> ## Documentation Index
> Fetch the complete documentation index at: https://agno-v2.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Agentic RAG

> Agentic RAG with PgVector hybrid search: the agent queries a recipe knowledge base on demand.

```python agentic_rag.py theme={null}
"""
Agentic Rag
=============================

1. Run: `./cookbook/scripts/run_pgvector.sh` to start a postgres container with pgvector.
"""

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.pgvector import PgVector, SearchType

db_url = "postgresql+psycopg://ai:ai@localhost:5532/ai"
knowledge = Knowledge(
    # Use PgVector as the vector database and store embeddings in the `ai.recipes` table
    vector_db=PgVector(
        table_name="recipes",
        db_url=db_url,
        search_type=SearchType.hybrid,
        embedder=OpenAIEmbedder(id="text-embedding-3-small"),
    ),
)

# ---------------------------------------------------------------------------
# Create Agent
# ---------------------------------------------------------------------------
agent = Agent(
    model=OpenAIResponses(id="gpt-5.2"),
    knowledge=knowledge,
    # Add a tool to search the knowledge base which enables agentic RAG.
    # This is enabled by default when `knowledge` is provided to the Agent.
    search_knowledge=True,
    markdown=True,
)

# ---------------------------------------------------------------------------
# Run Agent
# ---------------------------------------------------------------------------
if __name__ == "__main__":
    knowledge.insert(url="https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf")
    agent.print_response(
        "How do I make chicken and galangal in coconut milk soup", stream=True
    )
    # agent.print_response(
    #     "Hi, i want to make a 3 course meal. Can you recommend some recipes. "
    #     "I'd like to start with a soup, then im thinking a thai curry for the main course and finish with a dessert",
    #     stream=True,
    # )
```

## Run the Example

<Snippet file="create-venv-step.mdx" />

**1. Install dependencies**

```bash theme={null}
uv pip install -U agno "psycopg[binary]" beautifulsoup4 openai pgvector pypdf sqlalchemy
```

**2. Export your OpenAI API key**

```bash Mac/Linux theme={null}
export OPENAI_API_KEY="your_openai_api_key_here"
```

```bash Windows theme={null}
$Env:OPENAI_API_KEY="your_openai_api_key_here"
```

<Snippet file="run-pgvector-step.mdx" />

**3. Run the example**

Save the code above as `agentic_rag.py`, then run:

```bash theme={null}
python agentic_rag.py
```

Full source: [cookbook/02\_agents/07\_knowledge/agentic\_rag.py](https://github.com/agno-agi/agno/blob/v3.0.4/cookbook/02_agents/07_knowledge/agentic_rag.py)


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