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Fast and accurate production-grade RAG pipelines
Fully automatic AI vectorization, transforming pixels into full-color vector graphics.
sinequa
A vectorization tool developed by Sinequa that generates embedding vectors from input paragraphs or queries for sentence similarity calculation and retrieval tasks.
A vectorizer developed by Sinequa that generates embedding vectors based on input paragraphs or queries, used for sentence similarity calculation and retrieval tasks.
A vectorizer developed by Sinequa that generates embedding vectors from input paragraphs or queries for sentence similarity computation and retrieval tasks.
A multilingual vectorizer developed by Sinequa that generates embedding vectors for input paragraphs or queries, used for similarity calculation and information retrieval.
A vectorizer developed by Sinequa capable of generating embedding vectors from paragraphs or queries for sentence similarity computation and feature extraction.
The Vectorize MCP Server is a model context protocol server integrating the Vectorize service, providing functions such as vector retrieval, text extraction, and in - depth research.
An MCP server that provides relevant Ethereum Improvement Proposals (EIP) content for AI agents through semantic search, supporting Markdown document processing and vectorized retrieval.
The MCP Code Indexer is an intelligent code retrieval tool designed specifically for large AI language models. It improves the efficiency and accuracy of code processing through semantic understanding and vectorized indexing, supporting functions such as code analysis, quality assessment, and dependency management.
Vibe-Eyes is an MCP server project that enables LLMs to 'see' what's happening in browser-based games and applications through vectorized canvas visualization and debugging information.
A project that uses DuckDB and Plamo-Embedding-1B to implement RAG functionality, supporting vectorized storage and retrieval of markdown files and providing an MCP service interface.
An MCP tool for similarity search based on Bevy's English documentation. It realizes intelligent document query functions by converting HTML documents to Markdown and storing them in a vectorized form.
An AI knowledge base and data processing project that includes vector database processing, MCP protocol support, and core function libraries, supporting text chunking, vectorized search, and debugging tools.
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