data_indexing.py

import io
import os
import requests
import sys
# import tempfile
# import time
from typing import List, Dict, Tuple, Any, Optional
import uuid

# Add project root to Python path
project_root = os.path.abspath(os.path.join(os.path.dirname(__file__), '..', '..'))
if project_root not in sys.path:
    sys.path.insert(0, project_root)

from PIL import Image
from FlagEmbedding import BGEM3FlagModel
import gradio as gr
from langchain_core.documents import Document
from langchain_huggingface import HuggingFaceEmbeddings
import qdrant_client
from qdrant_client.http.models import Modifier, Distance, SparseVectorParams, VectorParams, SparseIndexParams
import torch
from transformers import EfficientNetModel, AutoImageProcessor
from pymongo import MongoClient

from config import (
    QDRANT_COLLECTION_NAME_SPCHIEUSANG,
    QDRANT_COLLECTION_NAME_SPCHUYENDUNG,
    QDRANT_COLLECTION_NAME_SPPHICHNUOC,
    QDRANT_COLLECTION_NAME_SPTHIETBIDIEN,
    QDRANT_COLLECTION_NAME_SPNHATHONGMINH,
    QDRANT_COLLECTION_NAME_GPNHATHONGMINH,
    QDRANT_COLLECTION_NAME_GPHOCDUONG,
    QDRANT_COLLECTION_NAME_GPNGUNGHIEP,
    QDRANT_COLLECTION_NAME_GPCANHQUAN,
    QDRANT_COLLECTION_NAME_GPNLMT,  
    QDRANT_COLLECTION_NAME_GPNNCNC,
    QDRANT_COLLECTION_NAME_GPDUONGPHO,
    QDRANT_COLLECTION_NAME_GPVPCS,
    QDRANT_COLLECTION_NAME_GPNMCN,
    QDRANT_COLLECTION_NAME_GPNOXH,
    IMAGE_EMBEDDING_SIZE,
    TEXT_EMBEDDING_SIZE,
    IMAGE_EMBEDDING_MODEL,
    TEXT_EMBEDDING_MODEL,
    MONGODB_URI,
    QDRANT_HOST,
    QDRANT_API_KEY,
    MONGODB_DATABASE
)

from data_helper import *
# from src.utils.helper import client

client = qdrant_client.QdrantClient(
    url=QDRANT_HOST,
    api_key=QDRANT_API_KEY,
    timeout=300.0
)

"""=================SETTINGS========================"""
device = torch.device(
    "cuda" if torch.cuda.is_available() else 
    "mps" if torch.mps.is_available() else 
    "cpu"
)

product_vectors_config = {
    "product": qdrant_client.http.models.VectorParams(
        size=TEXT_EMBEDDING_SIZE,
        distance=Distance.COSINE
    ),
    "image": qdrant_client.http.models.VectorParams(
        size=IMAGE_EMBEDDING_SIZE,
        distance=Distance.COSINE
    ),
    "product_bgem3_dense": qdrant_client.http.models.VectorParams(
        size=1024, 
        distance=Distance.COSINE,
    )
}

sparse_vectors_config={
    "product_bgem3_sparse": SparseVectorParams(
        index=SparseIndexParams(on_disk=False),
        modifier = Modifier.IDF
    )
}

product_collections = [
    QDRANT_COLLECTION_NAME_SPCHIEUSANG,
    QDRANT_COLLECTION_NAME_SPCHUYENDUNG,
    QDRANT_COLLECTION_NAME_SPPHICHNUOC,
    QDRANT_COLLECTION_NAME_SPTHIETBIDIEN,
    QDRANT_COLLECTION_NAME_SPNHATHONGMINH
]

product_types = [
    "chieu_sang",
    "chuyen_dung",
    "phich_nuoc",
    "thiet_bi_dien",
    "nha_thong_minh"
]

# MongoDB collections mapping for products
mongodb_product_collections = {
    "chieu_sang": "sp_chieu_sang",
    "chuyen_dung": "sp_chuyen_dung",
    "phich_nuoc": "sp_phich_nuoc",
    "thiet_bi_dien": "sp_thiet_bi_dien",
    "nha_thong_minh": "sp_nha_thong_minh"
}

solution_collections = [
    QDRANT_COLLECTION_NAME_GPCANHQUAN,
    QDRANT_COLLECTION_NAME_GPDUONGPHO,
    QDRANT_COLLECTION_NAME_GPHOCDUONG,
    QDRANT_COLLECTION_NAME_GPNHATHONGMINH,
    QDRANT_COLLECTION_NAME_GPNGUNGHIEP,
    QDRANT_COLLECTION_NAME_GPNLMT,
    QDRANT_COLLECTION_NAME_GPNNCNC,
    QDRANT_COLLECTION_NAME_GPVPCS,
    QDRANT_COLLECTION_NAME_GPNMCN,
    QDRANT_COLLECTION_NAME_GPNOXH
]

solution_types = [
    "canh_quan",
    "duong_pho",
    "hoc_duong",
    "nha_thong_minh",
    "ngu_nghiep",
    "nlmt",
    "nong_nghiep_cnc",
    "van_phong_cong_so",
    "nha_may_cong_nghiep",
    "nha_o_xa_hoi"
]

# MongoDB collections mapping for solutions
mongodb_solution_collections = {
    "canh_quan": "gp_canh_quan",
    "duong_pho": "gp_duong_pho",
    "hoc_duong": "gp_hoc_duong",
    "nha_thong_minh": "gp_nha_thong_minh",
    "ngu_nghiep": "gp_ngu_nghiep",
    "nlmt": "gp_he_thong_dien_nlmt",
    "nong_nghiep_cnc": "gp_nong_nghiep_cnc",
    "van_phong_cong_so": "gp_van_phong_cong_so",
    "nha_may_cong_nghiep": "gp_nha_may_cong_nghiep",
    "nha_o_xa_hoi": "gp_nha_o_xa_hoi"
}


