mirror of
https://github.com/hwchase17/langchain
synced 2024-10-29 17:07:25 +00:00
87e502c6bc
Co-authored-by: jacoblee93 <jacoblee93@gmail.com> Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
34 lines
2.0 KiB
Plaintext
34 lines
2.0 KiB
Plaintext
# 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](/docs/modules/data_connection/vectorstores/integrations/weaviate.html)
|