This page covers how to use the Weaviate ecosystem within LangChain.

What is Weaviate?

Weaviate in a nutshell:

  • Weaviate is an open-source ​database of the type ​vector search engine.

  • Weaviate allows you to store JSON documents in a class property-like fashion while attaching machine learning vectors to these documents to represent them in vector space.

  • Weaviate can be used stand-alone (aka bring your vectors) or with a variety of modules that can do the vectorization for you and extend the core capabilities.

  • Weaviate has a GraphQL-API to access your data easily.

  • We aim to bring your vector search set up to production to query in mere milliseconds (check our open source benchmarks to see if Weaviate fits your use case).

  • Get to know Weaviate in the basics getting started guide in under five minutes.

Weaviate in detail:

Weaviate is a low-latency vector search engine with out-of-the-box support for different media types (text, images, etc.). It offers Semantic Search, Question-Answer Extraction, Classification, Customizable Models (PyTorch/TensorFlow/Keras), etc. Built from scratch in Go, Weaviate stores both objects and vectors, allowing for combining vector search with structured filtering and the fault tolerance of a cloud-native database. It is all accessible through GraphQL, REST, and various client-side programming languages.

Installation and Setup#

  • Install the Python SDK with pip install weaviate-client



There exists a wrapper around Weaviate indexes, allowing you to use it as a vectorstore, whether for semantic search or example selection.

To import this vectorstore:

from langchain.vectorstores import Weaviate

For a more detailed walkthrough of the Weaviate wrapper, see this notebook