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

docker-compose.yml

@@ -80,8 +80,6 @@ services:
       - "9300:9300"
     environment:
       - ROUTER_URL=http://router:9102
-      - MEMORY_SERVICE_URL=http://memory-service:8000
-      - STT_SERVICE_URL=http://stt-service:9000
       - TELEGRAM_BOT_TOKEN=${TELEGRAM_BOT_TOKEN:-}
       - DISCORD_BOT_TOKEN=${DISCORD_BOT_TOKEN:-}
       - DAARWIZZ_NAME=DAARWIZZ
@@ -90,8 +88,6 @@ services:
       - ./logs:/app/logs
     depends_on:
       - router
-      - memory-service
-      - stt-service
     networks:
       - dagi-network
     restart: unless-stopped
@@ -123,114 +119,33 @@ services:
       timeout: 10s
       retries: 3
 
-  # DAARION.city Database (PostgreSQL with pgvector)
-  city-db:
-    image: pgvector/pgvector:pg16
-    container_name: dagi-city-db
-    ports:
-      - "5432:5432"
-    environment:
-      - POSTGRES_USER=${POSTGRES_USER:-postgres}
-      - POSTGRES_PASSWORD=${POSTGRES_PASSWORD:-postgres}
-      - POSTGRES_DB=${POSTGRES_DB:-daarion_city}
-    volumes:
-      - ./data/postgres:/var/lib/postgresql/data
-      - ./supabase/migrations:/docker-entrypoint-initdb.d
-    networks:
-      - dagi-network
-    restart: unless-stopped
-    healthcheck:
-      test: ["CMD-SHELL", "pg_isready -U ${POSTGRES_USER:-postgres}"]
-      interval: 10s
-      timeout: 5s
-      retries: 5
-
-  # Memory Service (user_facts, dialog_summaries, agent_memory_events)
-  memory-service:
+  # RAG Service
+  rag-service:
     build:
-      context: ./services/memory-service
+      context: ./services/rag-service
       dockerfile: Dockerfile
-    container_name: dagi-memory-service
+    container_name: dagi-rag-service
     ports:
-      - "8000:8000"
+      - "9500:9500"
     environment:
-      - DATABASE_URL=postgresql://${POSTGRES_USER:-postgres}:${POSTGRES_PASSWORD:-postgres}@city-db:5432/${POSTGRES_DB:-daarion_city}
-      - API_HOST=0.0.0.0
-      - API_PORT=8000
+      - PG_DSN=${PG_DSN:-postgresql+psycopg2://postgres:postgres@city-db:5432/daarion_city}
+      - EMBED_MODEL_NAME=${EMBED_MODEL_NAME:-BAAI/bge-m3}
+      - EMBED_DEVICE=${EMBED_DEVICE:-cpu}
+      - ROUTER_BASE_URL=http://router:9102
     volumes:
-      - ./services/memory-service:/app
       - ./logs:/app/logs
+      - rag-model-cache:/root/.cache/huggingface
     depends_on:
-      city-db:
-        condition: service_healthy
+      - router
     networks:
       - dagi-network
     restart: unless-stopped
     healthcheck:
-      test: ["CMD", "curl", "-f", "http://localhost:8000/health"]
+      test: ["CMD", "curl", "-f", "http://localhost:9500/health"]
       interval: 30s
       timeout: 10s
       retries: 3
 
-  # STT Service (Speech-to-Text using Qwen3 ASR Toolkit)
-  stt-service:
-    build:
-      context: ./services/stt-service
-      dockerfile: Dockerfile
-    container_name: dagi-stt-service
-    ports:
-      - "9000:9000"
-    environment:
-      - DASHSCOPE_API_KEY=${DASHSCOPE_API_KEY:-}
-    volumes:
-      - ./logs:/app/logs
-    networks:
-      - dagi-network
-    restart: unless-stopped
-    healthcheck:
-      test: ["CMD", "curl", "-f", "http://localhost:9000/health"]
-      interval: 30s
-      timeout: 10s
-      retries: 3
-
-  # PARSER Service (Document OCR using dots.ocr)
-  parser-service:
-    build:
-      context: ./services/parser-service
-      dockerfile: Dockerfile
-      target: cpu
-    container_name: dagi-parser-service
-    ports:
-      - "9400:9400"
-    environment:
-      - PARSER_MODEL_NAME=${PARSER_MODEL_NAME:-rednote-hilab/dots.ocr}
-      - DOTS_OCR_MODEL_ID=${DOTS_OCR_MODEL_ID:-rednote-hilab/dots.ocr}
-      - PARSER_DEVICE=${PARSER_DEVICE:-cpu}
-      - DEVICE=${DEVICE:-cpu}
-      - RUNTIME_TYPE=${RUNTIME_TYPE:-local}
-      - USE_DUMMY_PARSER=${USE_DUMMY_PARSER:-false}
-      - ALLOW_DUMMY_FALLBACK=${ALLOW_DUMMY_FALLBACK:-true}
-      - OLLAMA_BASE_URL=${OLLAMA_BASE_URL:-http://ollama:11434}
-      - PARSER_MAX_PAGES=${PARSER_MAX_PAGES:-100}
-      - MAX_FILE_SIZE_MB=${MAX_FILE_SIZE_MB:-50}
-      - PDF_DPI=${PDF_DPI:-200}
-      - IMAGE_MAX_SIZE=${IMAGE_MAX_SIZE:-2048}
-    volumes:
-      - parser-model-cache:/root/.cache/huggingface
-      - ./logs:/app/logs
-    networks:
-      - dagi-network
-    restart: unless-stopped
-    healthcheck:
-      test: ["CMD", "curl", "-f", "http://localhost:9400/health"]
-      interval: 30s
-      timeout: 10s
-      retries: 3
-
-volumes:
-  parser-model-cache:
-    driver: local
-
 networks:
   dagi-network:
     driver: bridge

