> ## Documentation Index
> Fetch the complete documentation index at: https://agno-v2.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}
uv pip install -U clickhouse-connect pypdf openai sqlalchemy agno
```

Run ClickHouse with Docker:

```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
```

Set your OpenAI API key. The examples below use the default OpenAI embedder and model.

```shell theme={null}
export OPENAI_API_KEY=xxx
```

## 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,
)

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

> **Async Support ⚡**
>
> ClickHouse also supports asynchronous operations, enabling concurrency and leading to better performance.
>
> ```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]
> >
> > Use `ainsert()` and `aprint_response()` methods with `asyncio.run()` for non-blocking operations in high-throughput applications.

## ClickHouse Params

<Snippet file="vectordb_clickhouse_params.mdx" />


This documentation is built and hosted on [Mintlify](https://mintlify.com), a developer documentation platform.