RAG

A machine learning library that extracts, parses and restructures raw scholarly documents such as PDFs into structured XML/TEI-encoded data, focused on technical and scientific publications.

• RAG

A tool that parses a multi-language codebase with Tree-sitter, builds a knowledge graph of its structure in Memgraph, and lets a developer query, edit and optimize the code in plain English.

• Agent memory
• RAG

An application that transforms unstructured data such as PDFs, documents, text files, YouTube videos and web pages into a structured knowledge graph stored in Neo4j using large language models.

• RAG
• Databases

A starter stack combining LangChain, Neo4j and Ollama with Docker, meant as a template and set of demo applications for building a GenAI application.

• Agent frameworks
• RAG

An enterprise-grade AI agent platform offering multi-provider LLM management, RAG-backed knowledge bases, visual workflow orchestration and multi-agent coordination.

• Agent frameworks
• RAG

A low-code RAG development framework built on the Model Context Protocol, letting a developer assemble retrieval-augmented-generation pipelines from independent MCP servers and orchestrate them with YAML.

• MCP servers
• RAG

A code search library built for AI agents that returns exact relevant code snippets using roughly 99% fewer tokens than a typical grep-and-read search.

• Agent memory
• RAG

A self-hosted, multi-tenant knowledge agent platform combining RAG, a knowledge graph, multi-agent orchestration, MCP/Skills, a sandbox and permission management in one workspace.

• Agent frameworks
• RAG

An LLM-driven platform that extracts structured JSON from unstructured documents such as PDFs, scans and images, deployable as an API or an ETL pipeline.

• RAG
• Data engineering

A sample application demonstrating a ChatGPT-style, Retrieval-Augmented Generation experience over a company's own documents, using Azure OpenAI and Azure AI Search.

• RAG

An open-source deep-research tool that combines large language models with vector databases to search, evaluate and reason over private data and produce a sourced report.

• RAG

A hands-on course that builds a production-grade retrieval-augmented-generation research assistant from scratch, called the arXiv Paper Curator, teaching keyword search first and then hybrid and agentic retrieval.

• RAG
• Tutorials

A self-hosted AI research assistant that runs agentic, multi-step research across many LLMs and more than ten search engines, including private documents, with proper citations.

• Agent frameworks
• RAG

A high-accuracy retrieval-augmented-generation package for answering questions from scientific PDFs, text, Office documents and source code, with citations back to the source passages.

• RAG

A polyglot document-intelligence engine that extracts text, tables, metadata and structured data from more than 100 file formats and code from hundreds of programming languages, with OCR, transcription and 15 language bindings.

• RAG
• Data engineering

A retrieval system that renders web pages, PDFs and images as screenshots and searches over the images directly, keeping visual structure such as tables and charts that text extraction would lose.

• RAG

A full-text, vector and hybrid search engine and RAG pipeline that runs in the browser, on a server or at the edge, with typo tolerance, facets, geosearch and a plugin system.

• RAG
• Databases

A full-stack, hands-on tutorial on retrieval-augmented generation for large-model application developers, covering data preparation, index construction, advanced retrieval, generation and evaluation.

• RAG
• Tutorials

An incremental data-indexing engine that keeps context such as codebases, meeting notes, inboxes, Slack, PDFs and video continuously fresh for AI agents and LLM apps, recomputing only what changed.

• Agent memory
• RAG

An open-source, enterprise-focused LLM application DevOps platform combining a visual generative-AI workflow builder, retrieval-augmented generation, agents, model management, evaluation and fine-tuning.

• Agent frameworks
• RAG

A one-stop retrieval toolkit for search and retrieval-augmented generation, providing a family of open embedding, reranking and multimodal-search models known as BGE.

• RAG
• Models

An MCP plugin that adds semantic code search over an entire codebase to Claude Code and other AI coding agents, storing embeddings in a vector database instead of loading whole directories into context.

• MCP servers
• RAG

An all-in-one Python framework for semantic search, LLM orchestration and language-model workflows, built around an embeddings database that combines vector search with graph and relational storage.

• RAG
• Databases

A framework for building local, private, enterprise retrieval-augmented generation applications, pairing a catalog of 300+ small, optimized language models with document parsing and knowledge-base pipelines.

• RAG
• Models