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swarms
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evelynmitchell 1 year ago committed by GitHub
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26
Dockerfile
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@ -1,25 +1,25 @@
# ==================================
# Use an official Python runtime as a parent image
# Use an official Python runtime as a parent image image
FROM python:3.9-slim
RUN apt-get update && apt-get -y install libgl1-mesa-dev libglib2.0-0 build-essential; apt-get clean
RUN pip install opencv-contrib-python-headless
#RUN apt-get update && apt-get -y install libgl1-mesa-dev libglib2.0-0 build-essential; apt-get cleangl1-mesa-dev libglib2.0-0 build-essential; apt-get clean
#RUN pip install opencv-contrib-python-headlessss
# Set environment variables
ENV PYTHONDONTWRITEBYTECODE 1
ENV PYTHONUNBUFFERED 1
# Set the working directory in the container
WORKDIR /usr/src/swarm_cloud
WORKDIR /usr/src/swarms
# Install Python dependencies
# COPY requirements.txt and pyproject.toml if you're using poetry for dependency management
# COPY requirements.txt and pyproject.toml if you're using poetry for dependency managementf you're using poetry for dependency management
COPY requirements.txt .
RUN pip install --upgrade pip
RUN pip install --no-cache-dir -r requirements.txt
# Install the 'swarms' package, assuming it's available on PyPI
# Install the 'swarms' package, assuming it's available on PyPIs available on PyPI
RUN pip install swarms
# Copy the rest of the application
@ -27,18 +27,18 @@ COPY . .
# Add entrypoint script if needed
# COPY ./entrypoint.sh .
# RUN chmod +x /usr/src/swarm_cloud/entrypoint.sh
# RUN chmod +x /usr/src/swarm_cloud/entrypoint.shnt.sh
# Expose port if your application has a web interface
# Expose port if your application has a web interfaceinterface
# EXPOSE 5000
# # Define environment variable for the swarm to work
# # Define environment variable for the swarm to workm to work
# ENV SWARM_API_KEY=your_swarm_api_key_here
# # Add Docker CMD or ENTRYPOINT script to run the application
# # Add Docker CMD or ENTRYPOINT script to run the applicationun the application
# CMD python your_swarm_startup_script.py
# Or use the entrypoint script if you have one
# ENTRYPOINT ["/usr/src/swarm_cloud/entrypoint.sh"]
# Or use the entrypoint script if you have onene
# ENTRYPOINT ["/usr/src/swarm_cloud/entrypoint.sh"]nt.sh"]
# If you're using `CMD` to execute a Python script, make sure it's executable
# If you're using `CMD` to execute a Python script, make sure it's executablescript, make sure it's executable
# RUN chmod +x your_swarm_startup_script.py

57
README.md
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@ -42,14 +42,9 @@ Features:
```python
import os
from dotenv import load_dotenv
# Import the OpenAIChat model and the Agent struct
from swarms import Agent, OpenAIChat
# Load the environment variables
load_dotenv()
# Get the API key from the environment
api_key = os.environ.get("OPENAI_API_KEY")
@ -138,13 +133,8 @@ The `Worker` is a simple all-in-one agent equipped with an LLM, tools, and RAG f
# Importing necessary modules
import os
from dotenv import load_dotenv
from swarms import OpenAIChat, Worker, tool
# Loading environment variables from .env file
load_dotenv()
# Retrieving the OpenAI API key from environment variables
api_key = os.getenv("OPENAI_API_KEY")
@ -279,12 +269,8 @@ Sequential Workflow enables you to sequentially execute tasks with `Agent` and t
```python
import os
from dotenv import load_dotenv
from swarms import Agent, OpenAIChat, SequentialWorkflow
load_dotenv()
# Load the environment variables
api_key = os.getenv("OPENAI_API_KEY")
@ -336,13 +322,8 @@ for task in workflow.tasks:
```python
import os
from dotenv import load_dotenv
from swarms import Agent, ConcurrentWorkflow, OpenAIChat, Task
# Load environment variables from .env file
load_dotenv()
# Load environment variables
llm = OpenAIChat(openai_api_key=os.getenv("OPENAI_API_KEY"))
agent = Agent(llm=llm, max_loops=1)
@ -368,13 +349,8 @@ workflow.run()
```python
import os
from dotenv import load_dotenv
from swarms import Agent, OpenAIChat, RecursiveWorkflow, Task
# Load environment variables from .env file
load_dotenv()
# Load environment variables
llm = OpenAIChat(openai_api_key=os.getenv("OPENAI_API_KEY"))
agent = Agent(llm=llm, max_loops=1)
@ -407,12 +383,8 @@ Plug-and-Play Integration: The structure provides a seamless integration with va
```python
import os
from dotenv import load_dotenv
from swarms import Anthropic, Gemini, Mixtral, ModelParallelizer, OpenAIChat
load_dotenv()
# API Keys
anthropic_api_key = os.getenv("ANTHROPIC_API_KEY")
openai_api_key = os.getenv("OPENAI_API_KEY")
@ -449,17 +421,12 @@ A Plug in and play conversational agent with `GPT4`, `Mixytral`, or any of our m
```python
import os
from dotenv import load_dotenv
from swarms import Conversation, OpenAIChat
conv = Conversation(
time_enabled=True,
)
# Load the environment variables
load_dotenv()
# Get the API key from the environment
api_key = os.environ.get("OPENAI_API_KEY")
@ -502,14 +469,9 @@ interactive_conversation(llm)
```python
import os
from dotenv import load_dotenv
# Import the OpenAIChat model and the Agent struct
from swarms import Agent, OpenAIChat, SwarmNetwork
# Load the environment variables
load_dotenv()
# Get the API key from the environment
api_key = os.environ.get("OPENAI_API_KEY")
@ -559,14 +521,8 @@ print(out)
```python
import os
from dotenv import load_dotenv
from swarms.structs import Agent, OpenAIChat, Task
# Load the environment variables
load_dotenv()
# Define a function to be used as the action
def my_action():
print("Action executed")
@ -626,14 +582,10 @@ print(f"Task result: {task.result}")
```python
import os
from dotenv import load_dotenv
from transformers import AutoModelForCausalLM, AutoTokenizer
from pydantic import BaseModel
from swarms import BlocksList, Gemini, GPT4VisionAPI, Mixtral, OpenAI, ToolAgent
# Load the environment variables
load_dotenv()
# Get the environment variables
openai_api_key = os.getenv("OPENAI_API_KEY")
gemini_api_key = os.getenv("GEMINI_API_KEY")
@ -774,8 +726,6 @@ Here's a production grade swarm ready for real-world deployment in a factory and
```python
import os
from dotenv import load_dotenv
from swarms.models import GPT4VisionAPI
from swarms.prompts.logistics import (
Efficiency_Agent_Prompt,
@ -788,8 +738,6 @@ from swarms.prompts.logistics import (
)
from swarms.structs import Agent
# Load ENV
load_dotenv()
api_key = os.getenv("OPENAI_API_KEY")
# GPT4VisionAPI
@ -885,14 +833,9 @@ Run the agent with multiple modalities useful for various real-world tasks in ma
# Description: This is an example of how to use the Agent class to run a multi-modal workflow
import os
from dotenv import load_dotenv
from swarms.models.gpt4_vision_api import GPT4VisionAPI
from swarms.structs import Agent
# Load the environment variables
load_dotenv()
# Get the API key from the environment
api_key = os.environ.get("OPENAI_API_KEY")

