Summarize and query from a lot of heterogeneous documents. Any LLM provider, any filetype, advanced RAG, advanced summaries, scriptable, etc

Massive Multimodal Open RAG & Extraction A scalable multimodal pipeline for processing, indexing, and querying multimodal documents Ever needed to take 8000 PDFs, 2000 videos, and 500 spreadsheets and feed them to an LLM as a knowledge base? Well, MMORE is here to help you!

A Retrieval-Augmented Generation (RAG) application for querying legal documents. It uses PostgreSQL, Elasticsearch, and LLM to provide summaries and suggestions based on user queries. Features data ingestion with Airflow, real-time monitoring with Grafana, and a Streamlit interface.

A RAG agent using Google's ADK & Vertex AI that lets set up semantic search across documents in under 2 minutes. Features GCS integration and natural language querying

🦜 🔗 Query and obtain data from Markdown documents with LangChain's RAG system

LM Studio Plugin to mark a directory full of documents as a RAG source and another directory as the Vectorstore and build a RAG for use in your queries. Built using Cursor.

A knowledge base backend system for LLMs with full-text search, semantic retrieval, and knowledge graph querying. Ready-to-use modules for document processing and RAG, enabling quick deployment of enterprise knowledge retrieval systems.

LifeSpring Clinic Intelligent RAG-Based Healthcare Assistant is an AI-powered chatbot that provides accurate clinic information using Retrieval-Augmented Generation. It answers patient queries about doctors, services, appointments, symptoms, and care policies with reliable, document-based responses.

A RAG system designed to process documents with multimodal content. It can generate factual, context-aware answers to user queries, based on the documents texts, tables, figures, ...

Multi-Modal RAG for .NET — query databases, documents, images and audio in natural language. Production-ready with multi-AI support, vector storage, and multi-database coordination.

LlamaIndex wrapper for doing LLM RAG queries on local/private documents

Open-source RAG engine for ingesting, indexing, and querying unstructured documents

🔍 AI-Powered Document Intelligence System | Retrieval-Augmented Generation (RAG) Advanced document processing platform that combines semantic embedding, intelligent retrieval, and generative AI to transform how you interact with documents. Extract insights, answer complex queries, and unlock knowledge across multiple document formats.

RAG-Microservice: A robust, scalable question-answering service leveraging the Retriever-Answer Generator (RAG) architecture. Built with [Tech/Models used, e.g., Elasticsearch, GPT-3], this service efficiently retrieves relevant documents and generates precise answers for complex queries.

Multimodal RAG with Docling that lets you query PDFs containing text, tables, images, and formulas using a Retrieval-Augmented Generation pipeline. It leverages Docling for structured PDF parsing and Qdrant for fast vector search over embedded document chunks.

Multi-granularity RAG framework that indexes documents at 5 levels and assembles optimal context at query time.

API to load and query documents using RAG

MODE (Mixture of Document Experts) is an advanced RAG framework that enhances query response quality by combining hierarchical document clustering, expert model specialization, and centroid-based retrieval. Ideal for small to medium-sized datasets, it delivers more accurat eand efficient document retrieval and synthesis.

RAG pipeline for querying and analyzing company documents using LLMs and vector search

An intelligent RAG system powered by Google's Gemini 2.0 Flash Thinking, Qdrant vector storage, and Agno agent orchestration. Upload documents, process web pages, and get AI-assisted answers with advanced query rewriting and web search capabilities.

Advanced RAG + Raptor: A sophisticated document processing and retrieval system combining hierarchical document clustering with advanced query processing. Features HTML-to-markdown conversion, recursive document clustering, query expansion, cross-encoder re-ranking, and contextual response generation using LangChain, Vertex AI, PostgreSQL/pgvector,

RagCheck is a proactive corpus quality assessment tool that analyses RAG application document collections before deployment, identifying content gaps and providing specific recommendations to improve query performance. The platform transforms reactive corpus fixes into proactive quality assurance, helping organisations achieve as high as 85% score.

RAG implemented from scratch without using LangChain and LangGraph - designed specifically for processing and querying PDF documents with advanced support for visual content like tables, charts, and mathematical formulas.

Ragnar is a pure Ruby command-line RAG (Retrieval-Augmented Generation) tool with zero external dependencies. It provides local document indexing, semantic search, and LLM-powered query processing. Built to be hackable, it lets Ruby developers experiment with agentic workflows and RAG pipelines natively in Ruby.

Production-ready Multi-Source RAG system with Text-to-SQL capabilities. Intelligent query routing, document processing, and SQL generation with comprehensive evaluation and monitoring.

Retrieval-Augmented Generation (RAG) combines retrieval of information from a document database with generative AI to provide accurate and contextually aware answers. In this article, we'll walk through how to build a basic RAG-based application using Python and Streamlit to create a conversational interface for document querying.

Retrieval-Augmented Generation (RAG) chatbot system using LangChain and LangGraph, incorporating various components such as document retrieval, relevance checking, and fallback mechanisms to answer computer science and NLP-related queries.

RAG‑PDF‑Analyzer is a Streamlit chatbot that lets users upload PDFs and query them with natural language. It uses PyPDF2 for text extraction, HuggingFace embeddings with FAISS for semantic search, and Mistral LLM via LangGraph to deliver context‑aware answers from documents.

Agentic RAG is a next-generation AI architecture that combines the precision of structured agent-based reasoning with the power of retrieval-augmented generation. Unlike traditional RAG pipelines that blindly retrieve documents for every query

AI-powered document query system using LangChain, ChromaDB, and OpenAI for efficient RAG-based information retrieval.