Distributed vector search for AI-native applications
The universal tool suite for vector database management. Manage Pinecone, Chroma, Qdrant, Weaviate and more vector databases with ease.
Parsing-free RAG supported by VLMs
High performance embedded vector database
Vector search demo with the arXiv paper dataset, RedisVL, HuggingFace, OpenAI, Cohere, FastAPI, React, and Redis.
🐊 Snappy's unique approach unifies vision-language late interaction with structured OCR for region-level knowledge retrieval. Like the project? Drop a star! ⭐
The first database built to let AI agents think their way to the right answer using structural reasoning, rather than guessing based on vector similarity.
A post-retrieval temporal layer for RAG systems — validity filtering, time decay, document kind classification, and hybrid reranking in one pipeline.
Pixel-native visual RAG ported to Rust on the ruvector ANN substrate (HNSW + IVF-Flat) — screenshot/document retrieval over visual embeddings, a Rust port of PixelRAG, with a metaharness benchmark CLI: npx rupixel
Knolo turns documents into a verifiable Knowledge Image (.knolo): one portable file you can mount, query, and prove. Deterministic retrieval, cryptographic roots, fail-closed verification. Local-first. No vector DB required.
A local-first Python personal AI assistant for reasoning, memory, voice, vision, automation, and device control.
[VLSP 2025] ViDRILL is a Vietnamese document retrieval system for VLSP 2025. It combines dense and sparse retrieval, reranking, and optional LLM-based query rewriting and reasoning to support high-accuracy information retrieval and future LLM-enhanced pipelines.
Vietnamese long form question answering system with documents retrieval.
Implementation of ECIR 2022 Paper: How Can Graph Neural Networks Help Document Retrieval: A Case Study on CORD19 with Concept Map Generation
Retrieves the top 10 documents from the Wikipedia corpus for a user inputted free-text query
PageIndex-inspired agentic RAG app for vectorless document QA, FastAPI, multi-document retrieval, context compaction, and self-hosted AI workspaces.
Document Querying with LLMs - Google PaLM API: Semantic Search With LLM Embeddings
Benchmark 9 retrieval architectures (vector, contextual, QnA, knowledge graph, hybrid, RAPTOR, PageIndex, BM25, rerank) on your own docs. Automated hyperparameter search with bootstrap CIs and significance tests.
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