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title: Multi Doc Rag V2
emoji: 📚
colorFrom: blue
colorTo: indigo
sdk: docker
app_port: 7860
pinned: false
Multi-Document Research Assistant — RAG with Retrieval Quality Evaluation
Upload multiple PDFs. Ask questions. Get cited answers grounded in your documents — with automatic retrieval quality scoring.
🎯 Problem
Most LLMs hallucinate when asked about private or domain-specific documents. They generate confident answers from training data — not from your actual content.
Standard RAG systems fix this but introduce a new problem: you can't tell when retrieval fails. Wrong chunks get retrieved. The LLM answers confidently from bad context. No warning.
This project solves both problems:
- Grounds all answers in your uploaded documents
- Scores retrieval quality on every query so you know when to trust the answer
🏗️ Architecture
PDF Documents
│
▼
┌─────────────────┐
│ PyPDFLoader │ Extract text page by page
└────────┬────────┘
│
▼
┌─────────────────┐
│ RecursiveText │ Split into 512-token chunks
│ Splitter │ with 50-token overlap
└────────┬────────┘
│
▼
┌─────────────────┐
│ all-MiniLM-L6 │ Embed each chunk → 384-dim vector
│ Embeddings │
└────────┬────────┘
│
▼
┌─────────────────┐
│ ChromaDB │ Store vectors + text + metadata
│ (Vector Store) │ (source filename + page number)
└────────┬────────┘
│
User Query
│
▼
┌─────────────────┐
│ Similarity │ Cosine search → Top-k chunks
│ Search │
└────────┬────────┘
│
▼
┌─────────────────┐
│ Gemini Flash │ Generate answer from chunks only
│ (LLM) │
└────────┬────────┘
│
▼
┌─────────────────┐
│ Custom Evaluator│ Score grounding, relevance,
│ │ completeness
└─────────────────┘
│
▼
Answer + Sources + Quality Scores
📊 Evaluation Metrics
Instead of using RAGAS (which requires paid OpenAI API), this project implements a custom lightweight evaluator — free, fast, and interpretable.
| Metric | What It Measures | How |
|---|---|---|
| Grounding Score | Is the answer based on retrieved chunks? | Word overlap: answer ∩ context / answer |
| Retrieval Relevance | Did we retrieve the right chunks? | Cosine similarity: query vector vs chunk vectors |
| Answer Completeness | Did the LLM use the retrieved context? | Word overlap: answer ∩ context / context |
Sample Results (Attention Is All You Need paper)
| Query | Grounding | Relevance | Completeness |
|---|---|---|---|
| "What is the attention mechanism?" | 0.97 | 0.61 | 0.16 |
| "Who are the authors?" | 0.95 | 0.58 | 0.12 |
| "What is the conclusion?" | 0.91 | 0.55 | 0.14 |
Key insight: High grounding (0.97) but low completeness (0.16) means the LLM extracted a precise answer from a small portion of retrieved context — expected behavior for factual queries.
🚀 Quick Start
Prerequisites
- Python 3.10+
- Google AI Studio API key (free at aistudio.google.com)
Installation
git clone https://github.com/aneebnaqvi15/rag-research-assistant
cd rag-research-assistant
python -m venv venv
venv\Scripts\activate # Windows
# source venv/bin/activate # Mac/Linux
pip install -r requirements.txt
Configuration
Create a .env file:
GOOGLE_API_KEY=your_key_here
Run
streamlit run app.py
Open http://localhost:8501
🛠️ Tech Stack
| Component | Tool | Why |
|---|---|---|
| PDF Loading | pypdf + LangChain |
Page-level metadata for citations |
| Chunking | RecursiveCharacterTextSplitter |
Respects sentence boundaries |
| Embeddings | all-MiniLM-L6-v2 |
Fast, free, 384-dim, runs on CPU |
| Vector Store | ChromaDB |
Local, zero-config, stores metadata |
| LLM | Gemini 1.5 Flash |
Free tier, strong reasoning |
| Orchestration | LangChain |
Connects all RAG components |
| UI | Streamlit |
Rapid prototyping, real-time logs |
| Evaluation | Custom (NumPy + sklearn) | Free, interpretable, no OpenAI dependency |
✨ Features
- Multi-document support — Upload and query across multiple PDFs simultaneously
- Source citations — Every answer shows exact filename and page number
- Real-time processing logs — Watch the pipeline run: load → chunk → embed → index
- Retrieval quality scores — Three metrics scored on every query
- Bring your own API key — Toggle in sidebar to use your own Gemini key
- Custom model selection — Enter any Gemini model string from AI Studio
- Adjustable retrieval — Control chunk size and top-k via sidebar sliders
💡 Key Learnings
1. Dependency hell is real.
ChromaDB, NumPy 2.0, and OpenTelemetry have a war inside Colab's environment. The fix: use EphemeralClient() instead of file-based persistence, and never pin NumPy manually.
2. RAGAS is not free.
RAGAS v0.4+ requires OpenAI API for their InstructorLLM. Building a custom evaluator is not a compromise — it's better engineering. You understand every metric you ship.
3. Chunk size is a hyperparameter. 512 tokens with 50-token overlap is a starting point, not a truth. Smaller chunks = sharper vectors but less context. Tune based on your document type.
4. Retrieval can fail silently. A grounding score of 1.0 does not mean a correct answer. It means every word in the answer exists somewhere in retrieved chunks. You need multiple metrics to trust a RAG system.
5. LLM quality matters more than RAG architecture.
flan-t5-base produced gibberish from perfect retrieval. Gemini 1.5 Flash produced accurate answers from the same chunks. The retrieval pipeline is only as useful as the model that reads it.
🔗 Related Projects
This project is Part 2 of a connected AI/ML portfolio:
| Project | Focus | Link |
|---|---|---|
| Banking77 Intent Classifier | NLP fine-tuning with DistilBERT + LoRA | GitHub |
| RAG Research Assistant | Retrieval-Augmented Generation + Evaluation | This repo |
Together these demonstrate: fine-tuning (shaping model behavior) vs RAG (shaping model knowledge) — two complementary approaches to applied NLP.
📄 References
- Attention Is All You Need — Vaswani et al. (2017)
- REALM: Retrieval-Augmented Language Model Pre-Training
- LangChain Documentation
- ChromaDB Documentation
- Sentence Transformers — all-MiniLM-L6-v2
👤 Author
Syed Muhammad Aneeb CS Graduate · Full-Stack Developer · AI Engineer
Built with zero GPU budget on Google Colab free tier. Constraints breed creativity.