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| .. | ||
| docker-compose.yml | ||
| generative-search-with-weaviate-and-openai.ipynb | ||
| getting-started-with-weaviate-and-openai.ipynb | ||
| hybrid-search-with-weaviate-and-openai.ipynb | ||
| question-answering-with-weaviate-and-openai.ipynb | ||
| README.md | ||
| Using_Weaviate_for_embeddings_search.ipynb | ||
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:
- Open source inside a Docker-container (example)
- Using the Weaviate Cloud Service (get started)
- 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 |