init: project setup with AGENTS.md, test structure, and plan directory
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# RAG Video Q&A — Project Knowledge Base
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**Generated:** 2026-04-22
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**Source:** development_plan.md
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**Status:** Greenfield (no code yet)
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---
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## OVERVIEW
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RAG-powered Video Q&A web app. Phase 1: text → ChromaDB retrieval → bullet-point answer. Phase 2: video upload → real-time ASR → auto/manual RAG query. FastAPI backend + React 18 (Vite) frontend.
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## STRUCTURE
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```
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app/
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├── backend/ # FastAPI (Python)
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│ ├── app/
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│ │ ├── main.py
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│ │ ├── routers/ # query.py, ingest.py, video.py, ws_asr.py
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│ │ ├── services/ # rag.py, llm_client.py, asr_client.py, video_service.py
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│ │ ├── models/ # Pydantic schemas
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│ │ ├── core/ # config.py, database.py
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│ │ └── utils/ # chunking.py, metadata_extraction.py
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│ ├── uploads/ # video storage (max 300MB)
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│ ├── requirements.txt
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│ └── .env.example
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├── frontend/ # React 18 + TS + Vite
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│ ├── src/
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│ │ ├── components/ # shadcn/ui + custom
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│ │ ├── pages/
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│ │ ├── lib/
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│ │ │ └── api.ts # API client (TanStack Query)
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│ │ └── App.tsx
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│ ├── package.json
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│ └── vite.config.ts
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├── chroma_db/ # Persistent vector store
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├── Dockerfile
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├── docker-compose.yml
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├── nginx.conf
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└── deploy.sh
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```
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## WHERE TO LOOK
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| Task | Location | Notes |
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|------|----------|-------|
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| API routes | `backend/app/routers/` | Versioned `/api/v1/...` |
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| Business logic | `backend/app/services/` | RAG, LLM, ASR, video |
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| Schemas | `backend/app/models/` | Pydantic request/response |
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| Config | `backend/app/core/config.py` | `.env` driven |
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| DB init | `backend/app/core/database.py` | ChromaDB persistent |
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| Frontend API | `frontend/src/lib/api.ts` | TanStack Query |
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| UI components | `frontend/src/components/` | shadcn/ui + Tailwind |
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## CODE MAP
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*Greenfield — no code yet. See development_plan.md for full specification.*
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## CONVENTIONS
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- **Backend**: `snake_case` files; routers thin, services thick; `.env` for all LLM/ASR config
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- **Frontend**: PascalCase components; `lib/api.ts` single API client; TanStack Query for server state
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- **API**: Path versioning `/api/v1/`; WebSocket at `/ws/asr/{video_id}`
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- **RAG**: Strict prompt — answer ONLY from retrieved context; bullet-point format
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- **Metadata**: Every doc chunk must have `filename`, `upload_date`, `content_summary`
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## ANTI-PATTERNS (THIS PROJECT)
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- Hardcode LLM URLs/keys — always `.env`
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- Business logic in routers — belongs in `services/`
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- Non-persistent ChromaDB — must use `chroma_db/` directory
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- LLM hallucination outside retrieved context — strict RAG prompt enforced
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- Plain text responses — always bullet points with source metadata
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- Missing document metadata — breaks source attribution
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- Add authentication — public demo only
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- Mobile-first design — desktop only at this stage
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## UNIQUE STYLES
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- **Dual ASR trigger**: automatic (on transcript update) + manual "Ask from Video" button
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- **Layout**: Top-Left video player | Top-Right transcript + input | Bottom RAG response
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- **Provider switching**: same codebase runs dev (OpenRouter/Alibaba Cloud) and prod (local vLLM)
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- **Video limit**: 300MB max, MP4 + common formats
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## TESTING
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**Backend test directory**: `backend/app/test/`
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**Naming convention** (pytest, flat structure, phase-prefixed):
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```
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test_phase<N>_<module_or_feature>.py
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```
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**Examples**:
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- `test_phase1_ingest.py` — Document upload & ChromaDB ingestion
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- `test_phase1_query.py` — RAG query endpoint
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- `test_phase1_rag_service.py` — RAG retrieval + strict prompt logic
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- `test_phase1_llm_client.py` — LLM client (mocked provider)
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- `test_phase1_chunking.py` — Document chunking utils
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- `test_phase1_metadata.py` — Metadata extraction
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- `test_phase2_video_upload.py` — Video upload (<300MB, format validation)
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- `test_phase2_asr_client.py` — ASR transcription client
