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title: RAG Document Q&A
emoji: 📄
colorFrom: indigo
colorTo: blue
sdk: docker
app_port: 7860
pinned: false
---
# RAG Document Q&A System
Upload PDF documents and get cited, grounded answers — with hybrid retrieval, cross-encoder re-ranking, and confidence-aware generation.
## Overview
This project implements a production-grade Retrieval-Augmented Generation pipeline that goes beyond the typical "embed + nearest-neighbor + prompt" tutorial. It combines semantic and keyword search with cross-encoder re-ranking, applies Anthropic's contextual retrieval pattern to enrich embeddings with document metadata, and gates LLM calls on retrieval confidence to avoid hallucinated answers. The system ships with both a streaming Gradio UI and a FastAPI backend with Server-Sent Events, plus an evaluation harness that benchmarks chunking strategies and scores end-to-end answer quality using an LLM-as-judge.
## Architecture
```
┌─────────────────────────────────────────┐
│ INGESTION │
│ │
PDF ──► pymupdf4llm ──► │ Chunking (3 strategies) │
(pdfplumber │ ├─ fixed_size sliding window │
fallback) │ ├─ recursive_char LangChain splits │
│ └─ semantic embedding sim │
│ │ │
│ ▼ │
│ Contextual Enrichment │
│ "filename | Page N | Section\ntext" │
│ │ │
│ ▼ │
│ all-MiniLM-L6-v2 (384-dim, L2-norm) │
│ │ │
│ ┌─────┴──────┐ │
│ ▼ ▼ │
│ FAISS BM25Okapi │
│ IndexFlatIP rank-bm25 │
└─────────────────────────────────────────┘
┌─────────────────────────────────────────┐
│ QUERY │
│ │
Question ──────────────►│ Adaptive Expansion (Groq) │
│ only if top_score < 0.45 │
│ │ │
│ ▼ │
│ Hybrid Search │
│ score = 0.7·semantic + 0.3·bm25 │
│ (top 20 candidates) │
│ │ │
│ ▼ │
│ Cross-Encoder Re-ranking │
│ ms-marco-MiniLM-L-6-v2 │
│ (20 → top k) │
│ │ │
│ ▼ │
│ Confidence Gating │
│ cosine sim < 0.3 → skip LLM │
│ │ │
│ ▼ │
│ Groq (Llama 3.3 70B) streaming │
│ │ │
│ ▼ │
│ Cited answer [Source N] notation │
└─────────────────────────────────────────┘
┌─────────────────────────────────────────┐
│ EVALUATION │
│ │
│ Retrieval: Acc@1/3/5, MRR │
│ End-to-end: faithfulness + relevance │
│ (LLM-as-judge, temp=0.0) │
│ Config sweep: chunking strategy grid │
└─────────────────────────────────────────┘
```
## Key Features
- **Cross-encoder re-ranking** — bi-encoder retrieval (FAISS) produces 20 candidates; a cross-encoder (`ms-marco-MiniLM-L-6-v2`) scores each `(query, chunk)` pair jointly and re-orders them. Cross-encoders are too slow for full-corpus search but dramatically improve precision over the shortlist.
- **Hybrid search** — FAISS inner-product search and BM25 run in parallel. Both score sets are normalized to `[0, 1]` then fused (`0.7·semantic + 0.3·keyword`), catching exact-match terms that dense embeddings tend to smooth over.
- **Adaptive query expansion** — the pipeline runs an initial search first. Only if the top cosine similarity falls below 0.45 does it call the LLM to generate 3 rephrased variants, search each, and merge results. High-confidence queries pay zero expansion cost.
- **Contextual chunk enrichment** — each chunk is embedded as `"<filename> | Page N | <section>\n<text>"` rather than raw text, following Anthropic's contextual retrieval pattern. The embedding captures document location and section context, not just lexical content.
- **Confidence-based response gating** — `max_score` is taken from cosine similarity *before* re-ranking (cross-encoder scores on a −10 to +10 scale would break the threshold). If `max_score < 0.3`, the LLM is skipped entirely and the user receives an honest "insufficient context" message. Scores between 0.3–0.5 append a low-confidence warning.
