langchain/docs/ecosystem/weaviate.md

# 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/utils/combine_docs_examples/vectorstores.ipynb)
-												Docs refactor (#480)

Big docs refactor! Motivation is to make it easier for people to find
resources they are looking for. To accomplish this, there are now three
main sections:

- Getting Started: steps for getting started, walking through most core
functionality
- Modules: these are different modules of functionality that langchain
provides. Each part here has a "getting started", "how to", "key
concepts" and "reference" section (except in a few select cases where it
didnt easily fit).
- Use Cases: this is to separate use cases (like summarization, question
answering, evaluation, etc) from the modules, and provide a different
entry point to the code base.

There is also a full reference section, as well as extra resources
(glossary, gallery, etc)

Co-authored-by: Shreya Rajpal <ShreyaR@users.noreply.github.com>
											
										
										
											2023-01-02 16:24:09 +00:00
+								# 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/utils/combine_docs_examples/vectorstores.ipynb)