"""=================MONGODB CONNECTION========================"""
class MongoDBConnection:
    def __init__(self, connection_string: str = None, db_name: str = MONGODB_DATABASE):
        """
        Initialize MongoDB connection
        Args:
            connection_string: MongoDB Atlas connection string
            db_name: Database name
        """
        self.connection_string = MONGODB_URI if connection_string is None else connection_string
        self.db_name = db_name
        self.client = None
        self.db = None
        
    def connect(self):
        """Establish connection to MongoDB"""
        try:
            self.client = MongoClient(self.connection_string)
            self.db = self.client[self.db_name]
            # Test connection
            self.client.admin.command('ping')
            print(f"✅ Connected to MongoDB: {self.db_name}")
            return True
        except Exception as e:
            print(f"❌ Failed to connect to MongoDB: {e}")
            return False
    
    def get_collection_data(self, collection_name: str) -> List[Dict]:
        """
        Retrieve all documents from a collection
        Args:
            collection_name: Name of the MongoDB collection
        Returns:
            List of documents
        """
        try:
            collection = self.db[collection_name]
            data = list(collection.find({}))
            # Convert ObjectId to string
            for item in data:
                if '_id' in item:
                    item['_id'] = str(item['_id'])
            print(f"✅ Retrieved {len(data)} documents from {collection_name}")
            return data
        except Exception as e:
            print(f"❌ Error retrieving data from {collection_name}: {e}")
            return []
    
    def close(self):
        """Close MongoDB connection"""
        if self.client:
            self.client.close()
            print("✅ MongoDB connection closed")


"""=================CLASS EMBEDDING========================"""
class DataEmbedding:
    def __init__(self):
        pass

    def embed_text_batch(self, contents: List[str], batch_size: int = 32, hybrid_mode: bool = False) -> List[Optional[torch.Tensor]]:
        """Create text embeddings using HuggingFaceEmbeddings (768 dimensions), and optionally BGEM3 (1024 dimensions) in batches."""
        normal_embeddings, bgem3_dense_embeddings, bgem3_sparse_embeddings = [], [], []

        # Filter out empty contents and keep track of original indices
        valid_contents = []
        valid_indices = []
        for i, content in enumerate(contents):
            if content:
                valid_contents.append(content)
                valid_indices.append(i)
        
        if not valid_contents:
            return [None] * len(contents)
        
        try:
            text_embedding_model = HuggingFaceEmbeddings(
                model_name=TEXT_EMBEDDING_MODEL,
                model_kwargs={'device': device},
                encode_kwargs={'normalize_embeddings': True}
            )
            if hybrid_mode:
                hybrid_embedding_model = BGEM3FlagModel(
                    "BAAI/bge-m3", 
                    use_fp16=True, 
                    devices=str(device)
                )
            
            for i in range(0, len(valid_contents), batch_size):
                batch_contents = valid_contents[i:i+batch_size]
                
                bgem3_dense_embeddings_list, bgem3_sparse_embeddings_list = [], []
                if hybrid_mode:
                    bgem3_embeddings = hybrid_embedding_model.encode(
                        sentences=batch_contents,
                        return_dense=True,
                        return_sparse=True
                    )
                
                    bgem3_dense_embeddings_list = bgem3_embeddings['dense_vecs']
                    bgem3_sparse_embeddings_list = bgem3_embeddings['lexical_weights'] 
                    bgem3_dense_embeddings.extend([
                        torch.tensor(emb, dtype=torch.float32) 
                        for emb in bgem3_dense_embeddings_list
                    ])
                    bgem3_sparse_embeddings.extend(bgem3_sparse_embeddings_list) 

                normal_embeddings_list = text_embedding_model.embed_documents(batch_contents)
                normal_embeddings.extend([torch.tensor(emb, dtype=torch.float32) for emb in normal_embeddings_list])
            
            # Map back to original order
            result = [None] * len(contents)
            for i, valid_idx in enumerate(valid_indices):
                if hybrid_mode:
                    result[valid_idx] = (normal_embeddings[i], bgem3_dense_embeddings[i], bgem3_sparse_embeddings[i])
                else:
                    result[valid_idx] = (normal_embeddings[i], [], [])
            
            return result
            
        except Exception as e:
            print(f"❌ Error in batch text embedding: {str(e)[:100]}...")
            return []
    
    def embed_images_batch(self, image_urls: List[str], batch_size: int = 32) -> List[Optional[torch.Tensor]]:
        """Create image embeddings in batches."""
        all_embeddings: List[Optional[torch.Tensor]] = [None] * len(image_urls)
        
        # Create a list of images and their original indices that need processing
        images_to_process: List[Tuple[Any, int]] = []
        for i, url in enumerate(image_urls):
            if url:
                try:
                    response = requests.get(url, timeout=30)
                    response.raise_for_status()
                    image = Image.open(io.BytesIO(response.content)).convert('RGB')
                    images_to_process.append((image, i))
                except requests.exceptions.RequestException as e:
                    print(f"❌ HTTP error for url {url}: {e}")
                    pass
                except Exception as e:
                    print(f"❌ Error loading image {url}: {e}")
                    pass

        if not images_to_process:
            return all_embeddings

        image_processor = AutoImageProcessor.from_pretrained(IMAGE_EMBEDDING_MODEL)
        image_embedding_model = EfficientNetModel.from_pretrained(IMAGE_EMBEDDING_MODEL).to(device)
        # Process images in batches
        for i in range(0, len(images_to_process), batch_size):
            batch_data = images_to_process[i:i+batch_size]
            batch_images = [d[0] for d in batch_data]
            batch_indices = [d[1] for d in batch_data]

            try:
                inputs = image_processor(images=batch_images, return_tensors="pt").to(device)
                
                with torch.no_grad():
                    outputs = image_embedding_model(**inputs)