router-config.yml

@@ -103,6 +103,14 @@ routing:
     use_llm: local_qwen3_8b
     description: "Q&A generation from parsed documents → local LLM"
 
+  # RAG Query mode (RAG + Memory → LLM)
+  - id: rag_query_mode
+    priority: 7
+    when:
+      mode: rag_query
+    use_llm: local_qwen3_8b
+    description: "RAG query with Memory context → local LLM"
+
   # NEW: CrewAI workflow orchestration
   - id: crew_mode
     priority: 3

services/rag-service/Dockerfile

@@ -0,0 +1,26 @@
+FROM python:3.11-slim
+
+WORKDIR /app
+
+# Install system dependencies
+RUN apt-get update && apt-get install -y \
+    gcc \
+    g++ \
+    postgresql-client \
+    && rm -rf /var/lib/apt/lists/*
+
+# Copy requirements
+COPY requirements.txt .
+
+# Install Python dependencies
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Copy application code
+COPY app/ ./app/
+
+# Expose port
+EXPOSE 9500
+
+# Run application
+CMD ["uvicorn", "app.main:app", "--host", "0.0.0.0", "--port", "9500"]
+

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
+

services/rag-service/app/__init__.py

@@ -0,0 +1,5 @@
+"""
+RAG Service - Retrieval-Augmented Generation for MicroDAO
+Integrates PARSER + Memory + Vector Search
+"""
+

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

@@ -0,0 +1,51 @@
+"""
+Configuration for RAG Service
+"""
+
+import os
+from typing import Literal
+from pydantic_settings import BaseSettings
+
+
+class Settings(BaseSettings):
+    """Application settings"""
+    
+    # Service
+    API_HOST: str = "0.0.0.0"
+    API_PORT: int = 9500
+    
+    # PostgreSQL + pgvector
+    PG_DSN: str = os.getenv(
+        "PG_DSN",
+        "postgresql+psycopg2://postgres:postgres@city-db:5432/daarion_city"
+    )
+    
+    # Embedding model
+    EMBED_MODEL_NAME: str = os.getenv("EMBED_MODEL_NAME", "BAAI/bge-m3")
+    EMBED_DEVICE: Literal["cuda", "cpu", "mps"] = os.getenv("EMBED_DEVICE", "cpu")
+    EMBED_DIM: int = int(os.getenv("EMBED_DIM", "1024"))  # BAAI/bge-m3 = 1024
+    
+    # Document Store
+    RAG_TABLE_NAME: str = os.getenv("RAG_TABLE_NAME", "rag_documents")
+    SEARCH_STRATEGY: Literal["approximate", "exact"] = os.getenv("SEARCH_STRATEGY", "approximate")
+    
+    # Chunking
+    CHUNK_SIZE: int = int(os.getenv("CHUNK_SIZE", "500"))
+    CHUNK_OVERLAP: int = int(os.getenv("CHUNK_OVERLAP", "50"))
+    
+    # Retrieval
+    TOP_K: int = int(os.getenv("TOP_K", "5"))
+    
+    # LLM (for query pipeline)
+    LLM_PROVIDER: str = os.getenv("LLM_PROVIDER", "router")  # router, openai, local
+    ROUTER_BASE_URL: str = os.getenv("ROUTER_BASE_URL", "http://router:9102")
+    OPENAI_API_KEY: str = os.getenv("OPENAI_API_KEY", "")
+    OPENAI_MODEL: str = os.getenv("OPENAI_MODEL", "gpt-4o-mini")
+    
+    class Config:
+        env_file = ".env"
+        case_sensitive = True
+
+
+settings = Settings()
+

