Knowledge: RAG for Agents

Give agents access to your documents, databases, and APIs through Retrieval-Augmented Generation.

Overview

Knowledge is Agno's RAG framework. It handles the full pipeline: reading documents, chunking them, embedding chunks, storing them in a vector database, and retrieving relevant content when agents need it.

Component	What It Does	Options
Readers	Extract text from files	PDF, DOCX, CSV, JSON, Web, YouTube, ArXiv
Chunking	Split text into searchable pieces	Fixed, Recursive, Semantic, Code, Markdown, Agentic
Embedders	Convert text to vectors	OpenAI, Cohere, Bedrock, Ollama, 14+ more
Vector DBs	Store and search vectors	Qdrant, LanceDB, ChromaDB, Pinecone, 14+ more
Rerankers	Re-score results for quality	Cohere, SentenceTransformer, Bedrock, Infinity

Quick Start

python

from agno.agent import Agent
from agno.knowledge.embedder.openai import OpenAIEmbedder
from agno.knowledge.knowledge import Knowledge
from agno.models.openai import OpenAIResponses
from agno.vectordb.qdrant import Qdrant, SearchType

knowledge = Knowledge(
    vector_db=Qdrant(
        collection="my_docs",
        url="http://localhost:6333",
        search_type=SearchType.hybrid,
        embedder=OpenAIEmbedder(id="text-embedding-3-small"),
    ),
)

knowledge.insert(url="https://example.com/document.pdf")

agent = Agent(
    model=OpenAIResponses(id="gpt-5.2"),
    knowledge=knowledge,
    search_knowledge=True,
    markdown=True,
)

agent.print_response("What does the document say about X?")

Cookbook Structure

cookbook/07_knowledge/
|-- 01_getting_started/        Start here
|   |-- 01_basic_rag.py            Traditional RAG with context injection
|   |-- 02_agentic_rag.py          Agent-driven search decisions
|   |-- 03_loading_content.py      All source types: file, URL, text, topics
|   +-- 04_choosing_components.md  Decision guide
|
|-- 02_building_blocks/        Core components
|   |-- 01_chunking_strategies.py  Side-by-side comparison
|   |-- 02_hybrid_search.py        Vector + keyword + hybrid
|   |-- 03_reranking.py            Two-stage retrieval
|   |-- 04_filtering.py            Dict + FilterExpr
|   |-- 05_agentic_filtering.py    Agent-driven filters
|   +-- 06_embedders.py            Embedder comparison
|
|-- 03_production/             Real-world patterns
|   |-- 01_multi_source_rag.py     Multiple content types
|   |-- 02_knowledge_lifecycle.py  Insert, update, remove, track
|   |-- 03_multi_tenant.py         Per-tenant isolation
|   +-- 04_error_handling.py       Robust ingestion
|
|-- 04_advanced/               Power user patterns
|   |-- 01_custom_retriever.py     Custom retrieval function
|   |-- 02_custom_chunking.py      Custom chunking strategy
|   |-- 03_graph_rag.py            LightRAG integration
|   |-- 04_knowledge_tools.py      Think/search/analyze tools
|   +-- 05_knowledge_protocol.py   Custom KnowledgeProtocol
|
|-- 05_integrations/           Specific providers
|   |-- readers/                   PDF, CSV, JSON, Web, etc.
|   |-- cloud/                     S3, Azure, GCS
|   +-- vector_dbs/                Qdrant, ChromaDB, Pinecone, etc.
|
+-- reference/                 Decision guides
    |-- vector_db_comparison.md
    |-- embedder_comparison.md
    +-- chunking_decision_guide.md

Running the Cookbooks

1. Start Qdrant

bash

./cookbook/scripts/run_qdrant.sh

2. Set API Keys

bash

export OPENAI_API_KEY=your-key

3. Run Examples

bash

# Start with basic RAG
.venvs/demo/bin/python cookbook/07_knowledge/01_getting_started/01_basic_rag.py

# Try agentic RAG
.venvs/demo/bin/python cookbook/07_knowledge/01_getting_started/02_agentic_rag.py

# Explore building blocks
.venvs/demo/bin/python cookbook/07_knowledge/02_building_blocks/01_chunking_strategies.py

Two RAG Modes

Mode	Parameter	How It Works
Basic RAG	`add_knowledge_to_context=True`	Context auto-injected into prompt
Agentic RAG	`search_knowledge=True`	Agent gets search tool, decides when to use it

Agentic RAG is the default and recommended for most use cases.