langchain/docs/integrations/clickhouse.md

# ClickHouse

This page covers how to use ClickHouse Vector Search within LangChain.

[ClickHouse](https://clickhouse.com) is a open source real-time OLAP database with full SQL support and a wide range of functions to assist users in writing analytical queries. Some of these functions and data structures perform distance operations between vectors, enabling ClickHouse to be used as a vector database.

Due to the fully parallelized query pipeline, ClickHouse can process vector search operations very quickly, especially when performing exact matching through a linear scan over all rows, delivering processing speed comparable to dedicated vector databases.

High compression levels, tunable through custom compression codecs, enable very large datasets to be stored and queried. ClickHouse is not memory-bound, allowing multi-TB datasets containing embeddings to be queried.

The capabilities for computing the distance between two vectors are just another SQL function and can be effectively combined with more traditional SQL filtering and aggregation capabilities. This allows vectors to be stored and queried alongside metadata, and even rich text, enabling a broad array of use cases and applications.

Finally, experimental ClickHouse capabilities like [Approximate Nearest Neighbour (ANN) indices](https://clickhouse.com/docs/en/engines/table-engines/mergetree-family/annindexes) support faster approximate matching of vectors and provide a promising development aimed to further enhance the vector matching capabilities of ClickHouse.

## Installation
- Install clickhouse server by [binary](https://clickhouse.com/docs/en/install) or [docker image](https://hub.docker.com/r/clickhouse/clickhouse-server/)
- Install the Python SDK with `pip install clickhouse-connect`

### Configure clickhouse vector index

Customize `ClickhouseSettings` object with parameters

    ```python
    from langchain.vectorstores import ClickHouse, ClickhouseSettings
    config = ClickhouseSettings(host="<clickhouse-server-host>", port=8123, ...)
    index = Clickhouse(embedding_function, config)
    index.add_documents(...)
    ```
  
## Wrappers
supported functions:
- `add_texts`
- `add_documents`
- `from_texts`
- `from_documents`
- `similarity_search`
- `asimilarity_search`
- `similarity_search_by_vector`
- `asimilarity_search_by_vector`
- `similarity_search_with_relevance_scores`

### VectorStore

There exists a wrapper around open source Clickhouse database, allowing you to use it as a vectorstore,
whether for semantic search or similar example retrieval.

To import this vectorstore:
```python
from langchain.vectorstores import Clickhouse
```

For a more detailed walkthrough of the MyScale wrapper, see [this notebook](../modules/indexes/vectorstores/examples/clickhouse.ipynb)
Integrate Clickhouse as Vector Store (#5650) <!-- Thank you for contributing to LangChain! Your PR will appear in our release under the title you set. Please make sure it highlights your valuable contribution. Replace this with a description of the change, the issue it fixes (if applicable), and relevant context. List any dependencies required for this change. After you're done, someone will review your PR. They may suggest improvements. If no one reviews your PR within a few days, feel free to @-mention the same people again, as notifications can get lost. Finally, we'd love to show appreciation for your contribution - if you'd like us to shout you out on Twitter, please also include your handle! --> #### Description This PR is mainly to integrate open source version of ClickHouse as Vector Store as it is easy for both local development and adoption of LangChain for enterprises who already have large scale clickhouse deployment. ClickHouse is a open source real-time OLAP database with full SQL support and a wide range of functions to assist users in writing analytical queries. Some of these functions and data structures perform distance operations between vectors, [enabling ClickHouse to be used as a vector database](https://clickhouse.com/blog/vector-search-clickhouse-p1). Recently added ClickHouse capabilities like [Approximate Nearest Neighbour (ANN) indices](https://clickhouse.com/docs/en/engines/table-engines/mergetree-family/annindexes) support faster approximate matching of vectors and provide a promising development aimed to further enhance the vector matching capabilities of ClickHouse. In LangChain, some ClickHouse based commercial variant vector stores like [Chroma](https://github.com/hwchase17/langchain/blob/master/langchain/vectorstores/chroma.py) and [MyScale](https://github.com/hwchase17/langchain/blob/master/langchain/vectorstores/myscale.py), etc are already integrated, but for some enterprises with large scale Clickhouse clusters deployment, it will be more straightforward to upgrade existing clickhouse infra instead of moving to another similar vector store solution, so we believe it's a valid requirement to integrate open source version of ClickHouse as vector store. As `clickhouse-connect` is already included by other integrations, this PR won't include any new dependencies. #### Before submitting <!-- If you're adding a new integration, please include: 1. Added a test for the integration: https://github.com/haoch/langchain/blob/clickhouse/tests/integration_tests/vectorstores/test_clickhouse.py 2. Added an example notebook and document showing its use: * Notebook: https://github.com/haoch/langchain/blob/clickhouse/docs/modules/indexes/vectorstores/examples/clickhouse.ipynb * Doc: https://github.com/haoch/langchain/blob/clickhouse/docs/integrations/clickhouse.md See contribution guidelines for more information on how to write tests, lint etc: https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md --> 1. Added a test for the integration: https://github.com/haoch/langchain/blob/clickhouse/tests/integration_tests/vectorstores/test_clickhouse.py 2. Added an example notebook and document showing its use: * Notebook: https://github.com/haoch/langchain/blob/clickhouse/docs/modules/indexes/vectorstores/examples/clickhouse.ipynb * Doc: https://github.com/haoch/langchain/blob/clickhouse/docs/integrations/clickhouse.md #### Who can review? Tag maintainers/contributors who might be interested: <!-- For a quicker response, figure out the right person to tag with @ @hwchase17 - project lead Tracing / Callbacks - @agola11 Async - @agola11 DataLoaders - @eyurtsev Models - @hwchase17 - @agola11 Agents / Tools / Toolkits - @vowelparrot VectorStores / Retrievers / Memory - @dev2049 --> @hwchase17 @dev2049 Could you please help review? --------- Co-authored-by: Dev 2049 <dev.dev2049@gmail.com> 1 year ago			`# ClickHouse`