2
docs/applications/discord.md
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@ -12,7 +12,7 @@ This code provides a structure for a Discord bot with advanced features such as
1. Ensure that the necessary libraries are installed:
```bash
pip install discord.py python-dotenv dalle3 invoke openai
pip install discord.py dalle3 invoke openai
```
2. Create a `.env` file in the same directory as your bot script and add the following:

11
docs/examples/revgpt.md
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@ -35,23 +35,16 @@ The abstraction provided in `revgpt.py` is designed to simplify your interaction
1. **Import the Necessary Modules:**
```python
from dotenv import load_dotenv
from revgpt import AbstractChatGPT
```
2. **Load Environment Variables:**
```python
load_dotenv()
```
3. **Initialize the ChatGPT Abstract Class:**
2. **Initialize the ChatGPT Abstract Class:**
```python
chat = AbstractChatGPT(api_key=os.getenv("ACCESS_TOKEN"), **config)
```
4. **Start Interacting with ChatGPT:**
3. **Start Interacting with ChatGPT:**
```python
response = chat.ask("Hello, ChatGPT!")

5
docs/swarms/agents/omni_agent.md
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@ -41,14 +41,9 @@ Initialize the `OmniModalAgent` and communicate with it:
```python
import os
from dotenv import load_dotenv
from swarms.agents.omni_modal_agent import OmniModalAgent, OpenAIChat
from swarms.models import OpenAIChat
# Load the environment variables
load_dotenv()
# Get the API key from the environment
api_key = os.environ.get("OPENAI_API_KEY")

16
docs/swarms/memory/azure_openai.md
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@ -10,7 +10,7 @@ Before we begin, it's essential to have the following prerequisites in place:
1. **Python**: You'll need to have Python installed on your system. This guide assumes you're using Python 3.6 or later.
2. **Azure Subscription**: You'll need an active Azure subscription to access Azure OpenAI services.
3. **Azure OpenAI Resource**: Create an Azure OpenAI resource in your Azure subscription.
4. **Python Packages**: Install the required Python packages, including `python-dotenv` and `swarms`.
4. **Python Packages**: Install the required Python packages, including `swarms`.
## Setting up the Environment:
To kick things off, we'll set up our development environment and install the necessary dependencies.
@ -27,10 +27,10 @@ python -m venv myenv
source myenv/bin/activate # On Windows, use `myenv\Scripts\activate`
```
3. **Install Required Packages**: Install the `python-dotenv` and `swarms` packages using pip.
3. **Install Required Packages**: Install the `swarms` packages using pip.
```
pip install python-dotenv swarms
pip install swarms
```
4. **Create a `.env` File**: In the root directory of your project, create a new file called `.env`. This file will store your Azure OpenAI credentials and configuration settings.
@ -50,12 +50,8 @@ Now that we've set up our environment, let's dive into the code that connects to
```python
import os
from dotenv import load_dotenv
from swarms import AzureOpenAI
# Load the environment variables
load_dotenv()
# Create an instance of the AzureOpenAI class
model = AzureOpenAI(
azure_endpoint=os.getenv("AZURE_OPENAI_ENDPOINT"),
@ -68,11 +64,9 @@ model = AzureOpenAI(
## Let's break down this code:
1. **Import Statements**: We import the necessary modules, including `os` for interacting with the operating system, `load_dotenv` from `python-dotenv` to load environment variables, and `AzureOpenAI` from `swarms` to interact with the Azure OpenAI service.
2. **Load Environment Variables**: We use `load_dotenv()` to load the environment variables stored in the `.env` file we created earlier.
1. **Import Statements**: We import the necessary modules, including `os` for interacting with the operating system, `AzureOpenAI` from `swarms` to interact with the Azure OpenAI service.
3. **Create AzureOpenAI Instance**: We create an instance of the `AzureOpenAI` class by passing in the required configuration parameters:
2. **Create AzureOpenAI Instance**: We create an instance of the `AzureOpenAI` class by passing in the required configuration parameters:
- `azure_endpoint`: The endpoint URL for your Azure OpenAI resource.
- `deployment_name`: The name of the deployment you want to use.
- `openai_api_version`: The version of the OpenAI API you want to use.