                embeddings = outputs.pooler_output.squeeze()                
                normalized_embeddings = torch.nn.functional.normalize(embeddings, p=2, dim=1)
                for j, embedding in enumerate(normalized_embeddings):
                    original_index = batch_indices[j]
                    all_embeddings[original_index] = embedding.squeeze()
            
            except Exception as e:
                print(f"❌ Error embedding image batch: {e}")
                pass
                
        return all_embeddings
    

class ProductEmbedding(DataEmbedding):
    def run_embedding(self, product_type: str, mongodb_conn: MongoDBConnection,
                     batch_size: int = 32, hybrid_mode: bool = False):
        """
        Generate embeddings for a specific product type from MongoDB
        Args:
            product_type: Type of product
            mongodb_conn: MongoDB connection object
            batch_size: Batch size for processing
            hybrid_mode: Whether to use hybrid text embedding (BGEM3)
        """
        embeddings = []
        
        processed_docs = self.prepare_docs(
            product_type=product_type,
            mongodb_conn=mongodb_conn
        )
        
        # Batch text embedding for speed
        text_contents = [doc.page_content for doc in processed_docs]
        text_embeddings = self.embed_text_batch(text_contents, batch_size, hybrid_mode)
        
        # Batch image embedding
        image_urls = [doc.metadata.get("image_url") for doc in processed_docs]
        image_embeddings = self.embed_images_batch(image_urls)
        
        # Create embeddings with optimized structure creation
        for i, doc in enumerate(processed_docs):
            if i < len(text_embeddings) and text_embeddings[i] is not None:
                normal_text_embedding, bgem3_dense_text_embedding, bgem3_sparse_text_embedding = text_embeddings[i]
            else:
                normal_text_embedding, bgem3_dense_text_embedding, bgem3_sparse_text_embedding = None, None, None
            
            image_embedding = image_embeddings[i] if i < len(image_embeddings) else None
            
            # Create vectors dict - ensure proper format
            vectors = {
                "product": normal_text_embedding.tolist() if normal_text_embedding is not None else [0.0] * TEXT_EMBEDDING_SIZE,
                "product_bgem3_dense": bgem3_dense_text_embedding.tolist() if bgem3_dense_text_embedding is not None else [0.0] * 1024,
                "image": image_embedding.tolist() if image_embedding is not None else [0.0] * IMAGE_EMBEDDING_SIZE
            }

            if bgem3_sparse_text_embedding is not None and bgem3_sparse_text_embedding:
                sparse_vectors = {
                    "product_bgem3_sparse": {
                        "indices": list(bgem3_sparse_text_embedding.keys()),
                        "values": [float(v) for v in bgem3_sparse_text_embedding.values()]
                    }
                }
            else:
                sparse_vectors = {"product_sparse": {"indices": [], "values": []}}

            # Create payload with optimized metadata processing
            payload = {
                "product": doc.page_content,
                "metadata": {key: value for key, value in doc.metadata.items()}
            }

            # Create and append point
            embeddings.append({
                "point_id": str(uuid.uuid4()),
                "vectors": vectors,
                "sparse_vectors": sparse_vectors,
                "payload": payload
            })
        
        print(f"Generated {len(embeddings)} embeddings for {product_type}")
        return embeddings

    def prepare_docs(self, product_type: str, mongodb_conn: MongoDBConnection):
        """
        Prepare documents from MongoDB
        Args:
            product_type: Type of product
            mongodb_conn: MongoDB connection object
        """
        if not mongodb_conn or mongodb_conn.db is None:
            raise ValueError("MongoDB connection not established")
        
        collection_name = mongodb_product_collections.get(product_type)
        if not collection_name:
            raise ValueError(f"No MongoDB collection mapping for product type: {product_type}")
        
        data = mongodb_conn.get_collection_data(collection_name)
        print(f"🗄️ Loaded data from MongoDB collection: {collection_name}")
        
        docs = []
        EXCLUDE_FROM_FLATTENING = {"tags"}
        for item in data:
            content = self.create_content(item)
            metadata = self.extract_metadata(item, product_type)
            # Create a flat metadata structure for indexing
            flat_metadata = {**metadata}
            for key, value in metadata.items():
                if isinstance(value, dict) and key not in EXCLUDE_FROM_FLATTENING:
                    flat_metadata.update({f"{key}_{sub_key}": sub_value for sub_key, sub_value in value.items()})
            
            doc = Document(page_content=content, metadata=flat_metadata)
            docs.append(doc)

        print(f"Prepared {len(docs)} documents")
        return docs

    def create_content(self, item: Dict) -> str:
        """Tạo document content cho sản phẩm"""
        product_name = item.get("Tên sản phẩm", "")
        model = item.get("Mã Sản Phẩm", "")
        summary_specs = item.get("Tóm tắt TSKT", "")
        summary_advantages = item.get("Tóm tắt ưu điểm, tính năng", "")
        specs = item.get("Thông số kỹ thuật", "")
        advantages = item.get("Nội dung Ưu điểm SP\n(- File word/Excel\n- Đặt tên file theo mã SAP)", "")
        instruction = item.get("HDSD", "")
        content = (
            f"# Tên sản phẩm: {product_name}\n\n"
            f"## Mã sản phẩm: {model}\n\n"
            f"## Tóm tắt TSKT\n{summary_specs}\n\n"
            f"### Thông số kỹ thuật chi tiết\n{specs}\n\n"
            f"## Tóm tắt ưu điểm & tính năng\n{summary_advantages}\n\n"
            f"### Ưu điểm & tính năng chi tiết\n{advantages}\n"
            f"## Hướng dẫn sử dụng: \n{instruction}\n"
        )

        return content
    
    def extract_metadata(self, item: Dict, product_type: str) -> Dict:
        """Extract metadata from a product item"""
        additional_info = ProductEmbedding.process_additional_metadata(item, product_type)
        tags = item.get("Tags", {})
        common_metadata = {
            "prod_id": item.get("Product_ID", None),
            "ten_san_pham": item.get("Tên sản phẩm", ""),
            "model": item.get("Mã Sản Phẩm", ""),
            "danh_muc_l1": item.get("category 1", ""),
            "danh_muc_l2": item.get("category 2", ""),
            "danh_muc_l3": item.get("category 3", ""),
            "url": str(item.get("Link sản phẩm", "")).strip(),
            "image_url": item.get("Link ảnh sản phẩm"),
            "buy_url": item.get("Link mua hàng online", ""),
            "gia": item.get("Giá", ""),
            "tags": tags,
            **tags,
            **additional_info
        }
        return common_metadata

