MyScale
This page covers how to use MyScale vector database within LangChain. It is broken into two parts: installation and setup, and then references to specific MyScale wrappers.
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.
Introductionโ
Overview to MyScale and High performance vector search
You can now register on our SaaS and start a cluster now!
If you are also interested in how we managed to integrate SQL and vector, please refer to this document for further syntax reference.
We also deliver with live demo on huggingface! Please checkout our huggingface space! They search millions of vector within a blink!
Installation and Setupโ
- Install the Python SDK with
pip install clickhouse-connect
Setting up environmentsโ
There are two ways to set up parameters for myscale index.
Environment Variables
Before you run the app, please set the environment variable with
export
:export MYSCALE_HOST='<your-endpoints-url>' MYSCALE_PORT=<your-endpoints-port> MYSCALE_USERNAME=<your-username> MYSCALE_PASSWORD=<your-password> ...
You can easily find your account, password and other info on our SaaS. For details please refer to this document Every attributes under
MyScaleSettings
can be set with prefixMYSCALE_
and is case insensitive.Create
MyScaleSettings
object with parameters
```python
from langchain_community.vectorstores import MyScale, MyScaleSettings
config = MyScaleSettings(host="<your-backend-url>", port=8443, ...)
index = MyScale(embedding_function, config)
index.add_documents(...)
```
Wrappersโ
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
delete
VectorStoreโ
There exists a wrapper around MyScale database, allowing you to use it as a vectorstore, whether for semantic search or similar example retrieval.
To import this vectorstore:
from langchain_community.vectorstores import MyScale
API Reference:
For a more detailed walkthrough of the MyScale wrapper, see this notebook