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https://github.com/hwchase17/langchain
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66 lines
2.6 KiB
Markdown
66 lines
2.6 KiB
Markdown
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# MyScale
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This page covers how to use MyScale vector database within LangChain.
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It is broken into two parts: installation and setup, and then references to specific MyScale wrappers.
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With MyScale, you can manage both structured and unstructured (vectorized) data, and perform joint queries and analytics on both types of data using SQL. Plus, MyScale's cloud-native OLAP architecture, built on top of ClickHouse, enables lightning-fast data processing even on massive datasets.
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## Introduction
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[Overview to MyScale and High performance vector search](https://docs.myscale.com/en/overview/)
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You can now register on our SaaS and [start a cluster now!](https://docs.myscale.com/en/quickstart/)
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If you are also interested in how we managed to integrate SQL and vector, please refer to [this document](https://docs.myscale.com/en/vector-reference/) for further syntax reference.
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We also deliver with live demo on huggingface! Please checkout our [huggingface space](https://huggingface.co/myscale)! They search millions of vector within a blink!
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## Installation and Setup
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- Install the Python SDK with `pip install clickhouse-connect`
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### Setting up envrionments
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There are two ways to set up parameters for myscale index.
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1. Environment Variables
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Before you run the app, please set the environment variable with `export`:
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`export MYSCALE_URL='<your-endpoints-url>' MYSCALE_PORT=<your-endpoints-port> MYSCALE_USERNAME=<your-username> MYSCALE_PASSWORD=<your-password> ...`
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You can easily find your account, password and other info on our SaaS. For details please refer to [this document](https://docs.myscale.com/en/cluster-management/)
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Every attributes under `MyScaleSettings` can be set with prefix `MYSCALE_` and is case insensitive.
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2. Create `MyScaleSettings` object with parameters
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```python
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from langchain.vectorstores import MyScale, MyScaleSettings
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config = MyScaleSetting(host="<your-backend-url>", port=8443, ...)
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index = MyScale(embedding_function, config)
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index.add_documents(...)
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```
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## Wrappers
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supported functions:
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- `add_texts`
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- `add_documents`
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- `from_texts`
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- `from_documents`
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- `similarity_search`
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- `asimilarity_search`
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- `similarity_search_by_vector`
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- `asimilarity_search_by_vector`
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- `similarity_search_with_relevance_scores`
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### VectorStore
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There exists a wrapper around MyScale database, allowing you to use it as a vectorstore,
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whether for semantic search or similar example retrieval.
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To import this vectorstore:
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```python
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from langchain.vectorstores import MyScale
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```
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For a more detailed walkthrough of the MyScale wrapper, see [this notebook](../modules/indexes/vectorstores/examples/myscale.ipynb)
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