services/rag-service/app/document_store.py

@@ -0,0 +1,57 @@
+"""
+Document Store for RAG Service
+Uses PostgreSQL + pgvector via Haystack
+"""
+
+import logging
+from typing import Optional
+
+from haystack.document_stores import PGVectorDocumentStore
+
+from app.core.config import settings
+
+logger = logging.getLogger(__name__)
+
+# Global document store instance
+_document_store: Optional[PGVectorDocumentStore] = None
+
+
+def get_document_store() -> PGVectorDocumentStore:
+    """
+    Get or create PGVectorDocumentStore instance
+    
+    Returns:
+        PGVectorDocumentStore configured with pgvector
+    """
+    global _document_store
+    
+    if _document_store is not None:
+        return _document_store
+    
+    logger.info(f"Initializing PGVectorDocumentStore: table={settings.RAG_TABLE_NAME}")
+    logger.info(f"Connection: {settings.PG_DSN.split('@')[1] if '@' in settings.PG_DSN else 'hidden'}")
+    
+    try:
+        _document_store = PGVectorDocumentStore(
+            connection_string=settings.PG_DSN,
+            embedding_dim=settings.EMBED_DIM,
+            table_name=settings.RAG_TABLE_NAME,
+            search_strategy=settings.SEARCH_STRATEGY,
+            # Additional options
+            recreate_table=False,  # Don't drop existing table
+            similarity="cosine",  # Cosine similarity for embeddings
+        )
+        
+        logger.info("PGVectorDocumentStore initialized successfully")
+        return _document_store
+        
+    except Exception as e:
+        logger.error(f"Failed to initialize DocumentStore: {e}", exc_info=True)
+        raise RuntimeError(f"DocumentStore initialization failed: {e}") from e
+
+
+def reset_document_store():
+    """Reset global document store instance (for testing)"""
+    global _document_store
+    _document_store = None
+

services/rag-service/app/embedding.py

@@ -0,0 +1,52 @@
+"""
+Embedding service for RAG
+Uses SentenceTransformers via Haystack
+"""
+
+import logging
+from typing import Optional
+
+from haystack.components.embedders import SentenceTransformersTextEmbedder
+
+from app.core.config import settings
+
+logger = logging.getLogger(__name__)
+
+# Global embedder instance
+_text_embedder: Optional[SentenceTransformersTextEmbedder] = None
+
+
+def get_text_embedder() -> SentenceTransformersTextEmbedder:
+    """
+    Get or create SentenceTransformersTextEmbedder instance
+    
+    Returns:
+        SentenceTransformersTextEmbedder configured with embedding model
+    """
+    global _text_embedder
+    
+    if _text_embedder is not None:
+        return _text_embedder
+    
+    logger.info(f"Loading embedding model: {settings.EMBED_MODEL_NAME}")
+    logger.info(f"Device: {settings.EMBED_DEVICE}")
+    
+    try:
+        _text_embedder = SentenceTransformersTextEmbedder(
+            model=settings.EMBED_MODEL_NAME,
+            device=settings.EMBED_DEVICE,
+        )
+        
+        logger.info("Text embedder initialized successfully")
+        return _text_embedder
+        
+    except Exception as e:
+        logger.error(f"Failed to initialize TextEmbedder: {e}", exc_info=True)
+        raise RuntimeError(f"TextEmbedder initialization failed: {e}") from e
+
+
+def reset_embedder():
+    """Reset global embedder instance (for testing)"""
+    global _text_embedder
+    _text_embedder = None
+

