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

memory_client.py

@@ -0,0 +1,87 @@
+"""
+Memory Service Client for Router
+Used to get memory context for RAG queries
+"""
+
+import os
+import logging
+from typing import Optional, Dict, Any
+import httpx
+
+logger = logging.getLogger(__name__)
+
+MEMORY_SERVICE_URL = os.getenv("MEMORY_SERVICE_URL", "http://memory-service:8000")
+
+
+class MemoryClient:
+    """Client for Memory Service"""
+    
+    def __init__(self, base_url: str = MEMORY_SERVICE_URL):
+        self.base_url = base_url.rstrip("/")
+        self.timeout = 10.0
+    
+    async def get_context(
+        self,
+        user_id: str,
+        agent_id: str,
+        team_id: str,
+        channel_id: Optional[str] = None,
+        limit: int = 10
+    ) -> Dict[str, Any]:
+        """
+        Get memory context for dialogue
+        
+        Returns:
+            Dictionary with facts, recent_events, dialog_summaries
+        """
+        try:
+            async with httpx.AsyncClient(timeout=self.timeout) as client:
+                # Get user facts
+                facts_response = await client.get(
+                    f"{self.base_url}/facts",
+                    params={"user_id": user_id, "team_id": team_id, "limit": limit}
+                )
+                facts = facts_response.json() if facts_response.status_code == 200 else []
+                
+                # Get recent memory events
+                events_response = await client.get(
+                    f"{self.base_url}/agents/{agent_id}/memory",
+                    params={
+                        "team_id": team_id,
+                        "channel_id": channel_id,
+                        "scope": "short_term",
+                        "kind": "message",
+                        "limit": limit
+                    }
+                )
+                events = events_response.json().get("items", []) if events_response.status_code == 200 else []
+                
+                # Get dialog summaries
+                summaries_response = await client.get(
+                    f"{self.base_url}/summaries",
+                    params={
+                        "team_id": team_id,
+                        "channel_id": channel_id,
+                        "agent_id": agent_id,
+                        "limit": 5
+                    }
+                )
+                summaries = summaries_response.json().get("items", []) if summaries_response.status_code == 200 else []
+                
+                return {
+                    "facts": facts,
+                    "recent_events": events,
+                    "dialog_summaries": summaries
+                }
+        except Exception as e:
+            logger.warning(f"Memory context fetch failed: {e}")
+            return {
+                "facts": [],
+                "recent_events": [],
+                "dialog_summaries": []
+            }
+
+
+# Global client instance
+memory_client = MemoryClient()
+