- **Streaming responses** — Gradio UI streams tokens via a generator; the FastAPI backend streams as Server-Sent Events (`text/event-stream`) with `{"text": chunk}` events and a terminal `{"done": true}`.
- **Layout-aware PDF parsing** — `pymupdf4llm` extracts structured markdown with headings and tables preserved. `pdfplumber` serves as a fallback for PDFs that `pymupdf4llm` cannot parse cleanly.
- **End-to-end evaluation** — the eval harness measures retrieval hit rate (Acc@K, MRR), then pipes results through the LLM-as-judge to score faithfulness (are claims grounded in context?) and relevance (does the answer address the question?) at temperature 0 for determinism.
## Tech Stack
| Component | Technology |
|---|---|
| Embedding model | `all-MiniLM-L6-v2` (sentence-transformers, 384-dim) |
| Vector index | FAISS `IndexFlatIP` (exact inner-product search) |
| Keyword index | BM25 (`rank-bm25`, `BM25Okapi`) |
| Re-ranker | `cross-encoder/ms-marco-MiniLM-L-6-v2` |
| LLM | Llama 3.3 70B via Groq API |
| PDF parsing | `pymupdf4llm` + `pdfplumber` fallback |
| Text splitting | LangChain `RecursiveCharacterTextSplitter` |
| Backend | FastAPI + Uvicorn |
| Frontend | Gradio (Blocks, streaming) |
| Containerization | Docker |
| Cloud deployment | HuggingFace Spaces |
## Quick Start
```bash
git clone <repo-url>
cd rag-qa
python -m venv venv && source venv/bin/activate
pip install -r requirements.txt
```
Create a `.env` file:
```
GROQ_API_KEY=your_key_here
```
Get a free key at [console.groq.com](https://console.groq.com) — the free tier allows 14,400 requests/day.
Launch the Gradio UI:
```bash
python app_gradio.py
# Opens at http://127.0.0.1:7860
```
Or run the FastAPI server:
```bash
uvicorn main:app --reload
# API docs at http://127.0.0.1:8000/docs
```
## API Endpoints
| Method | Endpoint | Description |
|---|---|---|
| `GET` | `/health` | Liveness check |
| `GET` | `/stats` | Index size, chunks ingested, model info |
| `POST` | `/ingest` | Upload a PDF; returns chunk count and IDs |
| `POST` | `/query` | Ask a question; returns answer + sources + confidence |
| `POST` | `/query/stream` | Same as `/query` but streams as SSE |
| `POST` | `/debug/chunks` | Return raw retrieved chunks without calling the LLM |
All `POST` query endpoints accept `{"question": "...", "top_k": 5}`.
The `/debug/chunks` endpoint is useful for diagnosing retrieval issues — it shows each chunk's rank, cosine similarity score, source, page, section header, and a 300-character text preview.