    @staticmethod
    def process_additional_metadata(item: Dict[str, Any], product_type) -> Dict[str, Any]:
        """Process an item and extract additional information"""
        tags = item.get("Tags", {})
        spec_text = item.get("Tóm tắt TSKT", "")
        model = item.get("Mã Sản Phẩm", "")
        prod_name = item.get("Tên sản phẩm", "")
        additional_info = {}

        # Extract cong_suat
        if "cong_suat" not in tags.keys() or tags["cong_suat"] == "":
            power = extract_power(spec_text)
            if power is not None:
                additional_info["cong_suat"] = power

        # Extract based on product type
        if product_type == "phich_nuoc":
            pass 

        elif product_type == "chieu_sang":
            ceiling_hole_diameter = extract_ceiling_hole_diameter2(spec_text)
            if ceiling_hole_diameter is not None:
                additional_info["duong_kinh_lo_khoet_tran"] = ceiling_hole_diameter
            
            tinh_nang = extract_tinh_nang(model, prod_name)
            if tinh_nang is not None:
                additional_info["tinh_nang"] = tinh_nang

        elif product_type == "chuyen_dung":
            he_thong_hoa_luoi_pha = extract_he_thong_hoa_luoi_pha(prod_name)
            if he_thong_hoa_luoi_pha is not None:
                additional_info["he_thong_hoa_luoi_pha"] = he_thong_hoa_luoi_pha

        elif product_type == "thiet_bi_dien":
            dong_danh_dinh = extract_dong_danh_dinh(spec_text)
            if dong_danh_dinh is not None:
                additional_info["dong_danh_dinh"] = dong_danh_dinh

        elif product_type == "nha_thong_minh":
            cable_length = extract_cable_length(spec_text)
            if cable_length is not None:
                additional_info["chieu_dai_day"] = cable_length

            plugs_max_current = extract_plugs_max_current(spec_text)
            if plugs_max_current is not None:
                additional_info["dong_dien_o_cam_toi_da"] = plugs_max_current

            voltage = extract_voltage(model)
            if voltage is not None:
                additional_info["dien_ap"] = voltage

        return additional_info


class SolutionEmbedding(DataEmbedding):
    def run_embedding(self, solution_type: str, mongodb_conn: MongoDBConnection, batch_size: int = 32):
        """Generate embeddings for a specific solution type from MongoDB"""
        embeddings = []
        
        processed_docs, docs_to_embed = self.prepare_docs(solution_type, mongodb_conn)
        
        embedding_contents = [doc.page_content for doc in docs_to_embed]
        text_embeddings = self.embed_text_batch(embedding_contents, batch_size)
        
        # Create embeddings with optimized structure creation
        for i, doc in enumerate(processed_docs):
            embedding_tuple = text_embeddings[i] if i < len(text_embeddings) else None
            text_embedding = embedding_tuple[0] if embedding_tuple is not None else None
            
            # Create payload with optimized metadata processing
            payload = {
                "content": doc.page_content,
                "metadata": {key: value for key, value in doc.metadata.items()}
            }

            # Create and append point
            embeddings.append({
                "point_id": str(uuid.uuid4()),
                "vectors": text_embedding.tolist() if text_embedding is not None else [0.0] * 768,
                "payload": payload
            })
        
        print(f"Generated {len(embeddings)} embeddings for {solution_type}")
        return embeddings

    def prepare_docs(self, solution_type: str, mongodb_conn: MongoDBConnection):
        """
        Prepare documents from MongoDB
        Args:
            solution_type: Type of solution
            mongodb_conn: MongoDB connection object
        """
        if not mongodb_conn or mongodb_conn.db is None:
            raise ValueError("MongoDB connection not established")
        
        collection_name = mongodb_solution_collections.get(solution_type)
        if not collection_name:
            raise ValueError(f"No MongoDB collection mapping for solution type: {solution_type}")
        
        data = mongodb_conn.get_collection_data(collection_name)
        print(f"🗄️ Loaded solution data from MongoDB collection: {collection_name}")

        docs = []
        docs_to_embed = []

        for item in data:
            # Assuming the MongoDB document structure matches the JSON structure
            for key, val in item.items():
                if key in ["_id", "san_pham"]:  # Skip MongoDB _id and san_pham
                    continue
                    
                if isinstance(val, list):
                    for d in val:
                        page_content = ". ".join([f"{k}: {v}" for k, v in d.items()])
                        docs.append(
                            Document(
                                page_content=page_content,
                                metadata={"category": key}
                            )
                        )

