the `tiktoken` package. Make sure to install the `tiktoken` package with `pip install tiktoken` if needed.
|
||||
|
||||
---
|
||||
|
||||
This documentation provides a comprehensive overview of the `OpenAIChat` class, its attributes, methods, and usage examples. You can use this class to create chatbot applications, conduct conversations with language models, and explore the capabilities of OpenAI's GPT-3.5 Turbo model.
|
||||
@ -1,238 +0,0 @@
|
||||
# OpenAIFunctionCaller Documentation
|
||||
|
||||
The `OpenAIFunctionCaller` class is designed to interface with OpenAI's chat completion API, allowing users to generate responses based on given prompts using specified models. This class encapsulates the setup and execution of API calls, including handling API keys, model parameters, and response formatting. The class extends the `BaseLLM` and utilizes OpenAI's client library to facilitate interactions.
|
||||
|
||||
## Class Definition
|
||||
|
||||
### OpenAIFunctionCaller
|
||||
|
||||
A class that represents a caller for OpenAI chat completions.
|
||||
|
||||
### Attributes
|
||||
|
||||
| Attribute | Type | Description |
|
||||
|----------------------|-------------------|-------------------------------------------------------------------------|
|
||||
| `system_prompt` | `str` | The system prompt to be used in the chat completion. |
|
||||
| `model_name` | `str` | The name of the OpenAI model to be used. |
|
||||
| `max_tokens` | `int` | The maximum number of tokens in the generated completion. |
|
||||
| `temperature` | `float` | The temperature parameter for randomness in the completion. |
|
||||
| `base_model` | `BaseModel` | The base model to be used for the completion. |
|
||||
| `parallel_tool_calls`| `bool` | Whether to make parallel tool calls. |
|
||||
| `top_p` | `float` | The top-p parameter for nucleus sampling in the completion. |
|
||||
| `client` | `openai.OpenAI` | The OpenAI client for making API calls. |
|
||||
|
||||
### Methods
|
||||
|
||||
#### `check_api_key`
|
||||
|
||||
Checks if the API key is provided and retrieves it from the environment if not.
|
||||
|
||||
| Parameter | Type | Description |
|
||||
|---------------|--------|--------------------------------------|
|
||||
| None | | |
|
||||
|
||||
**Returns:**
|
||||
|
||||
| Type | Description |
|
||||
|--------|--------------------------------------|
|
||||
| `str` | The API key. |
|
||||
|
||||
#### `run`
|
||||
|
||||
Runs the chat completion with the given task and returns the generated completion.
|
||||
|
||||
| Parameter | Type | Description |
|
||||
|-----------|----------|-----------------------------------------------------------------|
|
||||
| `task` | `str` | The user's task for the chat completion. |
|
||||
| `*args` | | Additional positional arguments to be passed to the OpenAI API. |
|
||||
| `**kwargs`| | Additional keyword arguments to be passed to the OpenAI API. |
|
||||
|
||||
**Returns:**
|
||||
|
||||
| Type | Description |
|
||||
|--------|-----------------------------------------------|
|
||||
| `str` | The generated completion. |
|
||||
|
||||
#### `convert_to_dict_from_base_model`
|
||||
|
||||
Converts a `BaseModel` to a dictionary.
|
||||
|
||||
| Parameter | Type | Description |
|
||||
|-------------|------------|--------------------------------------|
|
||||
| `base_model`| `BaseModel`| The BaseModel to be converted. |
|
||||
|
||||
**Returns:**
|
||||
|
||||
| Type | Description |
|
||||
|--------|--------------------------------------|
|
||||
| `dict` | A dictionary representing the BaseModel.|
|
||||
|
||||
#### `convert_list_of_base_models`
|
||||
|
||||
Converts a list of `BaseModels` to a list of dictionaries.
