mirror of
https://github.com/hwchase17/langchain
synced 2024-10-31 15:20:26 +00:00
f4e6eac3b6
The `self-que[ring` navbar](https://python.langchain.com/docs/modules/data_connection/retrievers/self_query/) has repeated `self-quering` repeated in each menu item. I've simplified it to be more readable - removed `self-quering` from a title of each page; - added description to the vector stores - added description and link to the Integration Card (`integrations/providers`) of the vector stores when they are missed.
39 lines
2.1 KiB
Plaintext
39 lines
2.1 KiB
Plaintext
# Weaviate
|
||
|
||
>[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:
|
||
|
||
```bash
|
||
pip install weaviate-client
|
||
```
|
||
|
||
|
||
## 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
|
||
from langchain.vectorstores import Weaviate
|
||
```
|
||
|
||
For a more detailed walkthrough of the Weaviate wrapper, see [this notebook](/docs/integrations/vectorstores/weaviate.html)
|