State-of-the-Art Embeddings, Retrieval, and Reranking
Use PEFT or Full-parameter to CPT/SFT/DPO/GRPO 600+ LLMs (Qwen3.8, DeepSeek-V4, GLM-5.1, InternLM3, Llama4, ...) and 300+ MLLMs (Qwen3-VL, Qwen3-Omni, InternVL3.5, Ovis2.5, GLM5.3, Gemma4, Llava, Phi4, ...) (AAAI 2025).
MTEB: State-of-the-art evaluation of embeddings across languages and modalities
Open-source inference server and production cluster for all the models your agent needs.
🥤 RAGLite is a Python toolkit for Retrieval-Augmented Generation (RAG) with DuckDB or PostgreSQL
Open-source infrastructure that turns scattered SKILL.md files into curated, retrieval-ready agent-skill corpora—with retrieval and evaluation tooling included.
RankLLM is a Python toolkit for reproducible information retrieval research using rerankers, with a focus on listwise reranking.
Agentic RAG for local and self-hosted document search: hybrid retrieval, reranking and multimodal RAG on embedded LanceDB, with Docling parsing and an MCP server
中文优先的企业 RAG 知识库:可控解析、治理、切块、混合检索、重排、引用、图谱、评测与 Dify 接入。
Inference engine for Intel devices. Serve LLMs, VLMs, Whisper, Kokoro-TTS, Embedding and Rerank models over OpenAI endpoints.
RAG ReAct Agent - A Retrieval-Augmented Generation system with ReAct (Reasoning+Acting) agent loop for intelligent question answering with multi-hop reasoning
OpenContextEngine is a self-hosted, ACE-compatible code retrieval service. It indexes source files with cAST-aware chunking, stores metadata in PostgreSQL or SQLite, performs dense vector retrieval in Milvus 3.0, and reranks results with an LLM before coverage-aware selection.
专为生产环境设计的 RAG 运维助手。核心采用父子块检索策略与稀疏/稠密混合检索机制,集成 BGE-M3 与 Reranker 本地模型,支持多跳故障推理、主动追问及知识溯源。
一个开箱即用的完整智能客服服务:基于 FastAPI + LangChain RAG + CrewAI 多 Agent 构建, 提供意图识别路由、知识库问答、多智能体协作、SSE 流式对话等核心客服能力,并内置 检索工程(BM25+RRF+reranker)、稳定性工程(重试/限流/语义缓存)、可观测性(实时监控控制台) 与效果评估体系(RAGAS 评测闭环),可直接部署上线,也可作为独立 AI 服务被现有业务系统(如 Spring Cloud / Node.js 后端)通过 HTTP/SSE 集成调用。
Full-stack multimodal product retrieval and personalized reranking with CLIP, FAISS, Spring Boot, FastAPI, and PostgreSQL.
本地私有化智能文档问答系统:BM25+向量混合检索、本地 Rerank 重排、完全离线、数据零外传(LangChain + Ollama + ChromaDB)
Typed judgment tools for MCP agents. TypeSafe's Jev model as verify, screen, find, classify, rerank, decide, compare, extract, review, gate, and score: the model judges, policy decides auto, review, or escalate.
Jev-powered relevance filtering and reranking for RAG in Python.
Zero-dependency local-first AI memory engine: graph memory, multimodal ingestion, reranking, temporal reasoning, hash-chained ledger, capsule compression, memory formation + runtime layers. 52 MCP tools (41 native + 11 compatible). MIT.
Small System One decision models (17M–400M) for Yes/No, Choice, Score, and reranking. Training toolkit, Python inference, and ONNX support for Node.js and browsers.
Local AI Personal Knowledge Base Q&A · Full‑Link RAG Practical Implementation (Hybrid Retrieval + Rerank Fine‑Ranking + Citation‑Enabled Generation)
Reproducible benchmark and reference pipeline for reranked long-term memory retrieval