ContextQMD
Back to Agno

Getting Started with Knowledge

cookbook/07_knowledge/01_getting_started/README.md

2.6.41.4 KB
Original Source

Getting Started with Knowledge

Start here to learn the basics of RAG (Retrieval-Augmented Generation) with Agno.

Prerequisites

  1. Run Qdrant: ./cookbook/scripts/run_qdrant.sh
  2. Set OPENAI_API_KEY environment variable

Examples

FileWhat It Shows
01_basic_rag.pyTraditional RAG with automatic context injection
02_agentic_rag.pyAgentic RAG where the agent decides when to search
03_loading_content.pyLoading from files, URLs, text, topics, and batches
04_choosing_components.mdDecision guide for vector DBs, embedders, and chunking

Start Here

bash
# Basic RAG (simplest pattern)
.venvs/demo/bin/python cookbook/07_knowledge/01_getting_started/01_basic_rag.py

# Agentic RAG (recommended for production)
.venvs/demo/bin/python cookbook/07_knowledge/01_getting_started/02_agentic_rag.py

Basic vs Agentic RAG

  • Basic RAG (add_knowledge_to_context=True): Context is fetched and injected into the prompt automatically. Simple, predictable, but always searches.
  • Agentic RAG (search_knowledge=True): Agent gets a search tool and decides when to use it. More flexible, can search multiple times or skip searching. This is the default.

Further Reading