Multi-Agent-RAG-Template

A production-ready template for building Multi-Agent RAG (Retrieval-Augmented Generation) systems using the Swarms Framework. This template demonstrates how to create a collaborative team of AI agents that work together to process, analyze, and generate insights from documents.

🌟 Features

• Plug-and-Play Agent Architecture

  • Easily swap or modify agents without disrupting the system
  • Add custom agents with specialized capabilities
  • Define your own agent interaction patterns
  • Scale from 2 to 100+ agents seamlessly
  • Any LLM can be used, this template uses GROQ but you can use any other LLM such as OpenAI, Anthropic, Cohere, etc.

• Adaptable Document Processing

  • Support for any document format through custom extractors
  • Flexible document storage options (local, cloud, or hybrid)
  • Customizable chunking and embedding strategies
  • Dynamic index updates without system restart
  • Any RAG system can be used, this template uses LlamaIndexDB but you can use any other RAG system.

• Configurable Workflows

  • Design custom agent communication patterns
  • Implement parallel or sequential processing
  • Add conditional logic and branching workflows
  • Adjust system behavior through environment variables

🚀 Quick Start

1. Clone the Repository

git clone https://github.com/The-Swarm-Corporation/Multi-Agent-RAG-Template.git
cd Multi-Agent-RAG-Template

2. Set Up Environment

# Create and activate virtual environment (optional but recommended)
python -m venv venv
source venv/bin/activate  # On Windows: .\venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

3. Configure Environment Variables

# Create .env file

# Edit .env file with your credentials
GROQ_API_KEY="your-api-key-here"
WORKSPACE_DIR="agent_workspace"
OPENAI_API_KEY="your-openai-api-key-here"

4. Run the Example

python main.py

🏗️ Project Structure

Multi-Agent-RAG-Template/
├── main.py                    # Main entry point
├── multi_agent_rag/
│   ├── agents.py             # Agent definitions
│   └── memory.py             # RAG implementation
├── docs/                      # Place your documents here
├── requirements.txt           # Project dependencies
└── .env                      # Environment variables

🔧 Customization

Adding New Agents

1. Open multi_agent_rag/agents.py
2. Create a new agent using the Agent class:

new_agent = Agent(
    agent_name="New-Agent",
    system_prompt="Your system prompt here",
    llm=model,
    max_loops=1,
    # ... additional configuration
)

Modifying the Agent Flow

In main.py, update the flow parameter in the AgentRearrange initialization:

flow=f"{agent1.agent_name} -> {agent2.agent_name} -> {new_agent.agent_name}"

Integrating RAG

• The memory_system parameter in the AgentRearrange initialization is used to configure the RAG system.
• The memory_system parameter is an instance of LlamaIndexDB, which is a database class for storing and retrieving medical documents.
• The memory_system class must have a query(query: str) method that returns a string for the agent to use it.

# Import the AgentRearrange class for coordinating multiple agents
from swarms import AgentRearrange

from multi_agent_rag.agents import (
    diagnostic_specialist,
    medical_data_extractor,
    patient_care_coordinator,
    specialist_consultant,
    treatment_planner,
)

from multi_agent_rag.memory import LlamaIndexDB

router = AgentRearrange(
    name="medical-diagnosis-treatment-swarm",
    description="Collaborative medical team for comprehensive patient diagnosis and treatment planning",
    max_loops=1,
    agents=[
        medical_data_extractor,
        diagnostic_specialist,
        treatment_planner,
        specialist_consultant,
        patient_care_coordinator,
    ],
    memory_system=LlamaIndexDB(
        data_dir="docs",
        filename_as_id=True,
        recursive=True,
        similarity_top_k=10,
    ),
    flow=f"{medical_data_extractor.agent_name} -> {diagnostic_specialist.agent_name} -> {treatment_planner.agent_name} -> {specialist_consultant.agent_name} -> {patient_care_coordinator.agent_name}",
)

if __name__ == "__main__":
    router.run(
        "Analyze this Lucas Brown's medical data to provide a diagnosis and treatment plan"
    )

Pinecone Example

Here is an example of how to use Pinecone as the RAG system.

• Make sure you have a Pinecone index created and the PINECONE_API_KEY, PINECONE_INDEX_NAME, and PINECONE_ENVIRONMENT environment variables set.
• See the pinecone_swarm.py file for the full example.
• The PineconeManager class is used to interface with the Pinecone API.

import os
from swarms import AgentRearrange
from multi_agent_rag.agents import (
    diagnostic_specialist,
    medical_data_extractor,
    patient_care_coordinator,
    specialist_consultant,
    treatment_planner,
)
from multi_agent_rag.pinecone_wrapper import PineconeManager

router = AgentRearrange(
    name="medical-diagnosis-treatment-swarm",
    description="Collaborative medical team for comprehensive patient diagnosis and treatment planning",
    max_loops=1,
    agents=[
        medical_data_extractor,
        diagnostic_specialist,
        treatment_planner,
        specialist_consultant,
        patient_care_coordinator,
    ],
    memory_system=PineconeManager(
        api_key=os.getenv("PINECONE_API_KEY"),
        index_name=os.getenv("PINECONE_INDEX_NAME"),
        environment=os.getenv("PINECONE_ENVIRONMENT"),
    ),
    flow=f"{medical_data_extractor.agent_name} -> {diagnostic_specialist.agent_name} -> {treatment_planner.agent_name} -> {specialist_consultant.agent_name} -> {patient_care_coordinator.agent_name}",
)

if __name__ == "__main__":
    router.run(
        "Analyze this Lucas Brown's medical data to provide a diagnosis and treatment plan"
    )

📚 Documentation

For detailed documentation on:

• Swarms Framework
• LlamaIndex
• GROQ

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

1. Fork the repository
2. Create your feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request g

🛠 Built With

• Swarms Framework
• Python 3.10+
• GROQ API Key or you can change it to use any model from Swarm Models
• LlamaIndexDB for storing and retrieving medical documents

📬 Contact

Questions? Reach out:

• Twitter: @kyegomez
• Email: [email protected]

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