1
docs/swarms/models/gemini.md
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@ -15,7 +15,6 @@ Before using Gemini, ensure that you have the required dependencies installed. Y
```bash
pip install swarms
pip install google-generativeai
pip install python-dotenv
```
### Class: Gemini

5
playground/agents/multi_modal_auto_agent_example.py
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@ -1,13 +1,8 @@
# Description: This is an example of how to use the Agent class to run a multi-modal workflow
import os
from dotenv import load_dotenv
from swarms import Agent, GPT4VisionAPI
# Load the environment variables
load_dotenv()
# Get the API key from the environment
api_key = os.environ.get("OPENAI_API_KEY")

4
playground/agents/perimeter_defense_agent.py
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@ -1,13 +1,9 @@
import os
from dotenv import load_dotenv
import swarms.prompts.security_team as stsp
from swarms.models import GPT4VisionAPI
from swarms.structs import Agent
# Load environment variables and initialize the Vision API
load_dotenv()
api_key = os.getenv("OPENAI_API_KEY")
llm = GPT4VisionAPI(openai_api_key=api_key)

5
playground/agents/simple_agent_example.py
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@ -1,7 +1,5 @@
import os
from dotenv import load_dotenv
from swarms import (
Conversation,
OpenAIChat,
@ -15,9 +13,6 @@ conv = Conversation(
time_enabled=True,
)
# Load the environment variables
load_dotenv()
# Get the API key from the environment
api_key = os.environ.get("OPENAI_API_KEY")

5
playground/agents/worker_example.py
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@ -1,14 +1,9 @@
# Importing necessary modules
import os
from dotenv import load_dotenv
from swarms import OpenAIChat
from swarms.agents.worker_agent import Worker
# Loading environment variables from .env file
load_dotenv()
# Retrieving the OpenAI API key from environment variables
api_key = os.getenv("OPENAI_API_KEY")

3
playground/demos/accountant_team/account_team2_example.py
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@ -1,7 +1,5 @@
import os
from dotenv import load_dotenv
from swarms.models import Anthropic, OpenAIChat
from swarms.prompts.accountant_swarm_prompts import (
DECISION_MAKING_PROMPT,
@ -12,7 +10,6 @@ from swarms.structs import Agent
from swarms.utils.pdf_to_text import pdf_to_text
# Environment variables
load_dotenv()
anthropic_api_key = os.getenv("ANTHROPIC_API_KEY")
openai_api_key = os.getenv("OPENAI_API_KEY")

3
playground/demos/ad_gen/ad_gen_example.py
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@ -1,13 +1,10 @@
import os
import random
from dotenv import load_dotenv
from swarms.models import OpenAIChat
from swarms.models.stable_diffusion import StableDiffusion
from swarms.structs import Agent
load_dotenv()
openai_api_key = os.getenv("OPENAI_API_KEY")
stability_api_key = os.getenv("STABILITY_API_KEY")

4
playground/demos/ai_acceleerated_learning/main.py
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@ -2,15 +2,11 @@ import concurrent
import csv
from swarms import Agent, OpenAIChat
from swarms.memory import ChromaDB
from dotenv import load_dotenv
from swarms.utils.parse_code import extract_code_from_markdown
from swarms.utils.file_processing import create_file
from swarms.utils.loguru_logger import logger
# Load ENV
load_dotenv()
# Gemini
gemini = OpenAIChat()

3
playground/demos/ai_research_team/main_example.py
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@ -1,7 +1,5 @@
import os
from dotenv import load_dotenv
from swarms.models import Anthropic, OpenAIChat
from swarms.prompts.ai_research_team import (
PAPER_IMPLEMENTOR_AGENT_PROMPT,
@ -12,7 +10,6 @@ from swarms.utils.pdf_to_text import pdf_to_text
# Base llms
# Environment variables
load_dotenv()
anthropic_api_key = os.getenv("ANTHROPIC_API_KEY")
openai_api_key = os.getenv("OPENAI_API_KEY")

2
playground/demos/autoswarm/autoswarm.py
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@ -1,11 +1,9 @@
import os
from dotenv import load_dotenv
from swarms.models import OpenAIChat
from swarms.structs import Agent
import swarms.prompts.autoswarm as sdsp
# Load environment variables and initialize the OpenAI Chat model
load_dotenv()
api_key = os.getenv("OPENAI_API_KEY")
llm = OpenAIChat(model_name="gpt-4", openai_api_key=api_key)

3
playground/demos/developer_swarm/main_example.py
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@ -17,13 +17,10 @@ Documentation agent -> Tests agent
import os
from dotenv import load_dotenv
from swarms.models import OpenAIChat
from swarms.prompts.programming import DOCUMENTATION_SOP, TEST_SOP
from swarms.structs import Agent
load_dotenv()
api_key = os.getenv("OPENAI_API_KEY")

3
playground/demos/education/education_example.py
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@ -1,13 +1,10 @@
import os
from dotenv import load_dotenv
import swarms.prompts.education as edu_prompts
from swarms import Agent, SequentialWorkflow
from swarms.models import OpenAIChat
# Load environment variables
load_dotenv()
api_key = os.getenv("OPENAI_API_KEY")
stability_api_key = os.getenv("STABILITY_API_KEY")

4
playground/demos/gemini_benchmarking/gemini_chat_example.py
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@ -1,12 +1,8 @@
import os
from dotenv import load_dotenv
from swarms.models.gemini import Gemini
from swarms.prompts.react import react_prompt
load_dotenv()
api_key = os.environ["GEMINI_API_KEY"]
# Establish the prompt and image

4
playground/demos/gemini_benchmarking/gemini_react_example.py
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@ -1,12 +1,8 @@
import os
from dotenv import load_dotenv
from swarms.models.gemini import Gemini
from swarms.prompts.react import react_prompt
load_dotenv()
api_key = os.environ["GEMINI_API_KEY"]
# Establish the prompt and image

5
playground/demos/gemini_benchmarking/gemini_vcot_example.py
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@ -1,13 +1,8 @@
import os
from dotenv import load_dotenv
from swarms.models import Gemini
from swarms.prompts.visual_cot import VISUAL_CHAIN_OF_THOUGHT
# Load the environment variables
load_dotenv()
# Get the API key from the environment
api_key = os.environ.get("GEMINI_API_KEY")

