Agent with Knowledge

Give your agent a searchable knowledge base (Agentic RAG).

Knowledge gives your agent information it can search at runtime. This pattern is known as Agentic RAG. The agent decides when to search based on the user’s question.

1. Create a Python file

   agent_with_knowledge.py

   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.lancedb import LanceDb, SearchType
   
   knowledge = Knowledge(
       vector_db=LanceDb(
           uri="tmp/lancedb",
           table_name="recipes",
           search_type=SearchType.hybrid,
           embedder=OpenAIEmbedder(id="text-embedding-3-small"),
       ),
   )
   
   # Load a PDF into the knowledge base
   knowledge.insert(
       url="https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf",
   )
   
   agent = Agent(
       model=OpenAIResponses(id="gpt-5.2"),
       knowledge=knowledge,
       instructions="Search your knowledge base for Thai recipes. Be concise.",
       markdown=True,
   )
   
   agent.print_response("How do I make Pad Thai?", stream=True)
   agent.print_response("What ingredients do I need for green curry?", stream=True)
   
2. Set up your virtual environment

   Mac

   uv venv --python 3.12
   source .venv/bin/activate
   

   uv venv --python 3.12
   .venv\Scripts\activate
   
3. Install dependencies

   uv pip install -U agno openai lancedb tantivy pypdf
   
4. Export your OpenAI API key

   Mac/Linux

   export OPENAI_API_KEY="your_openai_api_key_here"
   

   $Env:OPENAI_API_KEY="your_openai_api_key_here"
   
5. Run Agent

   python agent_with_knowledge.py
   

How It Works

1. Knowledge base: Documents are chunked, embedded, and stored in a vector database
2. Search: Agent searches the knowledge base using hybrid search (semantic + keyword)
3. Context: Relevant chunks are added to context before generating a response

Adding Different Content Types

# From a URL
knowledge.insert(url="https://example.com/document.pdf")

# From a local file
knowledge.insert(path="./documents/guide.pdf")

# From text
knowledge.insert(text="Your content here...")