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Weaviate is an open-source vector database that stores both objects and vectors, allowing for the combination of vector search with structured filtering with the fault tolerance and scalability of a cloud-native database.
Very fast SPARQL Engine, which can handle very large knowledge graphs like the complete Wikidata, offers context-sensitive autocompletion for SPARQL queries, and allows combination with text search. It's faster than engines like Blazegraph or Virtuoso, especially for queries involving large result sets.
A Python-based search engine for OpenAI's ChatGPT conversation history, enabling efficient semantic search and interactive engagement with archived chats using text embeddings
Leveraged natural language processing and machine learning techniques to enhance the relevance and accuracy of search results by building a semantic search engine.
A Flask app with a search engine for GoodReads books, featuring simple and neural search capabilities using Whoosh, Annoy and HuggingFace transformers.
[WIP] Behold, semantic-search, built over sentence-transformers to make it easy for search engineers to evaluate, optimise and deploy models for search.
Building a Custom Vector Search Engine with Weaviate : The project discusses the architecture of Weaviate, an open-source vector database and provides a tutorial implementation of a custom vector search engine using Weaviate Cloud Service(WCS).
PACE (Podcast AI for Chapters and Episodes) is a semantic search engine that helps you find the information you need, inter- and intra-podcasts (Project for the AssemblyAI Winter 2022 Hackathon).