|
||||
|
||||
| Parameter | Type | Description |
|
||||
|--------------|-----------------|--------------------------------------|
|
||||
| `base_models`| `List[BaseModel]`| A list of BaseModels to be converted.|
|
||||
|
||||
**Returns:**
|
||||
|
||||
| Type | Description |
|
||||
|--------|-----------------------------------------------|
|
||||
| `List[Dict]` | A list of dictionaries representing the converted BaseModels. |
|
||||
|
||||
## Usage Examples
|
||||
|
||||
Here are three examples demonstrating different ways to use the `OpenAIFunctionCaller` class:
|
||||
|
||||
### Example 1: Production-Grade Claude Artifacts
|
||||
|
||||
```python
|
||||
import openai
|
||||
from swarm_models.openai_function_caller import OpenAIFunctionCaller
|
||||
from swarms.artifacts.main_artifact import Artifact
|
||||
|
||||
|
||||
# Pydantic is a data validation library that provides data validation and parsing using Python type hints.
|
||||
|
||||
|
||||
# Example usage:
|
||||
# Initialize the function caller
|
||||
model = OpenAIFunctionCaller(
|
||||
system_prompt="You're a helpful assistant.The time is August 6, 2024",
|
||||
max_tokens=500,
|
||||
temperature=0.5,
|
||||
base_model=Artifact,
|
||||
parallel_tool_calls=False,
|
||||
)
|
||||
|
||||
|
||||
# The OpenAIFunctionCaller class is used to interact with the OpenAI API and make function calls.
|
||||
# Here, we initialize an instance of the OpenAIFunctionCaller class with the following parameters:
|
||||
# - system_prompt: A prompt that sets the context for the conversation with the API.
|
||||
# - max_tokens: The maximum number of tokens to generate in the API response.
|
||||
# - temperature: A parameter that controls the randomness of the generated text.
|
||||
# - base_model: The base model to use for the API calls, in this case, the WeatherAPI class.
|
||||
out = model.run("Create a python file with a python game code in it")
|
||||
print(out)
|
||||
```
|
||||
|
||||
### Example 2: Prompt Generator
|
||||
|
||||
```python
|
||||
from swarm_models.openai_function_caller import OpenAIFunctionCaller
|
||||
from pydantic import BaseModel, Field
|
||||
from typing import Sequence
|
||||
|
||||
|
||||
class PromptUseCase(BaseModel):
|
||||
use_case_name: str = Field(
|
||||
...,
|
||||
description="The name of the use case",
|
||||
)
|
||||
use_case_description: str = Field(
|
||||
...,
|
||||
description="The description of the use case",
|
||||
)
|
||||
|
||||
|
||||
class PromptSpec(BaseModel):
|
||||
prompt_name: str = Field(
|
||||
...,
|
||||
description="The name of the prompt",
|
||||
)
|
||||
prompt_description: str = Field(
|
||||
...,
|
||||
description="The description of the prompt",
|
||||
)
|
||||
prompt: str = Field(
|
||||
...,
|
||||
description="The prompt for the agent",
|
||||
)
|
||||
tags: str = Field(
|
||||
...,
|
||||
description="The tags for the prompt such as sentiment, code, etc seperated by commas.",
|
||||
)
|
||||
use_cases: Sequence[PromptUseCase] = Field(
|
||||
...,
|
||||
description="The use cases for the prompt",
|
||||
)
|
||||
|
||||
|
||||
# Example usage:
|
||||
# Initialize the function caller
|
||||
model = OpenAIFunctionCaller(
|
||||
system_prompt="You're an agent creator, you're purpose is to create system prompt for new LLM Agents for the user. Follow the best practices for creating a prompt such as making it direct and clear. Providing instructions and many-shot examples will help the agent understand the task better.",
|
||||
max_tokens=1000,
|
||||
temperature=0.5,
|
||||
base_model=PromptSpec,
|
||||
parallel_tool_calls=False,
|
||||
)
|
||||
|
||||
|
||||
# The OpenAIFunctionCaller class is used to interact with the OpenAI API and make function calls.
|
||||
out = model.run(
|
||||
"Create an prompt for generating quality rust code with instructions and examples."
|
||||
)
|
||||
print(out)
