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

services/parser-service/app/core/config.py

@@ -47,6 +47,10 @@ class Settings(BaseSettings):
     ROUTER_BASE_URL: str = os.getenv("ROUTER_BASE_URL", "http://router:9102")
     ROUTER_TIMEOUT: int = int(os.getenv("ROUTER_TIMEOUT", "60"))
     
+    # RAG Service configuration (for ingest pipeline)
+    RAG_BASE_URL: str = os.getenv("RAG_BASE_URL", "http://rag-service:9500")
+    RAG_TIMEOUT: int = int(os.getenv("RAG_TIMEOUT", "120"))
+    
     class Config:
         env_file = ".env"
         case_sensitive = True