RAG / 向量检索排行

Awesome-HCI （Ubiquitous, LLM, MLLM, Agent, RAG, Embodied-AI, RLHF)

A comprehensive, professional guide explaining the differences, strengths, and best practices of Retrieval-Augmented Generation (RAG) and Fine-Tuning for LLMs, including workflows, comparisons, decision frameworks, and real-world hybrid AI use cases.

An intelligent full-stack insurance claim decision-making system that combines FastAPI backend with React TypeScript frontend, powered by Hugging Face Transformers and Gemma LLM. This AI agent analyzes insurance clauses and customer queries to predict whether a claim should be Approved or Rejected, along with justification and estimated amounts.

Edge Vector search engine with Vulkan GPU acceleration, hierarchical indexing (HRM2), and native LangChain integration. Gaussian splat-based architecture for similarity search on resource-constrained devices.

Semantic search knowledge base for MCP-enabled AI assistants. Index local files or GitHub repos, query with natural language. Built on LanceDB vector storage. Works with Claude Desktop, Cursor, and other MCP clients.

Fast vector search in PostgreSQL

LLM-free, layout-aware PDF chunking for RAG pipelines. Preserves document structure via regex and font heuristics. Written in pure Rust.

Official Core Services for MemFuse - the lightning-fast open-source memory layer that gives LLMs persistent, queryable memory across conversations and sessions.

Persistent cognitive memory for AI agents — drop-in MCP server for Claude Code, Cursor, Windsurf. Temporal decay, contradiction detection, knowledge graph, autonomous consolidation. Backed by the YantrikDB Rust engine. Install: pip install yantrikdb-mcp.

Self-hosted AI knowledge base with hybrid semantic search (pgvector + FTS + RRF), MCP server, multi-provider LLM inference (Ollama, OpenAI, OpenRouter, llama.cpp), multimodal ingestion (vision, audio transcription, speaker diarization), and knowledge graph. Rust + PostgreSQL.

Local-first desktop assistant for the whole job hunt — scrape listings, match with a local LLM, generate applications in your own voice, and track everything on a Kanban board.

A structured knowledge base for large language model technologies, covering LLMs, Agents, RAG, model training, evaluation methodologies, and AI engineering practices.

A curated list of the best AI resources, courses, tools, and repositories. Covering Generative AI (GenAI), LLMs, RAG, AI Agents, Prompt Engineering, and more. Your go-to cheat sheet for all things AI.

Retrieval Augmented Generation (RAG) Application to offer Q&A like experience on a long format text using OpenAI and ElasticSearch as Vector DB.

🐬 Formation MariaDB 12.3 LTS : SQL, index, transactions, réplication, Galera, MaxScale, DevOps/Kubernetes et MariaDB Vector (IA/RAG). 20 modules.

Windows + Docker + Python 基于 Milvus + MySQL + Redis + 大模型的私有化 RAG 问答系统

Elixir in memory VectorDB build with Rust using rustler! It's small, fast, efficient, simple!

An application that enable the users to upload PDF files and ask questions regarding their content using Retrieval Augmented Generation (RAG)

Open-source, self-hosted AI gateway for multi-tenant orgs: OpenAI-compatible LLM routing, RAG & vector stores, MCP hub, GPU fleet (MIG slicing), sandboxed coding agents, AI red-teaming & guardrails, and cost optimization — all in one console.

A filesystem-native vector database built around Markdown files. For AGENTS and MEMORY in mind. CLI first, folder based, no servers, no containers. Build with Rust for Performance.