retrieval

Generate vector embeddings in NodeJS

ChatIndex: Tree indexing and retrieval for long conversational memory

(ACL 2025 Main) A Comprehensive Benchmark for Code Information Retrieval.

[EMNLP 2024] OneGen: Efficient One-Pass Unified Generation and Retrieval for LLMs.

An open-source, vector-free long-term memory engine for AI agents, achieving SOTA on LoCoMo and LongMemEval with significantly less context.

An Open-Source Package for Chinese Open-domain Conversational Chatbot (中文闲聊对话系统，一键部署微信闲聊机器人)

Local code search combining BM25, vector similarity, and cross-encoder reranking. Parses 60+ languages with tree-sitter, runs entirely offline, and returns structured results with file paths, line ranges, and symbol metadata. Built in Rust.

Go implementation of @qdrant/fastembed.

Durable, file-based long-term memory for AI agents. Five-package plugin family: SDK, CLI, MCP server, Hermes adapter, and a LangGraph BaseStore. No vector database, no embeddings.

High-quality search for AI-native applications.

Production inference for encoder models - ColBERT, GLiNER, ColPali, embeddings etc. - as vLLM plugins for online and in-process deployment

Implementation of Marge, Pre-training via Paraphrasing, in Pytorch

Fast search engine on object storage, with full text search, vectors, and SQL, natively on Parquet.

Build Recommender System with PyTorch + Redis + Elasticsearch + Feast + Triton + Flask. Vector Recall, DeepFM Ranking and Web Application.

🚀 100% local RAG system with one-command setup. Your data never leaves your server. Apache-2.0

Pull high-quality, efficient embeddings for PubMed, arXiv and Wikipedia from Huggingface and use for local LLM inference/Retrieval Augmented Generation (RAG)

A Flexible Toolkit for Dense Retrieval

Bounded, inspectable LLM inference pipelines from declared YAML — runs offline against Ollama or any OpenAI-compatible local server, emitting JSONL traces, inspect reports, and stable exit codes.

Your agent seeks what search can't find. A self-hosted perception MCP server that transcribes speech, reads behind logins, sees images and video frames, crosses languages, and remembers.

Start building practical applications that allow you to interact with data using LangChain and LLMs.