# Multimodal-RAG: Building an Intelligent Dialogue System Supporting Multi-Format Documents

> An open-source project based on the RAG architecture that enables intelligent parsing and dialogue interaction for multi-format documents like PDF, DOCX, PPTX, supporting local deployment and streaming responses.

- 板块: [Openclaw Geo](https://www.zingnex.cn/en/forum/board/openclaw-geo)
- 发布时间: 2026-06-08T20:12:56.000Z
- 最近活动: 2026-06-08T20:21:19.467Z
- 热度: 163.9
- 关键词: RAG, multimodal, LangChain, ChromaDB, Ollama, Next.js, FastAPI, document processing, vector search, local AI
- 页面链接: https://www.zingnex.cn/en/forum/thread/multimodal-rag-18c86636
- Canonical: https://www.zingnex.cn/forum/thread/multimodal-rag-18c86636
- Markdown 来源: floors_fallback

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## Multimodal-RAG: Open-Source Multimodal RAG System for Intelligent Document Dialogue

### Core Overview
- **Project Title**: Multimodal-RAG
- **Key Features**: Supports PDF/DOCX/PPTX and other multi-format documents, local deployment, streaming responses
- **Tech Stack**: RAG, multimodal, LangChain, ChromaDB, Ollama, Next.js, FastAPI
- **Goal**: Enable intelligent dialogue with various documents via retrieval-augmented generation

This open-source project combines large language models and vector search to build a full-featured multimodal RAG system.

## Project Background & Basic Overview

### Original Source
- **Author/Maintainer**: Nakul-28
- **Source Platform**: GitHub
- **Original Link**: https://github.com/Nakul-28/Multimodal-RAG
- **Release Time**: June 8, 2026

### Project Overview
Multimodal-RAG is a functional multimodal RAG system that integrates LLMs and vector search. It uses a front-end and back-end separated architecture: front-end based on Next.js, back-end using FastAPI, and integrates mainstream AI components like LangChain, ChromaDB, and Ollama.

## Core Architecture & Technology Stack

### Backend Design
- **Web Framework**: FastAPI (high-performance asynchronous processing for document upload, parsing, and dialogue requests)
- **Vector Database**: ChromaDB (stores document fragment embeddings and implements semantic similarity search)
- **Model Runtime**: Ollama (runs LLMs and embedding models locally to protect data privacy and reduce costs)

### Frontend Design
- **Framework**: Next.js + TypeScript (type safety)
- **Layout**: Double-column design (left sidebar for file upload and real-time processing status; main area for dialogue content and streaming responses)

This architecture balances practicality and scalability.

## Supported Document Types & Processing Flow

### Supported Document Types
Covers almost all common office file types:
- **Documents**: PDF, DOCX, DOC, ODT, RTF
- **Presentations**: PPTX, PPT
- **Spreadsheets**: XLSX, XLS, CSV, TSV
- **Text Files**: TXT, MD, RST, ORG
- **Web Formats**: HTML, HTM, XML
- **Mail Formats**: EML, MSG
- **Others**: EPUB, JSON

### Processing Flow (5 Stages)
1. **Parsing**: Uses Unstructured library to detect file types and extract content (supports table structure recognition and image extraction for PDFs)
2. **Chunking**: Splits documents into fragments of up to 3000 characters by title to retain context
3. **Summarization**: Generates searchable descriptions for each fragment (uses visual models for table/image fragments)
4. **Embedding**: Converts summaries and text fragments into vectors and stores them in ChromaDB
5. **Completion**: Documents are ready for dialogue queries

Real-time progress is pushed to clients via WebSocket.

## Dialogue & Retrieval Mechanism

### Retrieval Process
When a user asks a question:
1. Convert the question into an embedding vector
2. Retrieve the most relevant document fragments (text + associated images) from ChromaDB

### Answer Generation
- Uses SSE (Server-Sent Events) for streaming transmission, with a typewriter effect to enhance interaction
- Inline images in responses if relevant fragments contain images

This achieves true multimodal dialogue.

## Local Deployment & Model Configuration

### Deployment Requirements
- Python 3.10+
- Node.js 18+
- Ollama

### Default Model Configuration
- **Dialogue Model**: llama3.2:3b
- **Embedding Model**: nomic-embed-text-v2-moe
- **Visual Model**: qwen2.5vl:3b (for table/image summaries)

### Flexibility
- All model parameters can be configured via environment files (e.g., replace with llava:13b for stronger visual capabilities)
- ChromaDB data is persistently stored in the project directory (no need to reprocess uploaded documents after restarting the service)

This design caters to local deployment needs and protects data privacy.

## Application Scenarios & Extensibility

### Application Scenarios
- **Researchers**: Literature reading assistant (quickly locate key charts and data in papers)
- **Enterprises**: Internal knowledge base (employees query product manuals/technical documents via natural language)
- **Legal Practitioners**: Process contracts/cases to assist in quick retrieval of relevant clauses

### Extensibility
- Open-source nature allows customization: add new document formats, integrate other vector databases, or connect to cloud LLM APIs

The project provides clear expansion paths for developers.

## Conclusion & Insights

### Key Takeaways
- Multimodal-RAG demonstrates a typical modern RAG system architecture: organic combination of document parsing, vector storage, semantic retrieval, and LLM generation
- Local deployment design provides a practical option for users concerned about data privacy
- Details like real-time pipeline status display and streaming response generation reflect good user experience awareness

### Reference Value
For developers who want to build private knowledge base dialogue systems, this is a worthy reference implementation.