                        if key != "faq":
                            docs_to_embed.append(
                                Document(
                                    page_content=page_content,
                                    metadata={"category": key}
                                )
                            )
                        else:
                            page_content = f"Câu hỏi: {d.get('Câu hỏi', '')}"
                            docs_to_embed.append(
                                Document(
                                    page_content=page_content,
                                    metadata={"category": key}
                                )
                            )

                elif isinstance(val, dict):
                    for k, v in val.items():
                        docs_to_embed.append(Document(page_content=f"{k}: {v}", metadata={"category": key}))
                        docs.append(Document(page_content=f"{k}: {v}", metadata={"category": key}))
        
        print(f"Prepared {len(docs)} documents")
        return docs, docs_to_embed


"""=================CLASS INDEXING========================"""
class ProductIndexing:
    def __init__(self, vector_db_client=client):
        super().__init__()
        self.client = vector_db_client
        self.mongodb_conn = None
    
    def setup_mongodb(self, connection_string: str = None):
        """Setup MongoDB connection"""
        self.mongodb_conn = MongoDBConnection(connection_string)
        return self.mongodb_conn.connect()
    
    def index(
            self,
            embeddings: List[Dict],
            collection_name: str,
            batch_size: int = 100
        ):
        """Index embeddings to a Qdrant collection in batches"""

        total_docs = len(embeddings)
        success_count = 0
        error_count = 0
        
        print(f"Adding {total_docs} multimodal documents to '{collection_name}'...")
        
        for i in range(0, total_docs, batch_size):
            batch = embeddings[i:i+batch_size]
            points = []
            
            try:
                for embedding_data in batch:
                    combined_vectors = embedding_data["vectors"].copy()
                    combined_vectors.update(embedding_data["sparse_vectors"])
                    
                    point = qdrant_client.http.models.PointStruct(
                        id=embedding_data["point_id"],
                        vector=combined_vectors,
                        payload=embedding_data["payload"]
                    )
                    points.append(point)
                
                if points:
                    self.client.upsert(collection_name=collection_name, points=points)
                    success_count += len(batch)
                    
                    text_count = sum(1 for p in points if any(v != 0 for v in p.vector["product"]))
                    image_count = sum(1 for p in points if any(v != 0 for v in p.vector["image"]))
                    
                    print(f"✅ Batch {i//batch_size + 1}: {len(batch)} docs | {text_count} product | {image_count} images")
                else:
                    print(f"⚠️ Batch {i//batch_size + 1}: No valid points to upload")
                
            except Exception as e:
                error_count += len(batch)
                print(f"❌ Batch {i//batch_size + 1} failed: {e}")
        
        print(f"\n📊 Final Results:")
        print(f"   ✅ Successful: {success_count}")
        print(f"   ❌ Failed: {error_count}")
        print(f"   📈 Success Rate: {success_count/(success_count+error_count)*100:.1f}%")
    
    def run_indexing(self, reload: bool = True, hybrid_mode: bool = True):
        """
        Index all product data from MongoDB into Qdrant collections.
        Args:
            reload: Whether to recreate collections
            hybrid_mode: Whether to use hybrid text embedding (BGEM3)
        """
        if reload:
            try:
                for collection in product_collections:
                    self.client.recreate_collection(
                        collection_name=collection,
                        vectors_config=product_vectors_config,
                        sparse_vectors_config=sparse_vectors_config
                    )
                print("All product collections recreated.")
            except Exception as e:
                print(f"Error while recreating collections: {e}")
                return

        # Setup MongoDB connection
        if not self.mongodb_conn:
            if not self.setup_mongodb():
                print("❌ Failed to connect to MongoDB. Aborting indexing.")
                return

        # Create embedding processor
        embed_object = ProductEmbedding()
        
        for collection, product_type in zip(product_collections, product_types):
            print(f"\n🔄 Processing {product_type} data from MongoDB...")
            
            # Generate embeddings for specific product type
            embeddings = embed_object.run_embedding(
                product_type=product_type,
                mongodb_conn=self.mongodb_conn,
                hybrid_mode=hybrid_mode
            )
            
            # Index embeddings to specific collection
            self.index(embeddings, collection)
            self._create_payload_indexes_for_product_type(product_type, collection)
        
        # Close MongoDB connection
        if self.mongodb_conn:
            self.mongodb_conn.close()
            self.mongodb_conn = None
    
    def indexing_single_product_type(self, product_type: str, collection_name: str, hybrid_mode: bool = True) -> str:
        """
        Indexing a single product group into its Qdrant collection from MongoDB
        Args:
            product_type: Type of product
            collection_name: Qdrant collection name
            hybrid_mode: Whether to use hybrid text embedding (BGEM3)
        """
        buffer = io.StringIO()
        sys.stdout = buffer

        try:
            self.client.recreate_collection(
                collection_name=collection_name,
                vectors_config=product_vectors_config,
                sparse_vectors_config=sparse_vectors_config
            )
            print(f"Collection {collection_name} created")

            # Setup MongoDB connection
            if not self.mongodb_conn:
                if not self.setup_mongodb():
                    print("❌ Failed to connect to MongoDB")
                    sys.stdout = sys.__stdout__
                    return buffer.getvalue()

            # Create embedding processor
            embed_object = ProductEmbedding()

            print(f"\n🔄 Processing {product_type} data from MongoDB...")
            embeddings = embed_object.run_embedding(
                product_type=product_type,
                mongodb_conn=self.mongodb_conn,
                hybrid_mode=hybrid_mode
            )
            self.index(embeddings, collection_name)

            # Close MongoDB connection
            if self.mongodb_conn:
                self.mongodb_conn.close()
                self.mongodb_conn = None

        except Exception as e:
            print(f"Error while indexing product type {product_type}: {e}")
        
        self._create_payload_indexes_for_product_type(product_type, collection_name)
        sys.stdout = sys.__stdout__
        return buffer.getvalue()

    def _create_payload_indexes_for_product_type(self, product_type: str, collection_name: str):
        """Create payload indexes based on product type field schemas"""
        
        print(f"🔍 Creating payload indexes for {product_type}...")
        
        try:
            # Common fields across all product types
            self.client.create_payload_index(
                collection_name=collection_name,
                field_name="metadata.danh_muc_l2",
                field_schema=qdrant_client.http.models.KeywordIndexParams(type="keyword")
            )
            
            self.client.create_payload_index(
                collection_name=collection_name,
                field_name="metadata.danh_muc_l3", 
                field_schema=qdrant_client.http.models.KeywordIndexParams(type="keyword")
            )
            
            self.client.create_payload_index(
                collection_name=collection_name,
                field_name="metadata.gia",
                field_schema=qdrant_client.http.models.IntegerIndexParams(type="integer")
            )
            
            self.client.create_payload_index(
                collection_name=collection_name,
                field_name="metadata.cong_suat",
                field_schema=qdrant_client.http.models.FloatIndexParams(type="float")
            )
            