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- `test_phase2_ws_asr.py` — WebSocket audio streaming
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- `test_phase2_query_from_video.py` — Auto/manual trigger from transcript
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- `test_integration_phase1.py` — End-to-end text → RAG → answer
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- `test_integration_phase2.py` — End-to-end video → ASR → RAG → answer
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**Rules**:
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- Use `pytest` + `pytest-asyncio` for async tests
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- Mock all external LLM/ASR calls (do not hit live APIs in tests)
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- Use `tmp_path` fixture for ChromaDB test instances
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- Each test file must have a module-level docstring describing coverage
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## COMMANDS
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```bash
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# Dev
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backend: uvicorn app.main:app --reload --port 8000
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frontend: npm run dev
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# Test
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backend: cd backend && pytest app/test/ -v
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# Prod
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docker-compose up -d
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./deploy.sh
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```
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## PLAN STORAGE
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**All development plans** (including sub-plans, debug plans, and task breakdowns) **must be stored in `.plans/`**.
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```
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.plans/
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├── development_plan.md # Main development plan (root-level)
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├── phase1_backend_plan.md # Phase 1 backend tasks
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├── phase1_frontend_plan.md # Phase 1 frontend tasks
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├── phase2_backend_plan.md # Phase 2 backend tasks
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├── phase2_frontend_plan.md # Phase 2 frontend tasks
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├── debug_<date>_<issue>.md # Debug/diagnosis logs
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└── _template.md # Plan template (optional)
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```
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**Rules**:
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- Name format: `<purpose>_<optional_date>.md` (snake_case)
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- Use `debug_` prefix for troubleshooting logs
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- Root `development_plan.md` stays at root as canonical source
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- Sub-plans reference root plan, never duplicate it
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## NOTES
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- No routing library specified — single-page app likely sufficient
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- No client state library specified — `useState`/`useReducer` + TanStack Query
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- WebSocket client not specified — may need to expand `lib/api.ts`
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- shadcn/ui components are copied, not imported as npm package
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- Alibaba Cloud reference: https://modelstudio.console.alibabacloud.com/ap-southeast-1?switchAgent=101503&tab=doc&productCode=p_efm&switchUserType=3#/doc/?type=model&url=2989727
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"""Shared pytest fixtures for backend tests.
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All external LLM/ASR calls must be mocked. Use tmp_path for ChromaDB instances.
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"""
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import pytest
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@pytest.fixture
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def mock_llm_client(monkeypatch):
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"""Mock LLM client to avoid hitting live APIs."""
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pass # TODO: implement mock
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@pytest.fixture
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def mock_asr_client(monkeypatch):
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"""Mock ASR client to avoid hitting live APIs."""
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pass # TODO: implement mock
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@pytest.fixture
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def chroma_test_dir(tmp_path):
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"""Provide a temporary directory for isolated ChromaDB instances."""
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return tmp_path / "chroma_test"
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"""Phase 1 tests: Document chunking utilities.
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Covers:
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- Text splitting strategies
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- Chunk size and overlap parameters
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- Handling of different document formats
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"""
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import pytest
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class TestChunking:
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"""Document chunking utility tests."""
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def test_chunk_size_limit(self):
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"""Should respect maximum chunk size."""
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pass # TODO: implement
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def test_chunk_overlap(self):
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"""Should include overlap between adjacent chunks."""
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pass # TODO: implement
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def test_empty_document(self):
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"""Should handle empty or whitespace-only documents."""
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pass # TODO: implement
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"""Phase 1 tests: Document ingestion endpoint.
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Covers:
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- POST /api/v1/ingest with valid documents
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- Metadata extraction (filename, upload_date, content_summary)
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- ChromaDB persistence with embeddings
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- Error handling for unsupported file types
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"""
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import pytest
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class TestIngest:
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"""Document upload and ChromaDB ingestion tests."""
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def test_ingest_pdf_success(self):
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"""Should ingest PDF and return document ID with metadata."""
