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

# RAG with LanceDB and SQLite Storage

This example demonstrates how to implement RAG using LanceDB vector database with Ollama embeddings and SQLite for agent data storage, providing a complete local setup for document retrieval.

## Code

```python rag_with_lance_db_and_sqlite.py theme={null}
from agno.agent import Agent
from agno.db.sqlite.sqlite import SqliteDb
from agno.knowledge.embedder.ollama import OllamaEmbedder
from agno.knowledge.knowledge import Knowledge
from agno.models.ollama import Ollama
from agno.vectordb.lancedb import LanceDb

# Define the database URL where the vector database will be stored
db_url = "/tmp/lancedb"

# Configure the language model
model = Ollama(id="llama3.1:8b")

# Create Ollama embedder
embedder = OllamaEmbedder(id="nomic-embed-text", dimensions=768)

# Create the vector database
vector_db = LanceDb(
    table_name="recipes",  # Table name in the vector database
    uri=db_url,  # Location to initiate/create the vector database
    embedder=embedder,  # Without using this, it will use OpenAIChat embeddings by default
)

knowledge = Knowledge(
    vector_db=vector_db,
)

knowledge.add_content_sync(
    name="Recipes", url="https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"
)


# Set up SQL storage for the agent's data
db = SqliteDb(db_file="data.db")

# Initialize the Agent with various configurations including the knowledge base and storage
agent = Agent(
    session_id="session_id",  # use any unique identifier to identify the run
    user_id="user",  # user identifier to identify the user
    model=model,
    knowledge=knowledge,
    db=db,
)

# Use the agent to generate and print a response to a query, formatted in Markdown
agent.print_response(
    "What is the first step of making Gluai Buat Chi from the knowledge base?",
    markdown=True,
)
```

## Usage

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

  <Step title="Install libraries">
    ```bash theme={null}
    pip install -U agno lancedb ollama
    ```
  </Step>

  <Step title="Setup Ollama">
    ```bash theme={null}
    # Install and start Ollama
    # Pull required models
    ollama pull llama3.1:8b
    ollama pull nomic-embed-text
    ```
  </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>

  <Step title="Create a Python file">
    Create a Python file and add the above code.

    ```bash theme={null}
    touch rag_with_lance_db_and_sqlite.py
    ```
  </Step>

  <Step title="Run Agent">
    <CodeGroup>
      ```bash Mac theme={null}
      python rag_with_lance_db_and_sqlite.py
      ```

      ```bash Windows theme={null}
      python rag_with_lance_db_and_sqlite.py
      ```
    </CodeGroup>
  </Step>

  <Step title="Find All Cookbooks">
    Explore all the available cookbooks in the Agno repository. Click the link below to view the code on GitHub:

    <Link href="https://github.com/agno-agi/agno/tree/main/cookbook/agents/rag" target="_blank">
      Agno Cookbooks on GitHub
    </Link>
  </Step>
</Steps>