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feat: production upgrade — agentic RAG, OpenSearch, Redis, Langfuse, Docker, Gradio, Telegram
Nikhil Pravin PisePhase 1: Project structure & dev tooling - pyproject.toml with Ruff/MyPy/pytest config and optional deps - Makefile with dev/test/docker/lint targets - Multi-stage Dockerfile + docker-compose.yml (12 services) - .env.example, .pre-commit-config.yaml Phase 2: Core infrastructure - src/settings.py: hierarchical Pydantic Settings (env-driven) - src/exceptions.py: domain exception hierarchy (15+ classes) - src/database.py: SQLAlchemy engine/session factory - src/models/analysis.py: ORM models (PatientAnalysis, MedicalDocument, SOPVersion) - src/repositories/: data access layer Phase 3: Production services - OpenSearch client with BM25, KNN vector, and hybrid RRF search - Medical synonym analyzer + KNN index mapping (1024d, HNSW) - Multi-provider embedding service (Jina/Google/HuggingFace) with fallback - Redis cache with graceful degradation (NullCache) - Langfuse v3 observability tracer (NullSpan for no-ops) - Ollama REST client with streaming + LangChain integration - Medical-aware tex…1e732dd