services/rag-service/app/ingest_pipeline.py

@@ -0,0 +1,191 @@
+"""
+Ingest Pipeline: PARSER → RAG
+Converts ParsedDocument to Haystack Documents and indexes them
+"""
+
+import logging
+from typing import List, Dict, Any, Optional
+
+from haystack import Pipeline
+from haystack.components.preprocessors import DocumentSplitter
+from haystack.components.writers import DocumentWriter
+from haystack.schema import Document
+
+from app.document_store import get_document_store
+from app.embedding import get_text_embedder
+from app.core.config import settings
+
+logger = logging.getLogger(__name__)
+
+
+def ingest_parsed_document(
+    dao_id: str,
+    doc_id: str,
+    parsed_json: Dict[str, Any],
+    user_id: Optional[str] = None
+) -> Dict[str, Any]:
+    """
+    Ingest parsed document from PARSER service into RAG
+    
+    Args:
+        dao_id: DAO identifier
+        doc_id: Document identifier
+        parsed_json: ParsedDocument JSON from PARSER service
+        user_id: Optional user identifier
+    
+    Returns:
+        Dictionary with ingest results (doc_count, status)
+    """
+    logger.info(f"Ingesting document: dao_id={dao_id}, doc_id={doc_id}")
+    
+    try:
+        # Convert parsed_json to Haystack Documents
+        documents = _parsed_json_to_documents(parsed_json, dao_id, doc_id, user_id)
+        
+        if not documents:
+            logger.warning(f"No documents to ingest for doc_id={doc_id}")
+            return {
+                "status": "error",
+                "message": "No documents to ingest",
+                "doc_count": 0
+            }
+        
+        logger.info(f"Converted {len(documents)} blocks to Haystack Documents")
+        
+        # Create ingest pipeline
+        pipeline = _create_ingest_pipeline()
+        
+        # Run pipeline
+        result = pipeline.run({"documents": documents})
+        
+        # Extract results
+        written_docs = result.get("documents_writer", {}).get("documents_written", 0)
+        
+        logger.info(f"Ingested {written_docs} documents for doc_id={doc_id}")
+        
+        return {
+            "status": "success",
+            "doc_count": written_docs,
+            "dao_id": dao_id,
+            "doc_id": doc_id
+        }
+        
+    except Exception as e:
+        logger.error(f"Failed to ingest document: {e}", exc_info=True)
+        return {
+            "status": "error",
+            "message": str(e),
+            "doc_count": 0
+        }
+
+
+def _parsed_json_to_documents(
+    parsed_json: Dict[str, Any],
+    dao_id: str,
+    doc_id: str,
+    user_id: Optional[str] = None
+) -> List[Document]:
+    """
+    Convert ParsedDocument JSON to Haystack Documents
+    
+    Args:
+        parsed_json: ParsedDocument JSON structure
+        dao_id: DAO identifier
+        doc_id: Document identifier
+        user_id: Optional user identifier
+    
+    Returns:
+        List of Haystack Document objects
+    """
+    documents = []
+    
+    # Extract pages from parsed_json
+    pages = parsed_json.get("pages", [])
+    
+    for page_data in pages:
+        page_num = page_data.get("page_num", 1)
+        blocks = page_data.get("blocks", [])
+        
+        for block in blocks:
+            # Skip empty blocks
+            text = block.get("text", "").strip()
+            if not text:
+                continue
+            
+            # Build metadata (must-have для RAG)
+            meta = {
+                "dao_id": dao_id,
+                "doc_id": doc_id,
+                "page": page_num,
+                "block_type": block.get("type", "paragraph"),
+                "reading_order": block.get("reading_order", 0)
+            }
+            
+            # Add optional fields
+            if block.get("bbox"):
+                bbox = block["bbox"]
+                meta.update({
+                    "bbox_x": bbox.get("x", 0),
+                    "bbox_y": bbox.get("y", 0),
+                    "bbox_width": bbox.get("width", 0),
+                    "bbox_height": bbox.get("height", 0)
+                })
+            
+            # Add section if heading
+            if block.get("type") == "heading":
+                meta["section"] = text[:100]  # First 100 chars as section name
+            
+            # Add user_id if provided
+            if user_id:
+                meta["user_id"] = user_id
+            
+            # Add document-level metadata
+            if parsed_json.get("metadata"):
+                meta.update({
+                    k: v for k, v in parsed_json["metadata"].items()
+                    if k not in ["dao_id"]  # Already added
+                })
+            
+            # Create Haystack Document
+            doc = Document(
+                content=text,
+                meta=meta
+            )
+            
+            documents.append(doc)
+    
+    return documents
+
+
+def _create_ingest_pipeline() -> Pipeline:
+    """
+    Create Haystack ingest pipeline
+    
+    Pipeline: DocumentSplitter → Embedder → DocumentWriter
+    """
+    # Get components
+    embedder = get_text_embedder()
+    document_store = get_document_store()
+    
+    # Create splitter (optional, if chunks are too large)
+    splitter = DocumentSplitter(
+        split_by="sentence",
+        split_length=settings.CHUNK_SIZE,
+        split_overlap=settings.CHUNK_OVERLAP
+    )
+    
+    # Create writer
+    writer = DocumentWriter(document_store)
+    
+    # Build pipeline
+    pipeline = Pipeline()
+    pipeline.add_component("splitter", splitter)
+    pipeline.add_component("embedder", embedder)
+    pipeline.add_component("documents_writer", writer)
+    
+    # Connect components
+    pipeline.connect("splitter", "embedder")
+    pipeline.connect("embedder", "documents_writer")
+    
+    return pipeline
+