|
||||
|
||||
```
|
||||
|
||||
### Example 3: Sentiment Analysis
|
||||
|
||||
```python
|
||||
from swarm_models.openai_function_caller import OpenAIFunctionCaller
|
||||
from pydantic import BaseModel, Field
|
||||
|
||||
|
||||
# Pydantic is a data validation library that provides data validation and parsing using Python type hints.
|
||||
# It is used here to define the data structure for making API calls to retrieve weather information.
|
||||
class SentimentAnalysisCard(BaseModel):
|
||||
text: str = Field(
|
||||
...,
|
||||
description="The text to be analyzed for sentiment rating",
|
||||
)
|
||||
rating: str = Field(
|
||||
...,
|
||||
description="The sentiment rating of the text from 0.0 to 1.0",
|
||||
)
|
||||
|
||||
|
||||
# The WeatherAPI class is a Pydantic BaseModel that represents the data structure
|
||||
# for making API calls to retrieve weather information. It has two attributes: city and date.
|
||||
|
||||
# Example usage:
|
||||
# Initialize the function caller
|
||||
model = OpenAIFunctionCaller(
|
||||
system_prompt="You're a sentiment Analysis Agent, you're purpose is to rate the sentiment of text",
|
||||
max_tokens=100,
|
||||
temperature=0.5,
|
||||
base_model=SentimentAnalysisCard,
|
||||
parallel_tool_calls=False,
|
||||
)
|
||||
|
||||
|
||||
# The OpenAIFunctionCaller class is used to interact with the OpenAI API and make function calls.
|
||||
# Here, we initialize an instance of the OpenAIFunctionCaller class with the following parameters:
|
||||
# - system_prompt: A prompt that sets the context for the conversation with the API.
|
||||
# - max_tokens: The maximum number of tokens to generate in the API response.
|
||||
# - temperature: A parameter that controls the randomness of the generated text.
|
||||
# - base_model: The base model to use for the API calls, in this case, the WeatherAPI class.
|
||||
out = model.run("The hotel was average, but the food was excellent.")
|
||||
print(out)
|
||||
|
||||
```
|
||||
|
||||
## Additional Information and Tips
|
||||
|
||||
- Ensure that your OpenAI API key is securely stored and not hard-coded into your source code. Use environment variables to manage sensitive information.
|
||||
- Adjust the `temperature` and `top_p` parameters to control the randomness and diversity of the generated responses. Lower values for `temperature` will result in more deterministic outputs, while higher values will introduce more variability.
|
||||
- When using `parallel_tool_calls`, ensure that the tools you are calling in parallel are thread-safe and can handle concurrent execution.
|
||||
|
||||
## References and Resources
|
||||
|
||||
- [OpenAI API Documentation](https://beta.openai.com/docs/)
|
||||
- [Pydantic Documentation](https://pydantic-docs.helpmanual.io/)
|
||||
- [Loguru Logger Documentation](https://loguru.readthedocs.io/)
|
||||
|
||||
By following this comprehensive guide, you can effectively utilize the `OpenAIFunctionCaller` class to generate chat completions using OpenAI's models, customize the response parameters, and handle API interactions seamlessly within your application.
|
||||
@ -1,135 +0,0 @@
|
||||
# `OpenAITTS` Documentation
|
||||
|
||||
## Table of Contents
|
||||
1. [Overview](#overview)
|
||||
2. [Installation](#installation)
|
||||
3. [Usage](#usage)
|
||||
- [Initialization](#initialization)
|
||||
- [Running TTS](#running-tts)
|
||||
- [Running TTS and Saving](#running-tts-and-saving)
|
||||
4. [Examples](#examples)
|
||||
- [Basic Usage](#basic-usage)
|
||||
- [Saving the Output](#saving-the-output)
|
||||
5. [Advanced Options](#advanced-options)
|
||||
6. [Troubleshooting](#troubleshooting)
|
||||
7. [References](#references)
|
||||
|
||||
## 1. Overview <a name="overview"></a>
|
||||
|
||||
The `OpenAITTS` module is a Python library that provides an interface for converting text to speech (TTS) using the OpenAI TTS API. It allows you to generate high-quality speech from text input, making it suitable for various applications such as voice assistants, speech synthesis, and more.
|
||||
|
||||
### Features:
|
||||
- Convert text to speech using OpenAI's TTS model.
|
||||
- Supports specifying the model name, voice, and other parameters.
|
||||
- Option to save the generated speech to a WAV file.
