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/README.md

@@ -0,0 +1,206 @@
+# RAG Service
+
+Retrieval-Augmented Generation service for MicroDAO. Integrates PARSER + Memory + Vector Search.
+
+## Features
+
+- **Document Ingestion**: Convert ParsedDocument from PARSER service to vector embeddings
+- **Query Pipeline**: Retrieve relevant documents and generate answers using LLM
+- **Haystack Integration**: Uses Haystack 2.x with PostgreSQL + pgvector
+- **Memory Integration**: Combines RAG results with Memory context
+
+## Architecture
+
+```
+PARSER → parsed_json → RAG Service → Vector DB (pgvector)
+                                    ↓
+User Query → RAG Service → Retrieve Documents → LLM (DAGI Router) → Answer + Citations
+```
+
+## Configuration
+
+### Environment Variables
+
+```bash
+# PostgreSQL
+PG_DSN=postgresql+psycopg2://postgres:postgres@city-db:5432/daarion_city
+
+# Embedding Model
+EMBED_MODEL_NAME=BAAI/bge-m3  # or intfloat/multilingual-e5-base
+EMBED_DEVICE=cuda  # or cpu, mps
+EMBED_DIM=1024  # BAAI/bge-m3 = 1024
+
+# Document Store
+RAG_TABLE_NAME=rag_documents
+SEARCH_STRATEGY=approximate
+
+# LLM Provider
+LLM_PROVIDER=router  # router, openai, local
+ROUTER_BASE_URL=http://router:9102
+```
+
+## API Endpoints
+
+### POST /ingest
+
+Ingest parsed document from PARSER service.
+
+**Request:**
+```json
+{
+  "dao_id": "daarion",
+  "doc_id": "microdao-tokenomics-2025-11",
+  "parsed_json": { ... },
+  "user_id": "optional-user-id"
+}
+```
+
+**Response:**
+```json
+{
+  "status": "success",
+  "doc_count": 15,
+  "dao_id": "daarion",
+  "doc_id": "microdao-tokenomics-2025-11"
+}
+```
+
+### POST /query
+
+Query RAG system for answers.
+
+**Request:**
+```json
+{
+  "dao_id": "daarion",
+  "question": "Поясни токеноміку microDAO і роль стейкінгу",
+  "top_k": 5,
+  "user_id": "optional-user-id"
+}
+```
+
+**Response:**
+```json
+{
+  "answer": "MicroDAO використовує токен μGOV...",
+  "citations": [
+    {
+      "doc_id": "microdao-tokenomics-2025-11",
+      "page": 1,
+      "section": "Токеноміка MicroDAO",
+      "excerpt": "MicroDAO використовує токен μGOV..."
+    }
+  ],
+  "documents": [...]
+}
+```
+
+### GET /health
+
+Health check endpoint.
+
+## Usage
+
+### 1. Ingest Document
+
+After parsing document with PARSER service:
+
+```bash
+curl -X POST http://localhost:9500/ingest \
+  -H "Content-Type: application/json" \
+  -d '{
+    "dao_id": "daarion",
+    "doc_id": "microdao-tokenomics-2025-11",
+    "parsed_json": { ... }
+  }'
+```
+
+### 2. Query RAG
+
+```bash
+curl -X POST http://localhost:9500/query \
+  -H "Content-Type: application/json" \
+  -d '{
+    "dao_id": "daarion",
+    "question": "Поясни токеноміку microDAO"
+  }'
+```
+
+## Integration with PARSER
+
+After parsing document:
+
+```python
+# In parser-service
+parsed_doc = parse_document_from_images(images, output_mode="raw_json")
+
+# Send to RAG Service
+import httpx
+async with httpx.AsyncClient() as client:
+    response = await client.post(
+        "http://rag-service:9500/ingest",
+        json={
+            "dao_id": "daarion",
+            "doc_id": parsed_doc.doc_id,
+            "parsed_json": parsed_doc.model_dump(mode="json")
+        }
+    )
+```
+
+## Integration with Router
+
+Router handles `mode="rag_query"`:
+
+```python
+# In Router
+if req.mode == "rag_query":
+    # Call RAG Service
+    rag_response = await rag_client.query(
+        dao_id=req.dao_id,
+        question=req.payload.get("question")
+    )
+    
+    # Combine with Memory context
+    memory_context = await memory_client.get_context(...)
+    
+    # Build prompt with RAG + Memory
+    prompt = build_prompt_with_rag_and_memory(
+        question=req.payload.get("question"),
+        rag_documents=rag_response["documents"],
+        memory_context=memory_context
+    )
+    
+    # Call LLM
+    answer = await llm_provider.generate(prompt)
+```
+
+## Development
+
+### Local Setup
+
+```bash
+# Install dependencies
+pip install -r requirements.txt
+
+# Set environment variables
+export PG_DSN="postgresql+psycopg2://postgres:postgres@localhost:5432/daarion_city"
+export EMBED_MODEL_NAME="BAAI/bge-m3"
+export EMBED_DEVICE="cpu"
+
+# Run service
+uvicorn app.main:app --host 0.0.0.0 --port 9500 --reload
+```
+
+### Tests
+
+```bash
+pytest tests/
+```
+
+## Dependencies
+
+- **Haystack 2.x**: Document store, embedding, retrieval
+- **sentence-transformers**: Embedding models
+- **psycopg2**: PostgreSQL connection
+- **FastAPI**: API framework
+