## Evaluation
The evaluation framework has three layers:
**Retrieval metrics** (`evaluation/eval.py`) — given a test set of `(question, expected_source, expected_page)` tuples, measures whether the correct chunk appears in the top-k results:
| Metric | Definition |
|---|---|
| Accuracy@K | Fraction of queries where correct chunk is in top K |
| MRR | Mean Reciprocal Rank of the first correct result |
**End-to-end metrics** (`evaluation/eval_e2e.py`) — runs the full pipeline and scores each answer:
| Metric | Scorer |
|---|---|
| Faithfulness | LLM judge: are all claims grounded in retrieved context? |
| Relevance | LLM judge: does the answer address the question? |
| Keyword coverage | Fraction of expected answer keywords found in output |
**Chunking strategy comparison** (`evaluation/compare.py`) — grid search over chunk sizes, overlap values, and re-ranking on/off. Results table (fill in with your benchmark numbers):
| Strategy | Chunk size | Overlap | Reranking | Acc@5 | MRR | Faithfulness | Relevance |
|---|---|---|---|---|---|---|---|
| recursive_char | 300 | 0 | No | — | — | — | — |
| recursive_char | 500 | 50 | No | — | — | — | — |
| recursive_char | 500 | 50 | Yes | — | — | — | — |
| recursive_char | 1000 | 100 | Yes | — | — | — | — |
| semantic | 500 | — | Yes | — | — | — | — |
## Project Structure
```
rag-qa/
├── main.py # FastAPI server
├── app_gradio.py # Gradio web UI
├── requirements.txt
├── Dockerfile
├── .env.example
│
├── ingestion/
│ ├── pdf_reader.py # pymupdf4llm + pdfplumber extraction
│ ├── chunker.py # fixed_size, recursive_char, semantic strategies
│ ├── embedder.py # SentenceTransformer + contextual enrichment
│ └── pipeline.py # Orchestrates extract → chunk → embed → index
│
├── retrieval/
│ ├── index.py # FAISS IndexFlatIP wrapper
│ ├── bm25_index.py # BM25Okapi wrapper
│ ├── reranker.py # Cross-encoder re-ranking
│ └── searcher.py # Hybrid search + expansion + confidence score
│
├── generation/
│ └── generator.py # Groq client, confidence gating, streaming
│
├── evaluation/
│ ├── judge.py # LLM-as-judge (faithfulness + relevance)
│ ├── eval.py # Retrieval metrics: Acc@K, MRR
│ ├── eval_e2e.py # End-to-end pipeline evaluation
│ └── compare.py # Configuration grid comparison
│
└── data/ # Sample PDFs for testing
```
## How It Works
**PDF Extraction** — `pymupdf4llm` converts each page to structured markdown, preserving headings and table layout. If it fails, `pdfplumber` extracts flat text. Pages are processed independently so that `page_num` metadata stays accurate for citations.
**Chunking** — three strategies are available. `fixed_size` is a naive sliding window (fast baseline). `recursive_character` uses LangChain's splitter with markdown-aware separators (`\n## `, `\n\n`, `. `) and extracts the nearest heading above each chunk as `section_header` metadata. `semantic` embeds individual sentences and splits at cosine similarity drops below a threshold, grouping topically coherent content together.
**Embedding** — before embedding, each chunk's text is prefixed with `"<filename> | Page N [| Section]\n"`. This means the stored vector encodes not just the chunk's content but where in the document it came from, which improves retrieval precision for location-specific queries.
**Retrieval** — a query vector is searched against FAISS (semantic) and BM25 (keyword) simultaneously. Both result sets are min-max normalized to `[0, 1]` and fused by weighted sum. The top 20 fused candidates are then re-scored by the cross-encoder, which reads the full `(query, chunk)` string pair and produces a relevance score independent of embedding geometry.
**Generation** — `max_score` (cosine similarity of the top-ranked chunk before re-ranking) determines whether to call the LLM. Below 0.3, the call is skipped. Between 0.3 and 0.5, the answer is appended with a confidence warning. Above 0.5, the model receives a numbered context block and is prompted to cite every claim with `[Source N]` notation and synthesize across sources.
## Deployment
**Docker:**
```bash
docker build -t rag-qa .
docker run -p 7860:7860 -e GROQ_API_KEY=your_key rag-qa
```
**HuggingFace Spaces:**
Set `GROQ_API_KEY` in your Space's Settings → Repository secrets, then push the repository. The `Dockerfile` is picked up automatically by Spaces.
## Future Improvements
- **OCR support** — integrate `pytesseract` or `surya-ocr` for scanned PDFs that contain no extractable text layer
- **Persistent vector store** — replace the in-memory FAISS index with a disk-backed store (FAISS `write_index` with reload on startup, or a dedicated vector DB) so the index survives server restarts
- **Multi-document cross-referencing** — surface when the same fact is corroborated across multiple uploaded documents rather than citing only one source
- **GPU-accelerated embedding** — the sentence-transformers model runs on CPU by default; passing `device="cuda"` cuts batch embedding time significantly for large document sets
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