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

# Clickhouse Vector Database

> Use ClickHouse as a vector database for your Knowledge Base.

## Setup

```shell theme={null}
docker run -d \
  -e CLICKHOUSE_DB=ai \
  -e CLICKHOUSE_USER=ai \
  -e CLICKHOUSE_PASSWORD=ai \
  -e CLICKHOUSE_DEFAULT_ACCESS_MANAGEMENT=1 \
  -v clickhouse_data:/var/lib/clickhouse/ \
  -v clickhouse_log:/var/log/clickhouse-server/ \
  -p 8123:8123 \
  -p 9000:9000 \
  --ulimit nofile=262144:262144 \
  --name clickhouse-server \
  clickhouse/clickhouse-server
```

## Example

```python agent_with_knowledge.py theme={null}
from agno.agent import Agent
from agno.knowledge.knowledge import Knowledge
from agno.db.sqlite import SqliteDb
from agno.vectordb.clickhouse import Clickhouse

knowledge=Knowledge(
    vector_db=Clickhouse(
        table_name="recipe_documents",
        host="localhost",
        port=8123,
        username="ai",
        password="ai",
    ),
)

knowledge.insert(
  url="https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"
)

agent = Agent(
    db=SqliteDb(db_file="agno.db"),
    knowledge=knowledge,
    # Enable the agent to search the knowledge base
    search_knowledge=True,
    # Enable the agent to read the chat history
    read_chat_history=True,
)
# Comment out after first run
agent.knowledge.load(recreate=False)  # type: ignore

agent.print_response("How do I make pad thai?", markdown=True)
agent.print_response("What was my last question?", stream=True)
```

<Card title="Async Support ⚡">
  <div className="mt-2">
    <p>
      Clickhouse also supports asynchronous operations, enabling concurrency and leading to better performance.
    </p>

    ```python async_clickhouse.py theme={null}
    import asyncio

    from agno.agent import Agent
    from agno.knowledge.knowledge import Knowledge
    from agno.db.sqlite import SqliteDb
    from agno.vectordb.clickhouse import Clickhouse

    agent = Agent(
        db=SqliteDb(db_file="agno.db"),
        knowledge=Knowledge(
            vector_db=Clickhouse(
                table_name="recipe_documents",
                host="localhost",
                port=8123,
                username="ai",
                password="ai",
            ),
        ),
        # Enable the agent to search the knowledge base
        search_knowledge=True,
        # Enable the agent to read the chat history
        read_chat_history=True,
    )

    if __name__ == "__main__":
        # Comment out after first run
        asyncio.run(agent.knowledge.ainsert(
            url="https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"
          )
        )

        # Create and use the agent
        asyncio.run(agent.aprint_response("How to make Tom Kha Gai", markdown=True))
    ```

    <Tip className="mt-4">
      Use <code>aload()</code> and <code>aprint\_response()</code> methods with <code>asyncio.run()</code> for non-blocking operations in high-throughput applications.
    </Tip>
  </div>
</Card>

## Clickhouse Params

<Snippet file="vectordb_clickhouse_params.mdx" />