> ## Documentation Index
> Fetch the complete documentation index at: https://docs.langchain.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Weaviate integrations

> Integrate with Weaviate using LangChain Python.

> [Weaviate](https://weaviate.io/) is an open-source vector database. It allows you to store data objects and vector embeddings from
> your favorite ML models, and scale seamlessly into billions of data objects.

What is `Weaviate`?

* 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](https://weaviate.io/developers/weaviate/current/benchmarks/) to see if Weaviate fits your use case).
* Get to know Weaviate in the [basics getting started guide](https://weaviate.io/developers/weaviate/current/core-knowledge/basics.html) 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:

<CodeGroup>
  ```bash pip theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
  pip install langchain-weaviate
  ```

  ```bash uv theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
  uv add langchain-weaviate
  ```
</CodeGroup>

## Vector store

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:

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
from langchain_weaviate import WeaviateVectorStore
```

For a more detailed walkthrough of the Weaviate wrapper, see [this notebook](/oss/python/integrations/vectorstores/weaviate)

***

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