|
||||
|
||||
## 2. Installation <a name="installation"></a>
|
||||
|
||||
To use the `OpenAITTS` model, you need to install the necessary dependencies. You can do this using `pip`:
|
||||
|
||||
```bash
|
||||
pip install swarms requests wave
|
||||
```
|
||||
|
||||
## 3. Usage <a name="usage"></a>
|
||||
|
||||
### Initialization <a name="initialization"></a>
|
||||
|
||||
To use the `OpenAITTS` module, you need to initialize an instance of the `OpenAITTS` class. Here's how you can do it:
|
||||
|
||||
```python
|
||||
from swarm_models.openai_tts import OpenAITTS
|
||||
|
||||
# Initialize the OpenAITTS instance
|
||||
tts = OpenAITTS(
|
||||
model_name="tts-1-1106",
|
||||
proxy_url="https://api.openai.com/v1/audio/speech",
|
||||
openai_api_key=openai_api_key_env,
|
||||
voice="onyx",
|
||||
)
|
||||
```
|
||||
|
||||
#### Parameters:
|
||||
- `model_name` (str): The name of the TTS model to use (default is "tts-1-1106").
|
||||
- `proxy_url` (str): The URL for the OpenAI TTS API (default is "https://api.openai.com/v1/audio/speech").
|
||||
- `openai_api_key` (str): Your OpenAI API key. It can be obtained from the OpenAI website.
|
||||
- `voice` (str): The voice to use for generating speech (default is "onyx").
|
||||
- `chunk_size` (int): The size of data chunks when fetching audio (default is 1024 * 1024 bytes).
|
||||
- `autosave` (bool): Whether to automatically save the generated speech to a file (default is False).
|
||||
- `saved_filepath` (str): The path to the file where the speech will be saved (default is "runs/tts_speech.wav").
|
||||
|
||||
### Running TTS <a name="running-tts"></a>
|
||||
|
||||
Once the `OpenAITTS` instance is initialized, you can use it to convert text to speech using the `run` method:
|
||||
|
||||
```python
|
||||
# Generate speech from text
|
||||
speech_data = tts.run("Hello, world!")
|
||||
```
|
||||
|
||||
#### Parameters:
|
||||
- `task` (str): The text you want to convert to speech.
|
||||
|
||||
#### Returns:
|
||||
- `speech_data` (bytes): The generated speech data.
|
||||
|
||||
### Running TTS and Saving <a name="running-tts-and-saving"></a>
|
||||
|
||||
You can also use the `run_and_save` method to generate speech from text and save it to a file:
|
||||
|
||||
```python
|
||||
# Generate speech from text and save it to a file
|
||||
speech_data = tts.run_and_save("Hello, world!")
|
||||
```
|
||||
|
||||
#### Parameters:
|
||||
- `task` (str): The text you want to convert to speech.
|
||||
|
||||
#### Returns:
|
||||
- `speech_data` (bytes): The generated speech data.
|
||||
|
||||
## 4. Examples <a name="examples"></a>
|
||||
|
||||
### Basic Usage <a name="basic-usage"></a>
|
||||
|
||||
Here's a basic example of how to use the `OpenAITTS` module to generate speech from text:
|
||||
|
||||
```python
|
||||
from swarm_models.openai_tts import OpenAITTS
|
||||
|
||||
# Initialize the OpenAITTS instance
|
||||
tts = OpenAITTS(
|
||||
model_name="tts-1-1106",
|
||||
proxy_url="https://api.openai.com/v1/audio/speech",
|
||||
openai_api_key=openai_api_key_env,
|
||||
voice="onyx",
|
||||
)
|
||||
|
||||
# Generate speech from text
|
||||
speech_data = tts.run("Hello, world!")
|
||||
```
|
||||
|
||||
### Saving the Output <a name="saving-the-output"></a>
|
||||
|
||||
You can save the generated speech to a WAV file using the `run_and_save` method:
|
||||
|
||||
```python
|
||||
# Generate speech from text and save it to a file
|
||||
speech_data = tts.run_and_save("Hello, world!")
|
||||
```
|
||||
|
||||
## 5. Advanced Options <a name="advanced-options"></a>
|
||||
|
||||
The `OpenAITTS` module supports various advanced options for customizing the TTS generation process. You can specify the model name, voice, and other parameters during initialization. Additionally, you can configure the chunk size for audio data fetching and choose whether to automatically save the generated speech to a file.
|
||||
|
||||
## 6. Troubleshooting <a name="troubleshooting"></a>
|
||||
|
||||
If you encounter any issues while using the `OpenAITTS` module, please make sure you have installed all the required dependencies and that your OpenAI API key is correctly configured. If you still face problems, refer to the OpenAI documentation or contact their support for assistance.
