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

# Distributed RAG with LanceDB

> Coordinate agents that search vector and hybrid tables in one LanceDB database.

A coordinating team delegates retrieval and response tasks across agents with separate LanceDB knowledge tables.

```python distributed_rag_lancedb.py theme={null}
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.team import Team
from agno.vectordb.lancedb import LanceDb, SearchType

primary_knowledge = Knowledge(
    vector_db=LanceDb(
        table_name="recipes_primary",
        uri="tmp/lancedb",
        search_type=SearchType.vector,
        embedder=OpenAIEmbedder(id="text-embedding-3-small"),
    ),
)

context_knowledge = Knowledge(
    vector_db=LanceDb(
        table_name="recipes_context",
        uri="tmp/lancedb",
        search_type=SearchType.hybrid,
        embedder=OpenAIEmbedder(id="text-embedding-3-small"),
    ),
)

primary_retriever = Agent(
    name="Primary Retriever",
    model=OpenAIResponses(id="gpt-5-mini"),
    role="Retrieve primary documents and core information from knowledge base",
    knowledge=primary_knowledge,
    search_knowledge=True,
    instructions=[
        "Search the primary knowledge table.",
        "Return the matching recipe details and source context.",
    ],
    markdown=True,
)

context_expander = Agent(
    name="Context Expander",
    model=OpenAIResponses(id="gpt-5-mini"),
    role="Expand context by finding related and supplementary information",
    knowledge=context_knowledge,
    search_knowledge=True,
    instructions=[
        "Search the context knowledge table.",
        "Return related recipe details and source context.",
    ],
    markdown=True,
)

answer_synthesizer = Agent(
    name="Answer Synthesizer",
    model=OpenAIResponses(id="gpt-5-mini"),
    role="Synthesize retrieved information into an answer",
    instructions=[
        "Combine information from the Primary Retriever and Context Expander.",
        "Cite the supplied sources.",
    ],
    markdown=True,
)

quality_validator = Agent(
    name="Quality Validator",
    model=OpenAIResponses(id="gpt-5-mini"),
    role="Validate answer quality and suggest improvements",
    instructions=[
        "Compare the draft response with the retrieved information.",
        "Identify conflicts and unsupported details.",
    ],
    markdown=True,
)

distributed_rag_team = Team(
    name="Distributed RAG Team",
    model=OpenAIResponses(id="gpt-5-mini"),
    members=[
        primary_retriever,
        context_expander,
        answer_synthesizer,
        quality_validator,
    ],
    instructions=[
        "Coordinate the retrieval and response tasks in order.",
        "Primary Retriever: First retrieve core relevant information.",
        "Context Expander: Then expand with related context and background.",
        "Answer Synthesizer: Synthesize the retrieved information.",
        "Quality Validator: Finally check the response against the retrieved information.",
    ],
    show_members_responses=True,
    markdown=True,
)

if __name__ == "__main__":
    query = "How do I make chicken and galangal in coconut milk soup? Include cooking tips and variations."
    source_url = "https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"

    primary_knowledge.insert(name="Thai Recipes Primary", url=source_url)
    context_knowledge.insert(name="Thai Recipes Context", url=source_url)
    distributed_rag_team.print_response(input=query)
```

## Usage

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

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

  <Step title="Export the API key">
    ```bash theme={null}
    export OPENAI_API_KEY=your_openai_api_key_here
    ```
  </Step>

  <Step title="Run the team">
    ```bash theme={null}
    python distributed_rag_lancedb.py
    ```
  </Step>
</Steps>

## Next Steps

| Task                                | Guide                                                                      |
| ----------------------------------- | -------------------------------------------------------------------------- |
| Run the pattern with PostgreSQL     | [Distributed RAG with PgVector](/knowledge/teams/distributed-rag-pgvector) |
| Attach one knowledge base to a team | [Team with Knowledge Base](/knowledge/teams/team-with-knowledge)           |