Ingest documents into elasticaserch
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.
A high-performance, parallelized document ingestion and vectorization pipeline.
n8n community node for the Exado AI API (RAG chat, OpenAI-compatible completions/embeddings, document ingestion, conversations).
Document ingestion and retrieval-augmented generation
TypeScript-first, production-oriented RAG SDK for document ingestion, OCR, chunking, embeddings, indexing, retrieval, and answer generation.
OpenClaw plugin for Knowhere-powered document ingestion and automatic grounding.
n8n community node for the Exado AI API (RAG chat, OpenAI-compatible completions/embeddings, document ingestion, conversations).
RAG Engine — document ingestion, vector search, and knowledge base MCP server
Knowledge microservice — RAG collections, document ingestion, chunking, embeddings, semantic/text/hybrid retrieval
Transforms an excel spreadsheet into JSON for document ingestion into SOLR
JavaScript client for ingestion
I2E Policy Bridge - Document ingestion and policy compilation for AIGRC
Canonical table schema, normalization helpers, exports, and chunk builders for document ingestion workflows.
Normalize messy PDFs for fast web delivery and reliable document ingestion.
Ragie skills for AI coding agents — document ingestion, retrieval, RAG patterns, and MCP integration
MemTap — Graph-based long-term memory for AI agents. Knowledge graph with semantic recall, GraphRAG, entity management, decision tracking, auto-capture, document ingestion, onboarding, and multi-turn context.
A lightweight vector database and RAG storage layer for MCP servers. Supports document ingestion, chunking, embedding, and semantic search.
Production-ready MCP server for document ingestion and knowledge management with vector search. Supports PDF, DOCX, TXT, MD, CSV, JSON, HTML with ChromaDB and multiple embedding providers.
Azure Monitor Ingestion library
Self-improving cognitive memory engine with graph knowledge, document ingestion, and Ebbinghaus decay. By Framers.
An entity provider for streaming large asset sources into the catalog
The official TypeScript library for the Llama Cloud API
Dataset package for Workglow.
Rust client for bigRAG — a self-hostable RAG platform
Senior SysAdmin, Network Admin, Data Analyst, and Software Engineer living in your terminal. A high-precision local AI agent harness for LM Studio, Ollama, and other local OpenAI-compatible runtimes that runs 100% on your own silicon. Reads repos, edits files, runs builds, inspects full network state and workstation telemetry, and runs real Python/JS for data analysis.
Document corpus ingestion and knowledge query pipeline for LegionIO
The Ruby Kubernetes Controller allows users to interact with the core Kubernetes APIs natively from within their Ruby applications. This library is compatible with all leading Kubernetes Instances, including OpenShift Kubernetes, Azure Kubernetes Service, Amazon EKS, Google Kubernetes Service, IBM Kubernetes Service, and Rancher Orchestrated Kubernetes. This library also supports yaml ingestion for creating, patching, updating, or deleting existing Kubernetes types, including Pods, Services, Deployments, Endpoints, and Ingresses. Our documentation also contains complete examples for all operation types.
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