Kye
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# Contributing to Swarms
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Thank you for your interest in contributing to Swarms! We welcome contributions from the community to help improve usability and readability. By contributing, you can be a part of creating a dynamic and interactive AI system.
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To get started, please follow the guidelines below.
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## Join the Swarms Community
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Join the Swarms community on Discord to connect with other contributors, coordinate work, and receive support.
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- [Join the Swarms Discord Server](https://discord.gg/qUtxnK2NMf)
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## Taking on Tasks
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We have a growing list of tasks and issues that you can contribute to. To get started, follow these steps:
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1. Visit the [Swarms GitHub repository](https://github.com/kyegomez/swarms) and browse through the existing issues.
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2. Find an issue that interests you and make a comment stating that you would like to work on it. Include a brief description of how you plan to solve the problem and any questions you may have.
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3. Once a project coordinator assigns the issue to you, you can start working on it.
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If you come across an issue that is unclear but still interests you, please post in the Discord server mentioned above. Someone from the community will be able to help clarify the issue in more detail.
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We also welcome contributions to documentation, such as updating markdown files, adding docstrings, creating system architecture diagrams, and other related tasks.
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## Submitting Your Work
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To contribute your changes to Swarms, please follow these steps:
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1. Fork the Swarms repository to your GitHub account. You can do this by clicking on the "Fork" button on the repository page.
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2. Clone the forked repository to your local machine using the `git clone` command.
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3. Before making any changes, make sure to sync your forked repository with the original repository to keep it up to date. You can do this by following the instructions [here](https://docs.github.com/en/github/collaborating-with-pull-requests/syncing-a-fork).
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4. Create a new branch for your changes. This branch should have a descriptive name that reflects the task or issue you are working on.
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5. Make your changes in the branch, focusing on a small, focused change that only affects a few files.
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6. Run any necessary formatting or linting tools to ensure that your changes adhere to the project's coding standards.
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7. Once your changes are ready, commit them to your branch with descriptive commit messages.
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8. Push the branch to your forked repository.
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9. Create a pull request (PR) from your branch to the main Swarms repository. Provide a clear and concise description of your changes in the PR.
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10. Request a review from the project maintainers. They will review your changes, provide feedback, and suggest any necessary improvements.
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11. Make any required updates or address any feedback provided during the review process.
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12. Once your changes have been reviewed and approved, they will be merged into the main branch of the Swarms repository.
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13. Congratulations! You have successfully contributed to Swarms.
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Please note that during the review process, you may be asked to make changes or address certain issues. It is important to engage in open and constructive communication with the project maintainers to ensure the quality of your contributions.
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## Developer Setup
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If you are interested in setting up the Swarms development environment, please follow the instructions provided in the [developer setup guide](docs/developer-setup.md). This guide provides an overview of the different tools and technologies used in the project.
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## Optimization Priorities
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To continuously improve Swarms, we prioritize the following design objectives:
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1. **Usability**: Increase the ease of use and user-friendliness of the swarm system to facilitate adoption and interaction with basic input.
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2. **Reliability**: Improve the swarm's ability to obtain the desired output even with basic and un-detailed input.
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3. **Speed**: Reduce the time it takes for the swarm to accomplish tasks by improving the communication layer, critiquing, and self-alignment with meta prompting.
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4. **Scalability**: Ensure that the system is asynchronous, concurrent, and self-healing to support scalability.
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Our goal is to continuously improve Swarms by following this roadmap while also being adaptable to new needs and opportunities as they arise.
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## Join the Agora Community
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Swarms is brought to you by Agora, the open-source AI research organization. Join the Agora community to connect with other researchers and developers working on AI projects.
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- [Join the Agora Discord Server](https://discord.gg/qUtxnK2NMf)
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Thank you for your contributions and for being a part of the Swarms and Agora community! Together, we can advance Humanity through the power of AI.
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# Swarms Documentation
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## ClassName
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Swarms
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## Purpose
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The Swarms module provides a powerful framework for creating and managing swarms of autonomous agents to accomplish complex tasks. It consists of the `WorkerNode` and `BossNode` classes, along with the `LLM` utility class, which allow you to easily set up and run a swarm of agents to tackle any objective. The module is highly configurable and extensible, providing flexibility to accommodate various use cases.
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## Usage example
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```python
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from swarms import Swarms
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api_key = "your_openai_api_key"
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# Initialize Swarms with your API key
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swarm = Swarms(api_key=api_key)
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# Define an objective
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objective = "Please make a web GUI for using HTTP API server..."
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# Run Swarms
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result = swarm.run(objective)
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print(result)
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```
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## Constructor
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```python
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def __init__(self, openai_api_key)
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```
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- `openai_api_key` (required): The API key for OpenAI's models.
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## Methods
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### run(objective)
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Runs the swarm with the given objective by initializing the worker and boss nodes.
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- `objective` (required): The objective or task to be accomplished by the swarm.
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Returns the result of the swarm execution.
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## Example Usage
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```python
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from swarms import Swarms
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api_key = "your_openai_api_key"
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# Initialize Swarms with your API key
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swarm = Swarms(api_key=api_key)
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# Define an objective
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objective = "Please make a web GUI for using HTTP API server..."
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# Run Swarms
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result = swarm.run(objective)
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print(result)
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```
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## WorkerNode
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The `WorkerNode` class represents an autonomous agent instance that functions as a worker to accomplish complex tasks. It has the ability to search the internet, process and generate images, text, audio, and more.
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### Constructor
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```python
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def __init__(self, llm, tools, vectorstore)
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```
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- `llm` (required): The language model used by the worker node.
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- `tools` (required): A list of tools available to the worker node.
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- `vectorstore` (required): The vector store used by the worker node.
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### Methods
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- `create_agent(ai_name, ai_role, human_in_the_loop, search_kwargs)`: Creates an agent within the worker node.
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- `add_tool(tool)`: Adds a tool to the worker node.
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- `run(prompt)`: Runs the worker node to complete a task specified by the prompt.
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### Example Usage
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```python
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from swarms import worker_node
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# Your OpenAI API key
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api_key = "your_openai_api_key"
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# Initialize a WorkerNode with your API key
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node = worker_node(api_key)
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# Define an objective
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objective = "Please make a web GUI for using HTTP API server..."
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# Run the task
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task = node.run(objective)
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print(task)
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```
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## BossNode
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The `BossNode` class represents an agent responsible for creating and managing tasks for the worker agent(s). It interacts with the worker node(s) to delegate tasks and monitor their progress.
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### Constructor
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```python
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def __init__(self, llm, vectorstore, agent_executor, max_iterations)
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```
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- `llm` (required): The language model used by the boss node.
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- `vectorstore` (required): The vector store used by the boss node.
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- `agent_executor` (required): The agent executor used to execute tasks.
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- `max_iterations` (required): The maximum number of iterations for task execution.
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### Methods
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- `create_task(objective)`: Creates a task with the given objective.
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- `execute_task(task)`: Executes the given task by interacting with the worker agent(s).
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## LLM
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The `LLM` class is a utility class that provides an interface to different language models (LLMs) such as OpenAI's ChatGPT and Hugging Face models. It is used to initialize the language model for the worker and boss nodes.
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### Constructor
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```python
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def __init__(self, openai_api_key=None, hf_repo_id=None, hf_api_token=None, model_kwargs=None)
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```
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- `openai_api_key` (optional): The API key for OpenAI's models.
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- `hf_repo_id` (optional): The repository ID for the Hugging Face model.
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- `hf_api_token` (optional): The API token for the Hugging Face model.
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- `model_kwargs` (optional): Additional keyword arguments to pass to the language model.
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### Methods
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- `run(prompt)`: Runs the language model with the given prompt and returns the generated response.
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## Configuration
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The Swarms module can be configured by modifying the following parameters:
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### WorkerNode
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- `llm_class`: The language model class to use for the worker node (default: `ChatOpenAI`).
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- `temperature`: The temperature parameter for the language model (default: `0.5`).
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### BossNode
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- `llm_class`: The language model class to use for the boss node (default: `OpenAI`).
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- `max_iterations`: The maximum number of iterations for task execution (default: `5`).
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### LLM
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- `openai_api_key`: The API key for OpenAI's models.
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- `hf_repo_id`: The repository ID for the Hugging Face model.
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- `hf_api_token`: The API token for the Hugging Face model.
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- `model_kwargs`: Additional keyword arguments to pass to the language model.
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## Tool Configuration
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The Swarms module supports various tools that can be added to the worker node for performing specific tasks. The following tools are available:
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- `DuckDuckGoSearchRun`: A tool for performing web searches.
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- `WriteFileTool`: A tool for writing files.
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- `ReadFileTool`: A tool for reading files.
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- `process_csv`: A tool for processing CSV files.
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- `WebpageQATool`: A tool for performing question answering using web pages.
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Additional tools can be added by extending the functionality of the `Tool` class.
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## Advanced Usage
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For more advanced usage, you can customize the tools and parameters according to your specific requirements. The Swarms module provides flexibility and extensibility to accommodate various use cases.
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For example, you can add your own custom tools by extending the `Tool` class and adding them to the worker node. You can also modify the prompt templates used by the boss node to customize the interaction between the boss and worker agents.
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Please refer to the source code and documentation of the Swarms module for more details and examples.
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## Conclusion
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The Swarms module provides a powerful framework for creating and managing swarms of autonomous agents to accomplish complex tasks. With the `WorkerNode` and `BossNode` classes, along with the `LLM` utility class, you can easily set up and run a swarm of agents to tackle any objective. The module is highly configurable and extensible, allowing you to tailor it to your specific needs.
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## LLM
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### Purpose
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The `LLM` class provides an interface to different language models (LLMs) such as OpenAI's ChatGPT and Hugging Face models. It allows you to initialize and run a language model with a given prompt and obtain the generated response.
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### Systems Understanding
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The `LLM` class takes an OpenAI API key or Hugging Face repository ID and API token as input. It uses these credentials to initialize the language model, either from OpenAI's models or from a specific Hugging Face repository. The language model can then be run with a prompt, and the generated response is returned.
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### Usage Example
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```python
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from swarms import LLM
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# Create an instance of LLM with OpenAI API key
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llm_instance = LLM(openai_api_key="your_openai_key")
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# Run the language model with a prompt
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result = llm_instance.run("Who won the FIFA World Cup in 1998?")
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print(result)
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# Create an instance of LLM with Hugging Face repository ID and API token
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llm_instance = LLM(hf_repo_id="google/flan-t5-xl", hf_api_token="your_hf_api_token")
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# Run the language model with a prompt
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result = llm_instance.run("Who won the FIFA World Cup in 1998?")
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print(result)
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```
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### Constructor
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```python
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def __init__(self, openai_api_key: Optional[str] = None,
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hf_repo_id: Optional[str] = None,
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hf_api_token: Optional[str] = None,
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model_kwargs: Optional[dict] = None)
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```
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- `openai_api_key` (optional): The API key for OpenAI's models.
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- `hf_repo_id` (optional): The repository ID for the Hugging Face model.
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- `hf_api_token` (optional): The API token for the Hugging Face model.
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- `model_kwargs` (optional): Additional keyword arguments to pass to the language model.
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### Methods
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- `run(prompt: str) -> str`: Runs the language model with the given prompt and returns the generated response.
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### Args
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- `prompt` (str): The prompt to be passed to the language model.
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### Returns
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- `result` (str): The generated response from the language model.
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## Conclusion
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The `LLM` class provides a convenient way to initialize and run different language models using either OpenAI's API or Hugging Face models. By providing the necessary credentials and a prompt, you can obtain the generated response from the language model.
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# `GooglePalm` class:
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### Example 1: Using Dictionaries as Messages
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```python
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from google_palm import GooglePalm
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# Initialize the GooglePalm instance
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gp = GooglePalm(
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client=your_client,
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model_name="models/chat-bison-001",
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temperature=0.7,
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top_p=0.9,
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top_k=10,
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n=5
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)
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# Create some messages
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messages = [
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{"role": "system", "content": "You are a helpful assistant."},
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{"role": "user", "content": "Who won the world series in 2020?"},
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]
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# Generate a response
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response = gp.generate(messages)
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# Print the generated response
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print(response)
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```
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### Example 2: Using BaseMessage and Its Subclasses as Messages
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```python
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from google_palm import GooglePalm
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from langchain.schema.messages import SystemMessage, HumanMessage
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# Initialize the GooglePalm instance
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gp = GooglePalm(
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client=your_client,
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model_name="models/chat-bison-001",
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temperature=0.7,
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top_p=0.9,
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top_k=10,
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n=5
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)
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# Create some messages
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messages = [
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SystemMessage(content="You are a helpful assistant."),
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HumanMessage(content="Who won the world series in 2020?"),
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]
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# Generate a response
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response = gp.generate(messages)
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# Print the generated response
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print(response)
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```
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### Example 3: Using GooglePalm with Asynchronous Function
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```python
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import asyncio
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from google_palm import GooglePalm
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from langchain.schema.messages import SystemMessage, HumanMessage
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# Initialize the GooglePalm instance
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gp = GooglePalm(
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client=your_client,
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model_name="models/chat-bison-001",
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temperature=0.7,
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top_p=0.9,
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top_k=10,
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n=5
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)
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# Create some messages
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messages = [
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SystemMessage(content="You are a helpful assistant."),
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HumanMessage(content="Who won the world series in 2020?"),
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]
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# Define an asynchronous function
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async def generate_response():
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response = await gp._agenerate(messages)
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print(response)
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# Run the asynchronous function
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asyncio.run(generate_response())
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```
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Remember to replace `your_client` with an actual instance of your client. Also, ensure the `model_name` is the correct name of the model that you want to use.
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The `temperature`, `top_p`, `top_k`, and `n` parameters control the randomness and diversity of the generated responses. You can adjust these parameters based on your application's requirements.
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## `CodeInterpreter`:
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```python
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tool = CodeInterpreter("Code Interpreter", "A tool to interpret code and generate useful outputs.")
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tool.run("Plot the bitcoin chart of 2023 YTD")
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# Or with file inputs
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tool.run("Analyze this dataset and plot something interesting about it.", ["examples/assets/iris.csv"])
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```
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To use the asynchronous version, simply replace `run` with `arun` and ensure your calling code is in an async context:
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```python
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import asyncio
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tool = CodeInterpreter("Code Interpreter", "A tool to interpret code and generate useful outputs.")
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asyncio.run(tool.arun("Plot the bitcoin chart of 2023 YTD"))
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# Or with file inputs
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asyncio.run(tool.arun("Analyze this dataset and plot something interesting about it.", ["examples/assets/iris.csv"]))
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```
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The `CodeInterpreter` class is a flexible tool that uses the `CodeInterpreterSession` from the `codeinterpreterapi` package to run the code interpretation and return the result. It provides both synchronous and asynchronous methods for convenience, and ensures that exceptions are handled gracefully.
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@ -1,143 +0,0 @@
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## LLMs in Swarms Documentation
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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.
