semantic-search

Claude Skills for connecting Claude.ai to local Weaviate vector databases - manage collections, ingest data, and query with RAG

Native AI inference for PHP 8.3+ - run ONNX, GGUF (llama.cpp) and RubixML models directly in your PHP process via FFI. Chat, streaming, embeddings, RAG and vector search. No Python, no HTTP microservices, no Docker sidecars.

.NET library for local embedding generation using ONNX and Microsoft.Extensions.AI

Local-first hybrid semantic code search tool. Indexes codebases into PostgreSQL with pgvector embeddings via Ollama, combines vector similarity + keyword search with RRF fusion. Supports 30+ languages. Features CLI, MCP server, WEB dashboard and interactive REPL.

Harness the power of Retrieval-Augmented Generation with the Personal AI Assistant, an innovative tool designed to extract and synthesize information from web and PDF sources efficiently. This cutting-edge solution transforms complex data into concise, actionable insights, making it indispensable for researchers and professionals alike.

🏆 AI Procurement Assistant - Google Cloud Sweden Hackathon Winner. Natural language search for retail buyers using Superlinked semantic search.

Local FAISS vector store as an MCP server – Agent Memory, drop-in local semantic search for Claude / Copilot / Agents.

🧠 BrainX V6 — The First Brain for OpenClaw. Persistent AI agent memory with PostgreSQL, pgvector, OpenAI embeddings, semantic search, cross-agent learning, and an OpenClaw runtime plugin.

CLI tool for Weaviate

Semantic QA with a markdown database: Query any markdown file using vector embedding, Pinecone vector database and GPT (langchain). A weaker version of privateGPT

Simple customizable evaluation for text retrieval performance of Sentence Transformers embedders on PDFs

YouTube MCP Server is an AI-powered solution designed to revolutionize your YouTube experience. It empowers users to search for YouTube videos, retrieve detailed transcripts, and perform semantic searches over video content—all without relying on the official API. By integrating with a vector database, this server streamlines content discovery.

Anthropic's Contextual Retrieval implementation with visual chunk comparison. Preview context enrichment before/after embedding.

StickerSelector 是一个基于语义向量匹配的表情包选择系统，用于让 AI 在聊天中发送真正符合语境的表情包。 与传统依赖关键词或规则的方案不同，StickerSelector 会将 AI 生成的“表情包意图描述”与本地表情包进行语义匹配，从而选出在当前语境下最自然、最像真人会使用的表情。 适用于 AI 聊天机器人、QQ或微信的自动聊天、AI 女友等需要“拟人化表达”的场景。

Multi-Modal RAG for .NET — query databases, documents, images and audio in natural language. Production-ready with multi-AI support, vector storage, and multi-database coordination.

ContextAtlas — context infrastructure for AI coding agents: hybrid retrieval, project memory and retrieval observability via CLI, MCP server or embeddable library. Tree-sitter indexing, LanceDB vector search, FTS5 and token-aware context packing.

Train linear embedding adapters with triplet loss to align retrieval embeddings with your queries (RAG).

Absurdly fast embedded graph database with built-in vector search. Rust core, Node.js and Python connectors, GQL, graph algorithms, all in-process with no server.

A semantic code search tool for intelligent, cross-repo context retrieval.

High-performance infrastructure libraries built in Rust, with seamless bindings for Python, Node.js, and WebAssembly.