            # Product-specific fields
            if product_type == "phich_nuoc":
                self.client.create_payload_index(
                    collection_name=collection_name,
                    field_name="metadata.dung_tich",
                    field_schema=qdrant_client.http.models.FloatIndexParams(type="float")
                )
                self.client.create_payload_index(
                    collection_name=collection_name,
                    field_name="metadata.chat_lieu",
                    field_schema=qdrant_client.http.models.KeywordIndexParams(type="keyword")
                )
                self.client.create_payload_index(
                    collection_name=collection_name,
                    field_name="metadata.tinh_nang",
                    field_schema=qdrant_client.http.models.KeywordIndexParams(type="keyword")
                )
                
            elif product_type == "chieu_sang":
                self.client.create_payload_index(
                    collection_name=collection_name,
                    field_name="metadata.kich_thuoc",
                    field_schema=qdrant_client.http.models.FloatIndexParams(type="float")
                )
                self.client.create_payload_index(
                    collection_name=collection_name,
                    field_name="metadata.duong_kinh_lo_khoet_tran",
                    field_schema=qdrant_client.http.models.KeywordIndexParams(type="keyword")
                )
                self.client.create_payload_index(
                    collection_name=collection_name,
                    field_name="metadata.tinh_nang",
                    field_schema=qdrant_client.http.models.KeywordIndexParams(type="keyword")
                )
                
            elif product_type == "chuyen_dung":
                self.client.create_payload_index(
                    collection_name=collection_name,
                    field_name="metadata.nhiet_do_mau",
                    field_schema=qdrant_client.http.models.KeywordIndexParams(type="keyword")
                )
                self.client.create_payload_index(
                    collection_name=collection_name,
                    field_name="metadata.dien_ap",
                    field_schema=qdrant_client.http.models.KeywordIndexParams(type="keyword")
                )
                self.client.create_payload_index(
                    collection_name=collection_name,
                    field_name="metadata.cong_nghe_led",
                    field_schema=qdrant_client.http.models.KeywordIndexParams(type="keyword")
                )
                self.client.create_payload_index(
                    collection_name=collection_name,
                    field_name="metadata.loai_den",
                    field_schema=qdrant_client.http.models.KeywordIndexParams(type="keyword")
                )
                self.client.create_payload_index(
                    collection_name=collection_name,
                    field_name="metadata.he_thong_hoa_luoi",
                    field_schema=qdrant_client.http.models.KeywordIndexParams(type="keyword")
                )
                
            elif product_type == "thiet_bi_dien":
                self.client.create_payload_index(
                    collection_name=collection_name,
                    field_name="metadata.dong_danh_dinh",
                    field_schema=qdrant_client.http.models.FloatIndexParams(type="float")
                )
                self.client.create_payload_index(
                    collection_name=collection_name,
                    field_name="metadata.anh_sang",
                    field_schema=qdrant_client.http.models.KeywordIndexParams(type="keyword")
                )
                self.client.create_payload_index(
                    collection_name=collection_name,
                    field_name="metadata.so_hat",
                    field_schema=qdrant_client.http.models.KeywordIndexParams(type="keyword")
                )
                self.client.create_payload_index(
                    collection_name=collection_name,
                    field_name="metadata.so_cuc",
                    field_schema=qdrant_client.http.models.KeywordIndexParams(type="keyword")
                )
                self.client.create_payload_index(
                    collection_name=collection_name,
                    field_name="metadata.modules",
                    field_schema=qdrant_client.http.models.KeywordIndexParams(type="keyword")
                )
                self.client.create_payload_index(
                    collection_name=collection_name,
                    field_name="metadata.doi_tuong",
                    field_schema=qdrant_client.http.models.KeywordIndexParams(type="keyword")
                )
                self.client.create_payload_index(
                    collection_name=collection_name,
                    field_name="metadata.cong_nghe",
                    field_schema=qdrant_client.http.models.KeywordIndexParams(type="keyword")
                )
                self.client.create_payload_index(
                    collection_name=collection_name,
                    field_name="metadata.loai_den",
                    field_schema=qdrant_client.http.models.KeywordIndexParams(type="keyword")
                )
                self.client.create_payload_index(
                    collection_name=collection_name,
                    field_name="metadata.san_pham",
                    field_schema=qdrant_client.http.models.KeywordIndexParams(type="keyword")
                )
                
            elif product_type == "nha_thong_minh":
                self.client.create_payload_index(
                    collection_name=collection_name,
                    field_name="metadata.chieu_dai_day",
                    field_schema=qdrant_client.http.models.FloatIndexParams(type="float")
                )
                self.client.create_payload_index(
                    collection_name=collection_name,
                    field_name="metadata.lo_khoet_tran",
                    field_schema=qdrant_client.http.models.IntegerIndexParams(type="integer")
                )
                self.client.create_payload_index(
                    collection_name=collection_name,
                    field_name="metadata.nut_bam",
                    field_schema=qdrant_client.http.models.KeywordIndexParams(type="keyword")
                )
                self.client.create_payload_index(
                    collection_name=collection_name,
                    field_name="metadata.dong_dien_o_cam_toi_da",
                    field_schema=qdrant_client.http.models.IntegerIndexParams(type="integer")
                )
                self.client.create_payload_index(
                    collection_name=collection_name,
                    field_name="metadata.dien_ap",
                    field_schema=qdrant_client.http.models.IntegerIndexParams(type="integer")
                )
                self.client.create_payload_index(
                    collection_name=collection_name,
                    field_name="metadata.hinh_dang",
                    field_schema=qdrant_client.http.models.KeywordIndexParams(type="keyword")
                )
                self.client.create_payload_index(
                    collection_name=collection_name,
                    field_name="metadata.tinh_nang",
                    field_schema=qdrant_client.http.models.TextIndexParams(type="text")
                )
                self.client.create_payload_index(
                    collection_name=collection_name,
                    field_name="metadata.goc_chieu",
                    field_schema=qdrant_client.http.models.TextIndexParams(type="text")
                )
                self.client.create_payload_index(
                    collection_name=collection_name,
                    field_name="metadata.combo",
                    field_schema=qdrant_client.http.models.TextIndexParams(type="text")
                )
                self.client.create_payload_index(
                    collection_name=collection_name,
                    field_name="metadata.anh_sang",
                    field_schema=qdrant_client.http.models.TextIndexParams(type="text")
                )
                
