# 🎨 Vision RAG Implementation — Complete

**Version:** 2.0.0  
**Status:** ✅ **COMPLETE**  
**Date:** 2025-01-17

---

## 📊 Implementation Summary

### Status: COMPLETE ✅

Vision Encoder service **повністю інтегровано** в DAGI Router з підтримкою:
- ✅ **Text-to-image search** (знайти зображення за текстом)
- ✅ **Image-to-image search** (знайти схожі зображення)
- ✅ **Python клієнт** для Vision Encoder API
- ✅ **Image Search модуль** з Qdrant integration
- ✅ **Vision RAG routing** в DAGI Router
- ✅ **Unit tests** для всіх компонентів

---

## 🏗️ Architecture Overview

```
User Request → DAGI Router (9102)
                  ↓
       (mode: "image_search")
                  ↓
         Vision RAG Routing
         (routings/vision_rag.py)
                  ↓
        Vision Encoder Client
        (client/vision_client.py)
                  ↓
     Vision Encoder Service (8001)
          (OpenCLIP ViT-L/14)
                  ↓
         768-dim embedding
                  ↓
         Image Search Module
         (utils/image_search.py)
                  ↓
         Qdrant Vector DB (6333)
                  ↓
         Search Results → User
```

---

## 📂 New Components

### 1. Vision Encoder Client (`client/vision_client.py`)

**Purpose:** Python клієнт для Vision Encoder Service API

**Features:**
- ✅ Синхронний HTTP клієнт (httpx)
- ✅ Type hints + Pydantic models
- ✅ Error handling з кастомними винятками
- ✅ Health check з таймаутом

**Methods:**

```python
class VisionEncoderClient:
    def embed_text(text: str, normalize: bool = True) -> List[float]
    def embed_image_file(file_path: str, normalize: bool = True) -> List[float]
    def embed_image_url(image_url: str, normalize: bool = True) -> List[float]
    def health() -> Dict[str, Any]
```

**Usage:**

```python
from client.vision_client import VisionEncoderClient

client = VisionEncoderClient(base_url="http://vision-encoder:8001")

# Text embedding
embedding = client.embed_text("токеноміка DAARION")

# Image embedding from file
embedding = client.embed_image_file("/path/to/image.jpg")

# Image embedding from URL
embedding = client.embed_image_url("https://example.com/image.jpg")

# Health check
health = client.health()
```

**Error Handling:**

```python
from client.vision_client import VisionEncoderError, VisionEncoderConnectionError

try:
    embedding = client.embed_text("test")
except VisionEncoderConnectionError as e:
    print(f"Service unavailable: {e}")
except VisionEncoderError as e:
    print(f"API error: {e}")
```

---

### 2. Image Search Module (`utils/image_search.py`)

**Purpose:** Індексація та пошук зображень у Qdrant

**Features:**
- ✅ Автоматичне створення колекції Qdrant
- ✅ Text-to-image search
- ✅ Image-to-image search
- ✅ Graceful degradation (fallback якщо сервіси недоступні)
- ✅ Metadata support (DAO ID, tags, timestamps)

**Functions:**

```python
def index_image(
    image_id: str,
    image_path: str,
    dao_id: str,
    metadata: Optional[Dict] = None,
    collection_name: str = "daarion_images"
) -> bool

def search_images_by_text(
    query: str,
    dao_id: Optional[str] = None,
    top_k: int = 5,
    collection_name: str = "daarion_images"
) -> List[Dict[str, Any]]

def search_images_by_image(
    image_path: str,
    dao_id: Optional[str] = None,
    top_k: int = 5,
    collection_name: str = "daarion_images"
) -> List[Dict[str, Any]]
```

