Weaviate 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.
- 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 to see if Weaviate fits your use case).
- Get to know Weaviate in the basics getting started guide 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:
pip install weaviate-client
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:
from langchain_community.vectorstores import Weaviate
For a more detailed walkthrough of the Weaviate wrapper, see this notebook