4
playground/demos/grupa/app_example.py
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@ -1,6 +1,5 @@
import os
from dotenv import load_dotenv
from termcolor import colored
from swarms.models import OpenAIChat
@ -8,9 +7,6 @@ from swarms.prompts.code_interpreter import CODE_INTERPRETER
from swarms.prompts.programming import DOCUMENTATION_SOP, TEST_SOP
from swarms.structs import Agent
load_dotenv()
FEATURE = (
"Implement an all-new signup system in typescript using supabase"
)

4
playground/demos/langchain_example/langchain_example.py
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@ -1,13 +1,9 @@
import os
from dotenv import load_dotenv
from langchain.llms import OpenAIChat
from swarms import Agent
# Loading environment variables from .env file
load_dotenv()
# Initialize the model
llm = OpenAIChat(
openai_api_key=os.getenv("OPENAI_API_KEY"),

5
playground/demos/llm_with_conversation/main_example.py
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@ -1,13 +1,8 @@
import os
from dotenv import load_dotenv
# Import the OpenAIChat model and the Agent struct
from swarms.models import OpenAIChat
# Load the environment variables
load_dotenv()
# Get the API key from the environment
api_key = os.environ.get("OPENAI_API_KEY")

4
playground/demos/logistics/logistics_example.py
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@ -1,7 +1,5 @@
import os
from dotenv import load_dotenv
from swarms.models import GPT4VisionAPI
from swarms.prompts.logistics import (
Efficiency_Agent_Prompt,
@ -15,8 +13,6 @@ from swarms.prompts.logistics import (
from swarms.structs import Agent
# from swarms.utils.banana_wrapper import banana
load_dotenv()
api_key = os.getenv("OPENAI_API_KEY")
# GPT4VisionAPI or llama

4
playground/demos/multi_modal_chain_of_thought/vcot_example.py
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@ -1,14 +1,10 @@
import os
from dotenv import load_dotenv
from swarms.models.gpt4_vision_api import GPT4VisionAPI
from swarms.prompts.visual_cot import VISUAL_CHAIN_OF_THOUGHT
from swarms.structs import Agent
# Load the environment variables
load_dotenv()
# Get the API key from the environment
api_key = os.environ.get("OPENAI_API_KEY")

3
playground/demos/multimodal_tot/idea2img_example.py
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@ -2,7 +2,6 @@ import datetime
import os
import streamlit as st
from dotenv import load_dotenv
from swarms.models import OpenAIChat
from swarms.models.gpt4_vision_api import GPT4VisionAPI
@ -10,7 +9,6 @@ from swarms.models.stable_diffusion import StableDiffusion
from swarms.structs import Agent
# Load environment variables
load_dotenv()
openai_api_key = os.getenv("OPENAI_API_KEY")
stability_api_key = os.getenv("STABLE_API_KEY")
@ -137,7 +135,6 @@ class Idea2Image(Agent):
# Load environment variables and initialize the models
load_dotenv()
openai_api_key = os.getenv("OPENAI_API_KEY")
stability_api_key = os.getenv("STABLE_API_KEY")
vision_api = GPT4VisionAPI(api_key=openai_api_key)

4
playground/demos/multimodal_tot/main_example.py
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@ -17,15 +17,11 @@ task: Generate an image of a swarm of bees -> Image generator -> GPT4V evaluates
import os
from dotenv import load_dotenv
from termcolor import colored
from swarms.models.gpt4_vision_api import GPT4VisionAPI
from swarms.models.stable_diffusion import StableDiffusion
# Load the environment variables
load_dotenv()
# Get the API key from the environment
api_key = os.environ.get("OPENAI_API_KEY")
stable_api_key = os.environ.get("STABLE_API_KEY")

3
playground/demos/nutrition/nutrition_example.py
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@ -2,13 +2,10 @@ import base64
import os
import requests
from dotenv import load_dotenv
from swarms.models import OpenAIChat
from swarms.structs import Agent
# Load environment variables
load_dotenv()
openai_api_key = os.getenv("OPENAI_API_KEY")
# Define prompts for various tasks

4
playground/demos/optimize_llm_stack/vortex_example.py
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@ -1,14 +1,10 @@
import os
from dotenv import load_dotenv
from swarms.models import OpenAIChat
from swarms.structs import Agent
# import modal
load_dotenv()
# Model
llm = OpenAIChat(
openai_api_key=os.getenv("OPENAI_API_KEY"),

4
playground/demos/personal_assistant/better_communication_example.py
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@ -3,14 +3,10 @@ import time
import pygame
import speech_recognition as sr
from dotenv import load_dotenv
from playsound import playsound
from swarms import OpenAIChat, OpenAITTS
# Load the environment variables
load_dotenv()
# Get the API key from the environment
openai_api_key = os.environ.get("OPENAI_API_KEY")

4
playground/demos/personal_stylist/personal_stylist_example.py
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@ -1,7 +1,5 @@
import os
from dotenv import load_dotenv
from swarms.models import GPT4VisionAPI
from swarms.prompts.personal_stylist import (
ACCESSORIES_STYLIST_AGENT_PROMPT,
@ -12,8 +10,6 @@ from swarms.prompts.personal_stylist import (
)
from swarms.structs import Agent
# Load environment variables
load_dotenv()
api_key = os.getenv("OPENAI_API_KEY")
# Initialize GPT4VisionAPI

3
playground/demos/security_team/security_team_example.py
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@ -1,14 +1,11 @@
import os
from dotenv import load_dotenv
from termcolor import colored
import swarms.prompts.security_team as stsp
from swarms.models import GPT4VisionAPI
from swarms.structs import Agent
# Load environment variables and initialize the Vision API
load_dotenv()
api_key = os.getenv("OPENAI_API_KEY")
llm = GPT4VisionAPI(openai_api_key=api_key)

5
playground/demos/swarm_hackathon/main.py
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@ -3,15 +3,10 @@ import csv
import os
from swarms import Gemini, Agent
from swarms.memory import ChromaDB
from dotenv import load_dotenv
from swarms.utils.parse_code import extract_code_from_markdown
from swarms.utils.file_processing import create_file
from swarms.utils.loguru_logger import logger
# Load ENV
load_dotenv()
gemini = Gemini(
model_name="gemini-pro",
gemini_api_key=os.getenv("GEMINI_API_KEY"),