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### Table of Contents
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1. [OpenAI](#openai)
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2. [HuggingFace](#huggingface)
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3. [Google PaLM](#google-palm)
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4. [Anthropic](#anthropic)
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### 1. OpenAI (swarms.models.OpenAI)
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The OpenAI class provides an interface to interact with OpenAI's language models. It allows both synchronous and asynchronous interactions.
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**Constructor:**
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```python
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OpenAI(api_key: str, system: str = None, console: bool = True, model: str = None, params: dict = None, save_messages: bool = True)
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```
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**Attributes:**
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- `api_key` (str): Your OpenAI API key.
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- `system` (str, optional): A system message to be used in conversations.
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- `console` (bool, default=True): Display console logs.
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- `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:**
|
||||
- `generate(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
|
||||
from swarms import OpenAI
|
||||
import asyncio
|
||||
|
||||
chat = OpenAI(api_key="YOUR_OPENAI_API_KEY")
|
||||
|
||||
response = chat.generate("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 (swarms.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:**
|
||||
- `generate(prompt_text: str, max_length: int = None) -> str`: Generate text based on a prompt.
|
||||
|
||||
**Usage Example:**
|
||||
```python
|
||||
from swarms import HuggingFaceLLM
|
||||
|
||||
model_id = "gpt2"
|
||||
hugging_face_model = HuggingFaceLLM(model_id=model_id)
|
||||
|
||||
prompt = "Once upon a time"
|
||||
generated_text = hugging_face_model.generate(prompt)
|
||||
print(generated_text)
|
||||
```
|
||||
|
||||
### 3. Google PaLM (swarms.models.GooglePalm)
|
||||
|
||||
The GooglePalm class provides an interface for Google's PaLM Chat API.
|
||||
|
||||
**Constructor:**
|
||||
```python
|
||||
GooglePalm(model_name: str = "models/chat-bison-001", google_api_key: str = None, temperature: float = None, top_p: float = None, top_k: int = None, n: int = 1)
|
||||
```
|
||||
|
||||
**Attributes:**
|
||||
- `model_name` (str): Name of the Google PaLM model.
|
||||
- `google_api_key` (str, optional): Google API key.
|
||||
- `temperature` (float, optional): Temperature for text generation.
|
||||
- `top_p` (float, optional): Top-p sampling value.
|
||||
- `top_k` (int, optional): Top-k sampling value.
|
||||
- `n` (int, default=1): Number of candidate completions.
|
||||
|
||||
**Methods:**
|
||||
- `generate(messages: List[Dict[str, Any]], stop: List[str] = None, **kwargs) -> Dict[str, Any]`: Generate text based on a list of messages.
|
||||
- `__call__(messages: List[Dict[str, Any]], stop: List[str] = None, **kwargs) -> Dict[str, Any]`: Generate text using the call syntax.
|
||||
|
||||
**Usage Example:**
|
||||
```python
|
||||
from swarms import GooglePalm
|
||||
|
||||
google_palm = GooglePalm()
|
||||
messages = [{"role": "system", "content": "You are a helpful assistant"}, {"role": "user", "content": "Tell me a joke"}]
|
||||
|
||||
response = google_palm.generate(messages)
|
||||
print(response["choices"][0]["text"])
|
||||
```
|
||||
|
||||
### 4. Anthropic (swarms.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:**
|
||||
- `generate(prompt: str, stop: List[str] = None) -> str`: Generate text based on a prompt.
|
||||
|
||||
**Usage Example:**
|
||||
```python
|
||||
from swarms import Anthropic
|
||||
|
||||
anthropic = Anthropic()
|
||||
prompt = "Once upon a time"
|
||||
generated_text = anthropic.generate(prompt)
|
||||
print(generated_text)
|
||||
```
|
||||
|
||||
This concludes the documentation for the "swarms" package, providing you with tools to seamlessly integrate with various language models and APIs. Happy coding!
|
||||
@ -1,114 +0,0 @@
|
||||
Maximize Value Using Value Equation
|
||||
1. Maximize Dream Outcome: Solve Problems Worth $1 Billion
|
||||
Swarms empowers you to solve problems worth $1 billion, maximizing your dream outcome and the potential impact of your work.
|
||||
|
||||
2. Maximize Perceived Likelihood of Success: 99% Success Rate
|
||||
With a 99% success rate backed by testimonials and proven case studies, Swarms maximizes your confidence in achieving your desired outcomes.
|
||||
|
||||
3. Minimize Time to Success: Achieve Results 10x Faster
|
||||
Swarms minimizes the time it takes to achieve success by enabling you to accomplish tasks and goals 10 times faster than traditional methods.
|
||||
|
||||
4. Minimize Effort & Sacrifice: Save 100 Hours per Week
|
||||
By automating tasks and streamlining processes, Swarms saves you 100 hours per week, minimizing effort and sacrifice required to achieve your goals.
|
||||
|
||||
5. Maximize Efficiency: Increase Productivity by 300%
|
||||
Swarms optimizes your workflow, increasing productivity by 300% through intelligent automation and task optimization.
|
||||
|
||||
6. Minimize Errors: Ensure 99.9% Accuracy
|
||||
With Swarms' autonomous AI agents, you can achieve 99.9% accuracy, minimizing errors and ensuring the highest level of quality in your work.
|
||||
|
||||
7. Maximize Scalability: Handle 1 Million Transactions per Second
|
||||
Swarms scales with your growing needs, allowing you to handle up to 1 million transactions per second, ensuring seamless operations as your business expands.
|
||||
|
||||
8. Minimize Costs: Save $1 Million Annually
|
||||
By optimizing resource allocation and reducing manual labor, Swarms helps you save $1 million annually, minimizing costs and maximizing your bottom line.
|
||||
|
||||
9. Maximize Flexibility: Adapt to Changing Requirements in Minutes
|
||||
Swarms offers maximum flexibility, allowing you to adapt to changing requirements in minutes, ensuring you stay agile and responsive in a dynamic business environment.
|
||||
|
||||
10. Minimize Complexity: Simplify Complex Tasks by 90%
|
||||
Swarms simplifies complex tasks by 90%, breaking them down into manageable steps, minimizing complexity and enabling you to tackle even the most challenging projects.
|
||||
|
||||
11. Maximize Collaboration: Increase Team Efficiency by 200%
|
||||
With Swarms' coordination capabilities, you can increase team efficiency by 200%, fostering collaboration and driving innovation within your organization.
|
||||
|
||||
12. Minimize Downtime: Ensure 99.99% Uptime
|
||||
Swarms ensures 99.99% uptime, minimizing downtime and ensuring continuous operations, preventing costly disruptions to your business.
|
||||
|
||||
13. Maximize Security: Protect Your Data with Military-Grade Encryption
|
||||
Swarms prioritizes data security, providing military-grade encryption to protect your sensitive information, maximizing the security and confidentiality of your data.
|
||||
|
||||
14. Minimize Learning Curve: Get Up and Running in 1 Hour
|
||||
Swarms minimizes the learning curve, allowing you to get up and running in just 1 hour, maximizing your time and productivity.
|
||||
|
||||
15. Maximize Innovation: Stay Ahead with AI-Driven Insights
|
||||
Swarms leverages AI and autonomous agents to provide cutting-edge insights, enabling you to stay ahead of the competition and drive innovation in your industry.
|
||||
|
||||
16. Minimize Maintenance: Reduce Maintenance Costs by 80%
|
||||
Swarms reduces maintenance costs by 80%, minimizing the time and resources required for upkeep, allowing you to focus on your core business activities.
|
||||
|
||||
17. Maximize Adaptability: Customize to Your Needs with 100+ Configurable Options
|
||||
Swarms offers over 100 configurable options, maximizing adaptability and allowing you to customize the platform to suit your specific requirements.
|
||||
|
||||
|
||||
|
||||
1. Maximize Dream Outcome: Solve Problems Worth $1 Billion
|
||||
Swarms empowers you to solve problems worth $1 billion, maximizing your dream outcome and the potential impact of your work.
|
||||
|
||||
2. Maximize Perceived Likelihood of Success: 99% Success Rate
|
||||
With a 99% success rate backed by testimonials and proven case studies, Swarms maximizes your confidence in achieving your desired outcomes.
|
||||
|
||||
3. Minimize Time to Success: Achieve Results 10x Faster
|
||||
Swarms minimizes the time it takes to achieve success by enabling you to accomplish tasks and goals 10 times faster than traditional methods.
|
||||
|
||||
4. Minimize Effort & Sacrifice: Save 100 Hours per Week
|
||||
By automating tasks and streamlining processes, Swarms saves you 100 hours per week, minimizing effort and sacrifice required to achieve your goals.
|
||||
|
||||
5. Maximize Efficiency: Increase Productivity by 300%
|
||||
Swarms optimizes your workflow, increasing productivity by 300% through intelligent automation and task optimization.
|
||||
|
||||
6. Minimize Errors: Ensure 99.9% Accuracy
|
||||
With Swarms' autonomous AI agents, you can achieve 99.9% accuracy, minimizing errors and ensuring the highest level of quality in your work.
|
||||
|
||||
7. Maximize Scalability: Handle 1 Million Transactions per Second
|
||||
Swarms scales with your growing needs, allowing you to handle up to 1 million transactions per second, ensuring seamless operations as your business expands.
|
||||
|
||||
8. Minimize Costs: Save $1 Million Annually
|
||||
By optimizing resource allocation and reducing manual labor, Swarms helps you save $1 million annually, minimizing costs and maximizing your bottom line.
|
||||
|
||||
9. Maximize Flexibility: Adapt to Changing Requirements in Minutes
|
||||
Swarms offers maximum flexibility, allowing you to adapt to changing requirements in minutes, ensuring you stay agile and responsive in a dynamic business environment.
|
||||
|
||||
10. Minimize Complexity: Simplify Complex Tasks by 90%
|
||||
Swarms simplifies complex tasks by 90%, breaking them down into manageable steps, minimizing complexity and enabling you to tackle even the most challenging projects.
|
||||
|
||||
11. Maximize Collaboration: Increase Team Efficiency by 200%
|
||||
With Swarms' coordination capabilities, you can increase team efficiency by 200%, fostering collaboration and driving innovation within your organization.
|
||||
|
||||
12. Minimize Downtime: Ensure 99.99% Uptime
|
||||
Swarms ensures 99.99% uptime, minimizing downtime and ensuring continuous operations, preventing costly disruptions to your business.
|
||||
|
||||
13. Maximize Security: Protect Your Data with Military-Grade Encryption
|
||||
Swarms prioritizes data security, providing military-grade encryption to protect your sensitive information, maximizing the security and confidentiality of your data.
|
||||
|
||||
14. Minimize Learning Curve: Get Up and Running in 1 Hour
|
||||
Swarms minimizes the learning curve, allowing you to get up and running in just 1 hour, maximizing your time and productivity.
|
||||
|
||||
15. Maximize Innovation: Stay Ahead with AI-Driven Insights
|
||||
Swarms leverages AI and autonomous agents to provide cutting-edge insights, enabling you to stay ahead of the competition and drive innovation in your industry.
|
||||
|
||||
16. Minimize Maintenance: Reduce Maintenance Costs by 80%
|
||||
Swarms reduces maintenance costs by 80%, minimizing the time and resources required for upkeep, allowing you to focus on your core business activities.
|
||||
|
||||
17. Maximize Adaptability: Customize to Your Needs with 100+ Configurable Options
|
||||
Swarms offers over 100 configurable options, maximizing adaptability and allowing you to customize the platform to suit your specific requirements.
|
||||
|
||||
18. Minimize Risk: Mitigate Potential Losses by 95%
|
||||
Swarms helps you minimize risk by mitigating potential losses by 95%, providing a secure and reliable platform for your critical operations.
|
||||
|
||||
19. Maximize ROI: Achieve 500% Return on Investment
|
||||
With Swarms' efficiency and cost-saving capabilities, you can achieve a 500% return on investment, maximizing the value you get from your resources.
|
||||
|
||||
20. Minimize Waste: Reduce Resource Consumption by 70%
|
||||
Swarms minimizes waste by reducing resource consumption by 70%, optimizing resource allocation and promoting sustainability in your operations.
|
||||
|
||||
@ -1,7 +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
|
||||
@ -1,101 +0,0 @@
|
||||
# Flywheel Effect for Developer Acquisition and Incentivization
|
||||
|
||||
As with the sales model, the developer acquisition and incentivization model also relies on a flywheel effect. This effect is particularly potent in a community-driven ecosystem such as ours, where the value proposition continually grows as more developers join and contribute to our projects. Here's how we could apply this approach:
|
||||
|
||||
## Step 1: Initial Value Proposition for Developers
|
||||
The starting point of the flywheel is to provide an attractive value proposition for developers. This could include:
|
||||
|
||||
- The ability to work on cutting-edge technology (Swarms, in this case).
|
||||
- The opportunity to contribute to a community-driven, open-source project.
|
||||
- The chance to learn from and collaborate with a global network of highly skilled developers.
|
||||
- An incentivization structure that rewards contributions (more on this later).
|
||||
|
||||
## Step 2: Developer Acquisition
|
||||
With the initial value proposition in place, we can move on to the actual acquisition of developers. This could be accomplished through:
|
||||
|
||||
- Active recruitment from online developer communities.
|
||||
- Referral programs that incentivize current contributors to bring in new developers.
|
||||
- Partnerships with universities, boot camps, and other institutions to attract budding developers.
|
||||
|
||||
## Step 3: Collaboration and Learning
|
||||
Once developers join our ecosystem, they become part of a collaborative community where they can learn from each other, improve their skills, and work on exciting and meaningful projects. This, in turn, attracts more developers, adding momentum to the flywheel.
|
||||
|
||||
## Step 4: Recognizing and Rewarding Contributions
|
||||
To keep the flywheel spinning, it's crucial to recognize and reward the contributions made by developers. This can be done in various ways:
|
||||
|
||||
- Monetary rewards: Developers can be paid based on the value their contributions bring to the project. This could be determined through various metrics, such as the complexity of their contributions, the impact on the project, or the amount of their code that gets used in production.
|
||||
|
||||
- Reputation and recognition: The open-source nature of our project means that all contributions are public and can be used by developers to build their professional profiles. Contributors could also be highlighted on our website, in our communications, and at community events.
|
||||
|
||||
- Career advancement: Developers who consistently make valuable contributions could be offered positions of leadership within the project, such as becoming maintainers or joining a steering committee.
|
||||
|
||||
- Agora Tokens: We could create a system of tokens that are earned based on contributions. These tokens could be exchanged for various benefits, such as access to exclusive events, special training, or even physical goods.
|
||||
|
||||
## Step 5: Scaling the Flywheel
|
||||
With the flywheel in motion, the next step is to scale. As our community grows and our technology improves, we can attract more developers and create more value. This leads to a virtuous cycle of growth, where each new developer adds to the attractiveness of our project, which in turn brings in more developers.
|
||||
|
||||
In essence, this flywheel approach is about creating a community where everyone benefits from each other's contributions. The more value a developer adds, the more they are rewarded. The more developers contribute, the more value is created, attracting even more developers.
