State-of-the-Art Embeddings, Retrieval, and Reranking
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。
Full-stack multimodal product retrieval and personalized reranking with CLIP, FAISS, Spring Boot, FastAPI, and PostgreSQL.
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.
Reproducible benchmark and reference pipeline for reranked long-term memory retrieval