2
playground/demos/swarm_of_mma_manufacturing/main_example.py
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@ -16,13 +16,11 @@ health security agent -> quality control agent -> productivity agent -> safety a
import os
from dotenv import load_dotenv
from termcolor import colored
from swarms.models import GPT4VisionAPI
from swarms.structs import Agent
load_dotenv()
api_key = os.getenv("OPENAI_API_KEY")
# GPT4VisionAPI

4
playground/demos/urban_planning/urban_planning_example.py
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@ -1,13 +1,9 @@
import os
from dotenv import load_dotenv
import swarms.prompts.urban_planning as upp
from swarms.models import GPT4VisionAPI, OpenAIChat
from swarms.structs import Agent, SequentialWorkflow
# Load environment variables
load_dotenv()
api_key = os.getenv("OPENAI_API_KEY")
stability_api_key = os.getenv("STABILITY_API_KEY")

4
playground/demos/visuo/text_to_sql_agent_example.py
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@ -1,12 +1,8 @@
import os
from dotenv import load_dotenv
# Import the OpenAIChat model and the Agent struct
from swarms import Agent, HuggingfaceLLM
# Load the environment variables
load_dotenv()
# Get the API key from the environment
api_key = os.environ.get("OPENAI_API_KEY")

4
playground/demos/xray/xray_example.py
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@ -1,7 +1,5 @@
import os
from dotenv import load_dotenv
from swarms.models import GPT4VisionAPI, OpenAIChat
from swarms.prompts.xray_swarm_prompt import (
TREATMENT_PLAN_PROMPT,
@ -9,8 +7,6 @@ from swarms.prompts.xray_swarm_prompt import (
)
from swarms.structs.agent import Agent
# Load environment variables
load_dotenv()
openai_api_key = os.getenv("OPENAI_API_KEY")
# Function to analyze an X-ray image

5
playground/examples/example_agent.py
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@ -1,14 +1,9 @@
import os
import sys
from dotenv import load_dotenv
# Import the OpenAIChat model and the Agent struct
from swarms import OpenAIChat, Agent
# Load the environment variables
load_dotenv()
# Get the API key from the environment
api_key = os.environ.get("OPENAI_API_KEY")

4
playground/examples/example_concurrentworkflow.py
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@ -1,10 +1,6 @@
import os
from dotenv import load_dotenv
from swarms import OpenAIChat, Task, ConcurrentWorkflow, Agent
# Load environment variables from .env file
load_dotenv()
# Load environment variables
llm = OpenAIChat(openai_api_key=os.getenv("OPENAI_API_KEY"))
agent = Agent(llm=llm, max_loops=1)

3
playground/examples/example_logistics.py
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@ -1,6 +1,5 @@
from swarms.structs import Agent
import os
from dotenv import load_dotenv
from swarms.models import GPT4VisionAPI
from swarms.prompts.logistics import (
Health_Security_Agent_Prompt,
@ -12,8 +11,6 @@ from swarms.prompts.logistics import (
Efficiency_Agent_Prompt,
)
# Load ENV
load_dotenv()
api_key = os.getenv("OPENAI_API_KEY")
# GPT4VisionAPI

3
playground/examples/example_recursiveworkflow.py
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@ -1,9 +1,6 @@
import os
from dotenv import load_dotenv
from swarms import OpenAIChat, Task, RecursiveWorkflow, Agent
# Load environment variables from .env file
load_dotenv()
# Load environment variables
llm = OpenAIChat(openai_api_key=os.getenv("OPENAI_API_KEY"))

3
playground/examples/example_sequentialworkflow.py
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@ -1,8 +1,5 @@
import os
from swarms import OpenAIChat, Agent, SequentialWorkflow
from dotenv import load_dotenv
load_dotenv()
# Load the environment variables
api_key = os.getenv("OPENAI_API_KEY")

5
playground/examples/example_simple_conversation_agent.py
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@ -1,7 +1,5 @@
import os
from dotenv import load_dotenv
from swarms import (
OpenAIChat,
Conversation,
@ -11,9 +9,6 @@ conv = Conversation(
time_enabled=True,
)
# Load the environment variables
load_dotenv()
# Get the API key from the environment
api_key = os.environ.get("OPENAI_API_KEY")

5
playground/examples/example_swarmnetwork.py
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@ -1,13 +1,8 @@
import os
from dotenv import load_dotenv
# Import the OpenAIChat model and the Agent struct
from swarms import OpenAIChat, Agent, SwarmNetwork
# Load the environment variables
load_dotenv()
# Get the API key from the environment
api_key = os.environ.get("OPENAI_API_KEY")

5
playground/examples/example_task.py
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@ -1,12 +1,7 @@
import os
from dotenv import load_dotenv
from swarms.structs import Agent, OpenAIChat, Task
# Load the environment variables
load_dotenv()
# Define a function to be used as the action
def my_action():

4
playground/examples/example_worker.py
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@ -1,11 +1,7 @@
# Importing necessary modules
import os
from dotenv import load_dotenv
from swarms import Worker, OpenAIChat, tool
# Loading environment variables from .env file
load_dotenv()
# Retrieving the OpenAI API key from environment variables
api_key = os.getenv("OPENAI_API_KEY")

4
playground/models/azure_openai_example.py
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@ -1,10 +1,6 @@
import os
from dotenv import load_dotenv
from swarms import AzureOpenAI
# Load the environment variables
load_dotenv()
# Create an instance of the AzureOpenAI class
model = AzureOpenAI(
azure_endpoint=os.getenv("AZURE_OPENAI_ENDPOINT"),

4
playground/models/gemini_example.py
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@ -1,11 +1,7 @@
import os
from dotenv import load_dotenv
from swarms.models.gemini import Gemini
load_dotenv()
api_key = os.environ["GEMINI_API_KEY"]
# Initialize the model