|
||||
|
||||
Such a model not only aligns with our values of openness, collaboration, and shared success, but it also gives us a sustainable and scalable method for growing our developer community. It makes Agora not just a place to work, but also a place to learn, grow, and be recognized for one's contributions. This is a powerful way to ensure that we can continue to advance our technology and make a significant impact on the world.
|
||||
|
||||
|
||||
# Risks and mitigations
|
||||
|
||||
The open source engineering freelancer model brings with it its own set of potential risks and challenges. Here's an exploration of some of these, along with strategies for mitigation:
|
||||
|
||||
**1. Quality Control:** When dealing with a wide network of freelance contributors, ensuring a consistent standard of quality across all contributions can be challenging. This can be mitigated by implementing rigorous review processes and standards, establishing an automated testing infrastructure, and fostering a culture of quality among contributors. Providing clear contribution guidelines, code style guides, and other resources can help freelancers understand what's expected of them. Providing Educational resources such as sponsoring creators like Yannic, and even making our own courses and then building techno-monasteries where young people can come in and research for free.
|
||||
|
||||
**2. Security Risks:** Open-source projects can be susceptible to malicious contributors, who might introduce vulnerabilities into the codebase. To mitigate this, rigorous code review processes should be in place. Additionally, adopting a "trust but verify" approach, leveraging automated security scanning tools, and conducting periodic security audits can be beneficial.
|
||||
|
||||
**3. Intellectual Property Issues:** Open-source projects can face risks around intellectual property, such as contributors introducing code that infringes on someone else's copyrights. A clear Contributor License Agreement (CLA) should be in place, which contributors need to agree to before their contributions can be accepted. This helps protect the project and its users from potential legal issues.
|
||||
|
||||
**4. Loss of Core Focus:** With numerous contributors focusing on different aspects of the project, there can be a risk of losing sight of the project's core objectives. Maintaining a clear roadmap, having a strong leadership team, and ensuring open and regular communication can help keep the project focused.
|
||||
|
||||
**5. Contributor Burnout:** Freelancers contributing in their free time might face burnout, especially if they feel their contributions aren't being recognized or rewarded. To mitigate this, create a supportive environment where contributors' efforts are acknowledged and rewarded. This might include monetary rewards, but can also include non-monetary rewards like public recognition, advancement opportunities within the project, and so on.
|
||||
|
||||
**6. Fragmentation:** In open source projects, there is a risk of fragmentation where different contributors or groups of contributors might want to take the project in different directions. Strong project governance, a clear roadmap, and open, transparent decision-making processes can help mitigate this risk.
|
||||
|
||||
**7. Dependency on Key Individuals:** If key parts of the project are understood and maintained by only a single contributor, there is a risk if that individual decides to leave or is unable to contribute for some reason. This can be mitigated by ensuring knowledge is shared and responsibilities are spread among multiple contributors.
|
||||
|
||||
Overall, these risks can be managed with proper planning, clear communication, and the implementation of good governance and security practices. It's essential to approach the open source model with a clear understanding of these potential pitfalls and a plan to address them.
|
||||
|
||||
## Plan to Gain Open Source Developers for SWARMS
|
||||
|
||||
Attracting and retaining open-source developers is a challenge that requires a strategic approach. This plan emphasizes delivering value to the developers as well as providing recognition, community, and financial incentives.
|
||||
|
||||
### Step 1: Foster an Engaging and Inclusive Community
|
||||
|
||||
The first step is to foster an engaging and inclusive open-source community around SWARMS. This community should be a place where developers feel welcome and excited to contribute. Regular community events (both online and offline), engaging content, and a supportive environment can help attract and retain developers.
|
||||
|
||||
### Step 2: Provide Clear Contribution Guidelines
|
||||
|
||||
Providing clear and comprehensive contribution guidelines will make it easier for developers to get started. These guidelines should cover the basics of how to set up the development environment, how to submit changes, and how the code review process works.
|
||||
|
||||
### Step 3: Offer Educational Resources and Training
|
||||
|
||||
Providing training and educational resources can help developers grow their skills and contribute more effectively. These resources could include tutorials, webinars, workshops, documentation, and more.
|
||||
|
||||
### Step 4: Establish a Recognition and Reward System
|
||||
|
||||
Recognize and reward the contributions of developers. This could involve public recognition, like featuring contributors on the SWARMS website, as well as financial incentives. Implementing a system where developers earn a share of the revenue from SWARMS based on their contributions can be a strong motivator.
|
||||
|
||||
### Step 5: Implement a Strong Support System
|
||||
|
||||
Offer strong technical support to developers. This could include dedicated channels for developers to ask questions, request feedback, and share their progress. Having core team members available to provide assistance and mentorship can be hugely beneficial.
|
||||
|
||||
### Step 6: Regularly Solicit and Incorporate Feedback
|
||||
|
||||
Regularly ask for feedback from developers and incorporate their suggestions into future developments. This shows developers that their opinions are valued and can lead to improvements in SWARMS.
|
||||
|
||||
## Flywheel for Gaining More Open Source Developers
|
||||
|
||||
Now let's look at the flywheel effect that can result from this plan. The idea of the flywheel is that each part of the process feeds into the next, creating a cycle of growth that becomes self-sustaining over time.
|
||||
|
||||
1. We build an engaging and supportive community around SWARMS.
|
||||
2. This community attracts more developers who are interested in contributing to SWARMS.
|
||||
3. As more developers contribute, the quality and scope of SWARMS improve, making it more attractive to potential users.
|
||||
4. As SWARMS gains more users, the potential revenue from SWARMS increases, allowing for larger rewards to be distributed to developers.
|
||||
5. The prospect of these rewards attracts even more developers to the SWARMS community.
|
||||
6. The cycle repeats, with each iteration attracting more developers, improving SWARMS, increasing its user base, and raising potential rewards.
|
||||
|
||||
Through this plan and the resulting flywheel effect, we can attract a strong, committed team of open-source developers to build SWARMS and make it the best it can be.
|
||||
@ -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.
|
||||
@ -1,149 +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!
|
||||
|
||||
|
||||
--------------------
|
||||
# Super-Intelligence Roadmap
|
||||
|
||||
Creating a Super-Intelligent Swarm involves three main phases, where each phase has multiple sub-stages, each of which will require rigorous testing and evaluation to ensure progress towards super-intelligence.
|
||||
|
||||
## Phase 1: Narrow Intelligence
|
||||
|
||||
In this phase, the goal is to achieve high performance in specific tasks. These tasks will be predefined and the swarm will be trained and tested on these tasks.
|
||||
|
||||
1. **Single Task Mastery**: Focus on mastering one task at a time. This can range from simple tasks like image recognition to complex tasks like natural language processing.
|
||||
|
||||
2. **Task Switching**: Train the swarm to switch between different tasks effectively. This includes being able to stop one task and start another one without any loss in performance.
|
||||
|
||||
3. **Multi-tasking**: The swarm should be capable of performing multiple tasks simultaneously without any degradation in performance.
|
||||
|
||||
## Phase 2: General Intelligence
|
||||
|
||||
In this phase, the swarm will be trained to handle a variety of tasks that were not part of the original training set.
|
||||
|
||||
1. **Transfer Learning**: The swarm should be able to transfer knowledge learned in one context to another context. This means being able to apply knowledge learned in one task to a different but related task.
|
||||
|
||||
2. **Adaptive Learning**: The swarm should be capable of adapting its learning strategies based on the task at hand. This includes being able to adjust its learning rate, exploration vs exploitation balance, etc.
|
||||
|
||||
3. **Self-Learning**: The swarm should be able to learn new tasks on its own without any external guidance. This includes being able to understand the task requirements, find relevant information, learn the task, and evaluate its performance.
|
||||
|
||||
## Phase 3: Super Intelligence
|
||||
|
||||
In this phase, the swarm will surpass human-level performance in most economically valuable work. This involves the swarm being able to solve complex real-world problems, make accurate predictions, and generate innovative solutions.
|
||||
|
||||
1. **Complex Problem Solving**: The swarm should be able to solve complex real-world problems. This includes being able to understand the problem, identify relevant information, generate solutions, evaluate the solutions, and implement the best solution.
|
||||
|
||||
2. **Predictive Abilities**: The swarm should be able to make accurate predictions about future events based on past data. This includes being able to understand the data, identify relevant patterns, make accurate predictions, and evaluate the accuracy of its predictions.
|
||||
|
||||
3. **Innovation**: The swarm should be able to generate innovative solutions to problems. This includes being able to think creatively, generate novel ideas, evaluate the ideas, and implement the best idea.
|
||||
|
||||
4. **Self-improvement**: The swarm should be capable of improving its own capabilities. This includes being able to identify areas of weakness, find ways to improve, and implement the improvements.
|
||||
|
||||
5. **Understanding**: The swarm should be able to understand complex concepts, make inferences, and draw conclusions. This includes being able to understand natural language, reason logically, and make sound judgments.
|
||||
|
||||
Each of these stages will require extensive testing and evaluation to ensure progress towards super-intelligence.
|
||||
|
||||
# Reverse-Engineering Super-Intelligence
|
||||
|
||||
To reach the Phase 3 level of super-intelligence, we need to reverse engineer the tasks that need to be completed. Here's an outline of what this might look like:
|
||||
|
||||
1. **Setting Success Metrics**: For each stage, define clear success metrics. These metrics should be quantitative and measurable, and they should align with the objectives of the stage.
|
||||
|
||||
2. **Identifying Prerequisites**: Determine what needs to be in place before each stage can begin. This could include certain capabilities, resources, or technologies.
|
||||
|
||||
3. **Developing Training Programs**: For each stage, develop a comprehensive training program. This should include a variety of tasks that will challenge the swarm and push it to
|
||||
|
||||
develop the necessary capabilities.
|
||||
|
||||
4. **Creating Testing Protocols**: Develop rigorous testing protocols for each stage. These protocols should test all aspects of the swarm's performance and they should be designed to push the swarm to its limits.
|
||||
|
||||
5. **Iterating and Improving**: Based on the results of the tests, iterate and improve the swarm. This could involve adjusting the training program, modifying the swarm's architecture, or tweaking its learning algorithms.
|
||||
|
||||
6. **Moving to the Next Stage**: Once the swarm has met the success metrics for a stage, it can move on to the next stage. This process continues until the swarm has reached the level of super-intelligence.
|
||||
|
||||
This process will require a significant amount of time, resources, and effort. However, by following this structured approach, we can systematically guide the swarm towards super-intelligence.
|
||||
|
||||
@ -1,165 +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.
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
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.
|
||||
|
||||
*
|
||||
@ -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.
|
||||
|
||||
---
|
||||
@ -1,115 +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.
|
||||
|
||||
|
||||
## Optimization Priorities
|
||||
|
||||
1. **Reliability**: Increase the reliability of the swarm - obtaining the desired output with a basic and un-detailed input.
|
||||
|
||||
2. **Speed**: Reduce the time it takes for the swarm to accomplish tasks by improving the communication layer, critiquing, and self-alignment with meta prompting.
|
||||
|
||||
3. **Scalability**: Ensure that the system is asynchronous, concurrent, and self-healing to support scalability.
|
||||
|
||||
Our goal is to continuously improve Swarms by following this roadmap, while also being adaptable to new needs and opportunities as they arise.
|
||||
|
||||
# Open Source Roadmap
|
||||
|
||||
Here is the detailed roadmap of our priorities and planned features for the near term:
|
||||
|
||||
## TODO
|
||||
|
||||
* Create a non-langchain worker and swarm class and compare
|
||||
|
||||
* Create extensive documentation
|
||||
|
||||
* Make sure that the boss agent successfully calls the worker agent if when it's finished makinng a plan
|
||||
|
||||
* Make sure the worker agent can access tools like web browser, terminal, and code editor, and multi-modal agents
|
||||
|
||||
* Make sure inputs and outputs from boss to worker are well defined and are collaborating if not then readjust prompt
|
||||
|
||||
* Create a tool that creates other tools with access to write code, debug, and an architectural argent that creates the architecture and then another agent that creates the code[Architecter(with code examples), code generator (with access to writing code and terminalrools)] -- The Compiler?
|
||||
|
||||
* Create a screenshot tool that takes a screen shot and then passes it to a worker multi-modal agent for visual context.
|
||||
|
||||
* API endroute in FASTAPI
|
||||
|
||||
* Develop Conversational UI with Gradio
|
||||
|
||||
* Integrate omni agent as a worker tool
|
||||
|
||||
* Integrate Ocean Database as primary vectorstore
|
||||
|
||||
* Integrate visual agent
|
||||
|
||||
* Integrate quantized hf models as base models with langchain huggingface
|
||||
|
||||
1. **Multi-Agent Debate Integration**: Integrate multi-agent debate frameworks ([Multi Agent debate](https://github.com/Skytliang/Multi-Agents-Debate) and [Multi agent2 debate](https://github.com/composable-models/llm_multiagent_debate)) to improve decision-making.
|
||||
|
||||
2. **Meta Prompting Integration**: Include meta prompting across all worker agents to guide their actions.
|
||||
|
||||
3. **Swarms Class**: Create a main swarms class `swarms('Increase sales by 40$', workers=4)` for managing and coordinating multiple worker nodes.
|
||||
|
||||
4. **Integration of Additional Tools**: Integrate [Jarvis](https://github.com/microsoft/JARVIS) as worker nodes, add text to speech and text to script tools ([whisper x](https://github.com/kyegomez/youtubeURL-to-text)), and integrate Hugging Face agents and other external tools.
|
||||
|
||||
5. **Task Completion and Evaluation Logic**: Include task completion logic with meta prompting, and evaluate task completion on a scale from 0.0 to 1.0.
|
||||
|
||||
7. **Ocean Integration**: Use the [Ocean](https://github.com/kyegomez/Ocean) vector database as the main embedding database for all the agents, both boss and worker.
|
||||
|
||||
8. **Improved Communication**: Develop a universal vector database that is only used when a task is completed in this format `[TASK][COMPLETED]`.
|
||||
|
||||
9. **Testing and Evaluation**: Create unit tests, benchmarks, and evaluations for performance monitoring and continuous improvement.
|
||||
|
||||
10. **Worker Swarm Class**: Create a class for self-scaling worker swarms. If they need help, they can spawn an entirely new worker and more workers if needed.
|
||||
|
||||
## Documentation
|
||||
|
||||
1. **Examples**: Create extensive and useful examples for a variety of use cases.
|
||||
|
||||
2. **README**: Update the README to include the examples and usage instructions.
|
||||
|
||||
|
||||
# Mid-Long term
|
||||
Here are some potential middle-to-long-term improvements to consider for this project:
|
||||
|
||||
1. **Modular Design**: Aim to design a more modular and scalable framework, making it easy for developers to plug-and-play various components.
|
||||
|
||||
2. **Interactive User Interface**: Develop a more interactive, user-friendly GUI that allows users to interact with the system without needing to understand the underlying code.
|
||||
|
||||
3. **Advanced Error Handling**: Implement advanced error handling and debugging capabilities to make it easier for developers to diagnose and fix issues.