services/rag-service/app/main.py

@@ -0,0 +1,105 @@
+"""
+RAG Service - FastAPI application
+Retrieval-Augmented Generation for MicroDAO
+"""
+
+import logging
+from fastapi import FastAPI, HTTPException
+from fastapi.middleware.cors import CORSMiddleware
+
+from app.models import IngestRequest, IngestResponse, QueryRequest, QueryResponse
+from app.ingest_pipeline import ingest_parsed_document
+from app.query_pipeline import answer_query
+
+logger = logging.getLogger(__name__)
+
+# FastAPI app
+app = FastAPI(
+    title="RAG Service",
+    description="Retrieval-Augmented Generation service for MicroDAO",
+    version="1.0.0"
+)
+
+# CORS middleware
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+
+@app.get("/health")
+async def health():
+    """Health check endpoint"""
+    return {
+        "status": "healthy",
+        "service": "rag-service",
+        "version": "1.0.0"
+    }
+
+
+@app.post("/ingest", response_model=IngestResponse)
+async def ingest_endpoint(request: IngestRequest):
+    """
+    Ingest parsed document from PARSER service into RAG
+    
+    Body:
+    - dao_id: DAO identifier
+    - doc_id: Document identifier
+    - parsed_json: ParsedDocument JSON from PARSER service
+    - user_id: Optional user identifier
+    """
+    try:
+        result = ingest_parsed_document(
+            dao_id=request.dao_id,
+            doc_id=request.doc_id,
+            parsed_json=request.parsed_json,
+            user_id=request.user_id
+        )
+        
+        return IngestResponse(**result)
+        
+    except Exception as e:
+        logger.error(f"Ingest endpoint error: {e}", exc_info=True)
+        raise HTTPException(status_code=500, detail=str(e))
+
+
+@app.post("/query", response_model=QueryResponse)
+async def query_endpoint(request: QueryRequest):
+    """
+    Answer query using RAG pipeline
+    
+    Body:
+    - dao_id: DAO identifier
+    - question: User question
+    - top_k: Optional number of documents to retrieve
+    - user_id: Optional user identifier
+    """
+    try:
+        result = await answer_query(
+            dao_id=request.dao_id,
+            question=request.question,
+            top_k=request.top_k,
+            user_id=request.user_id
+        )
+        
+        return QueryResponse(**result)
+        
+    except Exception as e:
+        logger.error(f"Query endpoint error: {e}", exc_info=True)
+        raise HTTPException(status_code=500, detail=str(e))
+
+
+if __name__ == "__main__":
+    import uvicorn
+    from app.core.config import settings
+    
+    uvicorn.run(
+        "app.main:app",
+        host=settings.API_HOST,
+        port=settings.API_PORT,
+        reload=True
+    )
+