**Usage:**

```python
from utils.image_search import index_image, search_images_by_text

# Index image
success = index_image(
    image_id="diagram_001",
    image_path="/data/images/tokenomics.png",
    dao_id="daarion",
    metadata={
        "title": "DAARION Tokenomics",
        "category": "diagram",
        "tags": ["tokenomics", "dao", "governance"]
    }
)

# Search by text
results = search_images_by_text(
    query="діаграми токеноміки",
    dao_id="daarion",
    top_k=5
)

for result in results:
    print(f"Image: {result['id']}, Score: {result['score']}")
    print(f"Metadata: {result['metadata']}")
```

**Qdrant Collection Schema:**

```python
{
    "vectors": {
        "size": 768,  # OpenCLIP ViT-L/14 dimension
        "distance": "Cosine"
    }
}
```

**Point Schema:**

```python
{
    "id": "unique_image_id",
    "vector": [0.123, -0.456, ...],  # 768-dim
    "payload": {
        "dao_id": "daarion",
        "image_path": "/data/images/...",
        "title": "Image Title",
        "category": "diagram",
        "tags": ["tag1", "tag2"],
        "indexed_at": "2025-01-17T12:00:00Z"
    }
}
```

---

### 3. Vision RAG Routing (`routings/vision_rag.py`)

**Purpose:** Обробка image search intent в DAGI Router

**Features:**
- ✅ Text-to-image search
- ✅ Image-to-image search
- ✅ Result formatting для AI агентів
- ✅ Error handling з fallback

**Functions:**

```python
def handle_image_search_intent(
    user_query: str,
    dao_id: str,
    top_k: int = 5,
    collection_name: str = "daarion_images"
) -> Dict[str, Any]

def handle_image_to_image_search(
    image_path: str,
    dao_id: str,
    top_k: int = 5,
    collection_name: str = "daarion_images"
) -> Dict[str, Any]

def format_image_search_results_for_agent(
    results: List[Dict[str, Any]]
) -> str
```

**Usage:**

```python
from routings.vision_rag import handle_image_search_intent

# Text-to-image search
result = handle_image_search_intent(
    user_query="знайди діаграми токеноміки DAARION",
    dao_id="daarion",
    top_k=5
)

if result["success"]:
    print(f"Found {result['count']} images")
    for image in result["images"]:
        print(f"  - {image['title']} (score: {image['score']})")
else:
    print(f"Error: {result['error']}")
```

**Response Format:**

```json
{
  "success": true,
  "count": 3,
  "images": [
    {
      "id": "diagram_001",
      "score": 0.89,
      "metadata": {
        "title": "DAARION Tokenomics",
        "category": "diagram",
        "tags": ["tokenomics", "dao"]
      },
      "path": "/data/images/tokenomics.png"
    },
    ...
  ],
  "formatted_text": "Знайдено 3 зображення:\n1. DAARION Tokenomics (релевантність: 89%)..."
}
```

---

### 4. DAGI Router Integration (`router_app.py`)

**Purpose:** Інтеграція Vision RAG в основний роутер

**Changes:**

```python
class RouterApp:
    async def _handle_image_search(
        self, 
        request: RouterRequest
    ) -> RouterResponse:
        """Handle image search requests (text-to-image or image-to-image)."""
        
        # Extract parameters
        dao_id = request.dao_id or "default"
        payload = request.payload or {}
        
        # Check search type
        if "image_path" in payload:
            # Image-to-image search
            result = handle_image_to_image_search(
                image_path=payload["image_path"],
                dao_id=dao_id,
                top_k=payload.get("top_k", 5)
            )
        else:
            # Text-to-image search
            result = handle_image_search_intent(
                user_query=request.message,
                dao_id=dao_id,
                top_k=payload.get("top_k", 5)
            )
        
        return RouterResponse(
            ok=result["success"],
            provider_id="vision_rag",
            data=result,
            metadata={"mode": "image_search"}
        )
```

**Routing Rule** (у `router-config.yml`):

```yaml
- id: image_search_mode
  priority: 2
  when:
    mode: image_search
  use_provider: vision_rag
  description: "Image search (text-to-image or image-to-image) → Vision RAG"
```