|
||||
|
||||
4. **Optimized Resource Utilization**: Improve the efficiency of resource use, aiming to reduce memory consumption and improve speed without sacrificing accuracy.
|
||||
|
||||
5. **Collaborative Learning**: Integrate more sophisticated techniques for collaborative learning among the swarm, allowing them to share knowledge and learn from each other's successes and failures.
|
||||
|
||||
6. **Autonomous Self-Improvement**: Implement mechanisms that allow the swarm to autonomously learn from its past experiences and improve its performance over time.
|
||||
|
||||
7. **Security Enhancements**: Include robust security measures to protect sensitive data and prevent unauthorized access.
|
||||
|
||||
8. **Privacy-Preserving Techniques**: Consider incorporating privacy-preserving techniques such as differential privacy to ensure the confidentiality of user data.
|
||||
|
||||
9. **Support for More Languages**: Expand language support to allow the system to cater to a more global audience.
|
||||
|
||||
10. **Robustness and Resilience**: Improve the system's robustness and resilience, ensuring that it can operate effectively even in the face of hardware or software failures.
|
||||
|
||||
11. **Continual Learning**: Implement continual learning techniques to allow the system to adapt and evolve as new data comes in.
|
||||
|
||||
12. **More Contextual Understanding**: Enhance the system's capability to understand context better, making it more effective in handling real-world, complex tasks.
|
||||
|
||||
13. **Dynamic Task Prioritization**: Develop advanced algorithms for dynamic task prioritization, ensuring that the most important tasks are addressed first.
|
||||
|
||||
14. **Expanding the Swarm's Skills**: Train the swarm on a wider range of tasks, gradually expanding their skill set and problem-solving capabilities.
|
||||
|
||||
15. **Real-World Deployment**: Test and refine the system in real-world settings, learning from these experiences to further improve and adapt the system.
|
||||
|
||||
Remember, these are potential improvements. It's important to revisit your priorities regularly and adjust them based on project needs, feedback, and learning from both successes and failures.
|
||||
@ -1,146 +0,0 @@
|
||||
# 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
|
||||
|
||||
### AbstractSwarm
|
||||
|
||||
The AbstractSwarm 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 AbstractSwarm 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 AbstractSwarm.
|
||||
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(AbstractSwarm):
|
||||
# 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.
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
Here is an abstract class that provides the basic structure to process these components:
|
||||
|
||||
```python
|
||||
from abc import ABC, abstractmethod
|
||||
|
||||
class AbstractSwarm(ABC):
|
||||
|
||||
def __init__(self, agents, vectorstore, tools):
|
||||
self.agents = agents
|
||||
self.vectorstore = vectorstore
|
||||
self.tools = tools
|
||||
|
||||
@abstractmethod
|
||||
def initialize(self):
|
||||
pass
|
||||
|
||||
@abstractmethod
|
||||
def communicate(self):
|
||||
pass
|
||||
|
||||
@abstractmethod
|
||||
def process(self):
|
||||
pass
|
||||
|
||||
@abstractmethod
|
||||
def solve(self):
|
||||
pass
|
||||
```
|
||||
|
||||
This abstract class requires four methods to be implemented:
|
||||
|
||||
- `initialize`: This method is used to set up the initial state of the swarm, including setting up nodes and tools.
|
||||
- `communicate`: This method is responsible for facilitating communication between nodes.
|
||||
- `process`: This method handles the processing logic, which can be different based on the swarm architecture.
|
||||
- `solve`: This method is called to start the problem-solving process.
|
||||
|
||||
This abstract class can be inherited by specific swarm architecture classes to implement their specific behavior.
|
||||
|
||||
# 3 Ingredients
|
||||
|
||||
* The Individual Agent Configuration with a vectorstore and tools
|
||||
|
||||
* The Orchestrator, => task assignment, task completion handling, communication layer
|
||||
@ -1,53 +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.
|
||||
@ -1,19 +0,0 @@
|
||||
from swarms import Model, Agent, vectorstore, tools, orchestrator
|
||||
|
||||
# 1 model
|
||||
Model(openai)
|
||||
|
||||
# 2 agent level
|
||||
Agent(model, vectorstore, tools)
|
||||
|
||||
# 3 worker infrastructure level
|
||||
worker_node(Agent, human_input, tools)
|
||||
|
||||
# 4 swarm level basically handling infrastructure for multiple worker node
|
||||
swarm = orchestrator(worker_node, 100) # nodes
|
||||
|
||||
# 5
|
||||
hivemind = Hivemind(swarm * 100)
|
||||
|
||||
|
||||
# a market different pre built worker or boss agent that have access to different tools and memory, proompts
|
||||
@ -1,22 +0,0 @@
|
||||
Sure, here are some creative neo-futuristic names for your swarm worker agents:
|
||||
|
||||
QuantumPulse
|
||||
UltraNode
|
||||
FusionCortex
|
||||
HyperionCrawler
|
||||
XenoSync
|
||||
HoloWorker
|
||||
EtherEngine
|
||||
NebulaNexus
|
||||
OmegaOrbit
|
||||
ZetaZenith
|
||||
PhantomPivot
|
||||
VortexVector
|
||||
ChronoCluster
|
||||
SpecterSphere
|
||||
TitanTrace
|
||||
EchoEntity
|
||||
NovaNucleus
|
||||
AstroAssembler
|
||||
PulsarProcessor
|
||||
CosmicCrafter
|
||||
@ -1,69 +0,0 @@
|
||||
Guide to Product-Market Fit for HiveMind Class
|
||||
Risks and Mitigations
|
||||
Scalability: As the number of swarms increases, the computational resources required will also increase. This could lead to performance issues or high costs.
|
||||
|
||||
Mitigation: Implement efficient resource management and load balancing. Consider using cloud-based solutions that can scale up or down based on demand.
|
||||
|
||||
Concurrency Issues: With multiple swarms running concurrently, there could be issues with data consistency and synchronization.
|
||||
|
||||
Mitigation: Implement robust concurrency control mechanisms. Ensure that the shared vector store is thread-safe.
|
||||
|
||||
Error Propagation: Errors in one swarm could potentially affect other swarms or the entire HiveMind.
|
||||
|
||||
Mitigation: Implement robust error handling and isolation mechanisms. Errors in one swarm should not affect the operation of other swarms.
|
||||
|
||||
Complexity: The HiveMind class is complex and could be difficult to maintain and extend.
|
||||
|
||||
Mitigation: Follow best practices for software design, such as modularity, encapsulation, and separation of concerns. Write comprehensive tests to catch issues early.
|
||||
|
||||
User Experience: If the HiveMind class is not easy to use, it could deter potential users.
|
||||
|
||||
Mitigation: Provide clear documentation and examples. Implement a user-friendly API. Consider providing a high-level interface that abstracts away some of the complexity.
|
||||
|
||||
Mental Models and Design Paradigms
|
||||
Modularity: Each swarm should be a self-contained unit that can operate independently. This makes the system more flexible and easier to maintain.
|
||||
|
||||
Concurrency: The system should be designed to handle multiple swarms running concurrently. This requires careful consideration of issues such as data consistency and synchronization.
|
||||
|
||||
Fault Tolerance: The system should be able to handle errors gracefully. If one swarm encounters an error, it should not affect the operation of other swarms.
|
||||
|
||||
Scalability: The system should be able to handle an increasing number of swarms without a significant degradation in performance.
|
||||
|
||||
User-Centric Design: The system should be designed with the user in mind. It should be easy to use and provide value to the user.
|
||||
|
||||
Path to Product-Market Fit
|
||||
Identify Target Users: Determine who would benefit most from using the HiveMind class. This could be developers, data scientists, researchers, or businesses.
|
||||
|
||||
Understand User Needs: Conduct user research to understand the problems that users are trying to solve and how the HiveMind class can help.
|
||||
|
||||
Develop MVP: Develop a minimum viable product (MVP) that demonstrates the value of the HiveMind class. This should be a simple version of the product that solves a core user problem.
|
||||
|
||||
Gather Feedback: After releasing the MVP, gather feedback from users. This could be through surveys, interviews, or user testing.
|
||||
|
||||
Iterate and Improve: Use the feedback to iterate and improve the product. This could involve fixing bugs, adding new features, or improving usability.
|
||||
|
||||
Scale: Once the product has achieved product-market fit, focus on scaling. This could involve optimizing the product for performance, expanding to new markets, or developing partnerships.
|
||||
|
||||
|
||||
|
||||
Here are some features that could be added to the HiveMind class to provide maximum value for users:
|
||||
|
||||
Dynamic Scaling: The ability to automatically scale the number of swarms based on the complexity of the task or the load on the system. This would allow the system to handle a wide range of tasks efficiently.
|
||||
|
||||
Task Prioritization: The ability to prioritize tasks based on their importance or urgency. This would allow more important tasks to be completed first.
|
||||
|
||||
Progress Monitoring: The ability for users to monitor the progress of their tasks. This could include a progress bar, estimated completion time, or real-time updates.
|
||||
|
||||
Error Reporting: Detailed error reports that help users understand what went wrong if a task fails. This could include the error message, the swarm that encountered the error, and suggestions for how to fix the error.
|
||||
|
||||
Task Cancellation: The ability for users to cancel a task that is currently being processed. This could be useful if a user realizes they made a mistake or if a task is taking too long to complete.
|
||||
|
||||
Task Queuing: The ability for users to queue up multiple tasks. This would allow users to submit a batch of tasks and have them processed one after the other.
|
||||
|
||||
Result Formatting: The ability for users to specify how they want the results to be formatted. This could include options for plain text, JSON, XML, or other formats.
|
||||
|
||||
Integration with Other Services: The ability to integrate with other services, such as databases, cloud storage, or machine learning platforms. This would allow users to easily store results, access additional resources, or leverage advanced features.
|
||||
|
||||
Security Features: Features to ensure the security and privacy of user data, such as encryption, access controls, and audit logs.
|
||||
|
||||
User-Friendly API: A well-designed, user-friendly API that makes it easy for users to use the HiveMind class in their own applications. This could include clear documentation, examples, and error messages.
|
||||
@ -1,401 +0,0 @@
|
||||
## Swarming Architectures
|
||||
|
||||
Here are three examples of swarming architectures that could be applied in this context.
|
||||
|
||||
1. **Hierarchical Swarms**: In this architecture, a 'lead' agent coordinates the efforts of other agents, distributing tasks based on each agent's unique strengths. The lead agent might be equipped with additional functionality or decision-making capabilities to effectively manage the swarm.
|
||||
|
||||
2. **Collaborative Swarms**: Here, each agent in the swarm works in parallel, potentially on different aspects of a task. They then collectively determine the best output, often through a voting or consensus mechanism.
|
||||
|
||||
3. **Competitive Swarms**: In this setup, multiple agents work on the same task independently. The output from the agent which produces the highest confidence or quality result is then selected. This can often lead to more robust outputs, as the competition drives each agent to perform at its best.
|
||||
|
||||
4. **Multi-Agent Debate**: Here, multiple agents debate a topic. The output from the agent which produces the highest confidence or quality result is then selected. This can lead to more robust outputs, as the competition drives each agent to perform it's best.
|
||||
|
||||
|
||||
# Ideas
|
||||
|
||||
A swarm, particularly in the context of distributed computing, refers to a large number of coordinated agents or nodes that work together to solve a problem. The specific requirements of a swarm might vary depending on the task at hand, but some of the general requirements include:
|
||||
|
||||
1. **Distributed Nature**: The swarm should consist of multiple individual units or nodes, each capable of functioning independently.
|
||||
|
||||
2. **Coordination**: The nodes in the swarm need to coordinate with each other to ensure they're working together effectively. This might involve communication between nodes, or it could be achieved through a central orchestrator.
|
||||
|
||||
3. **Scalability**: A well-designed swarm system should be able to scale up or down as needed, adding or removing nodes based on the task load.
|
||||
|
||||
4. **Resilience**: If a node in the swarm fails, it shouldn't bring down the entire system. Instead, other nodes should be able to pick up the slack.
|
||||
|
||||
5. **Load Balancing**: Tasks should be distributed evenly across the nodes in the swarm to avoid overloading any single node.
|
||||
|
||||
6. **Interoperability**: Each node should be able to interact with others, regardless of differences in underlying hardware or software.
|
||||
|
||||
Integrating these requirements with Large Language Models (LLMs) can be done as follows:
|
||||
|
||||
1. **Distributed Nature**: Each LLM agent can be considered as a node in the swarm. These agents can be distributed across multiple servers or even geographically dispersed data centers.
|
||||
|
||||
2. **Coordination**: An orchestrator can manage the LLM agents, assigning tasks, coordinating responses, and ensuring effective collaboration between agents.
|
||||
|
||||
3. **Scalability**: As the demand for processing power increases or decreases, the number of LLM agents can be adjusted accordingly.
|
||||
|
||||
4. **Resilience**: If an LLM agent goes offline or fails, the orchestrator can assign its tasks to other agents, ensuring the swarm continues functioning smoothly.
|
||||
|
||||
5. **Load Balancing**: The orchestrator can also handle load balancing, ensuring tasks are evenly distributed amongst the LLM agents.
|
||||
|
||||
6. **Interoperability**: By standardizing the input and output formats of the LLM agents, they can effectively communicate and collaborate, regardless of the specific model or configuration of each agent.
|
||||
|
||||
In terms of architecture, the swarm might look something like this:
|
||||
|
||||
```
|
||||
(Orchestrator)
|
||||
/ \
|
||||
Tools + Vector DB -- (LLM Agent)---(Communication Layer) (Communication Layer)---(LLM Agent)-- Tools + Vector DB
|
||||
/ | | \
|
||||
(Task Assignment) (Task Completion) (Task Assignment) (Task Completion)
|
||||
```
|
||||
|
||||
Each LLM agent communicates with the orchestrator through a dedicated communication layer. The orchestrator assigns tasks to each LLM agent, which the agents then complete and return. This setup allows for a high degree of flexibility, scalability, and robustness.
|
||||
|
||||
|
||||
## Communication Layer
|
||||
|
||||
Communication layers play a critical role in distributed systems, enabling interaction between different nodes (agents) and the orchestrator. Here are three potential communication layers for a distributed system, including their strengths and weaknesses:
|
||||
|
||||
1. **Message Queuing Systems (like RabbitMQ, Kafka)**:
|
||||
|
||||
- Strengths: They are highly scalable, reliable, and designed for high-throughput systems. They also ensure delivery of messages and can persist them if necessary. Furthermore, they support various messaging patterns like publish/subscribe, which can be highly beneficial in a distributed system. They also have robust community support.
|
||||
|
||||
- Weaknesses: They can add complexity to the system, including maintenance of the message broker. Moreover, they require careful configuration to perform optimally, and handling failures can sometimes be challenging.
|
||||
|
||||
2. **RESTful APIs**:
|
||||
|
||||
- Strengths: REST is widely adopted, and most programming languages have libraries to easily create RESTful APIs. They leverage standard HTTP(S) protocols and methods and are straightforward to use. Also, they can be stateless, meaning each request contains all the necessary information, enabling scalability.
