https://github.com/the-swarm-corporation/multi-agent-rag-template
This template demonstrates how to create a collaborative team of AI agents that work together to process, analyze, and generate insights from documents.
https://github.com/the-swarm-corporation/multi-agent-rag-template
agents ai anthropic autogen chromadb crewai discord gpt-5 gpt35 gpt4 langchain multi-agent openai pinecone rag swarms weaviate
Last synced: 13 days ago
JSON representation
This template demonstrates how to create a collaborative team of AI agents that work together to process, analyze, and generate insights from documents.
- Host: GitHub
- URL: https://github.com/the-swarm-corporation/multi-agent-rag-template
- Owner: The-Swarm-Corporation
- License: mit
- Created: 2024-11-11T19:34:21.000Z (11 months ago)
- Default Branch: main
- Last Pushed: 2024-11-14T17:03:52.000Z (11 months ago)
- Last Synced: 2024-12-02T18:00:54.097Z (10 months ago)
- Topics: agents, ai, anthropic, autogen, chromadb, crewai, discord, gpt-5, gpt35, gpt4, langchain, multi-agent, openai, pinecone, rag, swarms, weaviate
- Language: Python
- Homepage: https://swarms.xyz/
- Size: 44.9 KB
- Stars: 9
- Watchers: 2
- Forks: 1
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- Funding: .github/FUNDING.yml
- License: LICENSE
Awesome Lists containing this project
README
# Multi-Agent-RAG-Template
[](https://discord.gg/agora-999382051935506503) [](https://www.youtube.com/@kyegomez3242) [](https://www.linkedin.com/in/kye-g-38759a207/) [](https://x.com/kyegomezb)
[](https://github.com/The-Swarm-Corporation/Legal-Swarm-Template)
[](https://github.com/kyegomez/swarms)
[](https://opensource.org/licenses/MIT)
[](https://www.python.org/downloads/)
[](https://swarms.xyz)
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**
```bash
git clone https://github.com/The-Swarm-Corporation/Multi-Agent-RAG-Template.git
cd Multi-Agent-RAG-Template
```
2. **Set Up Environment**
```bash
# 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**
```bash
# 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**
```bash
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:
```python
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:
```python
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.
```python
# 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.
```python
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](https://swarms.xyz)
- [LlamaIndex](https://docs.llamaindex.ai)
- [GROQ](https://groq.com)
## 🤝 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](https://github.com/kyegomez/swarms)
- Python 3.10+
- GROQ API Key or you can change it to use any model from [Swarm Models](https://github.com/The-Swarm-Corporation/swarm-models)
- LlamaIndexDB for storing and retrieving medical documents
## 📬 Contact
Questions? Reach out:
- Twitter: [@kyegomez](https://twitter.com/kyegomez)
- Email: kye@swarms.world
---
## Want Real-Time Assistance?
[Book a call with here for real-time assistance:](https://cal.com/swarms/swarms-onboarding-session)
---
⭐ Star us on GitHub if this project helped you!
Built with ♥ using [Swarms Framework](https://github.com/kyegomez/swarms)