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.

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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

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The agent uses:

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

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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?”

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Code

The complete code is available in the Agno repository.

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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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