			`This page covers how to use ClickHouse Vector Search within LangChain.`

			`[ClickHouse](https://clickhouse.com) is a open source real-time OLAP database with full SQL support and a wide range of functions to assist users in writing analytical queries. Some of these functions and data structures perform distance operations between vectors, enabling ClickHouse to be used as a vector database.`

			`Due to the fully parallelized query pipeline, ClickHouse can process vector search operations very quickly, especially when performing exact matching through a linear scan over all rows, delivering processing speed comparable to dedicated vector databases.`

			`High compression levels, tunable through custom compression codecs, enable very large datasets to be stored and queried. ClickHouse is not memory-bound, allowing multi-TB datasets containing embeddings to be queried.`

			`The capabilities for computing the distance between two vectors are just another SQL function and can be effectively combined with more traditional SQL filtering and aggregation capabilities. This allows vectors to be stored and queried alongside metadata, and even rich text, enabling a broad array of use cases and applications.`

			`Finally, experimental ClickHouse capabilities like [Approximate Nearest Neighbour (ANN) indices](https://clickhouse.com/docs/en/engines/table-engines/mergetree-family/annindexes) support faster approximate matching of vectors and provide a promising development aimed to further enhance the vector matching capabilities of ClickHouse.`

			`## Installation`
			`- Install clickhouse server by [binary](https://clickhouse.com/docs/en/install) or [docker image](https://hub.docker.com/r/clickhouse/clickhouse-server/)`
			- Install the Python SDK with `pip install clickhouse-connect`

			`### Configure clickhouse vector index`

			Customize `ClickhouseSettings` object with parameters

			```python
			`from langchain.vectorstores import ClickHouse, ClickhouseSettings`
			`config = ClickhouseSettings(host="<clickhouse-server-host>", port=8123, ...)`
			`index = Clickhouse(embedding_function, config)`
			`index.add_documents(...)`
			```

			`## Wrappers`
			`supported functions:`
			- `add_texts`
			- `add_documents`
			- `from_texts`
			- `from_documents`
			- `similarity_search`
			- `asimilarity_search`
			- `similarity_search_by_vector`
			- `asimilarity_search_by_vector`
			- `similarity_search_with_relevance_scores`

			`### VectorStore`

			`There exists a wrapper around open source Clickhouse database, allowing you to use it as a vectorstore,`
			`whether for semantic search or similar example retrieval.`

			`To import this vectorstore:`
			```python
			`from langchain.vectorstores import Clickhouse`
			```

			`For a more detailed walkthrough of the MyScale wrapper, see [this notebook](../modules/indexes/vectorstores/examples/clickhouse.ipynb)`