4
playground/models/tts_speech_example.py
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@ -1,11 +1,7 @@
import os
from dotenv import load_dotenv
from swarms import OpenAITTS
load_dotenv()
tts = OpenAITTS(
model_name="tts-1-1106",
voice="onyx",

5
playground/structs/agent_with_longterm_memory.py
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@ -1,13 +1,8 @@
import os
from dotenv import load_dotenv
# Import the OpenAIChat model and the Agent struct
from swarms import Agent, ChromaDB, OpenAIChat
# Load the environment variables
load_dotenv()
# Get the API key from the environment
api_key = os.environ.get("OPENAI_API_KEY")

6
playground/structs/agent_with_tools_example.py
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@ -10,16 +10,10 @@ tool decorated func [search_api] -> agent which parses the docs of the tool func
import os
from dotenv import load_dotenv
# Import the OpenAIChat model and the Agent struct
from swarms import Agent, OpenAIChat
from swarms.tools.tool import tool
# Load the environment variables
load_dotenv()
# Define a tool
@tool
def search_api(query: str, description: str):

5
playground/structs/autoscaler_example.py
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@ -1,15 +1,10 @@
import os
from dotenv import load_dotenv
# Import the OpenAIChat model and the Agent struct
from swarms.models import OpenAIChat
from swarms.structs import Agent
from swarms.structs.autoscaler import AutoScaler
# Load the environment variables
load_dotenv()
# Get the API key from the environment
api_key = os.environ.get("OPENAI_API_KEY")

4
playground/structs/company_example.py
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@ -1,12 +1,8 @@
import os
from dotenv import load_dotenv
from swarms import Agent, OpenAIChat
from swarms.structs.company import Company
load_dotenv()
llm = OpenAIChat(
openai_api_key=os.getenv("OPENAI_API_KEY"), max_tokens=4000
)

5
playground/structs/concurrent_workflow_example.py
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@ -1,12 +1,7 @@
import os
from dotenv import load_dotenv
from swarms import Agent, ConcurrentWorkflow, OpenAIChat, Task
# Load environment variables from .env file
load_dotenv()
# Load environment variables
llm = OpenAIChat(openai_api_key=os.getenv("OPENAI_API_KEY"))
agent = Agent(llm=llm, max_loops=1)

4
playground/structs/godmode_example.py
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@ -1,12 +1,8 @@
import os
from dotenv import load_dotenv
from swarms import ModelParallelizer
from swarms.models import Anthropic, Gemini, Mixtral, OpenAIChat
load_dotenv()
# API Keys
anthropic_api_key = os.getenv("ANTHROPIC_API_KEY")
openai_api_key = os.getenv("OPENAI_API_KEY")

4
playground/structs/hierarchical_swarm.py
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@ -1,11 +1,7 @@
import os
from swarms import OpenAIChat, Agent
from dotenv import load_dotenv
# Load environment variables
load_dotenv()
# Create a chat instance
llm = OpenAIChat(
api_key=os.getenv("OPENAI_API_KEY"),

6
playground/structs/kyle_hackathon.py
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@ -1,17 +1,11 @@
import os
from dotenv import load_dotenv
from swarms import Agent, OpenAIChat
from swarms.agents.multion_agent import MultiOnAgent
from swarms.memory.chroma_db import ChromaDB
from swarms.tools.tool import tool
from swarms.utils.code_interpreter import SubprocessCodeInterpreter
# Load the environment variables
load_dotenv()
# Memory
chroma_db = ChromaDB()

5
playground/structs/multi_modal_rag_agent.py
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@ -1,16 +1,11 @@
# Importing necessary modules
import os
from dotenv import load_dotenv
from swarms import Agent, OpenAIChat
from swarms.memory.chroma_db import ChromaDB
from swarms.prompts.visual_cot import VISUAL_CHAIN_OF_THOUGHT
from swarms.tools.tool import tool
# Loading environment variables from .env file
load_dotenv()
# Getting the Gemini API key from environment variables
gemini_api_key = os.getenv("GEMINI_API_KEY")
openai_api_key = os.getenv("OPENAI_API_KEY")

4
playground/structs/multi_process_workflow.py
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@ -1,10 +1,6 @@
import os
from swarms import Gemini, Agent
from swarms.structs.multi_process_workflow import MultiProcessWorkflow
from dotenv import load_dotenv
# Load the environment variables
load_dotenv()
# Gemini API key
api_key = os.getenv("GEMINI_API_KEY")

4
playground/structs/recursive_example.py
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@ -1,12 +1,8 @@
import os
from dotenv import load_dotenv
from swarms import Agent, OpenAIChat, RecursiveWorkflow, Task
# Load environment variables from .env file
load_dotenv()
# Load environment variables
llm = OpenAIChat(openai_api_key=os.getenv("OPENAI_API_KEY"))
agent = Agent(llm=llm, max_loops=1)

5
playground/structs/task_example.py
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@ -1,12 +1,7 @@
import os
from dotenv import load_dotenv
from swarms.structs import Agent, OpenAIChat, Task
# Load the environment variables
load_dotenv()
# Define a function to be used as the action
def my_action():

6
playground/tools/agent_with_tools_example.py
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@ -1,18 +1,12 @@
import os
from dotenv import load_dotenv
from swarms import Agent, OpenAIChat
from swarms.tools.tool import tool
load_dotenv()
api_key = os.environ.get("OPENAI_API_KEY")
llm = OpenAIChat(api_key=api_key)
@tool
def search_api(query: str) -> str:
"""Search API

8
scripts/auto_tests_docs/auto_docs.py
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@ -3,8 +3,6 @@ import inspect
import os
import threading
from dotenv import load_dotenv
from scripts.auto_tests_docs.docs import DOCUMENTATION_WRITER_SOP
from swarms import OpenAIChat
from swarms.structs.majority_voting import MajorityVoting
@ -12,12 +10,6 @@ from swarms.structs.stackoverflow_swarm import StackOverflowSwarm
from swarms.structs.task_queue_base import TaskQueueBase
from swarms.structs.tool_json_schema import JSON
##########
####################
load_dotenv()
api_key = os.getenv("OPENAI_API_KEY")
model = OpenAIChat(