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pass # TODO: implement
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def test_ingest_txt_success(self):
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"""Should ingest plain text and chunk correctly."""
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pass # TODO: implement
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def test_ingest_metadata_extraction(self):
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"""Should extract filename, upload_date, content_summary."""
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pass # TODO: implement
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def test_ingest_unsupported_format(self):
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"""Should reject unsupported file formats."""
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pass # TODO: implement
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"""Phase 1 tests: LLM client.
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Covers:
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- OpenAI-compatible API client for Qwen LLM
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- Provider switching via .env (OpenRouter, Alibaba Cloud, vLLM)
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- Error handling for API failures
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- Mocked responses in test mode
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"""
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import pytest
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class TestLLMClient:
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"""LLM client tests (all external calls mocked)."""
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def test_llm_call_success(self, mock_llm_client):
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"""Should return structured response from mocked LLM."""
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pass # TODO: implement
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def test_llm_provider_switching(self):
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"""Should switch base URL based on .env config."""
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pass # TODO: implement
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def test_llm_api_error_handling(self):
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"""Should handle HTTP errors from LLM provider."""
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pass # TODO: implement
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"""Phase 1 tests: Metadata extraction utilities.
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Covers:
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- Filename extraction
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- Upload date generation
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- Content summary generation
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- Metadata schema validation
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"""
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import pytest
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class TestMetadata:
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"""Metadata extraction utility tests."""
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def test_extract_filename(self):
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"""Should extract clean filename from path."""
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pass # TODO: implement
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def test_generate_upload_date(self):
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"""Should generate ISO format upload date."""
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pass # TODO: implement
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def test_content_summary(self):
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"""Should generate concise content summary."""
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pass # TODO: implement
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"""Phase 1 tests: RAG query endpoint.
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Covers:
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- POST /api/v1/query question → retrieve → LLM → bullet-point response
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- Strict RAG prompt enforcement (only use retrieved context)
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- Bullet-point response format
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- Source metadata inclusion
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"""
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import pytest
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class TestQuery:
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"""RAG query endpoint tests."""
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def test_query_returns_bullets(self):
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"""Should return bullet-point answer with source metadata."""
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pass # TODO: implement
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def test_query_strict_rag_no_hallucination(self):
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"""Should refuse to answer when no relevant context retrieved."""
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pass # TODO: implement
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def test_query_includes_source_metadata(self):
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"""Should include filename, upload_date in response."""
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pass # TODO: implement
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"""Phase 1 tests: RAG service logic.
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Covers:
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- ChromaDB retrieval with Qwen embeddings
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- Context assembly for LLM prompt
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- Strict prompt construction (answer ONLY from retrieved context)
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- Metadata handling per chunk
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"""
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import pytest
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class TestRAGService:
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"""RAG retrieval and prompt logic tests."""
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def test_retrieve_relevant_chunks(self):
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"""Should retrieve semantically relevant chunks from ChromaDB."""
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pass # TODO: implement
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def test_strict_prompt_format(self):
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"""Should construct prompt forbidding external knowledge."""
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pass # TODO: implement
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def test_chunk_metadata_preserved(self):
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"""Should preserve filename, upload_date, content_summary per chunk."""
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pass # TODO: implement
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"""Phase 2 tests: ASR transcription client.
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Covers:
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- Integration with Qwen/Qwen3-ASR-1.7B
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- File upload vs audio content input
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- Error handling for transcription failures
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- Mocked responses in test mode
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"""
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import pytest
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class TestASRClient:
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"""ASR client tests (all external calls mocked)."""
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def test_asr_transcribe_audio(self, mock_asr_client):
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"""Should return transcript from mocked ASR."""
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pass # TODO: implement
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def test_asr_file_upload_mode(self):
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"""Should support file path input."""
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pass # TODO: implement
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def test_asr_audio_content_mode(self):
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"""Should support raw audio bytes input."""
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pass # TODO: implement
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"""Phase 2 tests: Video upload endpoint.
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Covers:
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- POST /api/v1/upload-video with size validation (<300MB)
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- Format validation (MP4 and common formats)
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- Static file serving
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- Error handling for oversized/invalid files
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"""
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import pytest
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class TestVideoUpload:
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"""Video upload endpoint tests."""