            print(f"✅ All payload indexes created for {product_type}")
            
        except Exception as e:
            print(f"❌ Error creating payload indexes for {product_type}: {e}")

class SolutionIndexing:
    def __init__(self, vector_db_client=client):
        super().__init__()
        self.client = vector_db_client
        self.mongodb_conn = None
    
    def setup_mongodb(self, connection_string: str = None):
        """Setup MongoDB connection"""
        self.mongodb_conn = MongoDBConnection(connection_string)
        return self.mongodb_conn.connect()

    def index(
            self,
            embeddings: List[Dict],
            collection_name: str,
            batch_size: int = 10
        ):
        """Index embeddings to a Qdrant collection in batches"""

        total_docs = len(embeddings)
        success_count = 0
        error_count = 0
        
        print(f"Adding {total_docs} solution documents to '{collection_name}'...")
        
        for i in range(0, total_docs, batch_size):
            batch = embeddings[i:i+batch_size]
            points = []
            
            try:
                for embedding_data in batch:
                    # Create Qdrant point from embedding data
                    point = qdrant_client.http.models.PointStruct(
                        id=embedding_data["point_id"],
                        vector=embedding_data["vectors"],
                        payload=embedding_data["payload"]
                    )
                    points.append(point)
                
                # Upload batch to Qdrant
                if points:
                    self.client.upsert(collection_name=collection_name, points=points)
                    success_count += len(batch)
                    
                    # Count successful embeddings
                    text_count = sum(1 for p in points if any(v != 0 for v in p.vector))
                    
                    print(f"✅ Batch {i//batch_size + 1}: {len(batch)} docs | {text_count} contents")
                else:
                    print(f"⚠️ Batch {i//batch_size + 1}: No valid points to upload")
                
            except Exception as e:
                error_count += len(batch)
                print(f"❌ Batch {i//batch_size + 1} failed: {e}")
        
        print(f"\n📊 Final Results:")
        print(f"   ✅ Successful: {success_count}")
        print(f"   ❌ Failed: {error_count}")
        print(f"   📈 Success Rate: {success_count/(success_count+error_count)*100:.1f}%")

    def run_indexing(self, reload: bool = True):
        """Index all solution data from MongoDB into Qdrant collections."""
        if reload:
            try:
                for collection in solution_collections:
                    self.client.recreate_collection(
                        collection_name=collection,
                        vectors_config=qdrant_client.http.models.VectorParams(
                            size=768,
                            distance=qdrant_client.http.models.Distance.COSINE,
                        )
                    )
                print("All solution collections recreated.")
            except Exception as e:
                print(f"Error while recreating collections: {e}")
                return

        # Setup MongoDB connection
        if not self.mongodb_conn:
            if not self.setup_mongodb():
                print("❌ Failed to connect to MongoDB. Aborting indexing.")
                return

        # Create embedding processor
        embed_object = SolutionEmbedding()
        
        for collection, solution_type in zip(solution_collections, solution_types):
            print(f"\n🔄 Processing {solution_type} data from MongoDB...")
            embeddings = embed_object.run_embedding(solution_type, self.mongodb_conn)
            self.index(embeddings, collection)
        
        # Close MongoDB connection
        if self.mongodb_conn:
            self.mongodb_conn.close()
            self.mongodb_conn = None
    
    def indexing_single_solution(self, solution: str, collection_name: str) -> str:
        """Indexing a single solution into its Qdrant collection from MongoDB"""
        buffer = io.StringIO()
        sys.stdout = buffer

        try:
            self.client.recreate_collection(
                collection_name=collection_name,
                vectors_config=qdrant_client.http.models.VectorParams(
                    size=768,
                    distance=qdrant_client.http.models.Distance.COSINE,
                )
            )
            print(f"Collection {collection_name} created")

            # Setup MongoDB connection
            if not self.mongodb_conn:
                if not self.setup_mongodb():
                    print("❌ Failed to connect to MongoDB")
                    sys.stdout = sys.__stdout__
                    return buffer.getvalue()

            # Create embedding processor
            embed_object = SolutionEmbedding()

            print(f"\n🔄 Processing {solution} data from MongoDB...")
            embeddings = embed_object.run_embedding(solution, self.mongodb_conn)
            self.index(embeddings, collection_name)
            
            # Close MongoDB connection
            if self.mongodb_conn:
                self.mongodb_conn.close()
                self.mongodb_conn = None
                
        except Exception as e:
            print(f"Error while recreating collection and indexing solution {solution}: {e}")
        
        sys.stdout = sys.__stdout__
        return buffer.getvalue()


"""=================GRADIO UI========================"""
def create_indexing_interface():
    """Create Gradio interface for indexing from MongoDB"""
    product_indexing = ProductIndexing()
    solution_indexing = SolutionIndexing()

    with gr.Blocks(theme=gr.themes.Soft()) as demo:
        gr.Markdown("# 🗄️ Qdrant Data Indexing System (MongoDB)")
        gr.Markdown("Recreate Qdrant Collections and Index Data from MongoDB Atlas")
        
        output_box = gr.Textbox(lines=15, label="📋 Logs", interactive=False)
        
