Why:
- Need LLM-as-Judge evaluation for automated scoring
- Need benchmark orchestration to run questions × strategies
- Need HTML report generation with two views (managerial/technical)
Changes:
- evaluation.py: LLM-as-Judge scoring on 4 metrics (context, similarity, faithfulness, hallucination)
- benchmark_service.py: Orchestration with per-strategy failure isolation
- report.py: Dual-view HTML reports with dark mode, charts, and strategy cards
Why:
- Need structured logging for debugging query pipeline
- Need to mount benchmarking router
Changes:
- Added logging.basicConfig() with INFO level
- Mounted benchmarking router
- Registered QueryError exception handler
Why:
- Need to list documents to get document IDs for queries
- ProcessRequest should default to all 5 strategies
- paragraph_count should be optional (not always stored)
Changes:
- Added GET /documents endpoint with pagination
- Added DocumentListResponse model
- ProcessRequest now defaults to all 5 strategies
- DocumentResponse.paragraph_count now defaults to 0
Why:
- Qdrant was storing only metadata, not chunk text
- Queries returned empty text in retrieved chunks
- Scores were low (~0.25) because embeddings were on empty strings
Changes:
- Added 'text' field to ChunkMetadata model
- Updated chunk_to_metadata() to copy text
Why:
- Need implementation plan and task tracking documentation
- Need strategy explanations for reference
Changes:
- phases.md: 5-phase implementation plan with status tracking
- tasks.md: 25 tasks mapped to phases and steps
- chunking_strategies.md: detailed explanations of all 5 strategies
Why:
- App factory needs to mount document routes and initialize SQLite at startup
Changes:
- Import and mount documents_router
- Call init_db() on app creation
Why:
- Need abstract base class for all chunking strategies
- Need OpenAI embedding service (text-embedding-3-small)
- Need orchestration to run chunk → embed → store pipeline
Changes:
- Base: ChunkingStrategy ABC, token counting, chunk ID generation, sentence splitting
- Embedding: batch embedding with 2048 text limit per call
- Service: strategy registry, single/multi-strategy runners with per-strategy failure isolation
Why:
- Need to parse .docx/.doc files into DocumentTree + markdown
- Need API endpoints for document upload, processing, and deletion
Changes:
- Parser: .doc→.docx conversion via LibreOffice, XML-level extraction for table-heavy docs, heuristic heading detection
- Service: upload, process (delegates to chunking), delete orchestration
- Routes: POST /documents, POST /documents/{id}/process, DELETE /documents/{id}, GET /strategies