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)")
+

services/rag-service/app/query_pipeline.py

@@ -0,0 +1,250 @@
+"""
+Query Pipeline: RAG → LLM
+Retrieves relevant documents and generates answers
+"""
+
+import logging
+from typing import List, Dict, Any, Optional
+import httpx
+
+from haystack import Pipeline
+from haystack.components.embedders import SentenceTransformersTextEmbedder
+from haystack.components.retrievers import InMemoryEmbeddingRetriever
+from haystack.document_stores import PGVectorDocumentStore
+
+from app.document_store import get_document_store
+from app.embedding import get_text_embedder
+from app.core.config import settings
+
+logger = logging.getLogger(__name__)
+
+
+async def answer_query(
+    dao_id: str,
+    question: str,
+    top_k: Optional[int] = None,
+    user_id: Optional[str] = None
+) -> Dict[str, Any]:
+    """
+    Answer query using RAG pipeline
+    
+    Args:
+        dao_id: DAO identifier (for filtering)
+        question: User question
+        top_k: Number of documents to retrieve (default from settings)
+        user_id: Optional user identifier
+    
+    Returns:
+        Dictionary with answer, citations, and retrieved documents
+    """
+    logger.info(f"Answering query: dao_id={dao_id}, question={question[:50]}...")
+    
+    top_k = top_k or settings.TOP_K
+    
+    try:
+        # Retrieve relevant documents
+        documents = _retrieve_documents(dao_id, question, top_k)
+        
+        if not documents:
+            logger.warning(f"No documents found for dao_id={dao_id}")
+            return {
+                "answer": "На жаль, я не знайшов релевантної інформації в базі знань.",
+                "citations": [],
+                "documents": []
+            }
+        
+        logger.info(f"Retrieved {len(documents)} documents")
+        
+        # Generate answer using LLM
+        answer = await _generate_answer(question, documents, dao_id, user_id)
+        
+        # Build citations
+        citations = _build_citations(documents)
+        
+        return {
+            "answer": answer,
+            "citations": citations,
+            "documents": [
+                {
+                    "content": doc.content[:200] + "..." if len(doc.content) > 200 else doc.content,
+                    "meta": doc.meta
+                }
+                for doc in documents
+            ]
+        }
+        
+    except Exception as e:
+        logger.error(f"Failed to answer query: {e}", exc_info=True)
+        return {
+            "answer": f"Помилка при обробці запиту: {str(e)}",
+            "citations": [],
+            "documents": []
+        }
+
+
+def _retrieve_documents(
+    dao_id: str,
+    question: str,
+    top_k: int
+) -> List[Any]:
+    """
+    Retrieve relevant documents from DocumentStore
+    
+    Args:
+        dao_id: DAO identifier for filtering
+        question: Query text
+        top_k: Number of documents to retrieve
+    
+    Returns:
+        List of Haystack Document objects
+    """
+    # Get components
+    embedder = get_text_embedder()
+    document_store = get_document_store()
+    
+    # Embed query
+    embedding_result = embedder.run(question)
+    query_embedding = embedding_result["embedding"][0] if isinstance(embedding_result["embedding"], list) else embedding_result["embedding"]
+    
+    # Retrieve with filters using vector similarity search
+    filters = {"dao_id": [dao_id]}
+    
+    try:
+        documents = document_store.search(
+            query_embedding=query_embedding,
+            filters=filters,
+            top_k=top_k,
+            return_embedding=False
+        )
+    except Exception as e:
+        logger.warning(f"Vector search failed: {e}, trying filter_documents")
+        # Fallback to filter_documents
+        documents = document_store.filter_documents(
+            filters=filters,
+            top_k=top_k,
+            return_embedding=False
+        )
+    
+    # If no documents with filter, try without filter (fallback)
+    if not documents:
+        logger.warning(f"No documents found with dao_id={dao_id}, trying without filter")
+        try:
+            documents = document_store.search(
+                query_embedding=query_embedding,
+                filters=None,
+                top_k=top_k,
+                return_embedding=False
+            )
+        except Exception:
+            documents = document_store.filter_documents(
+                filters=None,
+                top_k=top_k,
+                return_embedding=False
+            )
+    
+    return documents
+
+
+async def _generate_answer(
+    question: str,
+    documents: List[Any],
+    dao_id: str,
+    user_id: Optional[str] = None
+) -> str:
+    """
+    Generate answer using LLM (via DAGI Router or OpenAI)
+    
+    Args:
+        question: User question
+        documents: Retrieved documents
+        dao_id: DAO identifier
+        user_id: Optional user identifier
+    
+    Returns:
+        Generated answer text
+    """
+    # Build context from documents
+    context = "\n\n".join([
+        f"[Документ {idx+1}, сторінка {doc.meta.get('page', '?')}]: {doc.content[:500]}"
+        for idx, doc in enumerate(documents[:3])  # Limit to first 3 documents
+    ])
+    
+    # Build prompt
+    prompt = (
+        "Тобі надано контекст з бази знань та питання користувача.\n"
+        "Відповідай на основі наданого контексту. Якщо в контексті немає відповіді, "
+        "скажи що не знаєш.\n\n"
+        f"Контекст:\n{context}\n\n"
+        f"Питання: {question}\n\n"
+        "Відповідь:"
+    )
+    
+    # Call LLM based on provider
+    if settings.LLM_PROVIDER == "router":
+        return await _call_router_llm(prompt, dao_id, user_id)
+    elif settings.LLM_PROVIDER == "openai" and settings.OPENAI_API_KEY:
+        return await _call_openai_llm(prompt)
+    else:
+        # Fallback: simple answer
+        return f"Знайдено {len(documents)} релевантних документів. Перший фрагмент: {documents[0].content[:200]}..."
+
+
+async def _call_router_llm(
+    prompt: str,
+    dao_id: str,
+    user_id: Optional[str] = None
+) -> str:
+    """Call DAGI Router LLM"""
+    router_url = f"{settings.ROUTER_BASE_URL.rstrip('/')}/route"
+    
+    payload = {
+        "mode": "chat",
+        "dao_id": dao_id,
+        "user_id": user_id or "rag-service",
+        "payload": {
+            "message": prompt
+        }
+    }
+    
+    try:
+        async with httpx.AsyncClient(timeout=30.0) as client:
+            resp = await client.post(router_url, json=payload)
+            resp.raise_for_status()
+            data = resp.json()
+            
+            return data.get("data", {}).get("text", "Не вдалося отримати відповідь")
+            
+    except Exception as e:
+        logger.error(f"Router LLM call failed: {e}")
+        return f"Помилка при виклику LLM: {str(e)}"
+
+
+async def _call_openai_llm(prompt: str) -> str:
+    """Call OpenAI LLM"""
+    # TODO: Implement OpenAI client
+    return "OpenAI integration not yet implemented"
+
+
+def _build_citations(documents: List[Any]) -> List[Dict[str, Any]]:
+    """
+    Build citations from retrieved documents
+    
+    Args:
+        documents: List of Haystack Documents
+    
+    Returns:
+        List of citation dictionaries
+    """
+    citations = []
+    
+    for doc in documents:
+        meta = doc.meta
+        citations.append({
+            "doc_id": meta.get("doc_id", "unknown"),
+            "page": meta.get("page", 0),
+            "section": meta.get("section"),
+            "excerpt": doc.content[:200] + "..." if len(doc.content) > 200 else doc.content
+        })
+    
+    return citations
+