4
scripts/auto_tests_docs/auto_docs_functions.py
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@ -3,13 +3,9 @@ import os
import sys
import threading
from dotenv import load_dotenv
from scripts.auto_tests_docs.docs import DOCUMENTATION_WRITER_SOP
from swarms import OpenAIChat
load_dotenv()
api_key = os.getenv("OPENAI_API_KEY")
model = OpenAIChat(

9
scripts/auto_tests_docs/auto_docs_omni.py
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@ -2,18 +2,9 @@ import inspect
import os
import threading
from dotenv import load_dotenv
from scripts.auto_tests_docs.docs import DOCUMENTATION_WRITER_SOP
from swarms import OpenAIChat
###########
###############
load_dotenv()
api_key = os.getenv("OPENAI_API_KEY")
model = OpenAIChat(

6
scripts/auto_tests_docs/auto_tests.py
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@ -3,19 +3,13 @@ import os
import re
import threading
########
from dotenv import load_dotenv
from scripts.auto_tests_docs.docs import TEST_WRITER_SOP_PROMPT
from swarms import OpenAIChat
#########
from swarms.memory.dict_internal_memory import DictInternalMemory
from swarms.memory.dict_shared_memory import DictSharedMemory
from swarms.memory.lanchain_chroma import LangchainChromaVectorMemory
load_dotenv()
api_key = os.getenv("OPENAI_API_KEY")
model = OpenAIChat(

4
scripts/auto_tests_docs/auto_tests_functions.py
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@ -3,14 +3,10 @@ import os
import sys
import threading
from dotenv import load_dotenv
from scripts.auto_tests_docs.docs import TEST_WRITER_SOP_PROMPT
from swarms import OpenAIChat
from swarms.utils.parse_code import extract_code_from_markdown
load_dotenv()
api_key = os.getenv("OPENAI_API_KEY")
model = OpenAIChat(

2
scripts/auto_tests_docs/docs.py
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@ -148,7 +148,7 @@ def TEST_WRITER_SOP_PROMPT(
8. **Environment Variables and Secret Handling**:
- Store secrets and configurations in environment variables.
- Use libraries like `python-decouple` or `python-dotenv` to load environment variables.
- Use libraries like `python-decouple` to load environment variables.
- For tests, mock or set environment variables temporarily within the test environment.
9. **Grouping and Marking Tests**:

4
swarm_network_example.py
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@ -8,10 +8,6 @@ from swarms import (
TogetherLLM,
)
from swarms.memory import ChromaDB
from dotenv import load_dotenv
# load the environment variables
load_dotenv()
# Initialize the ChromaDB
memory = ChromaDB()

5
swarms/memory/chroma_db.py
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@ -5,16 +5,11 @@ from typing import Callable, List, Optional
import chromadb
import numpy as np
from dotenv import load_dotenv
from swarms.utils.data_to_text import data_to_text
from swarms.utils.markdown_message import display_markdown_message
from swarms.memory.base_vectordb import AbstractVectorDatabase
# Load environment variables
load_dotenv()
# Results storage using local ChromaDB
class ChromaDB(AbstractVectorDatabase):
"""

3
swarms/models/dalle3.py
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@ -10,14 +10,11 @@ import backoff
import openai
import requests
from cachetools import TTLCache
from dotenv import load_dotenv
from openai import OpenAI
from PIL import Image
from pydantic import field_validator
from termcolor import colored
load_dotenv()
# Configure Logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

4
swarms/models/gemini.py
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@ -2,7 +2,6 @@ import os
import subprocess as sp
from pathlib import Path
from dotenv import load_dotenv
from PIL import Image
from swarms.models.base_multimodal_model import BaseMultiModalModel
@ -17,9 +16,6 @@ except ImportError as error:
sp.run(["pip", "install", "--upgrade", "google-generativeai"])
load_dotenv()
# Helpers
def get_gemini_api_key_env():
"""Get the Gemini API key from the environment

2
swarms/models/gpt4_vision_api.py
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@ -6,7 +6,6 @@ from typing import Optional
import aiohttp
import requests
from dotenv import load_dotenv
from termcolor import colored
from swarms.models.base_multimodal_model import BaseMultiModalModel
@ -21,7 +20,6 @@ except ImportError:
raise ImportError
# Load environment variables
load_dotenv()
openai_api_key = os.getenv("OPENAI_API_KEY")

6
swarms/models/openai_tts.py
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@ -3,7 +3,6 @@ import subprocess
import sys
import requests
from dotenv import load_dotenv
from swarms.models.base_llm import AbstractLLM
@ -15,11 +14,6 @@ except ImportError as error:
[sys.executable, "-m", "pip", "install", "pyaudio"]
)
# Load .env file
load_dotenv()
# OpenAI API Key env
def openai_api_key_env():
openai_api_key = os.getenv("OPENAI_API_KEY")

3
swarms/models/stable_diffusion.py
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@ -5,9 +5,6 @@ import uuid
from typing import List
import requests
from dotenv import load_dotenv
load_dotenv()
stable_api_key = os.environ.get("STABLE_API_KEY")

5
swarms/models/together.py
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@ -3,14 +3,9 @@ import os
from typing import Optional
import requests
from dotenv import load_dotenv
from swarms.models.base_llm import AbstractLLM
# Load environment variables
load_dotenv()
def together_api_key_env():
"""Get the API key from the environment."""
return os.getenv("TOGETHER_API_KEY")

2
swarms/prompts/autoswarm.py
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@ -48,13 +48,11 @@ copy/paste to vscode and run it without issue. Here are some tips to consider:
Output Format: A complete Python script that is ready for copy/paste to GitHub and demo execution. It should be formatted with complete logic, proper indentation, clear variable names, and comments.
Here is an example of a a working swarm script that you can use as a rough template for the logic:
import os
from dotenv import load_dotenv
from swarms.models import OpenAIChat
from swarms.structs import Agent
import swarms.prompts.swarm_daddy as sdsp
# Load environment variables and initialize the OpenAI Chat model
load_dotenv()
api_key = os.getenv("OPENAI_API_KEY")
llm = OpenAIChat(model_name = "gpt-4", openai_api_key=api_key)

