#bm25

MCP-native code retrieval for AI agents — 84-88% fewer read tokens, BM25F + semantic search, AST chunks, session dedup

实时面试提词器：本地 ASR + BM25 检索 + LLM 快答，说完 1.3~1.5 秒出答案（Windows）

基于 DeepSeek Harness 的智能客服工单处理 Agent：自动分类、优先级评估、知识库检索（BM25）与建议答复，结论写回 SQLite 并可全程审计

AI agent knowledge & long-term memory · MCP server, knowledge graph, BM25 + RRF retrieval · drop-in for Claude Code/ Openclaw/ Cursor / Codex / Hermes

AI document assistant with a production RAG pipeline — hybrid BM25 + semantic search, cross-encoder reranking, and streaming answers. Built with FastAPI, Streamlit, ChromaDB, and DeepSeek LLM. Fully Dockerized and deployed on Railway.

Agent + Multimodal RAG 全栈学习项目：LangGraph PAE/ReAct、Multi-Query、BM25 + pgvector 混合检索、RAG-Fusion/RRF、MinerU/OCR/ASR、evidence 引用与 HITL；React + Spring Boot + FastAPI。

Nokori (残り) — behavioral memory layer for Claude Code and Cursor. Turns your corrections into rules that come back when it counts. 经验留下的痕迹，比记忆更深。

A personal memory layer for your AI tools. Intelligent authoring (gap detection, self-enriching graph, session-synthesis loop) + measurably good retrieval (94.9% blind Cov@10, sub-ms latency). Domain-agnostic framework — works for any context you want to track. MCP-native, Claude-Code-first, works with Cursor/Continue/Cline/OpenAI/Gemini.

On-Premise RAG (продвинутая версия): vLLM/Ollama + bge-m3 + reranker + Qdrant + hybrid (BM25+RRF) + PDR + Basic Auth + Prometheus/Grafana + локальная оценка. Запускается на Ollama без GPU. Тех-задача Petrel AI (Astana Hub).

Enterprise RAG platform for secure document intelligence using hybrid retrieval, reranking, Gemini LLM, RBAC, HITL, citations, and web search.

Verbatim recall across Claude Code sessions, plus a gate against oversized skills. Pure Node, no dependencies, nothing leaves your machine.

Hands your coding agent the right skill for each prompt: BM25 recall + Jev, one hook binary for Claude Code, Codex, Gemini CLI, Hermes, Cline

Document-Driven Aviation RAG Assistant built using FastAPI, FAISS, BM25, Gemini API, and Streamlit. Provides grounded answers from aviation manuals with citations, hallucination prevention, and hybrid retrieval.

Reproducibility materials for: A Multi-Source RAG Framework for Intelligent Agent Orchestration in Enterprise CRM Systems (IEEE Access 2026)

A Retrieval-Augmented Generation (RAG) chatbot for answering LPDP scholarship questions using Hybrid Retrieval, Cross-Encoder Reranking, and Llama 3.3.

A retrieval-based medical Q&A system that gives accurate answers to diabetes-related questions by searching a curated medical knowledge base instead of generating text from scratch. Built with a hybrid FAISS + BM25 retrieval pipeline, cross-encoder reranking, and multi-layer safety gating and served through a Streamlit web app.

Evidence-first enterprise knowledge retrieval & compliance agent: BM25+dense RRF hybrid, SHA-256 stable chunking, provenance-audited NIST/OWASP corpus, Recall@K/MRR eval

End-to-end RAG system built from scratch with recursive chunking, persistent indexing, vector + BM25 hybrid retrieval, evaluation, FastAPI, Docker, and AWS deployment testing.

Hands-on implementation of Retrieval-Augmented Generation (RAG) using vector databases, embeddings, semantic search, and Anthropic Claude.

Enterprise-grade agentic RAG chatbot — Python 3.14 / FastAPI / LangGraph orchestration, PostgreSQL + pgvector hybrid (dense + BM25) retrieval with cross-encoder reranking, Keycloak OIDC auth, MinIO object storage, arq ingestion workers, DeepEval scoring, and a pixel-perfect React 19 + Vite + TypeScript UI.

Apex-relevance subgraph retrieval for AI agents — feed your LLM the most relevant slice of a knowledge graph, within a token budget.

Fast local search over your text and Markdown files, built for AI agents: keyword (BM25), GPU semantic search and reranked hybrid retrieval, served over MCP.