feat: complete RAG pipeline integration (ingest + query + Memory)

Parser Service:
- Add /ocr/ingest endpoint (PARSER → RAG in one call)
- Add RAG_BASE_URL and RAG_TIMEOUT to config
- Add OcrIngestResponse schema
- Create file_converter utility for PDF/image → PNG bytes
- Endpoint accepts file, dao_id, doc_id, user_id
- Automatically parses with dots.ocr and sends to RAG Service

Router Integration:
- Add _handle_rag_query() method in RouterApp
- Combines Memory + RAG → LLM pipeline
- Get Memory context (facts, events, summaries)
- Query RAG Service for documents
- Build prompt with Memory + RAG documents
- Call LLM provider with combined context
- Return answer with citations

Clients:
- Create rag_client.py for Router (query RAG Service)
- Create memory_client.py for Router (get Memory context)

E2E Tests:
- Create e2e_rag_pipeline.sh script for full pipeline test
- Test ingest → query → router query flow
- Add E2E_RAG_README.md with usage examples

Docker:
- Add RAG_SERVICE_URL and MEMORY_SERVICE_URL to router environment

docker-compose.yml

@@ -12,6 +12,8 @@ services:
       - RBAC_BASE_URL=http://rbac:9200
       - DEVTOOLS_BASE_URL=http://devtools:8008
       - CREWAI_BASE_URL=http://crewai:9010
+      - RAG_SERVICE_URL=http://rag-service:9500
+      - MEMORY_SERVICE_URL=http://memory-service:8000
     volumes:
       - ./router-config.yml:/app/router-config.yml:ro
       - ./logs:/app/logs