DocuMind: A Modular RAG System for Intelligent PDF Q&A

DocuMind is a Retrieval-Augmented Generation (RAG) system designed for production environments, specifically for intelligent Q&A over PDF documents. It supports local LLM inference (no external API dependency), combines multiple chunking strategies, FAISS vector retrieval, and other technologies to ensure data privacy and accurate Q&A. The original author/maintainer is Saurav-VK, project source is GitHub, original link: https://github.com/Saurav-VK/DocuMind, release date: June 12, 2026.

Traditional keyword search struggles to meet the complex query needs of enterprises/individuals (such as semantic understanding, question answering, and source citation). DocuMind combines RAG technology with local LLM to provide a high-quality intelligent Q&A experience while ensuring data privacy, addressing industry pain points.

End-to-end modular pipeline: PDF → Page filtering (remove table of contents/noise) → Chunking → Chunk filtering → Embedding vectors → FAISS index; When a question is asked: vector retrieval → Result cleaning → Context construction → LLM answer generation.

Supported four chunking strategies:

1. Token-based splitting: Fixed token segmentation, suitable for structured technical documents;
2. Sentence-transformer-based splitting: Semantic boundary recognition, maintaining coherence;
3. Semantic chunking: Clustering semantically similar sentences, suitable for concept-dense content;
4. Recursive character splitting: Recursive character segmentation, robustly handling long texts.

Multi-strategy adaptation to academic papers, legal contracts, and other document types improves versatility.

Ollama is used to run local LLM (default Mistral model), advantages: local data processing (privacy requirements), no API fees, low latency.

Expose RESTful interfaces via FastAPI, supporting PDF upload, real-time Q&A, and retrieval quality evaluation; Developers can test via Swagger UI/Postman or integrate into existing applications. Redis cache optimizes response speed for repeated queries.

Tech stack: Python, FastAPI, FAISS, Sentence Transformers, LangChain, PyPDF, Ollama.

Deployment steps: Clone the repository → Install dependencies → Start Redis container → Ollama loads the model → Start FastAPI service; It can be completed on a single machine with low hardware threshold.

Built-in retrieval quality evaluation endpoint, calculating coherence metrics and readability scores. Helps developers optimize chunking strategies and retrieval parameters, forming a data-driven improvement loop, identifying inefficient queries and adjusting strategies (such as chunk size, strategy switching).

Applicable scenarios: Enterprise knowledge base Q&A, personal document assistant, academic research assistance, legal document analysis.

Expansion directions: Multimodal support, multilingual processing, advanced query rewriting and reordering; Modular design allows component replacement (e.g., replacing FAISS with other vector databases, changing embedding models).