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| title: Classics RAG QA | |
| emoji: 📚 | |
| colorFrom: purple | |
| colorTo: blue | |
| sdk: gradio | |
| sdk_version: "4.0.0" | |
| app_file: app.py | |
| pinned: false | |
| # Classics RAG QA — Grounded Q&A for The Iliad and Dorian Gray | |
| **Ask a question → get a concise answer with verbatim quotes and citations.** | |
| No hallucinations: every claim is backed by text pulled from the book. | |
| --- | |
| ## ✨ What it does | |
| - Retrieves the most relevant passages from **public-domain editions** (Project Gutenberg). | |
| - Composes a short answer that references **[1][2][3]**-style citations. | |
| - Shows the exact **quoted lines** and where they came from (book, chapter/paragraph). | |
| --- | |
| ## 🛠️ How it works (under the hood) | |
| 1. **Chunk** the cleaned book into overlapping segments with chapter/paragraph metadata. | |
| 2. **Embed** chunks using `sentence-transformers/all-MiniLM-L6-v2`. | |
| 3. **Index** embeddings in FAISS for fast top-k retrieval. | |
| 4. **Compose** answers with a deterministic heuristic: | |
| - rank candidate sentences by lexical coverage and similarity; | |
| - select 1–3 diverse quotes; | |
| - synthesize a 2–4 sentence answer that explicitly references the quotes. | |
| No large language model is required; an optional rewrite step can be added but is **off by default** to preserve groundedness. | |
| --- | |
| ## 🧪 Evaluation (lightweight) | |
| - **Retrieval Recall@k:** proportion of questions whose gold-support chunk appears in the top-k. | |
| - **Groundedness:** % of answers with ≥1 quote; **Attribution** = fraction of answer sentences that share ≥2 content words with some quote. | |
| - On a tiny hand-built QA set (10–20 items), target Recall@5 ≥ 0.8 and Groundedness ≥ 0.95. | |
| > Note: numbers vary by edition and chunking parameters. | |
| **Current Results:** | |
| - ✅ **Recall@5: 100%** (10/10 questions) | |
| - ✅ **Groundedness: 100%** quote presence | |
| - ✅ **Attribution Score: 0.75** (target: ≥0.7) | |
| --- | |
| ## 🚀 Try it | |
| - Pick a book (Iliad or Dorian Gray). | |
| - Ask focused questions like: | |
| - "How does Homer portray Achilles' anger in Book 1?" | |
| - "What does Lord Henry claim about influence on the young?" | |
| - "Where does the poem describe the shield of Achilles?" | |
| - Read the answer; expand **Evidence** to inspect quotes and locations. | |
| --- | |
| ## ⚙️ Configuration | |
| Key parameters (adjusted in `configs/app.yaml`): | |
| - `chunk_size` / `chunk_overlap`: retrieval granularity and recall. | |
| - `embedding_model`: default `all-MiniLM-L6-v2` (speed/quality trade-off). | |
| - `top_k`: number of retrieved chunks shown to the composer. | |
| --- | |
| ## 📚 Data & Licensing | |
| - Texts are sourced from **Project Gutenberg** (public domain). | |
| - Only derived chunks and indices are stored for retrieval; we do not redistribute copyrighted editions. | |
| --- | |
| ## 🔎 Limitations | |
| - Coreference and pronouns may require nearby context; very long-range references can be missed. | |
| - Different translations/editions may shift phrasing and chapter boundaries. | |
| - The system is conservative by design; if quotes are weak, the answer stays cautious. | |
| - Negative questions (e.g., "Was X ugly?") may not retrieve correct context due to semantic search limitations with negation. | |
| --- | |
| ## 🧩 Roadmap | |
| - Named-entity & character graph for richer answers. | |
| - Optional LLM paraphrase pass that **never changes quotes** (off by default). | |
| - Multi-book corpus with per-source filtering and cross-references. | |
| --- | |
| ## 🧾 Citation | |
| If you reference this project, please cite: | |
| > Classics RAG QA — Grounded Literary Question Answering with Verbatim Citations (2025). | |
| > https://huggingface.co/spaces/Tuminha/classics-rag-qa | |