> ## Documentation Index
> Fetch the complete documentation index at: https://agno-v2-codex-docs-audit-20260719-0149.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Traditional RAG

> Traditional RAG that injects PgVector search results into the prompt instead of using a search tool.

```python traditional_rag.py theme={null}
"""
Traditional Rag
=============================

1. Run: `./cookbook/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,
    # Enable RAG by adding context from the `knowledge` to the user prompt.
    add_knowledge_to_context=True,
    # Set as False because Agents default to `search_knowledge=True`
    search_knowledge=False,
    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
    )
```

## Run the Example

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

  <Step title="Install dependencies">
    ```bash theme={null}
    uv pip install -U agno "psycopg[binary]" beautifulsoup4 openai pgvector pypdf sqlalchemy
    ```
  </Step>

  <Step title="Export your OpenAI API key">
    <CodeGroup>
      ```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"
      ```
    </CodeGroup>
  </Step>

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

  <Step title="Run the example">
    Save the code above as `traditional_rag.py`, then run:

    ```bash theme={null}
    python traditional_rag.py
    ```
  </Step>
</Steps>

Full source: [cookbook/02\_agents/07\_knowledge/traditional\_rag.py](https://github.com/agno-agi/agno/blob/v2.7.4/cookbook/02_agents/07_knowledge/traditional_rag.py)