Multi-Document RAG QA System

Multi-Document RAG QA System is a research-grade, open-source platform designed for question answering across multiple documents using Retrieval-Augmented Generation (RAG). It is engineered to be universal, scalable, ML-driven, and explainable—not just another document search tool.

This is not a simple search engine. It is a full end-to-end system with multi-document ingestion, embedding pipelines, vector DB retrieval, LLM-based QA, RAG explanations, evaluation metrics, and interactive UI.

---

🚀 Why This System Exists

Current limitations in document QA systems:

• No open-source, research-grade multi-document QA pipeline
• No universal ingestion for PDFs, DOCX, TXT, or other file types
• No explainable reasoning when the system fails to answer accurately

This project addresses these gaps by providing:

• ML-assisted retrieval for heterogeneous documents
• Multi-stage RAG pipelines for answer generation
• Explainable reasoning using RAG to prevent hallucination
• Transparent evaluation with QA metrics and benchmarks
• Reproducible, production-ready deployment

---

🧠 Core Design Goals

• 🧩 Universal – supports PDF, DOCX, TXT, and more
• ⚡ Adaptive & Efficient – retrieves and ranks relevant documents intelligently
• 🔍 Explainable – RAG provides reasoning and citations
• 🎯 Benchmark-first – evaluates QA performance on multi-document corpora
• 🛠️ Tool-aware – integrates logs, embeddings, and file context
• 🔓 Fully open – MIT / Apache 2.0 license

---

📐 System Overview

The system treats QA as a decision and retrieval problem rather than just text generation.

Stage	Description
Ingestion	Reads documents, extracts metadata, text, and structure
Feature Extraction	Chunking, tokenization, embedding creation
Vector DB Storage	FAISS / Pinecone to store embeddings for retrieval
Retriever	Finds relevant chunks for queries
LLM QA Pipeline	Generates context-aware answers from retrieved content
Explainer (RAG)	Provides reasoning and citations when answers are incomplete or uncertain
Evaluator	Computes F1, Exact Match, ROUGE, BLEU, hallucination metrics
Frontend Interaction	React-based interface for uploads, queries, and answers

---

📊 ML & Embedding Overview

The ML components do not answer questions directly but guide retrieval and ranking:

Attribute	Value
Model type	Random Forest / Transformer embeddings
Inputs	Chunk embeddings, document metadata, prior query relevance
Outputs	Ranked documents, predicted relevance scores
Libraries	Hugging Face, OpenAI embeddings, scikit-learn
License	MIT / Apache 2.0

---

🔍 Retrieval-Augmented Generation (RAG)

RAG ensures accurate, explainable answers:

• Uses embeddings and historical document context
• References only retrieved documents to prevent hallucination
• Generates actionable, human-readable explanations for uncertain answers
• Supports multi-document queries with context merging

---

🏗️ Tech Stack

Layer	Choice
Backend	FastAPI, Python 3.11
ML & Embeddings	Hugging Face, OpenAI, scikit-learn
Vector DB	FAISS, Pinecone
LLM QA Pipeline	OpenAI GPT / LLaMA or similar
RAG	LangChain + FAISS / Chroma
Frontend UI	React + Tailwind
PDF / DOCX Parsing	PyMuPDF, python-docx
Evaluation	QA metrics & hallucination detection
Deployment	Docker, Docker Compose, AWS EC2