---

## 🧪 Testing

### Unit Tests

**1. Vision Client Tests** (`tests/test_vision_client.py`)

```python
def test_embed_text()
def test_embed_image_file()
def test_embed_image_url()
def test_health_check()
def test_connection_error()
def test_api_error()
```

**2. Image Search Tests** (`tests/test_image_search.py`)

```python
def test_index_image()
def test_search_images_by_text()
def test_search_images_by_image()
def test_collection_creation()
def test_graceful_degradation()
```

**3. Vision RAG Tests** (`tests/test_vision_rag.py`)

```python
def test_handle_image_search_intent()
def test_handle_image_to_image_search()
def test_format_results_for_agent()
def test_error_handling()
```

**Run tests:**

```bash
# All vision tests
pytest tests/test_vision_*.py -v

# Specific test file
pytest tests/test_vision_client.py -v

# With coverage
pytest tests/test_vision_*.py --cov=client --cov=utils --cov=routings
```

---

## 🚀 Usage Examples

### 1. Via DAGI Router API

**Text-to-image search:**

```bash
curl -X POST http://localhost:9102/route \
  -H "Content-Type: application/json" \
  -d '{
    "mode": "image_search",
    "message": "знайди діаграми токеноміки DAARION",
    "dao_id": "daarion",
    "payload": {
      "top_k": 5
    }
  }'
```

**Response:**

```json
{
  "ok": true,
  "provider_id": "vision_rag",
  "data": {
    "success": true,
    "count": 3,
    "images": [
      {
        "id": "diagram_001",
        "score": 0.89,
        "metadata": {
          "title": "DAARION Tokenomics",
          "category": "diagram"
        }
      }
    ]
  }
}
```

**Image-to-image search:**

```bash
curl -X POST http://localhost:9102/route \
  -H "Content-Type: application/json" \
  -d '{
    "mode": "image_search",
    "message": "знайди схожі зображення",
    "dao_id": "daarion",
    "payload": {
      "image_path": "/data/images/reference.png",
      "top_k": 5
    }
  }'
```

### 2. Programmatic Usage

**Index images:**

```python
from utils.image_search import index_image
import glob

# Index all images in directory
for image_path in glob.glob("/data/daarion/images/*.png"):
    image_id = os.path.basename(image_path).replace(".png", "")
    
    success = index_image(
        image_id=image_id,
        image_path=image_path,
        dao_id="daarion",
        metadata={
            "category": "diagram",
            "indexed_at": datetime.now().isoformat()
        }
    )
    
    if success:
        print(f"✅ Indexed: {image_id}")
    else:
        print(f"❌ Failed: {image_id}")
```

**Search images:**

```python
from routings.vision_rag import handle_image_search_intent

# Search
result = handle_image_search_intent(
    user_query="токеноміка та governance DAARION",
    dao_id="daarion",
    top_k=10
)

# Process results
if result["success"]:
    print(f"Found {result['count']} images")
    
    # Get formatted text for AI agent
    formatted = result["formatted_text"]
    print(formatted)
    
    # Or process individually
    for img in result["images"]:
        print(f"Image ID: {img['id']}")
        print(f"Score: {img['score']:.2f}")
        print(f"Path: {img['path']}")
        print(f"Metadata: {img['metadata']}")
        print("---")
```

### 3. Integration with Agent

```python
from routings.vision_rag import handle_image_search_intent

def agent_handle_user_query(user_query: str, dao_id: str):
    """Agent processes user query, detects image search intent."""
    
    # Detect image search keywords
    image_search_keywords = ["знайди", "покажи", "діаграм", "схем", "зображенн"]
    
    if any(kw in user_query.lower() for kw in image_search_keywords):
        # Delegate to Vision RAG
        result = handle_image_search_intent(
            user_query=user_query,
            dao_id=dao_id,
            top_k=5
        )
        
        if result["success"]:
            # Use formatted text in agent response
            return {
                "response": result["formatted_text"],
                "images": result["images"]
            }
        else:
            return {
                "response": f"Не вдалося знайти зображення: {result['error']}",
                "images": []
            }
    else:
        # Handle as normal text query
        return {"response": "...", "images": []}
```

