A rag component for Convex.
A a collection of languages stemmers and stopwords for Lunr Javascript library
The Retrieval-Augmented Generation (RAG) module contains document processing and embedding utilities.
A agent component for Convex.
Extract clean, timestamped YouTube captions, subtitles, transcripts, and video metadata for AI summaries, RAG, search, and slide-ready workflows.
The official Pinecone TypeScript SDK for building vector search applications with AI/ML.
Local RAG MCP Server - Easy-to-setup document search with minimal configuration
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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.
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`.
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Retrivora AI is a plug-and-play AI engine for RAG chat experiences — generic vector DB + LLM provider, embeddable or standalone.
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Offline HTTP RAG (Retrieval-Augmented Generation) server for the Red Sift Design System — returns ranked, scored documentation chunks for any LLM/agent to consume.
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> 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.
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MediaPipe GenAI Experimental Tasks
Personal Data Hub — UnifiedSchema + validators + KG ingest helpers for the data-back-to-the-individual middleware
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QdrantRag vector store and QdrantRagProvider for @mcp-abap-adt/llm-agent.
Abstract RAG (Retrieval-Augmented Generation) interfaces and shared implementations
RAG (Retrieval-Augmented Generation) utilities for Wundr platform - embeddings, chunking, and vector operations
Standalone RAG ingestion, sync, retrieval, evaluation, clients, and framework adapters extracted from AbsoluteJS
RAG-based knowledge assistant using Apollo and LLM for LegionIO
CRUDRAG generates RubyLLM CRUD tools and a semantic RAG index that agents can use to interact with your data layer.
Glancer is a Ruby on Rails engine that enables natural language queries over your database using RAG and LLMs.
HTM (Hierarchical Temporal Memory) provides intelligent memory/context management for LLM-based applications. It implements a two-tier memory system with durable long-term storage (PostgreSQL) and token-limited working memory, enabling applications to recall context from past conversations using RAG (Retrieval-Augmented Generation) techniques.
Seamlessly integrate Cloudflare AutoRag into your Ruby on Rails applications with this gem. It provides a simple and idiomatic Ruby interface for leveraging AutoRag’s automatic retrieval-augmented generation (RAG) pipeline—making it easy to connect your app’s data with powerful AI models using Cloudflare Workers and Vectorize.
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