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def test_upload_mp4_success(self):
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"""Should accept valid MP4 under 300MB."""
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pass # TODO: implement
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def test_upload_size_limit(self):
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"""Should reject files over 300MB."""
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pass # TODO: implement
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def test_upload_invalid_format(self):
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"""Should reject non-video formats."""
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pass # TODO: implement
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def test_static_file_serving(self):
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"""Should serve uploaded video via static URL."""
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pass # TODO: implement
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"""Phase 2 tests: WebSocket ASR streaming.
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Covers:
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- /ws/asr/{video_id} connection lifecycle
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- Real-time audio chunk streaming
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- Transcript accumulation
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- Connection cleanup on disconnect
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"""
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import pytest
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class TestWebSocketASR:
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"""WebSocket ASR streaming tests."""
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@pytest.mark.asyncio
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async def test_ws_connection_established(self):
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"""Should accept WebSocket connection with valid video_id."""
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pass # TODO: implement
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@pytest.mark.asyncio
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async def test_ws_audio_chunk_streaming(self):
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"""Should process audio chunks and return transcripts."""
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pass # TODO: implement
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@pytest.mark.asyncio
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async def test_ws_disconnect_cleanup(self):
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"""Should clean up resources on client disconnect."""
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pass # TODO: implement
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# RAG Video Q&A Web Application - Development Plan
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**Project Overview**
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Web-based application built in two phases.
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- **Phase 1**: Text question → RAG retrieval → Point-form answer (strictly from database)
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- **Phase 2**: Video upload + player → real-time audio streaming → ASR transcription → question extraction → Phase 1 RAG flow
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**Tech Stack**
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- **Backend**: Python + FastAPI (REST + WebSocket)
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- **Frontend**: TypeScript + React 18 (Vite) + shadcn/ui + Tailwind CSS
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- **Server**: Linux Ubuntu 22.04
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- **RAG Database**: ChromaDB (persistent)
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- **LLM/ASR Integration**: Dynamic via `.env` (supports local vLLM, OpenRouter, Alibaba Cloud)
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- Alibaba Cloud reference: https://modelstudio.console.alibabacloud.com/ap-southeast-1?switchAgent=101503&tab=doc&productCode=p_efm&switchUserType=3#/doc/?type=model&url=2989727
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- **Models**:
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- Embedding: `qwen/qwen3-embedding-4b`
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- LLM: `qwen/qwen3.5-35b-a3b`
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- ASR: `Qwen/Qwen3-ASR-1.7B`
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**Deployment**
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- Development: Simple commands (`uvicorn` + `npm run dev`)
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- Production: Docker + Nginx
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---
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## Project Structure (Monorepo)
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app/
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├── backend/ # FastAPI
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│ ├── app/
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│ │ ├── main.py
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||||
│ │ ├── routers/ # query.py, ingest.py, video.py, ws_asr.py
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│ │ ├── services/ # rag.py, llm_client.py, asr_client.py, video_service.py
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│ │ ├── models/ # Pydantic schemas
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│ │ ├── core/ # config.py, database.py
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│ │ └── utils/ # chunking, metadata extraction
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│ ├── uploads/ # video storage (max 300MB)
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||||
│ ├── requirements.txt
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||||
│ └── .env.example
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├── frontend/ # React + TypeScript (Vite)
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│ ├── src/
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||||
│ │ ├── components/
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│ │ ├── pages/
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||||
│ │ ├── lib/ # api.ts
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||||
│ │ └── App.tsx
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||||
│ ├── package.json
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||||
│ └── vite.config.ts
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||||
├── chroma_db/ # Persistent vector store
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├── Dockerfile
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├── docker-compose.yml
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||||
├── nginx.conf
|
||||
└── deploy.sh
|
||||
|
||||
|
||||
---
|
||||
|
||||
## Key Requirements Incorporated
|
||||
|
||||
- **LLM/ASR Configuration**: Backend reads from `.env` for easy switching between development (OpenRouter / Alibaba Cloud) and production (local vLLM).
|
||||
- **RAG Database**: ChromaDB with metadata support (filename + extracted content metadata).
|
||||
- **Embedding Model**: `qwen/qwen3-embedding-4b` via sentence-transformers.