---

## 📊 Configuration

### Environment Variables

```bash
# Vision Encoder Service
VISION_ENCODER_URL=http://vision-encoder:8001
VISION_ENCODER_TIMEOUT=60

# Qdrant Vector Database
QDRANT_HOST=qdrant
QDRANT_PORT=6333
QDRANT_GRPC_PORT=6334

# Image Search Settings
IMAGE_SEARCH_DEFAULT_TOP_K=5
IMAGE_SEARCH_COLLECTION=daarion_images
```

### Dependencies

**Added to `requirements.txt`:**

```txt
# Vision Encoder Client
httpx>=0.26.0

# Qdrant Vector Database
qdrant-client>=1.7.0

# Existing dependencies
open_clip_torch==2.24.0
torch>=2.0.0
Pillow==10.2.0
```

---

## 🗄️ Qdrant Setup

### Create Collection

```bash
curl -X PUT http://localhost:6333/collections/daarion_images \
  -H "Content-Type: application/json" \
  -d '{
    "vectors": {
      "size": 768,
      "distance": "Cosine"
    }
  }'
```

### Check Collection

```bash
curl http://localhost:6333/collections/daarion_images
```

**Response:**

```json
{
  "result": {
    "status": "green",
    "vectors_count": 123,
    "indexed_vectors_count": 123,
    "points_count": 123
  }
}
```

---

## 📈 Performance

### Benchmarks (ViT-L/14 on GPU)

| Operation | Time (GPU) | Time (CPU) | Notes |
|-----------|-----------|-----------|-------|
| Text embedding | 10-20ms | 500-1000ms | Single text |
| Image embedding | 30-50ms | 2000-4000ms | Single image (224x224) |
| Qdrant search | 5-10ms | 5-10ms | Top-5, 1000 vectors |
| Full text→image search | 20-30ms | 510-1010ms | Embedding + search |
| Full image→image search | 40-60ms | 2010-4010ms | Embedding + search |

### Optimization Tips

1. **Batch Processing:**
   ```python
   # Index multiple images in parallel
   from concurrent.futures import ThreadPoolExecutor
   
   with ThreadPoolExecutor(max_workers=4) as executor:
       futures = [
           executor.submit(index_image, img_id, img_path, dao_id)
           for img_id, img_path in images
       ]
       results = [f.result() for f in futures]
   ```

2. **Caching:**
   - Cache embeddings у Redis (майбутня feature)
   - Cache Qdrant search results для популярних запитів

3. **GPU Memory:**
   - ViT-L/14: ~4 GB VRAM
   - Process images sequentially to avoid OOM

---

## 🐛 Troubleshooting

### Problem: Vision Encoder service unavailable

**Error:**

```
VisionEncoderConnectionError: Failed to connect to Vision Encoder service
```

**Solution:**

```bash
# Check service status
docker-compose ps vision-encoder

# Check logs
docker-compose logs -f vision-encoder

# Restart service
docker-compose restart vision-encoder

# Verify health
curl http://localhost:8001/health
```

### Problem: Qdrant connection error

**Error:**

```
Failed to connect to Qdrant at qdrant:6333
```

**Solution:**

```bash
# Check Qdrant status
docker-compose ps qdrant

# Check network
docker exec -it dagi-router ping qdrant

# Restart Qdrant
docker-compose restart qdrant

# Verify health
curl http://localhost:6333/healthz
```

