feat: add pgvector semantic search ...

- Add pgvector dependency and Alembic migration (vector extension, embedding
  column, HNSW index with cosine ops)
- Add nomic-embed-text embedding model to config
- Add OllamaClient.embed() method for /api/embed endpoint
- Add embedding generation stage to PropertyPipeline (_stage_embed)
- Add PropertyRepository.update_embedding() and search_similar() with
  cosine distance + optional filters (deal_type, city, price range)
- Add POST /api/v1/search/similar endpoint with query embedding + filters
- Add SimilarSearchRequest/Response schemas
- Add backfill script for existing listings
- Update docker-compose.yml to pgvector/pgvector:pg16 image
- Update .env to use Docker PostgreSQL on port 5433

Co-Authored-By: Claude <noreply@anthropic.com>

feat: implement review items 1-7 ...

- Decompose PropertyPipeline into 8 explicit stages with PipelineContext
- Add tenacity retry (3 attempts, exponential backoff) to OllamaClient and ImageDownloader
- Add simple in-memory circuit breaker for Ollama calls
- Resize images to 1024px before base64 encoding for vision model
- Add /health endpoint (DB, Ollama, disk checks)
- Add DB performance indexes + alembic migration
- Classify AI errors: OllamaRetryableError vs OllamaFatalError
- Add strict PayloadSchema validation for ingest endpoint

Co-Authored-By: Claude <noreply@anthropic.com>

feat: core pipeline + FastAPI API (Phases 0-6) ...

Implemented the full VMK Data Collector foundation and processing pipeline:

- Config, logging, exception hierarchy
- DB models (listings, raw data, images, snapshots, enrichments, custom fields)
- Alembic async migrations
- Repositories with upsert/snapshot support
- Domain entities and Pydantic schemas
- Ollama AI client with mock mode
- AI normalizer, image analyzer, enricher
- Image downloader with SHA-256 dedup
- PropertyPipeline: raw -> AI validate -> upsert/snapshot -> images -> enrich
- FastAPI app with /api/v1/ingest endpoint

Co-Authored-By: Claude <noreply@anthropic.com>