|
||||
- **Document Ingestion**: Via UI (project-based demo, no user authentication).
|
||||
- **Video**: MP4 and common formats, maximum 300MB.
|
||||
- **ASR Flow**: Both **automatic** (on transcript updates) and **manual** “Ask from Video” button.
|
||||
- **UI Layout**:
|
||||
- Top-Left: Video player
|
||||
- Top-Right: Real-time transcript + text input box
|
||||
- Bottom Half: RAG response (bullet points with source metadata)
|
||||
- **Authentication**: Public demo (no login required).
|
||||
- **Mobile**: Not required at this stage.
|
||||
|
||||
---
|
||||
|
||||
## Phase 1: Text Question → RAG → Point-Form Answer (5-7 days)
|
||||
|
||||
### Backend (FastAPI)
|
||||
- Dynamic configuration via `.env` (LLM base URL, API key, model names).
|
||||
- `services/rag.py`: Persistent ChromaDB + Qwen embedding + metadata extraction (filename, upload date, content summary).
|
||||
- `services/llm_client.py`: OpenAI-compatible client for Qwen LLM with **strict RAG prompt** (only use retrieved context).
|
||||
- Endpoints:
|
||||
- `POST /api/v1/ingest` – Document upload and ingestion with metadata.
|
||||
- `POST /api/v1/query` – Question → retrieve → LLM → bullet-point response.
|
||||
|
||||
### Frontend (React + TS)
|
||||
- Clean layout: Top-right input box, bottom response area.
|
||||
- Type-safe API calls using TanStack Query.
|
||||
- Display answer as clean bullet list with source metadata.
|
||||
|
||||
---
|
||||
|
||||
## Phase 2: Video Upload + Real-Time ASR → RAG (8-10 days)
|
||||
|
||||
### Backend Additions
|
||||
- Video upload (`POST /api/v1/upload-video`) with size/format validation (<300MB).
|
||||
- Static file serving for videos.
|
||||
- WebSocket `/ws/asr/{video_id}` for real-time audio chunk streaming.
|
||||
- ASR integration with `Qwen/Qwen3-ASR-1.7B` (file upload or audio content).
|
||||
- Question extraction via LLM, then trigger Phase 1 RAG (auto + manual support).
|
||||
|
||||
### Frontend Additions
|
||||
- Drag & drop video upload + progress.
|
||||
- Video player (`<video controls>`).
|
||||
- Live transcript display (scrolling box).
|
||||
- Top-Left: Video player | Top-Right: Live transcript + manual input.
|
||||
- Bottom: RAG response panel.
|
||||
- Support both automatic “Ask” on transcript updates and manual button.
|
||||
|
||||
---
|
||||
|
||||
## Development Timeline
|
||||
|
||||
| Phase | Duration | Key Deliverables |
|
||||
|-----------------------------|--------------|------------------|
|
||||
| Setup + Phase 1 Backend | 3-4 days | FastAPI + Chroma + Metadata + LLM client |
|
||||
| Phase 1 Frontend | 2-3 days | UI layout + text query flow |
|
||||
| Phase 2 Backend | 4-5 days | Video upload + WebSocket ASR + question extraction |
|
||||
| Phase 2 Frontend | 3-4 days | Video player + live transcript + auto/manual flow |
|
||||
| Testing & Polish | 1-2 days | End-to-end testing + deployment scripts |
|
||||
|
||||
**Total Estimated Effort**: 13-17 developer days (2-3 weeks)
|
||||
|
||||
---
|
||||
|
||||
## Deployment Strategy
|
||||
|
||||
**Development**:
|
||||
- Backend: `cd backend && uvicorn app.main:app --reload --port 8000`
|
||||
- Frontend: `cd frontend && npm run dev`
|
||||
|
||||
**Production**:
|
||||
- Use `docker-compose up -d` (includes backend, built frontend, Nginx reverse proxy).
|
||||
- Simple `deploy.sh` script for building and restarting.
|
||||
|
||||
|
||||
---
|
||||
|
||||
**File Information**
|
||||
- Filename: `development_plan.md`
|
||||
- Last Updated: April 2026
|
||||
- Status: Ready for implementation
|
||||
Loading…
Reference in New Issue