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openai-cookbook/examples/vector_databases/weaviate
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Logan Kilpatrick f1e13cfcc7
Misc updates (#1022) 		8 months ago
..
README.md docs: fix typos (#811) 11 months ago
Using_Weaviate_for_embeddings_search.ipynb Misc updates (#1022) 8 months ago
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README.md
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Weaviate <> OpenAI

​Weaviate is an open-source vector search engine (docs - Github) that can store and search through OpenAI embeddings and data objects. The database allows you to do similarity search, hybrid search (the combining of multiple search techniques, such as keyword-based and vector search), and generative search (like Q&A). Weaviate also supports a wide variety of OpenAI-based modules (e.g., text2vec-openai, qna-openai), allowing you to vectorize and query data fast and efficiently.

You can run Weaviate (including the OpenAI modules if desired) in three ways:

  1. Open source inside a Docker-container (example)
  2. Using the Weaviate Cloud Service (get started)
  3. In a Kubernetes cluster (learn more)

Examples

This folder contains a variety of Weaviate and OpenAI examples.

Name Description language Google Colab
Getting Started with Weaviate and OpenAI A simple getting started for semantic vector search using the OpenAI vectorization module in Weaviate (text2vec-openai) Python Notebook link
Hybrid Search with Weaviate and OpenAI A simple getting started for hybrid search using the OpenAI vectorization module in Weaviate (text2vec-openai) Python Notebook link
Question Answering with Weaviate and OpenAI A simple getting started for question answering (Q&A) using the OpenAI Q&A module in Weaviate (qna-openai) Python Notebook link
Docker-compose example A Docker-compose file with all OpenAI modules enabled Docker