---

🧱 Repository Structure

multi-doc-rag-qa/
├── README.md
├── LICENSE
├── .env.example
├── docker-compose.yml
├── backend/
│   ├── app.py                  # FastAPI main server
│   ├── config.py               # Configs (DB, embeddings, LLM)
│   ├── ingest.py               # Document ingestion pipeline
│   ├── retriever.py            # Vector DB retrieval
│   ├── llm.py                  # LLM query handling
│   ├── evaluator.py            # QA evaluation & metrics
│   ├── requirements.txt
│   └── utils/
│       ├── file_loader.py      # PDF, DOCX, TXT loaders
│       ├── text_splitter.py    # Chunking & tokenization
│       ├── embedding_utils.py  # Embedding creation & DB insertion
│       └── logger.py           # Logging utility
├── frontend/
│   ├── package.json
│   ├── public/
│   │   └── index.html
│   └── src/
│       ├── index.jsx
│       ├── App.jsx
│       ├── components/
│       │   ├── ChatWindow.jsx
│       │   ├── FileUploader.jsx
│       │   ├── QueryInput.jsx
│       │   └── EvaluationPanel.jsx
│       └── services/
│           ├── api.js          # API calls to backend
│           └── utils.js
├── ml/
│   ├── feature_engineering/
│   │   └── embedding_pipeline.py
│   ├── models/
│   │   └── local_models.py
│   └── training/
│       └── model_finetune.py
├── rag/
│   ├── pipelines/
│   │   ├── multi_doc_rag.py
│   │   ├── streaming_rag.py
│   │   └── evaluation_rag.py
│   └── vector_db/
│       ├── faiss_db.py         # FAISS vector DB integration
│       └── pinecone_db.py      # Pinecone alternative
├── tests/
│   ├── test_ingest.py
│   ├── test_retriever.py
│   ├── test_llm.py
│   └── test_api.py
├── .github/
│   └── workflows/
│       └── ci-cd.yml           # GitHub Actions CI/CD pipeline
└── docker/
    ├── Dockerfile.backend
    └── Dockerfile.frontend

---

🧱 ASCII Architecture Diagram

                                        ┌────────────────────────┐
                                        │       User / Client    │
                                        │  (Web UI, CLI, API)    │
                                        └────────────┬───────────┘
                                                     │
                                                     ▼
                                        ┌────────────────────────┐
                                        │     FastAPI Backend    │
                                        └────────────┬───────────┘
                                                     │
                                ┌───────────────┬───────────┬───────────────┐
                                │               │           │               │
                                ▼               ▼           ▼               ▼
                          ┌───────────┐   ┌───────────┐ ┌───────────┐ ┌───────────┐
                          │ Ingestion │   │ Retriever │ │ LLM QA    │ │ Evaluator │
                          └─────┬─────┘   └─────┬─────┘ └─────┬─────┘ └─────┬─────┘
                                │               │           │               │
                                ▼               ▼           ▼               ▼
                          ┌───────────┐   ┌───────────┐ ┌───────────┐ ┌───────────┐
                          │ File Load │   │ Vector DB │ │ RAG       │ │ Metrics   │
                          │ & Parsing │   │  (FAISS/  │ │ Explainer │ │ (F1, EM,  │
                          │ (PDF/DOCX)│   │ Pinecone) │ │           │ │ BLEU, etc)│
                          └───────────┘   └───────────┘ └───────────┘ └───────────┘

---

🧪 Training & Ingestion Pipeline

1. Collect diverse documents (PDF, DOCX, TXT)
2. Extract text and metadata
3. Chunk documents and generate embeddings
4. Store embeddings in vector DB (FAISS / Pinecone)
5. Evaluate QA retrieval, RAG explanations, and metrics
6. Fine-tune embedding model if necessary

---

🛠️ Tool-Aware Reasoning

• Grounded reasoning based on document embeddings
• Detects missing or ambiguous context
• Avoids hallucination by referencing only retrieved chunks

---

📊 Evaluation & Metrics

• F1-score, Exact Match, ROUGE, BLEU
• Hallucination detection
• Retrieval relevance & ML strategy accuracy

---

🌐 Quick Start (Docker)

docker build -t multi-doc-rag-qa .
docker run -p 8000:8000 multi-doc-rag-qa

Visit: http://localhost:8000

---

⚖️ License

Apache 2.0

---

⚠️ Disclaimer

Fully research-grade, open-source project. No magical QA claims. Fully transparent, reproducible, and explainable.

---

📘 Detailed Case Study:

🔗 Notion Portfolio: https://trail-bramble-8d5.notion.site/Naveen-Badiger-DevOps-Cloud-Engineer-300b680e255b80618978c2654214a6c6