A a collection of languages stemmers and stopwords for Lunr Javascript library
A rag component for Convex.
The Retrieval-Augmented Generation (RAG) module contains document processing and embedding utilities.
Image structural similarity (SSIM). In TypeScript/JavaScript. For browser/server.
LL(*) lookahead strategy for the Chevrotain parser library
A agent component for Convex.
A Node.js wrapper for the opendataloader-pdf Java CLI.
Phase 2 of the catalog plane. Adds vector embeddings, AI-agent access patterns, and the MCP server scaffolding on top of the Phase 1 foundation in `@voyantjs/catalog`.
The enrichment-grade MCP server for academic paper search and full-text extraction. For science.
Simple JS stack with auto run for node and browsers
The official Pinecone TypeScript SDK for building vector search applications with AI/ML.
BlockRun MCP Server - Give your AI agent web search, deep research, prediction markets, crypto data, X/Twitter intelligence. Paid via x402 micropayments.
> LlamaCloud is a new generation of managed parsing, ingestion, and retrieval services, designed to bring production-grade context-augmentation to your LLM and RAG applications.
Opencode plugin to fetch latest and trending research papers from arXiv and OpenAlex
Project-local RAG memory MCP server — knowledge graph + multilingual vector + FTS5 in a single SQLite file. Per-project isolation, 30 MCP tools, codepoint-safe chunking (Korean/CJK/emoji).
Retrivora AI is a plug-and-play AI engine for RAG chat experiences — generic vector DB + LLM provider, embeddable or standalone.
Keystone airgaped wallet SDK
No description provided.
Extract clean, timestamped YouTube captions, subtitles, transcripts, and video metadata for AI summaries, RAG, search, and slide-ready workflows.
Local RAG MCP Server - Easy-to-setup document search with minimal configuration
Straightforward fuzzy matching, information retrieval and NLP building blocks for JavaScript.
> TODO: description
The smallest and fastest pixel-level image comparison library.
A JavaScript library for Retrieval-Augmented Generation (RAG) within the QVAC ecosystem. Build powerful, context-aware AI applications with seamless document ingestion, vector search, and LLM integration.