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

rag_client.py

@@ -0,0 +1,74 @@
+"""
+RAG Service Client for Router
+Used to query RAG Service for document retrieval
+"""
+
+import os
+import logging
+from typing import Optional, Dict, Any
+import httpx
+
+logger = logging.getLogger(__name__)
+
+RAG_SERVICE_URL = os.getenv("RAG_SERVICE_URL", "http://rag-service:9500")
+
+
+class RAGClient:
+    """Client for RAG Service"""
+    
+    def __init__(self, base_url: str = RAG_SERVICE_URL):
+        self.base_url = base_url.rstrip("/")
+        self.timeout = 30.0
+    
+    async def query(
+        self,
+        dao_id: str,
+        question: str,
+        top_k: Optional[int] = None,
+        user_id: Optional[str] = None
+    ) -> Dict[str, Any]:
+        """
+        Query RAG Service for answer and documents
+        
+        Args:
+            dao_id: DAO identifier
+            question: User question
+            top_k: Number of documents to retrieve
+            user_id: Optional user identifier
+        
+        Returns:
+            Dictionary with answer, citations, and documents
+        """
+        try:
+            async with httpx.AsyncClient(timeout=self.timeout) as client:
+                response = await client.post(
+                    f"{self.base_url}/query",
+                    json={
+                        "dao_id": dao_id,
+                        "question": question,
+                        "top_k": top_k,
+                        "user_id": user_id
+                    }
+                )
+                response.raise_for_status()
+                return response.json()
+                
+        except httpx.HTTPError as e:
+            logger.error(f"RAG query failed: {e}")
+            return {
+                "answer": "Помилка при запиті до бази знань.",
+                "citations": [],
+                "documents": []
+            }
+        except Exception as e:
+            logger.error(f"RAG query error: {e}", exc_info=True)
+            return {
+                "answer": "Помилка при запиті до бази знань.",
+                "citations": [],
+                "documents": []
+            }
+
+
+# Global client instance
+rag_client = RAGClient()
+