|
||||
|
||||
- Weaknesses: For real-time applications, REST may not be the best fit due to its synchronous nature. Additionally, handling a large number of API requests can put a strain on the system, causing slowdowns or timeouts.
|
||||
|
||||
3. **gRPC (Google Remote Procedure Call)**:
|
||||
|
||||
- Strengths: gRPC uses Protocol Buffers as its interface definition language, leading to smaller payloads and faster serialization/deserialization compared to JSON (commonly used in RESTful APIs). It supports bidirectional streaming and can use HTTP/2 features, making it excellent for real-time applications.
|
||||
|
||||
- Weaknesses: gRPC is more complex to set up compared to REST. Protocol Buffers' binary format can be more challenging to debug than JSON. It's also not as widely adopted as REST, so tooling and support might be limited in some environments.
|
||||
|
||||
In the context of swarm LLMs, one could consider an **Omni-Vector Embedding Database** for communication. This database could store and manage the high-dimensional vectors produced by each LLM agent.
|
||||
|
||||
- Strengths: This approach would allow for similarity-based lookup and matching of LLM-generated vectors, which can be particularly useful for tasks that involve finding similar outputs or recognizing patterns.
|
||||
|
||||
- Weaknesses: An Omni-Vector Embedding Database might add complexity to the system in terms of setup and maintenance. It might also require significant computational resources, depending on the volume of data being handled and the complexity of the vectors. The handling and transmission of high-dimensional vectors could also pose challenges in terms of network load.
|
||||
|
||||
|
||||
|
||||
|
||||
# Technical Analysis Document: Particle Swarm of AI Agents using Ocean Database
|
||||
|
||||
## Overview
|
||||
|
||||
The goal is to create a particle swarm of AI agents using the OpenAI API for the agents and the Ocean database as the communication space, where the embeddings act as particles. The swarm will work collectively to perform tasks and optimize their behavior based on the interaction with the Ocean database.
|
||||
|
||||
## Algorithmic Overview
|
||||
|
||||
1. Initialize the AI agents and the Ocean database.
|
||||
2. Assign tasks to the AI agents.
|
||||
3. AI agents use the OpenAI API to perform tasks and generate embeddings.
|
||||
4. AI agents store their embeddings in the Ocean database.
|
||||
5. AI agents query the Ocean database for relevant embeddings.
|
||||
6. AI agents update their positions based on the retrieved embeddings.
|
||||
7. Evaluate the performance of the swarm and update the agents' behavior accordingly.
|
||||
8. Repeat steps 3-7 until a stopping criterion is met.
|
||||
|
||||
## Python Implementation Logic
|
||||
|
||||
1. **Initialize the AI agents and the Ocean database.**
|
||||
|
||||
```python
|
||||
import openai
|
||||
import oceandb
|
||||
from oceandb.utils.embedding_functions import ImageBindEmbeddingFunction
|
||||
|
||||
# Initialize Ocean database
|
||||
client = oceandb.Client()
|
||||
text_embedding_function = ImageBindEmbeddingFunction(modality="text")
|
||||
collection = client.create_collection("all-my-documents", embedding_function=text_embedding_function)
|
||||
|
||||
# Initialize AI agents
|
||||
agents = initialize_agents(...)
|
||||
```
|
||||
|
||||
2. **Assign tasks to the AI agents.**
|
||||
|
||||
```python
|
||||
tasks = assign_tasks_to_agents(agents, ...)
|
||||
```
|
||||
|
||||
3. **AI agents use the OpenAI API to perform tasks and generate embeddings.**
|
||||
|
||||
```python
|
||||
def agent_perform_task(agent, task):
|
||||
# Perform the task using the OpenAI API
|
||||
result = perform_task_with_openai_api(agent, task)
|
||||
# Generate the embedding
|
||||
embedding = generate_embedding(result)
|
||||
return embedding
|
||||
|
||||
embeddings = [agent_perform_task(agent, task) for agent, task in zip(agents, tasks)]
|
||||
```
|
||||
|
||||
4. **AI agents store their embeddings in the Ocean database.**
|
||||
|
||||
```python
|
||||
def store_embeddings_in_database(embeddings, collection):
|
||||
for i, embedding in enumerate(embeddings):
|
||||
document_id = f"agent_{i}"
|
||||
collection.add(documents=[embedding], ids=[document_id])
|
||||
|
||||
store_embeddings_in_database(embeddings, collection)
|
||||
```
|
||||
|
||||
5. **AI agents query the Ocean database for relevant embeddings.**
|
||||
|
||||
```python
|
||||
def query_database_for_embeddings(agent, collection, n_results=1):
|
||||
query_result = collection.query(query_texts=[agent], n_results=n_results)
|
||||
return query_result
|
||||
|
||||
queried_embeddings = [query_database_for_embeddings(agent, collection) for agent in agents]
|
||||
```
|
||||
|
||||
6. **AI agents update their positions based on the retrieved embeddings.**
|
||||
|
||||
```python
|
||||
def update_agent_positions(agents, queried_embeddings):
|
||||
for agent, embedding in zip(agents, queried_embeddings):
|
||||
agent.update_position(embedding)
|
||||
|
||||
update_agent_positions(agents, queried_embeddings)
|
||||
```
|
||||
|
||||
7. **Evaluate the performance of the swarm and update the agents' behavior accordingly.**
|
||||
|
||||
```python
|
||||
def evaluate_swarm_performance(agents, ...):
|
||||
# Evaluate the performance of the swarm
|
||||
performance = compute_performance_metric(agents, ...)
|
||||
return performance
|
||||
|
||||
def update_agent_behavior(agents, performance):
|
||||
# Update agents' behavior based on swarm performance
|
||||
for agent in agents:
|
||||
agent.adjust_behavior(performance)
|
||||
|
||||
performance = evaluate_swarm_performance(agents, ...)
|
||||
update_agent_behavior(agents, performance)
|
||||
```
|
||||
|
||||
8. **Repeat steps 3-7 until a stopping criterion is met.**
|
||||
|
||||
```python
|
||||
while not stopping_criterion_met():
|
||||
# Perform tasks and generate embeddings
|
||||
embeddings = [agent_perform_task(agent, task) for agent, task in zip(agents, tasks)]
|
||||
|
||||
# Store embeddings in the Ocean database
|
||||
store_embeddings_in_database(embeddings, collection)
|
||||
|
||||
# Query the Ocean database for relevant embeddings
|
||||
queried_embeddings = [query_database_for_embeddings(agent, collection) for agent in agents]
|
||||
|
||||
# Update AI agent positions based on the retrieved embeddings
|
||||
update_agent_positions(agents, queried_embeddings)
|
||||
|
||||
# Evaluate the performance of the swarm and update the agents' behavior accordingly
|
||||
performance = evaluate_swarm_performance(agents, ...)
|
||||
update_agent_behavior(agents, performance)
|
||||
```
|
||||
|
||||
This code demonstrates the complete loop to repeat steps 3-7 until a stopping criterion is met. You will need to define the `stopping_criterion_met()` function, which could be based on a predefined number of iterations, a target performance level, or any other condition that indicates that the swarm has reached a desired state.
|
||||
|
||||
|
||||
|
||||
|
||||
* Integrate petals to handle huggingface LLM
|
||||
|
||||
|
||||
|
||||
# Orchestrator
|
||||
* Takes in an agent class with vector store, then handles all the communication and scales up a swarm with number of agents and handles task assignment and task completion
|
||||
|
||||
```python
|
||||
|
||||
from swarms import OpenAI, Orchestrator, Swarm
|
||||
|
||||
orchestrated = Orchestrate(OpenAI, nodes=40) #handles all the task assignment and allocation and agent communication using a vectorstore as a universal communication layer and also handlles the task completion logic
|
||||
|
||||
Objective = "Make a business website for a marketing consultancy"
|
||||
|
||||
Swarms = (Swarms(orchestrated, auto=True, Objective))
|
||||
```
|
||||
|
||||
In terms of architecture, the swarm might look something like this:
|
||||
|
||||
```
|
||||
(Orchestrator)
|
||||
/ \
|
||||
Tools + Vector DB -- (LLM Agent)---(Communication Layer) (Communication Layer)---(LLM Agent)-- Tools + Vector DB
|
||||
/ | | \
|
||||
(Task Assignment) (Task Completion) (Task Assignment) (Task Completion)
|
||||
```
|
||||
|
||||
Each LLM agent communicates with the orchestrator through a dedicated communication layer. The orchestrator assigns tasks to each LLM agent, which the agents then complete and return. This setup allows for a high degree of flexibility, scalability, and robustness.
|
||||
|
||||
In the context of swarm LLMs, one could consider an **Omni-Vector Embedding Database** for communication. This database could store and manage the high-dimensional vectors produced by each LLM agent.
|
||||
|
||||
- Strengths: This approach would allow for similarity-based lookup and matching of LLM-generated vectors, which can be particularly useful for tasks that involve finding similar outputs or recognizing patterns.
|
||||
|
||||
- Weaknesses: An Omni-Vector Embedding Database might add complexity to the system in terms of setup and maintenance. It might also require significant computational resources, depending on the volume of data being handled and the complexity of the vectors. The handling and transmission of high-dimensional vectors could also pose challenges in terms of network load.
|
||||
|
||||
|
||||
|
||||
* Handling absurdly long sequences => first transform the objective if it's more than 1000tokens into a txt file similiar to how Claude works => then chunk it into sizes of 8000 seq length embeddings => then embed it and store in the vector database => then connext the agent model to it
|
||||
|
||||
|
||||
Given the complexity of the topic, please note that these simplified markdown documents are quite abstract and high level. They can be used as a starting point for further detailed design and implementation:
|
||||
|
||||
### Document 1: Hierarchical Swarms
|
||||
|
||||
#### Overall Architecture
|
||||
|
||||
1. Leader Agent (LA): This agent has the authority to manage and distribute tasks to the Worker Agents (WA).
|
||||
2. Worker Agents (WAs): These agents perform the tasks assigned by the LA.
|
||||
|
||||
#### Simplified Requirements
|
||||
|
||||
1. LA should be able to distribute tasks to WAs.
|
||||
2. WAs should be able to execute tasks and return results to LA.
|
||||
3. LA should be able to consolidate and process results.
|
||||
|
||||
#### Pseudocode
|
||||
|
||||
```
|
||||
create LA
|
||||
create WAs
|
||||
|
||||
for each task in tasks:
|
||||
LA.distribute_task(WAs, task)
|
||||
|
||||
for each WA in WAs:
|
||||
WA.execute_task()
|
||||
|
||||
LA.collect_results(WAs)
|
||||
|
||||
LA.process_results()
|
||||
```
|
||||
|
||||
#### General Classes
|
||||
|
||||
```python
|
||||
class LeaderAgent:
|
||||
def distribute_task(self, WAs, task):
|
||||
pass
|
||||
|
||||
def collect_results(self, WAs):
|
||||
pass
|
||||
|
||||
def process_results(self):
|
||||
pass
|
||||
|
||||
class WorkerAgent:
|
||||
def execute_task(self):
|
||||
pass
|
||||
```
|
||||
|
||||
### Document 2: Collaborative Swarms
|
||||
|
||||
#### Overall Architecture
|
||||
|
||||
1. Collaborative Agents (CAs): These agents work in parallel on different aspects of a task and then collectively determine the best output.
|
||||
|
||||
#### Simplified Requirements
|
||||
|
||||
1. CAs should be able to work on tasks in parallel.
|
||||
2. CAs should be able to collaborate to determine the best result.
|
||||
|
||||
#### Pseudocode
|
||||
|
||||
```
|
||||
create CAs
|
||||
|
||||
for each task in tasks:
|
||||
for each CA in CAs:
|
||||
CA.execute_task(task)
|
||||
|
||||
CA.collaborate()
|
||||
```
|
||||
|
||||
#### General Classes
|
||||
|
||||
```python
|
||||
class CollaborativeAgent:
|
||||
def execute_task(self, task):
|
||||
pass
|
||||
|
||||
def collaborate(self):
|
||||
pass
|
||||
```
|
||||
|
||||
### Document 3: Competitive Swarms
|
||||
|
||||
#### Overall Architecture
|
||||
|
||||
1. Competitive Agents (CompAs): These agents work independently on the same tasks, and the best result is selected.
|
||||
|
||||
#### Simplified Requirements
|
||||
|
||||
1. CompAs should be able to work independently on tasks.
|
||||
2. An evaluation method should be used to select the best result.
|
||||
|
||||
#### Pseudocode
|
||||
|
||||
```
|
||||
create CompAs
|
||||
|
||||
for each task in tasks:
|
||||
for each CompA in CompAs:
|
||||
CompA.execute_task(task)
|
||||
|
||||
evaluate_results(CompAs)
|
||||
```
|
||||
|
||||
#### General Classes
|
||||
|
||||
```python
|
||||
class CompetitiveAgent:
|
||||
def execute_task(self, task):
|
||||
pass
|
||||
|
||||
def evaluate_results(CompAs):
|
||||
pass
|
||||
```
|
||||
|
||||
Note: In the real world, the complexity of the architecture and requirements will significantly exceed what is presented here. These examples provide a basic starting point but should be expanded upon based on the specifics of the task or problem you're trying to solve.
|
||||
|
||||
|
||||
|
||||
# Swarms
|
||||
|
||||
BabyAGI -> Autogpt's -> tools -> other agents
|
||||
|
||||
- Host it on server, on premise, private learning, no learning is translating out
|
||||
- companies are sensitive with data, models are firewalled, need privacy, huggingface,
|
||||
- Does not transmit information,
|
||||
- see agent activity, task history,
|
||||
- optimize which agents for each task
|
||||
- Assist or provide feedback to management agent
|
||||
- overview see the whole swarm, modify each agent, visualize the communication stream with blue,
|
||||
- Work optimization routines
|
||||
- output monitoring
|
||||
- stop output, agent looping,
|
||||
-quality assurance checker, adversarial agent
|
||||
- see a holistic diagram of all agents, how are they being utilized, see number of iterations, query responses, balance loading,
|
||||
- summary of tasks completed with critique, type of summary, ceo summary, manager summary
|
||||
- outside of browser and accross whole operating system, switch apps, mac, linux, and windows
|
||||
-what are the skillsets behind the dev team, can be modified by experts, ui agent, manager agent, personalize agents with prompt and tools, and orca like explain your solutions, critique them then return the final output
|
||||
|
||||
|
||||
|
||||
|
||||
@ -1,11 +0,0 @@
|
||||
* The Platform, where users can have a conversation with the domain
|
||||
|
||||
* An developer platform where people can build swarms through a UI in nodes, connect and play
|
||||
|
||||
* SIMS like UI, where you see every node accomplishing their tasks around the internet
|
||||
|
||||
* Swarms Discord BOT
|
||||
|
||||
* PAID API
|
||||
|
||||
* MARKETPLACE FOR PREBUILT SWARMS WITH SPECIFIC PROMPTS, MODELS, TOOLS, AND MEMORIES,
|
||||
@ -1,42 +0,0 @@
|
||||
Research Proposal: Creating a Swarm of LLM Agents for Operating Systems
|
||||
Introduction
|
||||
The goal of this research is to explore the feasibility and requirements of creating a swarm of Language Learning Model (LLM) agents that can autonomously operate the kernel of an operating system. This swarm of AI agents would be capable of performing tasks such as process scheduling, memory management, device management, and system calls, among others.
