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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
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>[Weaviate](https://weaviate.io/) is an open-source vector database. It allows you to store data objects and vector embeddings from
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>your favorite ML models, and scale seamlessly into billions of data objects.
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What is `Weaviate`?
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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:
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```bash
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pip install weaviate-client
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```
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## Vector Store
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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](/docs/integrations/vectorstores/weaviate.html)
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