Agentic RAG

This example application shows how to build a sophisticated RAG (Retrieval Augmented Generation) system that leverages search of a knowledge base with LLMs to provide deep insights into the data.

The agent can:

• Process and understand documents from multiple sources (PDFs, websites, text files)
• Build a searchable knowledge base using vector embeddings
• Maintain conversation context and memory across sessions
• Provide relevant citations and sources for its responses
• Generate summaries and extract key insights
• Answer follow-up questions and clarifications

The agent uses:

• Vector similarity search for relevant document retrieval
• Conversation memory for contextual responses
• Citation tracking for source attribution
• Dynamic knowledge base updates

Example queries to try:

• “What are the key points from this document?”
• “Can you summarize the main arguments and supporting evidence?”
• “What are the important statistics and findings?”
• “How does this relate to [topic X]?”
• “What are the limitations or gaps in this analysis?”
• “Can you explain [concept X] in more detail?”
• “What other sources support or contradict these claims?”

Code

The complete code is available in the Agno repository.

Usage

1. Clone the repository

   git clone https://github.com/agno-agi/agno.git
   cd agno
   

2. Create virtual environment

   python3 -m venv .venv
   source .venv/bin/activate
   

3. Install dependencies

   pip install -r cookbook/examples/streamlit_apps/agentic_rag/requirements.txt
   

4. Run PgVector

   First, install Docker Desktop.

   Then run either using the helper script:

   ./cookbook/scripts/run_pgvector.sh
   

   Or directly with Docker:

   docker run -d \
     -e POSTGRES_DB=ai \
     -e POSTGRES_USER=ai \
     -e POSTGRES_PASSWORD=ai \
     -e PGDATA=/var/lib/postgresql/data/pgdata \
     -v pgvolume:/var/lib/postgresql/data \
     -p 5532:5432 \
     --name pgvector \
     agnohq/pgvector:16
   

5. Set up API keys

   # Required
   export OPENAI_API_KEY=***
   # Optional
   export ANTHROPIC_API_KEY=***
   export GOOGLE_API_KEY=***
   
   

   We recommend using gpt-4o for optimal performance.

6. Launch the app

   streamlit run cookbook/examples/streamlit_apps/agentic_rag/app.py
   

   Open localhost:8501 to start using the Agentic RAG.

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Assistant

Responses are generated using AI and may contain mistakes.