| ## Project Vision |
| Create a Streamlit-powered "Accelerated Book Copilot" that ingests books or long-form documents, generates stacked summaries, enables adjustable-speed listening, answers questions with grounded citations, and enriches context via focused web crawling. |
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| ## User Journeys |
| - **Rapid Onboarding:** Upload a book or paste a URL, receive quick, detailed, and action-focused summaries in minutes. |
| - **Listening Mode:** Play text-to-speech audio with adjustable pace and language options. |
| - **Knowledge Dive:** Ask free-form questions, get cited answers, and review referenced passages. |
| - **Context Expansion:** Trigger a crawl for related resources (reviews, interviews, scholarly notes) and fold distilled insights into the knowledge base. |
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|
| ## Core Features |
| - PDF ingestion with reliable text extraction and structure retention. |
| - Chunking and embedding using SentenceTransformer or `text-embedding-3-small` for retrieval-ready vectors. |
| - Summaries: tiered (flash, detailed, character-focused) powered by Azure/OpenAI prompts. |
| - Q&A agent built on LangChain with streaming answers and citation highlighting. |
| - Listening: FOMO TTS modules with pitch/speed controls and downloadable clips. |
| - Crawl mode leveraging Newspaper3k/Requests + safety filters to augment knowledge base. |
| - Session management with Streamlit state and optional persistence (SQLite/JSON) for revisit. |
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|
| ## Architecture Outline |
| 1. **Ingestion Layer** |
| - PDF loader pipeline with robust parsing and structure detection. |
| - Background task queue (async workers) to preprocess large uploads. |
| 2. **Processing Layer** |
| - Text splitter (RecursiveCharacter) feeding embeddings stored in FAISS or Pinecone. |
| - Summarization orchestrator reusing FOMO prompt stacks. |
| - Q&A chain: RetrievalQA + chat memory + citation packaging. |
| 3. **Experience Layer** |
| - Streamlit tabs for Read, Summaries, Listen, Q&A, Crawl Insights. |
| - Interactive timeline/outline with Altair visualization for pacing metrics. |
| - Audio controls and transcript alignment. |
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|
| ## Tech Stack |
| - Streamlit UI (existing scaffold). |
| - LangChain for orchestration and tools. |
| - Azure/OpenAI LLM + embeddings; fallback to OpenAI endpoints. |
| - Pinecone or FAISS vector store depending on hackathon infra. |
| - SentenceTransformer local model for offline embeddings if needed. |
| - TTS via FOMO `load_tts` or Azure Speech (optional). |
| - Newspaper3k, BeautifulSoup, or HuggingFace datasets for crawl augmentation. |
| - Storage: lightweight JSON/SQLite, with hooks for cloud object storage if available. |
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|
| ## Milestones |
| 1. **Day 1 – Foundations** |
| - Wireframe Streamlit tabs and session state. |
| - Implement PDF ingestion -> chunk -> embed pipeline with local FAISS. |
| 2. **Day 2 – Intelligence Layer** |
| - Integrate summarization tiers and Q&A citations. |
| - Add TTS playback using existing FOMO audio utilities. |
| 3. **Day 3 – Crawl & Polish** |
| - Build targeted crawl + summarization flow. |
| - Add analytics (reading time estimator, compression ratios). |
| - Prep demo script and sample datasets. |
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| ## Risks & Mitigations |
| - **Large file latency:** Use background processing and progressive loading. |
| - **API rate limits:** Cache embeddings/summaries, throttle crawls. |
| - **Crawl reliability:** Predefine trusted domains, handle failures gracefully. |
| - **UX overload:** Start with core tabs, keep optional features toggled off by default. |
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| ## Next Steps |
| - Confirm target vector store (FAISS vs Pinecone) and credential availability. |
| - Draft Streamlit layout skeleton and assign owners per milestone. |
| - Prepare sample books and reliability test cases (short story, technical manual, novel). |
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