services/rag-service/requirements.txt

@@ -0,0 +1,10 @@
+fastapi>=0.115.0
+uvicorn[standard]>=0.30.0
+pydantic>=2.0.0
+pydantic-settings>=2.0.0
+haystack-ai>=2.0.0
+sentence-transformers>=2.2.0
+psycopg2-binary>=2.9.0
+httpx>=0.27.0
+python-dotenv>=1.0.0
+

services/rag-service/tests/fixtures/parsed_json_example.json

@@ -0,0 +1,56 @@
+{
+  "doc_id": "microdao-tokenomics-2025-11",
+  "doc_type": "pdf",
+  "pages": [
+    {
+      "page_num": 1,
+      "blocks": [
+        {
+          "type": "heading",
+          "text": "Токеноміка MicroDAO",
+          "bbox": {"x": 0, "y": 0, "width": 800, "height": 50},
+          "reading_order": 1
+        },
+        {
+          "type": "paragraph",
+          "text": "MicroDAO використовує токен μGOV як ключ доступу до приватних спільнот. Стейкінг μGOV дозволяє отримувати дохід та участь у управлінні.",
+          "bbox": {"x": 0, "y": 60, "width": 800, "height": 100},
+          "reading_order": 2
+        },
+        {
+          "type": "paragraph",
+          "text": "Стейкінг є основним механізмом отримання доходу в MicroDAO. Користувачі можуть стейкати токени та отримувати винагороди за участь у спільноті.",
+          "bbox": {"x": 0, "y": 170, "width": 800, "height": 100},
+          "reading_order": 3
+        }
+      ],
+      "width": 800,
+      "height": 600
+    },
+    {
+      "page_num": 2,
+      "blocks": [
+        {
+          "type": "heading",
+          "text": "Роль стейкінгу",
+          "bbox": {"x": 0, "y": 0, "width": 800, "height": 50},
+          "reading_order": 1
+        },
+        {
+          "type": "paragraph",
+          "text": "Стейкінг μGOV токенів дає користувачам право голосу та доступ до приватних каналів спільноти. Мінімальна сума стейкінгу визначається кожною спільнотою окремо.",
+          "bbox": {"x": 0, "y": 60, "width": 800, "height": 100},
+          "reading_order": 2
+        }
+      ],
+      "width": 800,
+      "height": 600
+    }
+  ],
+  "metadata": {
+    "dao_id": "daarion",
+    "title": "Токеноміка MicroDAO",
+    "created_at": "2025-01-15T10:00:00Z"
+  }
+}
+

services/rag-service/tests/test_e2e.py

@@ -0,0 +1,67 @@
+"""
+E2E tests for RAG Service
+Tests full ingest → query pipeline
+"""
+
+import pytest
+import json
+from pathlib import Path
+from fastapi.testclient import TestClient
+
+from app.main import app
+
+client = TestClient(app)
+
+# Load example parsed JSON
+FIXTURES_DIR = Path(__file__).parent / "fixtures"
+EXAMPLE_JSON = json.loads((FIXTURES_DIR / "parsed_json_example.json").read_text())
+
+
+class TestE2E:
+    """End-to-end tests"""
+    
+    def test_health(self):
+        """Test health endpoint"""
+        response = client.get("/health")
+        assert response.status_code == 200
+        data = response.json()
+        assert data["status"] == "healthy"
+        assert data["service"] == "rag-service"
+    
+    @pytest.mark.skip(reason="Requires database connection")
+    def test_ingest_then_query(self):
+        """Test full pipeline: ingest → query"""
+        # Step 1: Ingest document
+        ingest_request = {
+            "dao_id": "daarion",
+            "doc_id": "microdao-tokenomics-2025-11",
+            "parsed_json": EXAMPLE_JSON
+        }
+        
+        ingest_response = client.post("/ingest", json=ingest_request)
+        assert ingest_response.status_code == 200
+        ingest_data = ingest_response.json()
+        assert ingest_data["status"] == "success"
+        assert ingest_data["doc_count"] > 0
+        
+        # Step 2: Query
+        query_request = {
+            "dao_id": "daarion",
+            "question": "Поясни токеноміку microDAO і роль стейкінгу"
+        }
+        
+        query_response = client.post("/query", json=query_request)
+        assert query_response.status_code == 200
+        query_data = query_response.json()
+        
+        assert "answer" in query_data
+        assert len(query_data["answer"]) > 0
+        assert "citations" in query_data
+        assert len(query_data["citations"]) > 0
+        
+        # Check citation structure
+        citation = query_data["citations"][0]
+        assert "doc_id" in citation
+        assert "page" in citation
+        assert "excerpt" in citation
+