|
||||
|
||||
Objectives
|
||||
To investigate the feasibility of using LLM agents to autonomously operate the kernel of an operating system.
|
||||
To identify the requirements and challenges of implementing such a system.
|
||||
To develop a prototype system as a proof of concept.
|
||||
Methodology
|
||||
Literature Review: Conduct a comprehensive review of existing research on AI in operating systems, swarm intelligence, and LLMs.
|
||||
|
||||
Feasibility Study: Analyze the capabilities of current LLMs and assess whether they can be adapted to operate an OS kernel.
|
||||
|
||||
Requirement Analysis: Identify the hardware, software, and data requirements for implementing a swarm of LLM agents in an OS.
|
||||
|
||||
System Design: Design a prototype system that uses LLM agents to perform basic kernel operations.
|
||||
|
||||
Implementation and Testing: Implement the prototype system and conduct rigorous testing to evaluate its performance.
|
||||
|
||||
Requirements
|
||||
Hardware: A high-performance computing system would be required to handle the computational load of millions of LLM agents. This system would need to have a powerful CPU, a large amount of RAM, and possibly a GPU for machine learning tasks.
|
||||
|
||||
Software: The system would require an operating system that is compatible with the LLM agents. This could be a popular OS like Linux, which is open-source and widely used in AI research.
|
||||
|
||||
LLM Agents: The LLM agents would need to be trained to perform kernel operations. This would require a large dataset of kernel operations and their outcomes.
|
||||
|
||||
Swarm Intelligence Framework: A framework for swarm intelligence would be needed to manage the LLM agents and coordinate their activities.
|
||||
|
||||
Monitoring and Debugging Tools: Tools for monitoring the performance of the LLM agents and debugging any issues would be essential.
|
||||
|
||||
Potential Challenges
|
||||
Complexity of Kernel Operations: Kernel operations are complex and low-level. Training LLM agents to perform these operations accurately and efficiently could be challenging.
|
||||
|
||||
Coordination of LLM Agents: Coordinating the activities of millions of LLM agents could be a complex task. The swarm intelligence framework would need to be robust and efficient.
|
||||
|
||||
Security: The system would need to be secure to prevent unauthorized access and ensure the integrity of the kernel operations.
|
||||
|
||||
Performance: The system would need to be able to handle a high load and perform operations quickly to avoid slowing down the OS.
|
||||
|
||||
Conclusion
|
||||
Creating a swarm of LLM agents for operating systems is a challenging but potentially rewarding endeavor. This research aims to explore the feasibility of this idea and identify the requirements for its implementation. If successful, this could open up new possibilities for AI in operating systems and beyond.
|
||||
|
||||
@ -1,80 +0,0 @@
|
||||
## **Product Feature Document: Multi-Agent Distributed Collaboration Framework**
|
||||
|
||||
---
|
||||
|
||||
**Introduction**:
|
||||
In the modern age of AI, the potential of harnessing multiple intelligent agents to automate real-world tasks offers unprecedented value. We're building a framework that enables multi-agent distributed collaboration, akin to working with millions of GPT-3 nodes, and this document outlines the features which will bring tremendous value to early adopters.
|
||||
|
||||
---
|
||||
|
||||
**1. Data Security & Privacy**
|
||||
- **On-Premise Hosting**: Users can deploy the framework on their server to ensure data doesn't leave their ecosystem.
|
||||
- **Private Learning**: The agents can learn without transmitting sensitive data out.
|
||||
- **Firewalled Models**: Ensures that all model data remains secured behind barriers, preventing unauthorized data access.
|
||||
- **HuggingFace Integration**: For those comfortable with the HuggingFace ecosystem.
|
||||
- **Transparency**: Ability to see agent activity, task history, which aids in accountability.
|
||||
|
||||
---
|
||||
|
||||
**2. Agent & Swarm Management**
|
||||
- **Optimized Task Allocation**: System can decide which agents are best suited for each task, based on their learning and past performance.
|
||||
- **Agent Feedback System**: Enables users to assist or provide feedback to the managing agent.
|
||||
- **Holistic Swarm View**: Visualize the entire swarm, the communication streams, and individually modify agent behavior.
|
||||
- **Work Optimization**: Routines to determine the most efficient distribution of tasks amongst agents.
|
||||
- **Quality Assurance Agent**: A specialized agent to ensure the outputs meet the required standards.
|
||||
|
||||
---
|
||||
|
||||
**3. Output Management & Monitoring**
|
||||
- **Stop Looping**: If an agent gets stuck in a task loop, the system can intervene.
|
||||
- **Output Monitoring**: Real-time surveillance of what each agent produces.
|
||||
- **Task Summaries**: An overview of tasks, tailored to different management levels (CEO summary, manager summary).
|
||||
|
||||
---
|
||||
|
||||
**4. Cross-Platform Integration**
|
||||
- **OS Compatibility**: Seamlessly operate across Mac, Linux, and Windows.
|
||||
- **Beyond Browser**: Ability to interact with different applications across the entire OS.
|
||||
|
||||
---
|
||||
|
||||
**5. Customization & Training**
|
||||
- **Agent Personalization**: Tailor each agent's prompts, tools, and behavior to better fit specific tasks.
|
||||
- **Training Agent for SMBs**: Simplified input prompting system to guide small-medium businesses.
|
||||
- **Human Training Agent**: Uses visual aids, questions, and references to train human users. Incorporates meta prompting for dynamic environments.
|
||||
|
||||
---
|
||||
|
||||
**6. Installation & Deployment**
|
||||
- **Easy Install Process**: Streamlined installation process, with real-time troubleshooting support.
|
||||
- **Cloud Setup for Non-GPU**: For those without local GPU, a straightforward cloud setup guide.
|
||||
|
||||
---
|
||||
|
||||
**7. Advanced Agent Dynamics**
|
||||
- **Non-Hierarchical Structure**: Worker agents autonomously pick tasks based on their strengths.
|
||||
- **Knowledge Store**: A separate or integrated system where agents access and augment their knowledge.
|
||||
- **API Integration**: Ability to easily integrate different APIs like LLM, GPT-4, and Anthropic.
|
||||
|
||||
---
|
||||
|
||||
**8. Memory & Knowledge Management**
|
||||
- **Differentiated Memory**: Separate memory storage for individual agents and the entire swarm, aiding in more efficient task delegation and execution.
|
||||
|
||||
---
|
||||
|
||||
**Events and Workshops (for community involvement & onboarding)**
|
||||
- **Monthly Webinars**: Dive deep into feature releases, use cases, and best practices.
|
||||
- **Next Session**: August 25th, 2023 - "Harnessing the Power of Multi-Agent Systems"
|
||||
- **Quarterly Workshops**: Hands-on sessions for businesses to understand how to best leverage the framework.
|
||||
- **Upcoming Workshop**: September 15th-17th, 2023 - "Optimizing Agent Performance for Business Tasks"
|
||||
- **Annual Swarm Conclave**: A grand gathering of all community members, developers, and businesses to discuss future roadmaps and celebrate successes.
|
||||
- **Swarm Conclave 2023**: December 2nd-4th, 2023, San Francisco, CA.
|
||||
|
||||
---
|
||||
|
||||
**Conclusion**:
|
||||
This framework is not merely a technological tool, but a step into the future of collaborative AI. By combining the strengths of multiple intelligent agents, we can redefine how businesses operate, innovate, and grow.
|
||||
|
||||
**Join the revolution. Become part of Agora.**
|
||||
[**Discord Link**](https://discord.gg/qUtxnK2NMf)
|
||||
@ -1,75 +0,0 @@
|
||||
### Plan:
|
||||
|
||||
1. **Example Creation**:
|
||||
- Develop several usage examples, each one demonstrating a different configuration or set of parameters for the `VortexWorkerAgent` class.
|
||||
|
||||
2. **Documentation**:
|
||||
- Create a clear and concise documentation for each method in the class. Ensure that each method's purpose, input parameters, and return values (if any) are described.
|
||||
|
||||
3. **Rules and Guidelines**:
|
||||
- Establish a set of general usage rules and guidelines for effectively using the `VortexWorkerAgent` class without running into common pitfalls or misconfigurations.
|
||||
|
||||
### Code:
|
||||
|
||||
#### Examples:
|
||||
|
||||
```python
|
||||
# Example 1: Basic Initialization
|
||||
agent1 = VortexWorkerAgent(openai_api_key="YOUR_OPENAI_API_KEY")
|
||||
agent1.run("Help me find resources about renewable energy.")
|
||||
|
||||
# Example 2: Custom Name & Role
|
||||
agent2 = VortexWorkerAgent(openai_api_key="YOUR_OPENAI_API_KEY", worker_name="EcoHelper", worker_role="Researcher")
|
||||
agent2.run("Fetch me the latest data on solar energy advancements.")
|
||||
|
||||
# Example 3: Human-in-the-Loop Configuration
|
||||
agent3 = VortexWorkerAgent(openai_api_key="YOUR_OPENAI_API_KEY", human_in_the_loop=True)
|
||||
agent3.run("Provide me with a summary of the top AI advancements in 2023, and if unsure, ask me.")
|
||||
|
||||
# Example 4: Custom LLM & Tools Initialization
|
||||
custom_llm = InMemoryDocstore({ "answer": "This is a custom answer." })
|
||||
custom_tools = [WebpageQATool(qa_chain=load_qa_with_sources_chain(custom_llm))]
|
||||
|
||||
agent4 = VortexWorkerAgent(openai_api_key="YOUR_OPENAI_API_KEY", llm=custom_llm, tools=custom_tools)
|
||||
agent4.run("What's the answer?")
|
||||
```
|
||||
|
||||
#### Documentation:
|
||||
|
||||
```python
|
||||
class VortexWorkerAgent:
|
||||
"""An autonomous agent instance that accomplishes complex tasks.
|
||||
|
||||
Args:
|
||||
openai_api_key (str): The API key for OpenAI.
|
||||
llm (Optional[Union[InMemoryDocstore, ChatOpenAI]]): The Language Model to use. Defaults to ChatOpenAI.
|
||||
tools (Optional[List[Tool]]): Tools to be used by the agent. Defaults to a predefined list.
|
||||
embedding_size (Optional[int]): Size for embeddings. Defaults to 8192.
|
||||
worker_name (Optional[str]): Name of the worker. Defaults to "Swarm Worker AI Assistant".
|
||||
worker_role (Optional[str]): Role of the worker. Defaults to "Assistant".
|
||||
human_in_the_loop (Optional[bool]): Flag to specify if a human will be in the loop. Defaults to False.
|
||||
search_kwargs (dict): Additional keyword arguments for search. Empty by default.
|
||||
verbose (Optional[bool]): Verbose flag. Defaults to False.
|
||||
chat_history_file (str): File path to store chat history. Defaults to "chat_history.txt".
|
||||
|
||||
Methods:
|
||||
add_tool(tool: Tool): Adds a new tool to the agent's toolset.
|
||||
run(prompt: str) -> str: Executes a given task or query using the agent.
|
||||
"""
|
||||
```
|
||||
|
||||
#### Rules and Guidelines:
|
||||
|
||||
1. **Mandatory OpenAI API Key**: Always initialize the `VortexWorkerAgent` with a valid OpenAI API key. It's essential for its proper functioning.
|
||||
|
||||
2. **Custom LLMs & Tools**: When providing custom LLMs or tools, ensure they are compatible with the system and the rest of the agent's components.
|
||||
|
||||
3. **Human-in-the-Loop**: When `human_in_the_loop` is set to `True`, always ensure you have a mechanism to interact with the agent, especially if it prompts for human input.
|
||||
|
||||
4. **Verbose Mode**: Turning on the verbose mode (`verbose=True`) can be useful for debugging but might clutter the console during standard operations.
|
||||
|
||||
5. **Memory & Performance**: If you're working with large datasets or demanding tasks, ensure you have sufficient computational resources. The agent can be resource-intensive, especially with bigger embedding sizes.
|
||||
|
||||
6. **Safety & Security**: Always be cautious about the data you provide and fetch using the agent. Avoid sharing sensitive or personal information unless necessary.
|
||||
|
||||
7. **Chat History**: By default, the chat history is saved in a file named "chat_history.txt". Ensure you have the appropriate write permissions in the directory or specify a different path if needed.
|
||||
@ -0,0 +1,299 @@
|
||||
"""Sampling parameters for text generation."""
|
||||
from enum import IntEnum
|
||||
from functools import cached_property
|
||||
from typing import Callable, List, Optional, Union
|
||||
|
||||
import torch
|
||||
|
||||
_SAMPLING_EPS = 1e-5
|
||||
|
||||
|
||||
class SamplingType(IntEnum):
|
||||
GREEDY = 0
|
||||
RANDOM = 1
|
||||
BEAM = 2
|
||||
|
||||
|
||||
LogitsProcessor = Callable[[List[int], torch.Tensor], torch.Tensor]
|
||||
"""LogitsProcessor is a function that takes a list of previously generated
|
||||
tokens and a tensor of the logits for the next token, and returns a modified
|
||||
tensor of logits to sample from."""
|
||||
|
||||
|
||||
class SamplingParams:
|
||||
"""Sampling parameters for text generation.
|
||||
|
||||
Overall, we follow the sampling parameters from the OpenAI text completion
|
||||
API (https://platform.openai.com/docs/api-reference/completions/create).
|
||||
In addition, we support beam search, which is not supported by OpenAI.
|
||||
|
||||
Args:
|
||||
n: Number of output sequences to return for the given prompt.
|
||||
best_of: Number of output sequences that are generated from the prompt.
|
||||
From these `best_of` sequences, the top `n` sequences are returned.
|
||||
`best_of` must be greater than or equal to `n`. This is treated as
|
||||
the beam width when `use_beam_search` is True. By default, `best_of`
|
||||
is set to `n`.
|
||||
presence_penalty: Float that penalizes new tokens based on whether they
|
||||
appear in the generated text so far. Values > 0 encourage the model
|
||||
to use new tokens, while values < 0 encourage the model to repeat
|
||||
tokens.
|
||||
frequency_penalty: Float that penalizes new tokens based on their
|
||||
frequency in the generated text so far. Values > 0 encourage the
|
||||
model to use new tokens, while values < 0 encourage the model to
|
||||
repeat tokens.
|
||||
repetition_penalty: Float that penalizes new tokens based on whether
|
||||
they appear in the prompt and the generated text so far. Values > 1
|
||||
encourage the model to use new tokens, while values < 1 encourage
|
||||
the model to repeat tokens.
|
||||
temperature: Float that controls the randomness of the sampling. Lower
|
||||
values make the model more deterministic, while higher values make
|
||||
the model more random. Zero means greedy sampling.