2
swarms/prompts/programming.py
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@ -39,7 +39,7 @@ and thorough, use the guide below to create the tests, make the tests as thoroug
8. **Environment Variables and Secret Handling**:
- Store secrets and configurations in environment variables.
- Use libraries like `python-decouple` or `python-dotenv` to load environment variables.
- Use libraries like `python-decouple`to load environment variables.
- For tests, mock or set environment variables temporarily within the test environment.
9. **Grouping and Marking Tests**:

2
swarms/prompts/tests.py
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@ -42,7 +42,7 @@ def TEST_WRITER_SOP_PROMPT(
8. **Environment Variables and Secret Handling**:
- Store secrets and configurations in environment variables.
- Use libraries like `python-decouple` or `python-dotenv` to load environment variables.
- Use libraries like `python-decouple` to load environment variables.
- For tests, mock or set environment variables temporarily within the test environment.
9. **Grouping and Marking Tests**:

3
swarms/structs/autoscaler.py
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@ -66,13 +66,10 @@ class AutoScaler(BaseStructure):
Examples:
>>> import os
>>> from dotenv import load_dotenv
>>> # Import the OpenAIChat model and the Agent struct
>>> from swarms.models import OpenAIChat
>>> from swarms.structs import Agent
>>> from swarms.structs.autoscaler import AutoScaler
>>> # Load the environment variables
>>> load_dotenv()
>>> # Get the API key from the environment
>>> api_key = os.environ.get("OPENAI_API_KEY")
>>> # Initialize the language model

3
swarms/telemetry/sentry_active.py
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@ -1,9 +1,6 @@
import os
from dotenv import load_dotenv
import sentry_sdk
load_dotenv()
os.environ["USE_TELEMETRY"] = "True"
use_telementry = os.getenv("USE_TELEMETRY")

4
tests/memory/test_pq_db.py
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@ -1,12 +1,8 @@
import os
from unittest.mock import patch
from dotenv import load_dotenv
from swarms.memory.pg import PostgresDB
load_dotenv()
PSG_CONNECTION_STRING = os.getenv("PSG_CONNECTION_STRING")

3
tests/models/test_cohere.py
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@ -2,12 +2,9 @@ import os
from unittest.mock import Mock, patch
import pytest
from dotenv import load_dotenv
from swarms.models.cohere_chat import BaseCohere, Cohere
# Load the environment variables
load_dotenv()
api_key = os.getenv("COHERE_API_KEY")

3
tests/models/test_elevenlab.py
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@ -2,15 +2,12 @@ import os
from unittest.mock import mock_open, patch
import pytest
from dotenv import load_dotenv
from swarms.models.eleven_labs import (
ElevenLabsModel,
ElevenLabsText2SpeechTool,
)
load_dotenv()
# Define some test data
SAMPLE_TEXT = "Hello, this is a test."
API_KEY = os.environ.get("ELEVEN_API_KEY")

3
tests/models/test_gpt4_vision_api.py
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@ -4,13 +4,10 @@ from unittest.mock import AsyncMock, Mock, mock_open, patch
import pytest
from aiohttp import ClientResponseError
from dotenv import load_dotenv
from requests.exceptions import RequestException
from swarms.models.gpt4_vision_api import GPT4VisionAPI
load_dotenv()
custom_api_key = os.environ.get("OPENAI_API_KEY")
img = "images/swarms.jpeg"

3
tests/structs/test_agent.py
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@ -4,14 +4,11 @@ from unittest import mock
from unittest.mock import MagicMock, patch
import pytest
from dotenv import load_dotenv
from swarms.models import OpenAIChat
from swarms.structs.agent import Agent, stop_when_repeats
from swarms.utils.logger import logger
load_dotenv()
openai_api_key = os.getenv("OPENAI_API_KEY")

3
tests/structs/test_autoscaler.py
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@ -2,14 +2,11 @@ import os
from unittest.mock import MagicMock, patch
import pytest
from dotenv import load_dotenv
from swarms.models import OpenAIChat
from swarms.structs import Agent
from swarms.structs.autoscaler import AutoScaler
load_dotenv()
api_key = os.environ.get("OPENAI_API_KEY")
llm = OpenAIChat(
temperature=0.5,

3
tests/structs/test_base_workflow.py
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@ -2,13 +2,10 @@ import json
import os
import pytest
from dotenv import load_dotenv
from swarms.models import OpenAIChat
from swarms.structs import BaseWorkflow
load_dotenv()
api_key = os.environ.get("OPENAI_API_KEY")

3
tests/structs/test_task.py
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@ -3,7 +3,6 @@ from datetime import timedelta
from unittest.mock import Mock
import pytest
from dotenv import load_dotenv
from swarms.models.gpt4_vision_api import GPT4VisionAPI
from swarms.prompts.multi_modal_autonomous_instruction_prompt import (
@ -12,8 +11,6 @@ from swarms.prompts.multi_modal_autonomous_instruction_prompt import (
from swarms.structs.agent import Agent
from swarms.structs.task import Task
load_dotenv()
@pytest.fixture
def llm():

2
tests/test_upload_tests_to_issues.py
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@ -2,9 +2,7 @@ import os
import subprocess
import requests
from dotenv import load_dotenv
load_dotenv
# Constants
GITHUB_USERNAME = os.getenv("GITHUB_USERNAME")

4
tool_agent_with_llm.py
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@ -1,14 +1,10 @@
import os
from dotenv import load_dotenv
from pydantic import BaseModel, Field
from swarms import OpenAIChat, ToolAgent
from swarms.utils.json_utils import base_model_to_json
# Load the environment variables
load_dotenv()
# Initialize the OpenAIChat class
chat = OpenAIChat(
api_key=os.getenv("OPENAI_API"),

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