Results for rag-module

A TypeScript/JavaScript module for implementing Retrieval-Augmented Generation (RAG) using Qdrant vector database, Google's Generative AI embeddings, and Groq LLM.

The Retrieval-Augmented Generation (RAG) module contains document processing and embedding utilities.

An rag module for Angular.

RAG module for the UBC GenAI Toolkit

Enterprise RAG module with chat context storage, vector search, and session management. Complete chat history retrieval and streaming content extraction for Electron apps.

Strav RAG module — vector store abstraction, pgvector + in-memory drivers, chunking strategies. Composes with @strav/brain for embeddings and @strav/database for persistence.

The Retrieval-Augmented Generation (RAG) module contains document processing and embedding utilities.

The Retrieval-Augmented Generation (RAG) module contains document processing and embedding utilities.

A a collection of languages stemmers and stopwords for Lunr Javascript library

A rag component for Convex.

A Node.js TypeScript module for RAG functionality using nomic-embed-text-v1.5 and sqlite-vec

A agent component for Convex.

No description provided.

No description provided.

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 official Pinecone TypeScript SDK for building vector search applications with AI/ML.

Unified CLI for a local markdown-as-memory vault (Obsidian-compatible, not required), code graph (graphify), and cross-repo intelligence. Hosts: Claude Code + Codex CLI. Mistborn-themed with a neutral alias set.

No description provided.

The bedrock backend module for the rag-ai plugin.

The OpenAI backend module for the @roadiehq/rag-ai plugin.

> 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.

Retrivora AI is a plug-and-play AI engine for RAG chat experiences — generic vector DB + LLM provider, embeddable or standalone.

Extract clean, timestamped YouTube captions, subtitles, transcripts, and video metadata for AI summaries, RAG, search, and slide-ready workflows.

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).