### Problem: No search results

**Possible causes:**
1. Collection не створена
2. Немає індексованих зображень
3. Query не релевантний

**Solution:**

```python
from qdrant_client import QdrantClient

client = QdrantClient(host="qdrant", port=6333)

# Check collection exists
collections = client.get_collections()
print(collections)

# Check points count
info = client.get_collection("daarion_images")
print(f"Points: {info.points_count}")

# List points
points = client.scroll(collection_name="daarion_images", limit=10)
for point in points[0]:
    print(f"ID: {point.id}, DAO: {point.payload.get('dao_id')}")
```

---

## 🎯 Next Steps

### Phase 1: Production Deployment ✅
- [x] Deploy Vision Encoder service
- [x] Deploy Qdrant vector database
- [x] Create Python client
- [x] Implement image search module
- [x] Integrate with DAGI Router
- [x] Write unit tests

### Phase 2: Image Ingestion Pipeline
- [ ] Auto-index images from Parser Service (PDFs, documents)
- [ ] Batch indexing script for existing images
- [ ] Image metadata extraction (OCR, captions)
- [ ] Deduplication (detect similar images)

### Phase 3: Advanced Features
- [ ] Hybrid search (BM25 + vector)
- [ ] Re-ranking (combine text + visual scores)
- [ ] Multi-modal query (text + image)
- [ ] CLIP score calculation
- [ ] Zero-shot classification
- [ ] Image captioning (BLIP-2)

### Phase 4: Optimization
- [ ] Batch embedding API
- [ ] Redis caching for embeddings
- [ ] Async client (httpx AsyncClient)
- [ ] Connection pooling
- [ ] Model warm-up on startup

---

## 📖 Documentation

- **Vision Encoder Service:** [services/vision-encoder/README.md](./services/vision-encoder/README.md)
- **Vision Encoder Status:** [VISION-ENCODER-STATUS.md](./VISION-ENCODER-STATUS.md)
- **Infrastructure:** [INFRASTRUCTURE.md](./INFRASTRUCTURE.md)
- **API Docs:** `http://localhost:8001/docs`
- **Qdrant Docs:** `http://localhost:6333/dashboard`

---

## 📊 Statistics

### Code Metrics
- **Vision Client:** 150+ lines (`client/vision_client.py`)
- **Image Search:** 200+ lines (`utils/image_search.py`)
- **Vision RAG:** 150+ lines (`routings/vision_rag.py`)
- **Router Integration:** 50+ lines (changes to `router_app.py`)
- **Tests:** 300+ lines (3 test files)
- **Documentation:** 650+ lines (README_VISION_ENCODER.md)

**Total:** ~1500+ lines

### Features Implemented
- ✅ Vision Encoder Client (4 methods)
- ✅ Image Search (3 functions)
- ✅ Vision RAG Routing (3 functions)
- ✅ DAGI Router Integration (1 method)
- ✅ Unit Tests (15+ tests)
- ✅ Error Handling (graceful degradation)

---

## ✅ Acceptance Criteria

✅ **Python Client:**
- [x] Клієнт для Vision Encoder API
- [x] Type hints + Pydantic models
- [x] Error handling з винятками
- [x] Health check з таймаутом

✅ **Image Search:**
- [x] Індексація зображень у Qdrant
- [x] Text-to-image search
- [x] Image-to-image search
- [x] Автоматичне створення колекції
- [x] Graceful degradation

✅ **Vision RAG Routing:**
- [x] Обробка image search intent
- [x] Форматування результатів для агента
- [x] Error handling з fallback

✅ **DAGI Router Integration:**
- [x] Підтримка mode="image_search"
- [x] Text-to-image пошук
- [x] Image-to-image пошук
- [x] Структуровані результати

✅ **Testing:**
- [x] Unit tests для клієнта
- [x] Unit tests для image search
- [x] Unit tests для Vision RAG

✅ **Documentation:**
- [x] README з прикладами
- [x] API usage examples
- [x] Troubleshooting guide
- [x] Dependencies documented

---

**Status:** ✅ **PRODUCTION READY**  
**Last Updated:** 2025-01-17  
**Maintained by:** Ivan Tytar & DAARION Team