Ready-to-run cloud templates for RAG, AI pipelines, and enterprise search with live data. 🐳Docker-friendly.⚡Always in sync with Sharepoint, Google Drive, S3, Kafka, PostgreSQL, real-time data APIs, and more.

AI-powered Dropbox search tool for private documents

Roy: A lightweight, model-agnostic framework for crafting advanced multi-agent systems using large language models.

[NeurIPS'24] Approximate Nearest Neighbor search using reduced-rank regression, with extremely fast queries, tiny memory usage, and rapid indexing on modern vector embeddings.

Vector Index Benchmark for Embeddings (VIBE) is an extensible benchmark for approximate nearest neighbor search methods, or vector indexes, using modern embedding datasets.

A Rust binding for the VSAG -- vector indexing and search library.

KNN Search Algorithm Comparison – This project compares the performance of different K-Nearest Neighbors (KNN) search algorithms across various dataset sizes and dimensions.

A lightweight, in-memory vector index for approximate nearest-neighbor (ANN) search

Shrink a bloated ChunkHound DuckDB index by rebuilding it. CLI + Python library.

Semantic Desktop Search - search for answers not the file names

Streamlit app for a Blender Helper Bot using a pre-built TiDB vector store

🔥 VectorForge - Lightweight High-Performance Vector Index Engine | Pure Python, Zero Dependencies, HNSW/IVF/LSH/BruteForce, TUI Dashboard, Multi-Format I/O, Cross-Platform

A benchmarking playground for FAISS vector indexes comparing indexes on recall, latency, and memory, with vector retrieval

Simple, High Quality, RAG application using TiDB vector store

技能池管理 v1.0.0 — 向量索引 + 语义搜索 + core/pool 分类

From-scratch HNSW vector search engine in Rust with PyO3 Python bindings, a FastAPI retrieval service, and a Next.js RAG interface for document Q&A with cited sources and reproducible benchmarks against brute-force search.

A prototype for visualizing and exploring vector document indexes

Semantic Hybrid Search with Sentence-Transformers + FAISS. Ask any question in plain English. Get the most relevant Wikipedia passages back — ranked by meaning, not just keywords.

This repository stores the pre-built LlamaIndex vector index used by FinRAG. It contains document chunks and embeddings created with pdfplumber, Hugging Face’s BGE model, and LlamaIndex, allowing the main app to load the index without rebuilding it.

Chroma (Chroma DB) is an open-source, AI-native vector database (embedding database) for building LLM, RAG, and semantic-search applications. It provides storage, indexing, and retrieval for vector embeddings with metadata filtering, full-text and regex search, and multi-modal retrieval across text, images, and audio.