feat: implement RAG Service MVP with PARSER + Memory integration

RAG Service Implementation:
- Create rag-service/ with full structure (config, document_store, embedding, pipelines)
- Document Store: PostgreSQL + pgvector via Haystack
- Embedding: BAAI/bge-m3 (multilingual, 1024 dim)
- Ingest Pipeline: Convert ParsedDocument to Haystack Documents, embed, index
- Query Pipeline: Retrieve documents, generate answers via DAGI Router
- FastAPI endpoints: /ingest, /query, /health

Tests:
- Unit tests for ingest and query pipelines
- E2E test with example parsed JSON
- Test fixtures with real PARSER output example

Router Integration:
- Add mode='rag_query' routing rule in router-config.yml
- Priority 7, uses local_qwen3_8b for RAG queries

Docker:
- Add rag-service to docker-compose.yml
- Configure dependencies (router, city-db)
- Add model cache volume

Documentation:
- Complete README with API examples
- Integration guides for PARSER and Router

services/rag-service/app/models.py

@@ -0,0 +1,47 @@
+"""
+Pydantic models for RAG Service API
+"""
+
+from typing import Optional, List, Dict, Any
+from pydantic import BaseModel, Field
+
+
+class IngestRequest(BaseModel):
+    """Request for document ingestion"""
+    dao_id: str = Field(..., description="DAO identifier")
+    doc_id: str = Field(..., description="Document identifier")
+    parsed_json: Dict[str, Any] = Field(..., description="ParsedDocument JSON from PARSER service")
+    user_id: Optional[str] = Field(None, description="User identifier")
+
+
+class IngestResponse(BaseModel):
+    """Response from document ingestion"""
+    status: str = Field(..., description="Status: success or error")
+    doc_count: int = Field(..., description="Number of documents ingested")
+    dao_id: str = Field(..., description="DAO identifier")
+    doc_id: str = Field(..., description="Document identifier")
+    message: Optional[str] = Field(None, description="Error message if status=error")
+
+
+class QueryRequest(BaseModel):
+    """Request for RAG query"""
+    dao_id: str = Field(..., description="DAO identifier")
+    question: str = Field(..., description="User question")
+    top_k: Optional[int] = Field(None, description="Number of documents to retrieve")
+    user_id: Optional[str] = Field(None, description="User identifier")
+
+
+class Citation(BaseModel):
+    """Citation from retrieved document"""
+    doc_id: str = Field(..., description="Document identifier")
+    page: int = Field(..., description="Page number")
+    section: Optional[str] = Field(None, description="Section name")
+    excerpt: str = Field(..., description="Document excerpt")
+
+
+class QueryResponse(BaseModel):
+    """Response from RAG query"""
+    answer: str = Field(..., description="Generated answer")
+    citations: List[Citation] = Field(..., description="List of citations")
+    documents: List[Dict[str, Any]] = Field(..., description="Retrieved documents (for debugging)")
+