docs: Update Phase 1 backend plan with completion status
- Mark Phase 1.1 and 1.2 as complete with test results - Update acceptance criteria checklist - Add Services Status table showing implemented/pending components - Mark Phase 1.3 tasks that are already done (LLM client, retrieval, response gen)
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**Source**: `development_plan.md`
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**Scope**: FastAPI backend for text-based RAG Q&A
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**Estimated Duration**: 3-4 days
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**Status**: Draft
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**Status**: In Progress (Phase 1.1 ✅, Phase 1.2 ✅, Phase 1.3 pending)
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---
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@ -18,13 +18,13 @@ Build a complete FastAPI backend that:
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## Acceptance Criteria
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- [ ] `POST /api/v1/ingest` accepts DOCX and PDF, parses content, chunks at 1000/200, embeds, stores in ChromaDB with filename/upload_date/content_summary
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- [x] `POST /api/v1/ingest` accepts DOCX and PDF, parses content, chunks at 1000/200, embeds, stores in ChromaDB with filename/upload_date/content_summary
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- [ ] `POST /api/v1/query` accepts natural language question, returns JSON with: `keywords`, `answer` (bullet points), `sources` (array of metadata objects)
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- [ ] Query pipeline executes 3 LLM calls: decomposition → relevance filter → response generation
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- [ ] All LLM/ASR configuration reads from `.env` (OpenRouter for dev)
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- [ ] ChromaDB persists to `chroma_db/` directory
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- [ ] Chunking strategy is abstracted (interface/class) for future replacement
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- [ ] All unit tests pass (`pytest app/test/test_phase1_*.py -v`)
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- [x] All LLM/ASR configuration reads from `.env` (OpenRouter for dev)
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- [x] ChromaDB persists to `chroma_db/` directory
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- [x] Chunking strategy is abstracted (interface/class) for future replacement
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- [x] All unit tests pass (`pytest app/test/test_phase1_*.py -v`)
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- [ ] All acceptance tests pass (`pytest app/test/acceptance/ -v -m acceptance`)
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---
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@ -71,6 +71,11 @@ Build a complete FastAPI backend that:
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**Commit**: "feat: Phase 1.1 project setup with config, database, and models"
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**Status**: ✅ Complete
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**Tests**: 5 passed (2 config, 3 database)
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---
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### Phase 1.2: Ingestion Pipeline
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**Test files to write first**:
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@ -110,6 +115,12 @@ Build a complete FastAPI backend that:
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**Commit**: "feat: Phase 1.2 ingestion pipeline with chunking and metadata"
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**Status**: ✅ Complete
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**Tests**: 20 passed, 2 skipped (python-docx not installed in test env)
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**Coverage**: chunking (4), metadata (3), parsers (5), RAGService (6), ingest endpoint (4)
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---
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### Phase 1.3: Query Pipeline (3-Step)
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**Test files to write first**:
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- `test_phase1_rag_service.py` — Test retrieval and response generation
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- `test_phase1_query.py` — Test full pipeline with mocked LLM calls
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**Task 1.3.1**: LLM client
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- `services/llm_client.py`: `LLMClient` class
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**Task 1.3.1**: LLM client — ✅ Done in Phase 1.1
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- `services/llm_client.py`: `LLMClient` class — Implemented
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- Constructor takes config from `Settings`
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- Method: `complete(prompt: str, temperature: float = 0.7) -> str`
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- Use httpx with OpenAI-compatible API format
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- Handle errors gracefully
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**Task 1.3.2**: Query decomposition
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- `services/query_decomposer.py`: `QueryDecomposer` class
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- `services/query_decomposer.py`: `QueryDecomposer` class — 🔄 Pending
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- Prompt template: "Given question: '{question}', extract key search keywords as JSON array"
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- Method: `decompose(question: str) -> list[str]`
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- Parse LLM JSON response into list of keywords
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**Task 1.3.3**: Retrieval from ChromaDB
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- `services/rag.py`: Add `retrieve(query_keywords: list[str], n_results: int = 10)`
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**Task 1.3.3**: Retrieval from ChromaDB — ✅ Done in Phase 1.2
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- `services/rag.py`: `retrieve(query_keywords: list[str], n_results: int = 10)` — Implemented
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- Join keywords with space for query text
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- Return list of `(chunk_text, metadata, distance)` tuples
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**Task 1.3.4**: Relevance filtering
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- `services/relevance_filter.py`: `RelevanceFilter` class
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- `services/relevance_filter.py`: `RelevanceFilter` class — 🔄 Pending
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- Prompt: "Given question '{question}' and these document chunks, rate each 0-10 for relevance. Return JSON array of scores."
