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

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.

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.

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

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.

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.

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

• 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.

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.