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.
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 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 langchain-weaviate

​
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: 
from langchain_weaviate import WeaviateVectorStore
For a more detailed walkthrough of the Weaviate wrapper, see this notebook