An intelligent assistant serving the entire software development lifecycle, powered by a Multi-Agent Framework, working with DevOps Toolkits, Code&Doc Repo RAG, etc.

(RAG) Developing a chatbot capable of utilizing PDF files as a knowledge base to answer questions. Additionally, we employed the vision capability of GPT-4V to identify and describe graphs from each page. Describing the charts and their illustrated trends improves the chatbot's accuracy in answering and providing additional context.

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Intelligent multilingual PDF Q&A system for Indian professionals. Sarvam-30B + multilingual-e5-large. 22 Indian languages, no translation layer.

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A team of AI agents that answer document related questions (RAG alternative)

Retrieval-Augmented Generation (RAG) toolkit for Laravel: async docs, chat, outlines, multi-provider.

This reprository is a part of workshop: Best Practices for Building Graph-based RAG From Multiple Documents With Python

Local RAG system for document Q&A — hybrid search, multi-doc reasoning, PDF viewer

M3ARAG = Multi-Modal Multi-Agent RAG system for comprehensive document understanding through specialized AI agents processing PDFs, Office docs, and web content with iterative quality improvement.

Multi Doc RAG

RAG Chatbot for interacting with multiple documents using LangChain, Faiss, and Streamlit

focuses on building from scratch to understand the core components of RAG systems—document chunking, embedding generation, vector storage, and retrieval-based prompting.

SomaBrain is a production-ready cognitive memory and RAG platform for AI agents. Features: multi-tenant memory, advanced math, secure FastAPI, full test coverage, and modular RAG pipeline. Real math, real memory, real docs.

A RAG to deal with multiple documents simultaneously using Weaviate, Mistral-7B-v0.1 and Langchain.

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Learn to analyze documents like PDFs and images using Gemini’s multimodal AI. This course covers combining text and visuals with RAG techniques to extract insights from rich documents using Vertex AI.

一个基于 Python、Streamlit、FAISS 和大模型 API 构建的多文档 RAG 问答系统，支持 PDF / DOCX 文档解析、向量检索、多轮对话、Query Rewrite、来源引用展示以及增量索引更新。 系统通过 向量检索 + 大模型生成 的方式实现知识库问答，并展示引用来源，提高回答的可解释性。

🚀 A beginner-friendly, multi-scenario Generative AI starter kit. Includes end-to-end RAG, multimodal, industry solutions, model fine-tuning, and one-click scripts. 中文/English docs. Perfect for learning, rapid prototyping, and real-world deployment.

Vortex (Ω Team) — a hybrid AI repo for token‑level ESC patterns with stable semantic anchors; near‑zero hallucinations via RAG, SelfCheck, and conformal abstention; multi‑agent consensus (Kuramoto/ADMM); causal world models (RT‑2, Dreamer); default interpretability (Integrated Gradients, ESC telemetry); reproducible CI notebooks and docs.

RAG application to query multiple docs. Built to query 10K reports of companies.

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MCP based multi-doc type agentic RAG chatbot

Production-ready multi-language RAG assistant for querying multiple documents with source attribution.

[ACL2026 Main] Scaling Beyond Context: A Survey of Multimodal Retrieval-Augmented Generation for Document Understanding

AskIt is a Q&A application that allows users to upload and interact with multiple types of documents (PDF, DOCX, TXT, etc.) or provide links to webpages. The application uses Retrieval-Augmented Generation (RAG) to process the documents, index them, and answer questions based on their content.

RAG Chatbot is an AI-powered system that allows users to upload multi-format documents and ask questions about their content. Built with an agent-based architecture and uses MCP format (not using the real MCP), it uses document ingestion, semantic retrieval (FAISS), and LLM to provide accurate, contextual answers via via a chat-style Streamlit UI.

Chatbot to compare multiple PDF documents with RAG, Chat GPT and LlamaIndex

Terminal-first control plane for NVIDIA Nemotron — agentic coding, RAG, doc-ops, and multi-model formations.

Semantic, multi-hop RAG-powered code intelligence over Strato Mercata's blockchain stack – enabling devs to ask complex questions about the codebase and docs, and get LLM-powered answers grounded in embeddings, call graphs, and definitions.