| # Weaviate | |
| 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](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 with `pip install weaviate-client` | |
| ## Wrappers | |
| ### VectorStore | |
| 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 | |
| from langchain.vectorstores import Weaviate | |
| ``` | |
| For a more detailed walkthrough of the Weaviate wrapper, see [this notebook](../modules/indexes/examples/vectorstores.ipynb) | |