|
||||
top_p: Float that controls the cumulative probability of the top tokens
|
||||
to consider. Must be in (0, 1]. Set to 1 to consider all tokens.
|
||||
top_k: Integer that controls the number of top tokens to consider. Set
|
||||
to -1 to consider all tokens.
|
||||
min_p: Float that represents the minimum probability for a token to be
|
||||
considered, relative to the probability of the most likely token.
|
||||
Must be in [0, 1]. Set to 0 to disable this.
|
||||
use_beam_search: Whether to use beam search instead of sampling.
|
||||
length_penalty: Float that penalizes sequences based on their length.
|
||||
Used in beam search.
|
||||
early_stopping: Controls the stopping condition for beam search. It
|
||||
accepts the following values: `True`, where the generation stops as
|
||||
soon as there are `best_of` complete candidates; `False`, where an
|
||||
heuristic is applied and the generation stops when is it very
|
||||
unlikely to find better candidates; `"never"`, where the beam search
|
||||
procedure only stops when there cannot be better candidates
|
||||
(canonical beam search algorithm).
|
||||
stop: List of strings that stop the generation when they are generated.
|
||||
The returned output will not contain the stop strings.
|
||||
stop_token_ids: List of tokens that stop the generation when they are
|
||||
generated. The returned output will contain the stop tokens unless
|
||||
the stop tokens are special tokens.
|
||||
include_stop_str_in_output: Whether to include the stop strings in output
|
||||
text. Defaults to False.
|
||||
ignore_eos: Whether to ignore the EOS token and continue generating
|
||||
tokens after the EOS token is generated.
|
||||
max_tokens: Maximum number of tokens to generate per output sequence.
|
||||
logprobs: Number of log probabilities to return per output token.
|
||||
Note that the implementation follows the OpenAI API: The return
|
||||
result includes the log probabilities on the `logprobs` most likely
|
||||
tokens, as well the chosen tokens. The API will always return the
|
||||
log probability of the sampled token, so there may be up to
|
||||
`logprobs+1` elements in the response.
|
||||
prompt_logprobs: Number of log probabilities to return per prompt token.
|
||||
skip_special_tokens: Whether to skip special tokens in the output.
|
||||
spaces_between_special_tokens: Whether to add spaces between special
|
||||
tokens in the output. Defaults to True.
|
||||
logits_processors: List of functions that modify logits based on
|
||||
previously generated tokens.
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
n: int = 1,
|
||||
best_of: Optional[int] = None,
|
||||
presence_penalty: float = 0.0,
|
||||
frequency_penalty: float = 0.0,
|
||||
repetition_penalty: float = 1.0,
|
||||
temperature: float = 1.0,
|
||||
top_p: float = 1.0,
|
||||
top_k: int = -1,
|
||||
min_p: float = 0.0,
|
||||
use_beam_search: bool = False,
|
||||
length_penalty: float = 1.0,
|
||||
early_stopping: Union[bool, str] = False,
|
||||
stop: Optional[Union[str, List[str]]] = None,
|
||||
stop_token_ids: Optional[List[int]] = None,
|
||||
include_stop_str_in_output: bool = False,
|
||||
ignore_eos: bool = False,
|
||||
max_tokens: Optional[int] = 16,
|
||||
logprobs: Optional[int] = None,
|
||||
prompt_logprobs: Optional[int] = None,
|
||||
skip_special_tokens: bool = True,
|
||||
spaces_between_special_tokens: bool = True,
|
||||
logits_processors: Optional[List[LogitsProcessor]] = None,
|
||||
) -> None:
|
||||
self.n = n
|
||||
self.best_of = best_of if best_of is not None else n
|
||||
self.presence_penalty = presence_penalty
|
||||
self.frequency_penalty = frequency_penalty
|
||||
self.repetition_penalty = repetition_penalty
|
||||
self.temperature = temperature
|
||||
self.top_p = top_p
|
||||
self.top_k = top_k
|
||||
self.min_p = min_p
|
||||
self.use_beam_search = use_beam_search
|
||||
self.length_penalty = length_penalty
|
||||
self.early_stopping = early_stopping
|
||||
if stop is None:
|
||||
self.stop = []
|
||||
elif isinstance(stop, str):
|
||||
self.stop = [stop]
|
||||
else:
|
||||
self.stop = list(stop)
|
||||
if stop_token_ids is None:
|
||||
self.stop_token_ids = []
|
||||
else:
|
||||
self.stop_token_ids = list(stop_token_ids)
|
||||
self.ignore_eos = ignore_eos
|
||||
self.max_tokens = max_tokens
|
||||
self.logprobs = logprobs
|
||||
self.prompt_logprobs = prompt_logprobs
|
||||
self.skip_special_tokens = skip_special_tokens
|
||||
self.spaces_between_special_tokens = (
|
||||
spaces_between_special_tokens
|
||||
)
|
||||
self.logits_processors = logits_processors
|
||||
self.include_stop_str_in_output = include_stop_str_in_output
|
||||
self._verify_args()
|
||||
if self.use_beam_search:
|
||||
self._verify_beam_search()
|
||||
else:
|
||||
self._verify_non_beam_search()
|
||||
if self.temperature < _SAMPLING_EPS:
|
||||
# Zero temperature means greedy sampling.
|
||||
self.top_p = 1.0
|
||||
self.top_k = -1
|
||||
self.min_p = 0.0
|
||||
self._verify_greedy_sampling()
|
||||
|
||||
def _verify_args(self) -> None:
|
||||
if self.n < 1:
|
||||
raise ValueError(f"n must be at least 1, got {self.n}.")
|
||||
if self.best_of < self.n:
|
||||
raise ValueError(
|
||||
"best_of must be greater than or equal to n, "
|
||||
f"got n={self.n} and best_of={self.best_of}."
|
||||
)
|
||||
if not -2.0 <= self.presence_penalty <= 2.0:
|
||||
raise ValueError(
|
||||
"presence_penalty must be in [-2, 2], got "
|
||||
f"{self.presence_penalty}."
|
||||
)
|
||||
if not -2.0 <= self.frequency_penalty <= 2.0:
|
||||
raise ValueError(
|
||||
"frequency_penalty must be in [-2, 2], got "
|
||||
f"{self.frequency_penalty}."
|
||||
)
|
||||
if not 0.0 < self.repetition_penalty <= 2.0:
|
||||
raise ValueError(
|
||||
"repetition_penalty must be in (0, 2], got "
|
||||
f"{self.repetition_penalty}."
|
||||
)
|
||||
if self.temperature < 0.0:
|
||||
raise ValueError(
|
||||
"temperature must be non-negative, got"
|
||||
f" {self.temperature}."
|
||||
)
|
||||
if not 0.0 < self.top_p <= 1.0:
|
||||
raise ValueError(
|
||||
f"top_p must be in (0, 1], got {self.top_p}."
|
||||
)
|
||||
if self.top_k < -1 or self.top_k == 0:
|
||||
raise ValueError(
|
||||
"top_k must be -1 (disable), or at least 1, "
|
||||
f"got {self.top_k}."
|
||||
)
|
||||
if not 0.0 <= self.min_p <= 1.0:
|
||||
raise ValueError(
|
||||
f"min_p must be in [0, 1], got {self.min_p}."
|
||||
)
|
||||
if self.max_tokens is not None and self.max_tokens < 1:
|
||||
raise ValueError(
|
||||
"max_tokens must be at least 1, got"
|
||||
f" {self.max_tokens}."
|
||||
)
|
||||
if self.logprobs is not None and self.logprobs < 0:
|
||||
raise ValueError(
|
||||
f"logprobs must be non-negative, got {self.logprobs}."
|
||||
)
|
||||
if (
|
||||
self.prompt_logprobs is not None
|
||||
and self.prompt_logprobs < 0
|
||||
):
|
||||
raise ValueError(
|
||||
"prompt_logprobs must be non-negative, got "
|
||||
f"{self.prompt_logprobs}."
|
||||
)
|
||||
|
||||
def _verify_beam_search(self) -> None:
|
||||
if self.best_of == 1:
|
||||
raise ValueError(
|
||||
"best_of must be greater than 1 when using beam "
|
||||
f"search. Got {self.best_of}."
|
||||
)
|
||||
if self.temperature > _SAMPLING_EPS:
|
||||
raise ValueError(
|
||||
"temperature must be 0 when using beam search."
|
||||
)
|
||||
if self.top_p < 1.0 - _SAMPLING_EPS:
|
||||
raise ValueError(
|
||||
"top_p must be 1 when using beam search."
|
||||
)
|
||||
if self.top_k != -1:
|
||||
raise ValueError(
|
||||
"top_k must be -1 when using beam search."
|
||||
)
|
||||
if self.early_stopping not in [True, False, "never"]:
|
||||
raise ValueError(
|
||||
"early_stopping must be True, False, or 'never', "
|
||||
f"got {self.early_stopping}."
|
||||
)
|
||||
|
||||
def _verify_non_beam_search(self) -> None:
|
||||
if self.early_stopping is not False:
|
||||
raise ValueError(
|
||||
"early_stopping is not effective and must be "
|
||||
"False when not using beam search."
|
||||
)
|
||||
if (
|
||||
self.length_penalty < 1.0 - _SAMPLING_EPS
|
||||
or self.length_penalty > 1.0 + _SAMPLING_EPS
|
||||
):
|
||||
raise ValueError(
|
||||
"length_penalty is not effective and must be the "
|
||||
"default value of 1.0 when not using beam search."
|
||||
)
|
||||
|
||||
def _verify_greedy_sampling(self) -> None:
|
||||
if self.best_of > 1:
|
||||
raise ValueError(
|
||||
"best_of must be 1 when using greedy sampling."
|
||||
f"Got {self.best_of}."
|
||||
)
|
||||
|
||||
@cached_property
|
||||
def sampling_type(self) -> SamplingType:
|
||||
if self.use_beam_search:
|
||||
return SamplingType.BEAM
|
||||
if self.temperature < _SAMPLING_EPS:
|
||||
return SamplingType.GREEDY
|
||||
return SamplingType.RANDOM
|
||||
|
||||
def __repr__(self) -> str:
|
||||
return (
|
||||
f"SamplingParams(n={self.n}, "
|
||||
f"best_of={self.best_of}, "
|
||||
f"presence_penalty={self.presence_penalty}, "
|
||||
f"frequency_penalty={self.frequency_penalty}, "
|
||||
f"repetition_penalty={self.repetition_penalty}, "
|
||||
f"temperature={self.temperature}, "
|
||||
f"top_p={self.top_p}, "
|
||||
f"top_k={self.top_k}, "
|
||||
f"min_p={self.min_p}, "
|
||||
f"use_beam_search={self.use_beam_search}, "
|
||||
f"length_penalty={self.length_penalty}, "
|
||||
f"early_stopping={self.early_stopping}, "
|
||||
f"stop={self.stop}, "
|
||||
f"stop_token_ids={self.stop_token_ids}, "
|
||||
f"include_stop_str_in_output={self.include_stop_str_in_output}, "
|
||||
f"ignore_eos={self.ignore_eos}, "
|
||||
f"max_tokens={self.max_tokens}, "
|
||||
f"logprobs={self.logprobs}, "
|
||||
f"prompt_logprobs={self.prompt_logprobs}, "
|
||||
f"skip_special_tokens={self.skip_special_tokens}, "
|
||||
"spaces_between_special_tokens="
|
||||
f"{self.spaces_between_special_tokens})"
|
||||
)
|
||||
@ -0,0 +1,80 @@
|
||||
# Copyright (c) OpenMMLab. All rights reserved.
|
||||
import torch
|
||||
|
||||
|
||||
def continuous_tensor(
|
||||
inputs: torch.Tensor, seq_length: torch.LongTensor
|
||||
):
|
||||
"""Convert batched tensor to continuous tensor.
|
||||
|
||||
Args:
|
||||
inputs (Tensor): batched tensor.
|
||||
seq_length (Tensor): length of each sequence.
|
||||
|
||||
Return:
|
||||
Tensor: continuoused tensor.
|
||||
"""
|
||||
assert inputs.dim() > 1
|
||||
if inputs.size(1) == 1:
|
||||
return inputs.reshape(1, -1)
|
||||
|
||||
inputs = [inp[:slen] for inp, slen in zip(inputs, seq_length)]
|
||||
|
||||
inputs = torch.cat(inputs).unsqueeze(0)
|
||||
return inputs
|
||||
|
||||
|
||||
def batch_tensor(inputs: torch.Tensor, seq_length: torch.LongTensor):
|
||||
"""Convert continuoused tensor to batched tensor.
|
||||
|
||||
Args:
|
||||
inputs (Tensor): continuoused tensor.
|
||||
seq_length (Tensor): length of each sequence.
|
||||
|
||||
Return:
|
||||
Tensor: batched tensor.
|
||||
"""
|
||||
from torch.nn.utils.rnn import pad_sequence
|
||||
|
||||
end_loc = seq_length.cumsum(0)
|
||||
start_loc = end_loc - seq_length
|
||||
|
||||
inputs = [
|
||||
inputs[0, sloc:eloc] for sloc, eloc in zip(start_loc, end_loc)
|
||||
]
|
||||
inputs = pad_sequence(inputs, batch_first=True)
|
||||
return inputs
|
||||
|
||||
|
||||
def page_cache(
|
||||
paged_cache: torch.Tensor,
|
||||
batched_cache: torch.Tensor,
|
||||
cache_length: torch.Tensor,
|
||||
block_offsets: torch.Tensor,
|
||||
permute_head: bool = True,
|
||||
):
|
||||
"""Convert batched cache to paged cache.
|
||||
|
||||
Args:
|
||||
paged_cache (Tensor): Output paged cache.
|
||||
batched_cache (Tensor): Input batched cache.
|
||||
cache_length (Tensor): length of the cache.
|
||||
block_offsets (Tensor): Offset of each blocks.
|
||||
"""
|
||||
assert block_offsets.dim() == 2
|
||||
block_size = paged_cache.size(1)
|
||||
batch_size = batched_cache.size(0)
|
||||
if permute_head:
|
||||
batched_cache = batched_cache.permute(0, 2, 1, 3)
|
||||
|
||||
for b_idx in range(batch_size):
|
||||
cache_len = cache_length[b_idx]
|
||||
b_cache = batched_cache[b_idx]
|
||||
block_off = block_offsets[b_idx]
|
||||
block_off_idx = 0
|
||||
for s_start in range(0, cache_len, block_size):
|
||||
s_end = min(s_start + block_size, cache_len)
|
||||
s_len = s_end - s_start
|
||||
b_off = block_off[block_off_idx]
|
||||
paged_cache[b_off, :s_len] = b_cache[s_start:s_end]
|
||||
block_off_idx += 1
|
||||
@ -0,0 +1,315 @@
|
||||
from typing import Callable, Union
|
||||
|
||||
import torch
|
||||
from torch import Tensor, nn
|
||||
from torch.distributed._tensor import (
|
||||
DeviceMesh,
|
||||
DTensor,
|
||||
Replicate,
|
||||
Shard,
|
||||
distribute_tensor,
|
||||
)
|
||||
from zeta.nn import QuantizedLN
|
||||
|
||||
|
||||
try:
|
||||
from peft.tuners.lora import Linear as LoRALinear
|
||||
except ImportError:
|
||||
|
||||
class LoRALinear:
|
||||
pass
|
||||
|
||||
|
||||
def try_to_local(tensor: Union[Tensor, DTensor]):
|
||||
"""Try to convert DTensor to Tensor.
