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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>
34 lines
2.0 KiB
Markdown
34 lines
2.0 KiB
Markdown
# Weaviate
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This page covers how to use the Weaviate ecosystem within LangChain.
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What is Weaviate?
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**Weaviate in a nutshell:**
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- Weaviate is an open-source database of the type vector search engine.
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- 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.
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- 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.
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- Weaviate has a GraphQL-API to access your data easily.
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- 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).
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- Get to know Weaviate in the [basics getting started guide](https://weaviate.io/developers/weaviate/current/core-knowledge/basics.html) in under five minutes.
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**Weaviate in detail:**
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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.
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## Installation and Setup
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- Install the Python SDK with `pip install weaviate-client`
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## Wrappers
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### VectorStore
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There exists a wrapper around Weaviate indexes, allowing you to use it as a vectorstore,
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whether for semantic search or example selection.
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To import this vectorstore:
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```python
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from langchain.vectorstores import Weaviate
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```
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For a more detailed walkthrough of the Weaviate wrapper, see [this notebook](../modules/utils/combine_docs_examples/vectorstores.ipynb)
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