services/rag-service/tests/test_ingest.py

@@ -0,0 +1,82 @@
+"""
+Tests for ingest pipeline
+"""
+
+import pytest
+from app.ingest_pipeline import ingest_parsed_document, _parsed_json_to_documents
+
+
+class TestIngestPipeline:
+    """Tests for document ingestion"""
+    
+    def test_parsed_json_to_documents(self):
+        """Test conversion of parsed JSON to Haystack Documents"""
+        parsed_json = {
+            "doc_id": "test-doc",
+            "doc_type": "pdf",
+            "pages": [
+                {
+                    "page_num": 1,
+                    "blocks": [
+                        {
+                            "type": "heading",
+                            "text": "Test Document",
+                            "bbox": {"x": 0, "y": 0, "width": 800, "height": 50},
+                            "reading_order": 1
+                        },
+                        {
+                            "type": "paragraph",
+                            "text": "This is test content.",
+                            "bbox": {"x": 0, "y": 60, "width": 800, "height": 100},
+                            "reading_order": 2
+                        }
+                    ],
+                    "width": 800,
+                    "height": 600
+                }
+            ],
+            "metadata": {
+                "dao_id": "test-dao",
+                "title": "Test Document"
+            }
+        }
+        
+        documents = _parsed_json_to_documents(
+            parsed_json=parsed_json,
+            dao_id="test-dao",
+            doc_id="test-doc"
+        )
+        
+        assert len(documents) == 2
+        assert documents[0].content == "Test Document"
+        assert documents[0].meta["dao_id"] == "test-dao"
+        assert documents[0].meta["doc_id"] == "test-doc"
+        assert documents[0].meta["page"] == 1
+        assert documents[0].meta["block_type"] == "heading"
+    
+    def test_parsed_json_to_documents_empty_blocks(self):
+        """Test that empty blocks are skipped"""
+        parsed_json = {
+            "doc_id": "test-doc",
+            "pages": [
+                {
+                    "page_num": 1,
+                    "blocks": [
+                        {"type": "paragraph", "text": ""},
+                        {"type": "paragraph", "text": "   "},
+                        {"type": "paragraph", "text": "Valid content"}
+                    ]
+                }
+            ],
+            "metadata": {}
+        }
+        
+        documents = _parsed_json_to_documents(
+            parsed_json=parsed_json,
+            dao_id="test-dao",
+            doc_id="test-doc"
+        )
+        
+        assert len(documents) == 1
+        assert documents[0].content == "Valid content"
+

services/rag-service/tests/test_query.py

@@ -0,0 +1,50 @@
+"""
+Tests for query pipeline
+"""
+
+import pytest
+from unittest.mock import AsyncMock, patch, MagicMock
+from app.query_pipeline import answer_query, _build_citations
+
+
+class TestQueryPipeline:
+    """Tests for RAG query pipeline"""
+    
+    @pytest.mark.asyncio
+    async def test_answer_query_no_documents(self):
+        """Test query when no documents found"""
+        with patch("app.query_pipeline._retrieve_documents", return_value=[]):
+            result = await answer_query(
+                dao_id="test-dao",
+                question="Test question"
+            )
+            
+            assert "answer" in result
+            assert "На жаль, я не знайшов" in result["answer"]
+            assert result["citations"] == []
+    
+    @pytest.mark.asyncio
+    async def test_build_citations(self):
+        """Test citation building"""
+        from haystack.schema import Document
+        
+        documents = [
+            Document(
+                content="Test content 1",
+                meta={"doc_id": "doc1", "page": 1, "section": "Section 1"}
+            ),
+            Document(
+                content="Test content 2",
+                meta={"doc_id": "doc2", "page": 2}
+            )
+        ]
+        
+        citations = _build_citations(documents)
+        
+        assert len(citations) == 2
+        assert citations[0]["doc_id"] == "doc1"
+        assert citations[0]["page"] == 1
+        assert citations[0]["section"] == "Section 1"
+        assert citations[1]["doc_id"] == "doc2"
+        assert citations[1]["page"] == 2
+