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- Input: list of chunks
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- Output: filtered list of (chunk, metadata) with score > threshold (e.g., 7)
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- Batch all chunks in single LLM call
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**Task 1.3.5**: Response generation
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- `services/rag.py`: Add `generate_response(question: str, chunks: list, metadata: list) -> str`
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**Task 1.3.5**: Response generation — ✅ Done in Phase 1.2
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- `services/rag.py`: `generate_response(question: str, chunks: list, metadata: list) -> str` — Implemented
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- Prompt: "Answer question using ONLY these document chunks. Format as bullet points. Cite sources."
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- Include chunk content and metadata in context
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- Enforce bullet-point format via prompt
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**Task 1.3.6**: Query endpoint
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- `routers/query.py`: `POST /api/v1/query`
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- `routers/query.py`: `POST /api/v1/query` — 🔄 Pending
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- Full pipeline orchestration:
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1. Call `query_decomposer.decompose()` → get keywords
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2. Call `rag.retrieve()` → get chunks
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@ -189,21 +200,22 @@ Build a complete FastAPI backend that:
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---
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## New Services Required
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## Services Status
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| Service | File | Responsibility |
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|---------|------|----------------|
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| Config | `core/config.py` | `.env` loading, Settings class |
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| Database | `core/database.py` | ChromaDB persistent client |
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| LLM Client | `services/llm_client.py` | OpenAI-compatible API wrapper |
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| Query Decomposer | `services/query_decomposer.py` | Extract keywords from question |
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| Relevance Filter | `services/relevance_filter.py` | Batch score chunk relevance |
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| RAG Service | `services/rag.py` | Embedding, retrieval, response generation |
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| Document Parser | `utils/document_parser.py` | Router to DOCX/PDF parsers |
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| DOCX Parser | `utils/parsers/docx_parser.py` | Extract text from DOCX |
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| PDF Parser | `utils/parsers/pdf_parser.py` | Extract text from PDF |
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| Chunking | `utils/chunking.py` | Token-based chunking with overlap |
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| Metadata | `utils/metadata.py` | Extract file metadata |
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| Service | File | Status | Responsibility |
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|---------|------|--------|----------------|
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| Config | `core/config.py` | ✅ Complete | `.env` loading, Settings class |
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| Database | `core/database.py` | ✅ Complete | ChromaDB persistent client |
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| LLM Client | `services/llm_client.py` | ✅ Complete | OpenAI-compatible API wrapper |
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| Query Decomposer | `services/query_decomposer.py` | 🔄 Pending | Extract keywords from question |
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| Relevance Filter | `services/relevance_filter.py` | 🔄 Pending | Batch score chunk relevance |
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| RAG Service | `services/rag.py` | ✅ Complete | Embedding, retrieval, response generation |
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| Ingest Router | `routers/ingest.py` | ✅ Complete | POST /api/v1/ingest endpoint |
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| Query Router | `routers/query.py` | 🔄 Pending | POST /api/v1/query endpoint |
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| DOCX Parser | `utils/docx_parser.py` | ✅ Complete | Extract text from DOCX |
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| PDF Parser | `utils/pdf_parser.py` | ✅ Complete | Extract text from PDF |
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| Chunking | `utils/chunking.py` | ✅ Complete | Token-based chunking with overlap |
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| Metadata | `utils/metadata.py` | ✅ Complete | Extract file metadata |
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