|
||||
|
||||
Args:
|
||||
tensor (Tensor|DTensor): Tensor to convert.
|
||||
"""
|
||||
if isinstance(tensor, DTensor):
|
||||
tensor = tensor.to_local()
|
||||
return tensor
|
||||
|
||||
|
||||
def module_to_local(module: nn.Module):
|
||||
"""convert all DTensor parameters to Tensor parameters in module.
|
||||
|
||||
Args:
|
||||
module (Module): Module to convert.
|
||||
"""
|
||||
for name, mod in module.named_children():
|
||||
module_to_local(mod)
|
||||
|
||||
for name, param in module.named_parameters(recurse=False):
|
||||
module.register_parameter(
|
||||
name, nn.Parameter(try_to_local(param))
|
||||
)
|
||||
|
||||
for name, buf in module.named_buffers(recurse=False):
|
||||
module.register_buffer(name, try_to_local(buf))
|
||||
|
||||
|
||||
def rowwise_parallelize_linear(
|
||||
module: nn.Module, device_mesh: DeviceMesh, to_local: bool = False
|
||||
) -> None:
|
||||
"""
|
||||
This function parallelizes the input :class:`nn.Linear` module in
|
||||
:class:`RowwiseParallel` style.
|
||||
|
||||
Args:
|
||||
module (:class:`nn.Module`):
|
||||
The :class:`nn.Linear` module to be parallelized.
|
||||
device_mesh (:class:`DeviceMesh`):
|
||||
Object which describes the mesh topology of devices.
|
||||
|
||||
Returns:
|
||||
None
|
||||
"""
|
||||
for name, param in module.named_parameters():
|
||||
dist_spec = (
|
||||
[Shard(1)]
|
||||
if name == "weight"
|
||||
else [Replicate()] # type: ignore[list-item]
|
||||
)
|
||||
|
||||
dist_tensor = distribute_tensor(param, device_mesh, dist_spec)
|
||||
if to_local:
|
||||
dist_tensor = try_to_local(dist_tensor)
|
||||
if name == "bias":
|
||||
# rowwise linear would add bias more than ones.
|
||||
dist_tensor /= device_mesh.size()
|
||||
dist_param = torch.nn.Parameter(dist_tensor)
|
||||
module.register_parameter(name, dist_param)
|
||||
|
||||
# Weight, bias and scale are registered as buffer in QLinear
|
||||
for name, buffer in module.named_buffers():
|
||||
dist_spec = (
|
||||
[Shard(1)]
|
||||
if name == "weight"
|
||||
else [Replicate()] # type: ignore[list-item]
|
||||
)
|
||||
|
||||
dist_tensor = distribute_tensor(
|
||||
buffer, device_mesh, dist_spec
|
||||
)
|
||||
if to_local:
|
||||
dist_tensor = try_to_local(dist_tensor)
|
||||
if name == "bias":
|
||||
# rowwise linear would add bias more than ones.
|
||||
dist_tensor /= device_mesh.size()
|
||||
module.register_buffer(name, dist_tensor)
|
||||
|
||||
dist_tensor = distribute_tensor(
|
||||
buffer, device_mesh, dist_spec
|
||||
)
|
||||
if to_local:
|
||||
dist_tensor = try_to_local(dist_tensor)
|
||||
module.register_buffer(name, dist_tensor)
|
||||
|
||||
|
||||
def rowwise_parallelize_loralinear(
|
||||
module: LoRALinear,
|
||||
device_mesh: DeviceMesh,
|
||||
to_local: bool = False,
|
||||
) -> None:
|
||||
"""rowwize parallelize lora linear.
|
||||
|
||||
Read S-LoRA for more detail.
|
||||
"""
|
||||
rowwise_parallelize_linear(
|
||||
module.base_layer, device_mesh=device_mesh, to_local=to_local
|
||||
)
|
||||
for mod in module.lora_A.values():
|
||||
rowwise_parallelize_linear(
|
||||
mod, device_mesh=device_mesh, to_local=to_local
|
||||
)
|
||||
for mod in module.lora_B.values():
|
||||
colwise_parallelize_linear(
|
||||
mod, device_mesh=device_mesh, to_local=to_local
|
||||
)
|
||||
module._tp_mode = "rowwise"
|
||||
|
||||
|
||||
def rowwise_parallelize_linear_fn(
|
||||
module: nn.Module, device_mesh: DeviceMesh, to_local: bool = False
|
||||
) -> None:
|
||||
"""
|
||||
This function parallelizes the input :Linear module in
|
||||
:class:`RowwiseParallel` style.
|
||||
|
||||
Args:
|
||||
module (:class:`nn.Module`):
|
||||
The :class:`nn.Linear` module to be parallelized.
|
||||
device_mesh (:class:`DeviceMesh`):
|
||||
Object which describes the mesh topology of devices.
|
||||
|
||||
Returns:
|
||||
None
|
||||
"""
|
||||
if isinstance(module, (torch.nn.Linear, QuantizedLN)):
|
||||
return rowwise_parallelize_linear(
|
||||
module, device_mesh=device_mesh, to_local=to_local
|
||||
)
|
||||
elif isinstance(module, LoRALinear):
|
||||
return rowwise_parallelize_loralinear(
|
||||
module, device_mesh=device_mesh, to_local=to_local
|
||||
)
|
||||
else:
|
||||
raise TypeError(f"Unsupported module: {type(module)}")
|
||||
|
||||
|
||||
def colwise_parallelize_linear(
|
||||
module: nn.Module, device_mesh: DeviceMesh, to_local: bool = False
|
||||
) -> None:
|
||||
"""
|
||||
This function parallelizes the input :class:`nn.Linear` module in
|
||||
:class:`ColwiseParallel` style.
|
||||
|
||||
Args:
|
||||
module (:class:`nn.Module`):
|
||||
The :class:`nn.Linear` module to be parallelized.
|
||||
device_mesh (:class:`DeviceMesh`):
|
||||
Object which describes the mesh topology of devices.
|
||||
|
||||
Returns:
|
||||
None
|
||||
"""
|
||||
|
||||
for name, param in module.named_parameters():
|
||||
dist_tensor = distribute_tensor(
|
||||
param, device_mesh, [Shard(0)]
|
||||
)
|
||||
if to_local:
|
||||
dist_tensor = try_to_local(dist_tensor)
|
||||
dist_param = torch.nn.Parameter(dist_tensor)
|
||||
module.register_parameter(name, dist_param)
|
||||
# Weight, bias and scale are registered as buffer in QLinear
|
||||
for name, buffer in module.named_buffers():
|
||||
dist_tensor = distribute_tensor(
|
||||
buffer, device_mesh, [Shard(0)]
|
||||
)
|
||||
if to_local:
|
||||
dist_tensor = try_to_local(dist_tensor)
|
||||
module.register_buffer(name, dist_tensor)
|
||||
|
||||
|
||||
def colwise_parallelize_loralinear(
|
||||
module: nn.Module, device_mesh: DeviceMesh, to_local: bool = False
|
||||
) -> None:
|
||||
"""colwise parallelize lora linear."""
|
||||
colwise_parallelize_linear(
|
||||
module.base_layer, device_mesh=device_mesh, to_local=to_local
|
||||
)
|
||||
for mod in module.lora_A.values():
|
||||
colwise_parallelize_linear(
|
||||
mod, device_mesh=device_mesh, to_local=to_local
|
||||
)
|
||||
for mod in module.lora_B.values():
|
||||
colwise_parallelize_linear(
|
||||
mod, device_mesh=device_mesh, to_local=to_local
|
||||
)
|
||||
module._tp_mode = "colwise"
|
||||
|
||||
|
||||
def colwise_parallelize_linear_fn(
|
||||
module: nn.Module, device_mesh: DeviceMesh, to_local: bool = False
|
||||
) -> None:
|
||||
"""
|
||||
This function parallelizes the input :Linear module in
|
||||
:class:`ColwiseParallel` style.
|
||||
|
||||
Args:
|
||||
module (:class:`nn.Module`):
|
||||
The :class:`nn.Linear` module to be parallelized.
|
||||
device_mesh (:class:`DeviceMesh`):
|
||||
Object which describes the mesh topology of devices.
|
||||
|
||||
Returns:
|
||||
None
|
||||
"""
|
||||
if isinstance(module, (torch.nn.Linear, QuantizedLN)):
|
||||
return colwise_parallelize_linear(
|
||||
module, device_mesh=device_mesh, to_local=to_local
|
||||
)
|
||||
elif isinstance(module, LoRALinear):
|
||||
return colwise_parallelize_loralinear(
|
||||
module, device_mesh=device_mesh, to_local=to_local
|
||||
)
|
||||
else:
|
||||
raise TypeError(f"Unsupported module: {type(module)}")
|
||||
|
||||
|
||||
def _partition_module(
|
||||
mod_name: str,
|
||||
prefix: str,
|
||||
module: nn.Module,
|
||||
device_mesh: DeviceMesh,
|
||||
func: Callable,
|
||||
):
|
||||
"""partition module.
|
||||
|
||||
Parameters in module won't be force Replicated.
|
||||
|
||||
Args:
|
||||
mod_name (str): module name.
|
||||
prefix (str): Parameter prefix.
|
||||
module (Module): Module to be partitioned.
|
||||
device_mesh (DeviceMesh): The device mesh.
|
||||
func (Callable): partition callback
|
||||
"""
|
||||
for name, mod in module.named_children():
|
||||
child_name = f"{prefix}{name}"
|
||||
_partition_module(
|
||||
child_name,
|
||||
child_name + ".",
|
||||
module=mod,
|
||||
device_mesh=device_mesh,
|
||||
func=func,
|
||||
)
|
||||
|
||||
func(mod_name, module, device_mesh)
|
||||
|
||||
|
||||
def partition_module(
|
||||
module: nn.Module,
|
||||
device_mesh: DeviceMesh,
|
||||
func: Callable,
|
||||
to_local: bool = False,
|
||||
):
|
||||
"""partition module.
|
||||
|
||||
Parameters in module won't be force Replicated.
|
||||
|
||||
Args:
|
||||
module (Module): Module to be partitioned.
|
||||
device_mesh (DeviceMesh): The device mesh.
|
||||
func (Callable): partition callback.
|
||||
to_local (bool): Convert all DTensor parameters to Tensor parameters.
|
||||
"""
|
||||
_partition_module(
|
||||
"", "", module=module, device_mesh=device_mesh, func=func
|
||||
)
|
||||
|
||||
if to_local:
|
||||
module_to_local(module)
|
||||
|
||||
|
||||
def replicate_module(model: nn.Module, device_mesh: DeviceMesh):
|
||||
"""Replicate all parameters in module.
|
||||
|
||||
Args:
|
||||
model (Module): Module to perform replicate.
|
||||
device_mesh (DeviceMesh): The distribution device mesh.
|
||||
"""
|
||||
for name, param in model.named_parameters(recurse=False):
|
||||
param = distribute_tensor(
|
||||
param, device_mesh=device_mesh, placements=[Replicate()]
|
||||
).to_local()
|
||||
param = nn.Parameter(param)
|
||||
model.register_parameter(name, param)
|
||||
|
||||
for name, buf in model.named_buffers(recurse=False):
|
||||
buf = distribute_tensor(
|
||||
buf, device_mesh=device_mesh, placements=[Replicate()]
|
||||
).to_local()
|
||||
model.register_buffer(name, buf)
|
||||
@ -0,0 +1,79 @@
|
||||
import torch
|
||||
|
||||
|
||||
def continuous_tensor(
|
||||
inputs: torch.Tensor, seq_length: torch.LongTensor
|
||||
):
|
||||
"""Convert batched tensor to continuous tensor.
|
||||
|
||||
Args:
|
||||
inputs (Tensor): batched tensor.
|
||||
seq_length (Tensor): length of each sequence.
|
||||
|
||||
Return:
|
||||
Tensor: continuoused tensor.
|
||||
"""
|
||||
assert inputs.dim() > 1
|
||||
if inputs.size(1) == 1:
|
||||
return inputs.reshape(1, -1)
|
||||
|
||||
inputs = [inp[:slen] for inp, slen in zip(inputs, seq_length)]
|
||||
|
||||
inputs = torch.cat(inputs).unsqueeze(0)
|
||||
return inputs
|
||||
|
||||
|
||||
def batch_tensor(inputs: torch.Tensor, seq_length: torch.LongTensor):
|
||||
"""Convert continuoused tensor to batched tensor.
|
||||
|
||||
Args:
|
||||
inputs (Tensor): continuoused tensor.
|
||||
seq_length (Tensor): length of each sequence.
|
||||
|
||||
Return:
|
||||
Tensor: batched tensor.
|
||||
"""
|
||||
from torch.nn.utils.rnn import pad_sequence
|
||||
|
||||
end_loc = seq_length.cumsum(0)
|
||||
start_loc = end_loc - seq_length
|
||||
|
||||
inputs = [
|
||||
inputs[0, sloc:eloc] for sloc, eloc in zip(start_loc, end_loc)
|
||||
]
|
||||
inputs = pad_sequence(inputs, batch_first=True)
|
||||
return inputs
|
||||
|
||||
|
||||
def page_cache(
|
||||
paged_cache: torch.Tensor,
|
||||
batched_cache: torch.Tensor,
|
||||
cache_length: torch.Tensor,
|
||||
block_offsets: torch.Tensor,
|
||||
permute_head: bool = True,
|
||||
):
|
||||
"""Convert batched cache to paged cache.
|
||||
|
||||
Args:
|
||||
paged_cache (Tensor): Output paged cache.
|
||||
batched_cache (Tensor): Input batched cache.
|
||||
cache_length (Tensor): length of the cache.
|
||||
block_offsets (Tensor): Offset of each blocks.
|
||||
"""
|
||||
assert block_offsets.dim() == 2
|
||||
block_size = paged_cache.size(1)
|
||||
batch_size = batched_cache.size(0)
|
||||
if permute_head:
|
||||
batched_cache = batched_cache.permute(0, 2, 1, 3)
|
||||
|
||||
for b_idx in range(batch_size):
|
||||
cache_len = cache_length[b_idx]
|
||||
b_cache = batched_cache[b_idx]
|
||||
block_off = block_offsets[b_idx]
|
||||
block_off_idx = 0
|
||||
for s_start in range(0, cache_len, block_size):
|
||||
s_end = min(s_start + block_size, cache_len)
|
||||
s_len = s_end - s_start
|
||||
b_off = block_off[block_off_idx]
|
||||
paged_cache[b_off, :s_len] = b_cache[s_start:s_end]
|
||||
block_off_idx += 1
|
||||
Loading…
Reference in new issue