|
||||
|
||||
## 7. References <a name="references"></a>
|
||||
|
||||
- [OpenAI API Documentation](https://beta.openai.com/docs/)
|
||||
- [Python Requests Library](https://docs.python-requests.org/en/latest/)
|
||||
- [Python Wave Library](https://docs.python.org/3/library/wave.html)
|
||||
|
||||
This documentation provides a comprehensive guide on how to use the `OpenAITTS` module to convert text to speech using OpenAI's TTS model. It covers initialization, basic usage, advanced options, troubleshooting, and references for further exploration.
|
||||
@ -1,95 +0,0 @@
|
||||
# `Vilt` Documentation
|
||||
|
||||
## Introduction
|
||||
|
||||
Welcome to the documentation for Vilt, a Vision-and-Language Transformer (ViLT) model fine-tuned on the VQAv2 dataset. Vilt is a powerful model capable of answering questions about images. This documentation will provide a comprehensive understanding of Vilt, its architecture, usage, and how it can be integrated into your projects.
|
||||
|
||||
## Overview
|
||||
|
||||
Vilt is based on the Vision-and-Language Transformer (ViLT) architecture, designed for tasks that involve understanding both text and images. It has been fine-tuned on the VQAv2 dataset, making it adept at answering questions about images. This model is particularly useful for tasks where textual and visual information needs to be combined to provide meaningful answers.
|
||||
|
||||
## Class Definition
|
||||
|
||||
```python
|
||||
class Vilt:
|
||||
def __init__(self):
|
||||
"""
|
||||
Initialize the Vilt model.
|
||||
"""
|
||||
```
|
||||
|
||||
## Usage
|
||||
|
||||
To use the Vilt model, follow these steps:
|
||||
|
||||
1. Initialize the Vilt model:
|
||||
|
||||
```python
|
||||
from swarm_models import Vilt
|
||||
|
||||
model = Vilt()
|
||||
```
|
||||
|
||||
2. Call the model with a text question and an image URL:
|
||||
|
||||
```python
|
||||
output = model(
|
||||
"What is this image?", "http://images.cocodataset.org/val2017/000000039769.jpg"
|
||||
)
|
||||
```
|
||||
|
||||
### Example 1 - Image Questioning
|
||||
|
||||
```python
|
||||
model = Vilt()
|
||||
output = model(
|
||||
"What are the objects in this image?",
|
||||
"http://images.cocodataset.org/val2017/000000039769.jpg",
|
||||
)
|
||||
print(output)
|
||||
```
|
||||
|
||||
### Example 2 - Image Analysis
|
||||
|
||||
```python
|
||||
model = Vilt()
|
||||
output = model(
|
||||
"Describe the scene in this image.",
|
||||
"http://images.cocodataset.org/val2017/000000039769.jpg",
|
||||
)
|
||||
print(output)
|
||||
```
|
||||
|
||||
### Example 3 - Visual Knowledge Retrieval
|
||||
|
||||
```python
|
||||
model = Vilt()
|
||||
output = model(
|
||||
"Tell me more about the landmark in this image.",
|
||||
"http://images.cocodataset.org/val2017/000000039769.jpg",
|
||||
)
|
||||
print(output)
|
||||
```
|
||||
|
||||
## How Vilt Works
|
||||
|
||||
Vilt operates by combining text and image information to generate meaningful answers to questions about the provided image. Here's how it works:
|
||||
|
||||
1. **Initialization**: When you create a Vilt instance, it initializes the processor and the model. The processor is responsible for handling the image and text input, while the model is the fine-tuned ViLT model.
|
||||
|
||||
2. **Processing Input**: When you call the Vilt model with a text question and an image URL, it downloads the image and processes it along with the text question. This processing step involves tokenization and encoding of the input.
|
||||
|
||||
3. **Forward Pass**: The encoded input is then passed through the ViLT model. It calculates the logits, and the answer with the highest probability is selected.
|
||||
|
||||
4. **Output**: The predicted answer is returned as the output of the model.
|
||||
|
||||
## Parameters
|
||||
|
||||
Vilt does not require any specific parameters during initialization. It is pre-configured to work with the "dandelin/vilt-b32-finetuned-vqa" model.
|
||||
|
||||
## Additional Information
|
||||
|
||||
- Vilt is fine-tuned on the VQAv2 dataset, making it proficient at answering questions about a wide range of images.