        # gr.Markdown("---")
        # gr.Markdown("## 🏢 Giải pháp (Solutions)")
        
        # with gr.Row():
        #     gr.Button("GP Ngư nghiệp").click(
        #         solution_indexing.indexing_single_solution,
        #         inputs=[gr.State("ngu_nghiep"), gr.State(QDRANT_COLLECTION_NAME_GPNGUNGHIEP)],
        #         outputs=output_box)
            
        #     gr.Button("GP Học đường").click(
        #         solution_indexing.indexing_single_solution,
        #         inputs=[gr.State("hoc_duong"), gr.State(QDRANT_COLLECTION_NAME_GPHOCDUONG)],
        #         outputs=output_box)
            
        #     gr.Button("GP Nhà thông minh").click(
        #         solution_indexing.indexing_single_solution,
        #         inputs=[gr.State("nha_thong_minh"), gr.State(QDRANT_COLLECTION_NAME_GPNHATHONGMINH)],
        #         outputs=output_box)
            
        #     gr.Button("GP Nông nghiệp CNC").click(
        #         solution_indexing.indexing_single_solution,
        #         inputs=[gr.State("nong_nghiep_cnc"), gr.State(QDRANT_COLLECTION_NAME_GPNNCNC)],
        #         outputs=output_box)
        
        # with gr.Row():
        #     gr.Button("GP Cảnh quan").click(
        #         solution_indexing.indexing_single_solution,
        #         inputs=[gr.State("canh_quan"), gr.State(QDRANT_COLLECTION_NAME_GPCANHQUAN)],
        #         outputs=output_box)
            
        #     gr.Button("GP HTĐ NLMT").click(
        #         solution_indexing.indexing_single_solution,
        #         inputs=[gr.State("nlmt"), gr.State(QDRANT_COLLECTION_NAME_GPNLMT)],
        #         outputs=output_box)
            
        #     gr.Button("GP Đường phố").click(
        #         solution_indexing.indexing_single_solution,
        #         inputs=[gr.State("duong_pho"), gr.State(QDRANT_COLLECTION_NAME_GPDUONGPHO)],
        #         outputs=output_box)
            
        #     gr.Button("GP Văn phòng công sở").click(
        #         solution_indexing.indexing_single_solution,
        #         inputs=[gr.State("van_phong_cong_so"), gr.State(QDRANT_COLLECTION_NAME_GPVPCS)],
        #         outputs=output_box)
        
        # with gr.Row():
        #     gr.Button("GP Nhà máy CN").click(
        #         solution_indexing.indexing_single_solution,
        #         inputs=[gr.State("nha_may_cong_nghiep"), gr.State(QDRANT_COLLECTION_NAME_GPNMCN)],
        #         outputs=output_box)
            
        #     gr.Button("GP Nhà ở xã hội").click(
        #         solution_indexing.indexing_single_solution,
        #         inputs=[gr.State("nha_o_xa_hoi"), gr.State(QDRANT_COLLECTION_NAME_GPNOXH)],
        #         outputs=output_box)
            
        #     def index_all_solutions():
        #         buffer = io.StringIO()
        #         sys.stdout = buffer
        #         solution_indexing.run_indexing(reload=True)
        #         sys.stdout = sys.__stdout__
        #         return buffer.getvalue()
            
        #     gr.Button("✨ Tất cả GP", variant="primary").click(
        #         index_all_solutions,
        #         outputs=output_box)
        
        gr.Markdown("---")
        gr.Markdown("## 📦 Sản phẩm (Products)")
        
        # Individual product buttons
        with gr.Row():
            btn_phich = gr.Button("SP Phích nước")
            btn_chieu_sang = gr.Button("SP Chiếu sáng")
            btn_chuyen_dung = gr.Button("SP Chuyên dụng")
            btn_ntm = gr.Button("SP Nhà thông minh")
            btn_thiet_bi = gr.Button("SP Thiết bị điện")
        
        with gr.Row():
            btn_all_products = gr.Button("✨ Tất cả SP", variant="primary", scale=2)
        
        # Setup click handlers
        btn_phich.click(
            product_indexing.indexing_single_product_type,
            inputs=[gr.State("phich_nuoc"), gr.State(QDRANT_COLLECTION_NAME_SPPHICHNUOC), gr.State(True)],
            outputs=output_box)
        
        btn_chieu_sang.click(
            product_indexing.indexing_single_product_type,
            inputs=[gr.State("chieu_sang"), gr.State(QDRANT_COLLECTION_NAME_SPCHIEUSANG), gr.State(True)],
            outputs=output_box)
        
        btn_chuyen_dung.click(
            product_indexing.indexing_single_product_type,
            inputs=[gr.State("chuyen_dung"), gr.State(QDRANT_COLLECTION_NAME_SPCHUYENDUNG), gr.State(True)],
            outputs=output_box)
        
        btn_ntm.click(
            product_indexing.indexing_single_product_type,
            inputs=[gr.State("nha_thong_minh"), gr.State(QDRANT_COLLECTION_NAME_SPNHATHONGMINH), gr.State(True)],
            outputs=output_box)
        
        btn_thiet_bi.click(
            product_indexing.indexing_single_product_type,
            inputs=[gr.State("thiet_bi_dien"), gr.State(QDRANT_COLLECTION_NAME_SPTHIETBIDIEN), gr.State(True)],
            outputs=output_box)
        
        def index_all_products():
            buffer = io.StringIO()
            sys.stdout = buffer
            product_indexing.run_indexing(reload=True, hybrid_mode=True)
            sys.stdout = sys.__stdout__
            return buffer.getvalue()
        
        btn_all_products.click(
            index_all_products,
            outputs=output_box)
    
    return demo


if __name__ == "__main__":
    demo = create_indexing_interface()
    demo.launch()