# Basic RAG: Context Injection (/examples/knowledge/getting-started/basic-rag)



`add_knowledge_to_context=True` retrieves documents before the model call and appends available references to the user message. The retained source docstring’s “system prompt” wording is outdated. `search_knowledge=False` disables the separate search tool. If retrieval yields no documents or fails, no references are added.

```python title="basic_rag.py"
"""
Basic RAG: Context Injection
=============================
The simplest way to give an agent access to documents. Content is automatically
retrieved and injected into the system prompt before the agent responds.

This pattern works well for simple Q&A over documents. The agent doesn't need
to decide whether to search - it always gets relevant context.

Steps:
1. Create a Knowledge base with a vector database
2. Load a document
3. Create an Agent with add_knowledge_to_context=True
4. Ask questions - context is injected automatically

See also: 02_agentic_rag.py for agent-driven search decisions.
"""

import asyncio

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
from agno.vectordb.search import SearchType

# ---------------------------------------------------------------------------
# Setup
# ---------------------------------------------------------------------------

qdrant_url = "http://localhost:6333"

knowledge = Knowledge(
    vector_db=Qdrant(
        collection="basic_rag",
        url=qdrant_url,
        search_type=SearchType.hybrid,
        embedder=OpenAIEmbedder(id="text-embedding-3-small"),
    ),
)

# ---------------------------------------------------------------------------
# Create Agent
# ---------------------------------------------------------------------------

# Traditional RAG: context is fetched and injected into the prompt automatically.
# The agent doesn't get a search tool - it just sees the relevant context.
agent = Agent(
    model=OpenAIResponses(id="gpt-5.2"),
    knowledge=knowledge,
    add_knowledge_to_context=True,
    search_knowledge=False,
    markdown=True,
)

# ---------------------------------------------------------------------------
# Run Demo
# ---------------------------------------------------------------------------

if __name__ == "__main__":

    async def main():
        await knowledge.ainsert(
            url="https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"
        )

        print("\n" + "=" * 60)
        print("Basic RAG: Context injected into prompt automatically")
        print("=" * 60 + "\n")

        agent.print_response(
            "How do I make chicken and galangal in coconut milk soup",
            stream=True,
        )

    asyncio.run(main())
```

## Run the Example [#run-the-example]

<Steps>
    <Step title="Set up your virtual environment">
      <CodeBlockTabs defaultValue="Mac">
        <CodeBlockTabsList>
          <CodeBlockTabsTrigger value="Mac">
            Mac
          </CodeBlockTabsTrigger>

          <CodeBlockTabsTrigger value="Windows">
            Windows
          </CodeBlockTabsTrigger>
        </CodeBlockTabsList>

        <CodeBlockTab value="Mac">
          ```bash
          uv venv --python 3.12
          source .venv/bin/activate
          ```
        </CodeBlockTab>

        <CodeBlockTab value="Windows">
          ```bash
          uv venv --python 3.12
          .venv\Scripts\activate
          ```
        </CodeBlockTab>
      </CodeBlockTabs>
    </Step>

  <Step title="Install dependencies">
    ```bash
    uv pip install -U agno beautifulsoup4 fastembed openai pypdf qdrant-client
    ```
  </Step>

  <Step title="Export your OpenAI API key">
    <CodeBlockTabs defaultValue="Mac/Linux">
      <CodeBlockTabsList>
        <CodeBlockTabsTrigger value="Mac/Linux">
          Mac/Linux
        </CodeBlockTabsTrigger>

        <CodeBlockTabsTrigger value="Windows">
          Windows
        </CodeBlockTabsTrigger>
      </CodeBlockTabsList>

      <CodeBlockTab value="Mac/Linux">
        ```bash
        export OPENAI_API_KEY="your_openai_api_key_here"
        ```
      </CodeBlockTab>

      <CodeBlockTab value="Windows">
        ```bash
        $Env:OPENAI_API_KEY="your_openai_api_key_here"
        ```
      </CodeBlockTab>
    </CodeBlockTabs>
  </Step>

  <Step title="Run Qdrant">
    ```bash
    docker run -d --name qdrant -p 6333:6333 qdrant/qdrant:latest
    ```
  </Step>

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

    ```bash
    python basic_rag.py
    ```
  </Step>
</Steps>

Full source: [cookbook/07\_knowledge/01\_getting\_started/01\_basic\_rag.py](https://github.com/agno-agi/agno/blob/8f36eaf2d18e91afa7b327eec66a3cd3685dcb87/cookbook/07_knowledge/01_getting_started/01_basic_rag.py)