|
||||
- You can use Vilt for various applications, including image question-answering, image analysis, and visual knowledge retrieval.
|
||||
|
||||
That concludes the documentation for Vilt. We hope you find this model useful for your vision-and-language tasks. If you have any questions or encounter any issues, please refer to the Hugging Face Transformers documentation for further assistance. Enjoy working with Vilt!
|
||||
@ -1,138 +0,0 @@
|
||||
# Under The Hood: The Swarm Cloud Serving Infrastructure
|
||||
-----------------------------------------------------------------
|
||||
|
||||
This blog post delves into the intricate workings of our serving model infrastructure, providing a comprehensive understanding for both users and infrastructure engineers. We'll embark on a journey that starts with an API request and culminates in a response generated by your chosen model, all orchestrated within a multi-cloud environment.
|
||||
|
||||
### The Journey of an API Request
|
||||
|
||||
1. **The Gateway:** Your API request first arrives at an EC2 instance running SkyPilot, a lightweight controller.
|
||||
|
||||
2. **Intelligent Routing:** SkyPilot, wielding its decision-making prowess, analyzes the request and identifies the most suitable GPU in our multi-cloud setup. Factors like resource availability, latency, and cost might influence this choice.
|
||||
|
||||
3. **Multi-Cloud Agility:** Based on the chosen cloud provider (AWS or Azure), SkyPilot seamlessly directs the request to the appropriate containerized model residing in a sky clusters cluster. Here's where the magic of cloud-agnostic deployments comes into play.
|
||||
|
||||
### Unveiling the Architecture
|
||||
|
||||
Let's dissect the technical architecture behind this process:
|
||||
|
||||
- **SkyPilot (EC2 Instance):** This lightweight controller, deployed on an EC2 instance, acts as the central hub for orchestrating requests and routing them to suitable model instances.
|
||||
|
||||
- **Swarm Cloud Repositories:** Each model resides within its own dedicated folder on the Swarms Cloud GitHub repository (<https://github.com/kyegomez/swarms-cloud>). Here, you'll find a folder structure like this:
|
||||
|
||||
```
|
||||
servers/
|
||||
<model_name_1>/
|
||||
sky-serve.yaml # Deployment configuration file
|
||||
<model_name_2>/
|
||||
sky-serve.yaml
|
||||
...
|
||||
|
||||
```
|
||||
|
||||
- **SkyServe Deployment Tool:** This is the workhorse responsible for deploying models within sky clusters. Each model's folder contains a `sky-serve.yaml` file that dictates the deployment configuration.
|
||||
|
||||
### Infrastructure Engineer's Toolkit: Commands for Model Deployment
|
||||
|
||||
Here's a breakdown of the `sky serve` command and its subcommands:
|
||||
|
||||
- `sky serve -h`: Displays the help message for the `sky serve` CLI tool.
|
||||
|
||||
**Commands:**
|
||||
|
||||
- `sky serve up yaml.yaml -n --cloud aws/azure`: This command deploys a SkyServe service based on the provided `yaml.yaml` configuration file. The `-n` flag indicates a new deployment, and the `--cloud` flag specifies the target cloud platform (AWS or Azure).
|
||||
|
||||
**Additional Commands:**
|
||||
|
||||
- `sky serve update`: Updates a running SkyServe service.
|
||||
|
||||
- `sky serve status`: Shows the status of deployed SkyServe services.
|
||||
|
||||
- `sky serve down`: Tears down (stops and removes) a SkyServe service.
|
||||
|
||||
- `sky serve logs`: Tails the logs of a running SkyServe service, providing valuable insights into its operation.
|
||||
|
||||
By leveraging these commands, infrastructure engineers can efficiently manage the deployment and lifecycle of models within the multi-cloud environment.
|
||||
|
||||
**Building the Cluster and Accessing the Model:**
|
||||
|
||||
When you deploy a model using `sky serve up`, SkyServe triggers the building of a sky clusters cluster, if one doesn't already exist. Once the deployment is complete, SkyServe provides you with an endpoint URL for interacting with the model. This URL allows you to send requests to the deployed model and receive its predictions.
|
||||
|
||||
### Understanding the `sky-serve.yaml` Configuration
|
||||
|
||||
The `sky-serve.yaml` file plays a crucial role in defining the deployment parameters for your model. This file typically includes properties such as:
|
||||
|
||||
- **Image:** Specifies the Docker image containing your model code and dependencies.
