🚀 Swarms LlamaIndex RAG Template

A production-ready template for building RAG (Retrieval-Augmented Generation) applications using Swarms and LlamaIndex. This template provides everything you need to get started with document-based AI interactions.

🌟 Features

• 📚 Built-in RAG implementation with LlamaIndex
• 🤖 Advanced AI agent for healthcare data summarization
• 🔄 Interactive chat interface
• 📊 Sample data generator for testing
• 🛠️ Easy-to-customize architecture

🚀 Quick Start

1. Clone the repository

git clone https://github.com/The-Swarm-Corporation/Swarms-LlamaIndex-RAG-Template.git
cd Swarms-LlamaIndex-RAG-Template

2. Install dependencies

pip install -r requirements.txt

3. Set up your environment

# Create a .env file and add your API keys and configurations
touch .env
# export OPENAI_API_KEY=<your-openai-api-key>
# export GROQ_API_KEY=<your-groq-api-key>
# export WORKSPACE_DIR=<your-workspace-directory>

4. Generate sample data or add your own

# To generate sample data:
python -m llamaindex_rag.fake_data_generator

# OR add your own documents to the 'docs' folder

5. Run the application

python main.py

Example Usage

• The healthcare_summarizer agent is a pre-built agent that summarizes medical data.
• You can customize the prompt to fit your needs.
• The all_cores=True argument is optional, it allows the agent to use all available cores on your machine for faster processing.
• The agent will first query the LlamaIndexDB for relevant documents, then use the LLM to summarize the data.

from llamaindex_rag.agent import healthcare_summarizer

# Example usage
print(
    healthcare_summarizer.run(
        """
        What is the medical history of patient 1? Create a report with the following format:
        - Chief Complaint
        - Vitals
        - Assessment
        - Medications
        - Plan
        """,
        all_cores=True,
    )
)

Custom Agent with RAG

• You can create your own agent with RAG by using the Agent class.
• The system_prompt argument is the prompt that the agent will use to summarize the data.
• The llm argument is the LLM model that the agent will use to summarize the data.
• The max_loops argument is the number of loops the agent will run.
• The long_term_memory argument is the LlamaIndexDB instance that the agent will use to query the vector database. It can use any RAG system you want it just needs to be a class with a query method that intakes a query string and returns a response string.

# Initialize the healthcare summarization agent
healthcare_summarizer = Agent(
    agent_name="Healthcare-Data-Summarizer",
    system_prompt=HEALTHCARE_SUMMARY_PROMPT,
    llm=model,
    max_loops=2,
    autosave=True,
    dashboard=False,
    verbose=True,
    dynamic_temperature_enabled=False,
    saved_state_path="healthcare_summarizer.json",
    user_name="Human:",
    retry_attempts=1,
    context_length=250000,
    return_step_meta=True,
    output_type="json",
    streaming_on=False,
    long_term_memory=database,
    auto_generate_prompt=False,
    # output_file="healthcare_report.md",
    # state_save_file_type="json",
    rag_every_loop=True,
    interactive=True,
)

Memory Example

• LlamaIndexDB is a class that allows you to query a vector database of documents.
• Add your documents to the docs folder or use the fake data generator to populate the database.
• The data_dir argument is the directory containing your documents.
• similarity_top_k is the number of similar documents to retrieve, it can make the response more relevant and longer.

from llamaindex_rag.memory import LlamaIndexDB

# Example usage
llama_index_db = LlamaIndexDB(
    data_dir="docs",
    filename_as_id=True,
    recursive=True,
    required_exts=[".txt", ".pdf", ".docx"],
    similarity_top_k=10
)

llama_index_db.query(
    "What is the medical history of patient 1?",
    streaming=True,
    response_mode="compact"
)

📁 Project Structure

.
├── llamaindex_rag/
│   ├── agent.py          # Main RAG agent implementation
│   ├── memory.py         # LlamaIndex database integration
│   └── fake_data_generator.py  # Sample data generator
├── docs/                 # Document storage directory
├── requirements.txt      # Project dependencies
└── main.py              # Application entry point

🛠️ Built With

• Swarms Framework - AI agent framework
• LlamaIndex - Data framework for LLM applications
• Python 3.10+
• GROQ/OpenAI API

📖 Usage

1. Add Your Documents

   • Place your documents in the docs folder, or
   • Use the fake data generator for testing

2. Interact with the Agent

   • Run main.py to start the interactive session
   • Ask questions about your documents
   • Get AI-powered insights and summaries

🤝 Contributing

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

🛠 Built With

• Swarms Framework
• Python 3.10+
• GROQ API Key or you can change it to use any model from Swarm Models

📬 Contact

Questions? Reach out:

• Twitter: @kyegomez
• Email: kye@swarms.world

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