RAG / 向量检索排行

Comprehensive LLMs repo, where I cover both theoretical and practical aspects of LLMs.

A curated full-stack LLM engineering playbook — from AI infrastructure and model lifecycle to agents, MCP, memory, and evaluation.

chew through any source into clean datasets! a fast ingestion, RAG & fine-tuning toolkit

Visual tool to compare 6 RAG chunking strategies side-by-side with grading and query selection

In-memory vector database with pluggable indexing algorithms, metadata filtering, and a FastAPI-based REST API.

NeuralCache is a drop-in reranker for Retrieval-Augmented Generation (RAG) that learns which context the model actually uses.

WMB-100K — The first 100,000-turn benchmark for AI memory systems

An AI-powered search engine to interact with documentation using RAG and local LLMs. Privately deployable with vector search and a modern frontend.

NeuroLens is a premium, state-of-the-art Document Retrieval-Augmented Generation (RAG) system. It combines an immersive, holographic, sci-fi-themed React frontend with a high-performance FastAPI backend.

Deterministic context database for AI agents. Same query → same context, every time.

This is a repository to let you know the implementation of a basic RAG pipeline using LangChain in Supabase Edge Functions.

Persistent memory and identity protocol for AI systems with evidence-aware recall and inspectable memory.

A collection of ready-to-use n8n workflow templates for building intelligent automations using Pinecone.

Memory architectures that evolve with your agent

项目描述：为解决电商客服在物流、售后等高频咨询场景下，人工重复查询、知识定位慢及信息需反复核对等痛点，搭建 Agent 智能客服系统。通过意图分流、业务工具调用与 RAG 知识库检索，完成订单物流查询、售后信息收集和商品问答，提升响应效率与回答准确性。

Context Runtime — a database query planner for LLM context. Decides what a model sees before it answers; plans it, runs it through reused substrate, and learns from the outcome.

This repository contains practical AI projects applying machine learning techniques to real-world problems.

Enterprise multi-source RAG platform with multimodal embedding, hybrid vector/BM25 retrieval, MCP & RBAC support

Development and deployment on AWS of a question-answer LLM model using Llama2 with 7B parameters and RAG with LangChain

Kikiola is a high-performance vector database written in Go.