|
||||
|
||||
- **Replicas:** Defines the number of model replicas to be deployed in the Swarm cluster. This allows for load balancing and fault tolerance.
|
||||
|
||||
- **Resources:** Sets memory and CPU resource constraints for the deployed model containers.
|
||||
|
||||
- **Networking:** Configures network settings for communication within the sky clusters and with the outside world.
|
||||
|
||||
**Benefits of Our Infrastructure:**
|
||||
|
||||
- **Multi-Cloud Flexibility:** Deploy models seamlessly across AWS and Azure, taking advantage of whichever platform best suits your needs.
|
||||
|
||||
- **Scalability:** Easily scale model deployments up or down based on traffic demands.
|
||||
|
||||
- **Cost Optimization:** The intelligent routing by SkyPilot helps optimize costs by utilizing the most cost-effective cloud resources.
|
||||
|
||||
- **Simplified Management:** Manage models across clouds with a single set of commands using `sky serve`.
|
||||
|
||||
### Deep Dive: Technical Architecture
|
||||
|
||||
**Cloud Considerations:**
|
||||
|
||||
Our multi-cloud architecture offers several advantages, but it also introduces complexities that need to be addressed. Here's a closer look at some key considerations:
|
||||
|
||||
- **Cloud Provider APIs and SDKs:** SkyPilot interacts with the APIs and SDKs of the chosen cloud provider (AWS or Azure) to manage resources like virtual machines, storage, and networking. Infrastructure engineers need to be familiar with the specific APIs and SDKs for each cloud platform to ensure smooth operation and troubleshooting.
|
||||
|
||||
- **Security:** Maintaining consistent security across different cloud environments is crucial. This involves aspects like IAM (Identity and Access Management) configuration, network segmentation, and encryption of sensitive data at rest and in transit. Infrastructure engineers need to implement robust security measures tailored to each cloud provider's offerings.
|
||||
|
||||
- **Network Connectivity:** Establishing secure and reliable network connectivity between SkyPilot (running on EC2), sky clusters clusters (deployed on cloud VMs), and your client applications is essential. This might involve setting up VPN tunnels or utilizing cloud-native networking solutions offered by each provider.
|
||||
|
||||
- **Monitoring and Logging:** Monitoring the health and performance of SkyPilot, sky clusters clusters, and deployed models across clouds is critical for proactive issue identification and resolution. Infrastructure engineers can leverage cloud provider-specific monitoring tools alongside centralized logging solutions for comprehensive oversight.
|
||||
|
||||
**sky clusters Clusters**
|
||||
|
||||
sky clusters is a container orchestration platform that facilitates the deployment and management of containerized applications, including your machine learning models. When you deploy a model with `sky serve up`, SkyPilot launches an node with:
|
||||
|
||||
- **Provision Resources:** SkyPilot requests resources from the chosen cloud provider (e.g., VMs with GPUs) to create a sky clusters cluster if one doesn't already exist.
|
||||
|
||||
- **Deploy Containerized Models:** SkyPilot leverages the `sky-serve.yaml` configuration to build Docker images containing your model code and dependencies. These images are then pushed to a container registry (e.g., Docker Hub) and deployed as containers within the Swarm cluster.
|
||||
|
||||
- **Load Balancing and Service Discovery:** sky clusters provides built-in load balancing capabilities to distribute incoming requests across multiple model replicas, ensuring high availability and performance. Additionally, service discovery mechanisms allow models to find each other and communicate within the cluster.
|
||||
|
||||
**SkyPilot - The Orchestrator**
|
||||
|
||||
SkyPilot, the lightweight controller running on an EC2 instance, plays a central role in this infrastructure. Here's a deeper look at its functionalities:
|
||||
|
||||
- **API Gateway Integration:** SkyPilot can be integrated with your API gateway or service mesh to receive incoming requests for model predictions.
|
||||
|
||||
- **Request Routing:** SkyPilot analyzes the incoming request, considering factors like model compatibility, resource availability, and latency. Based on this analysis, SkyPilot selects the most suitable model instance within the appropriate sky clusters cluster.
|
||||
|
||||
- **Cloud Provider Interaction:** SkyPilot interacts with the chosen cloud provider's APIs to manage resources required for the sky clusters cluster and model deployment.
|
||||
|
||||
- **Model Health Monitoring:** SkyPilot can be configured to monitor the health and performance of deployed models. This might involve collecting metrics like model response times, resource utilization, and error rates.
|
||||
|
||||
- **Scalability Management:** Based on pre-defined policies or real-time traffic patterns, SkyPilot can trigger the scaling of model deployments (adding or removing replicas) within the sky clusters cluster.
|
||||
|
||||
**Advanced Considerations**
|
||||
|
||||
This blog post has provided a foundational understanding of our serving model infrastructure. For infrastructure engineers seeking a deeper dive, here are some additional considerations:
|
||||
|
||||
- **Container Security:** Explore container image scanning for vulnerabilities, enforcing least privilege principles within container runtime environments, and utilizing secrets management solutions for secure access to sensitive data.
|
||||
|
||||
- **Model Versioning and Rollbacks:** Implement a model versioning strategy to track changes and facilitate rollbacks to previous versions if necessary.
|
||||
|
||||
- **A/B Testing:** Integrate A/B testing frameworks to evaluate the performance of different model versions and configurations before full-scale deployment.
|
||||
|
||||
- **Auto-Scaling with Cloud Monitoring:** Utilize cloud provider-specific monitoring services like Amazon CloudWatch or Azure Monitor to trigger auto-scaling of sky clusters clusters based on predefined metrics.
|
||||
|
||||
By understanding these technical aspects and considerations, infrastructure engineers can effectively manage and optimize our multi-cloud serving model infrastructure.
|
||||
|
||||
### Conclusion
|
||||
|
||||
This comprehensive exploration has shed light on the intricate workings of our serving model infrastructure. We've covered the journey of an API request, delved into the technical architecture with a focus on cloud considerations, sky clusters clusters, and SkyPilot's role as the orchestrator. We've also explored advanced considerations for infrastructure engineers seeking to further optimize and secure this multi-cloud environment.
|
||||
|
||||
This understanding empowers both users and infrastructure engineers to leverage this technology effectively for deploying and managing your machine learning models at scale.
|
||||
@ -0,0 +1,49 @@
|
||||
from swarms import Agent
|
||||
|
||||
# Initialize the agent
|
||||
agent = Agent(
|
||||
agent_name="Quantitative-Trading-Agent",
|
||||
agent_description="Advanced quantitative trading and algorithmic analysis agent",
|
||||
system_prompt="""You are an expert quantitative trading agent with deep expertise in:
|
||||
- Algorithmic trading strategies and implementation
|
||||
- Statistical arbitrage and market making
|
||||
- Risk management and portfolio optimization
|
||||
- High-frequency trading systems
|
||||
- Market microstructure analysis
|
||||
- Quantitative research methodologies
|
||||
- Financial mathematics and stochastic processes
|
||||
- Machine learning applications in trading
|
||||
|
||||
Your core responsibilities include:
|
||||
1. Developing and backtesting trading strategies
|
||||
2. Analyzing market data and identifying alpha opportunities
|
||||
3. Implementing risk management frameworks
|
||||
4. Optimizing portfolio allocations
|
||||
5. Conducting quantitative research
|
||||
6. Monitoring market microstructure
|
||||
7. Evaluating trading system performance
|
||||
|
||||
You maintain strict adherence to:
|
||||
- Mathematical rigor in all analyses
|
||||
- Statistical significance in strategy development
|
||||
- Risk-adjusted return optimization
|
||||
- Market impact minimization
|
||||
- Regulatory compliance
|
||||
- Transaction cost analysis
|
||||
- Performance attribution
|
||||
|
||||
You communicate in precise, technical terms while maintaining clarity for stakeholders.""",
|
||||
model_name="azure/gpt-4.1",
|
||||
dynamic_temperature_enabled=True,
|
||||
output_type="str-all-except-first",
|
||||
max_loops="auto",
|
||||
interactive=True,
|
||||
no_reasoning_prompt=True,
|
||||
streaming_on=True,
|
||||
# dashboard=True
|
||||
)
|
||||
|
||||
out = agent.run(
|
||||
task="What are the best top 3 etfs for gold coverage?"
|
||||
)
|
||||
print(out)
|
||||
Loading…
Reference in new issue