.gitignore

@@ -1,9 +0,0 @@
-.env
-**venv**
-**data**
-*pyc**
-**pycache**
-*agent/**
-**downloaded_images**
-**model_export**
-**cpython**

dev_backend/db_setup.py

@@ -1,44 +0,0 @@
-from __future__ import annotations
-import os
-from contextlib import asynccontextmanager
-from typing import AsyncGenerator
-from dotenv import load_dotenv
-from qdrant_client import AsyncQdrantClient, models
-from typing import Annotated
-from sqlalchemy.ext.asyncio import create_async_engine, AsyncSession, async_sessionmaker
-from sqlalchemy.orm import declarative_base
-from sqlalchemy.orm import sessionmaker
-# pyrefly: ignore [missing-import]
-from fastapi import FastAPI, Depends
-from sqlalchemy.engine.url import URL
-from dotenv import load_dotenv
-load_dotenv()
-
-DATABASE_URL = URL.create(
-    drivername="mysql+asyncmy",
-    username=os.getenv("MYSQL_USER"),
-    password=os.getenv("MYSQL_PASSWORD"),
-    host=os.getenv("MYSQL_HOST"),
-    port=os.getenv("MYSQL_PORT"),
-    database=os.getenv("MYSQL_DATABASE"),
-)
-
-
-async def get_qdrant_client()->AsyncGenerator[AsyncQdrantClient,None]:
-    # Replace with your Qdrant URL
-    client = AsyncQdrantClient(url="http://localhost:6333", timeout=60)
-    try:
-        yield client
-    finally:
-        # Properly close the async client
-        await client.close()
-
-async def get_session():
-    engine = create_async_engine(DATABASE_URL, echo=True)
-    async_session = async_sessionmaker(bind=engine, class_=AsyncSession, expire_on_commit=False)
-    session = async_session()
-    Base = declarative_base()
-    try:
-        yield session
-    finally:
-        await session.close()

dev_backend/main.py

@@ -1,21 +0,0 @@
-import sys
-import os
-
-# Add the project root to sys.path to allow imports from model_export
-sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
-
-from fastapi import FastAPI, status
-from starlette.middleware.base import BaseHTTPMiddleware, RequestResponseEndpoint
-from dotenv import load_dotenv
-from db_setup import get_qdrant_client,get_session
-from mysql_process.views import app_router as mysql_router
-from vector_db_router.views import app_router as vector_db_router
-load_dotenv()
-
-api = FastAPI(
-    docs_url="/docs",
-    redoc_url="/redocs",
-)
-
-api.include_router(mysql_router,prefix="/mysql",tags=["mysql_process"])
-api.include_router(vector_db_router,prefix="/collection",tags=["vector_db"])

dev_backend/mysql_process/models.py

@@ -1,12 +0,0 @@
-from fastapi import FastAPI
-from sqlmodel import SQLModel, Field
-
-class Memory(SQLModel,table=True):
-    id: int = Field(default=None,primary_key=True)
-    product_link: str = Field(default=None,index=True)
-    price: float = Field(default=None)
-    product_image: str = Field(default=None)
-    product_name: str = Field(default=None)
-    product_description: str = Field(default=None)
-    product_rating: float = Field(default=None)
-    product_review: str = Field(default=None)

dev_backend/mysql_process/serializers.py

@@ -1,16 +0,0 @@
-from fastapi import FastAPI
-from pydantic import BaseModel
-from .models import Memory
-
-class MemorySerializer(BaseModel):
-    id: int
-    product_link: str
-    price: float
-    product_image: str
-    product_name: str
-    product_description: str
-    product_rating: float
-    product_review: str
-
-    class Config:
-        orm_mode = True

dev_backend/mysql_process/views.py

@@ -1,20 +0,0 @@
-from typing import Annotated
-from fastapi import Depends, Header, HTTPException,APIRouter
-from typing import List,Optional
-from db_setup import get_session,get_qdrant_client
-# from .models import Product
-from sqlalchemy.ext.asyncio import AsyncSession
-from qdrant_client import AsyncQdrantClient
-from fastapi.responses import JSONResponse
-app_router = APIRouter()
-
-
-@app_router.get("/products")
-async def get_all_products(
-    session: Annotated[AsyncSession, Depends(get_session)],
-    vector_db:Annotated[AsyncQdrantClient, Depends(get_qdrant_client)],
-):
-    try:
-        return JSONResponse(content={"message": "Hello World"})
-    except Exception as e:
-        raise HTTPException(status_code=500, detail=str(e))

dev_backend/vector_db_router/models.py

@@ -1,108 +0,0 @@
-from qdrant_client import AsyncQdrantClient, models
-from qdrant_client.models import PointStruct
-from typing import Dict, Any
-import logging
-logging.basicConfig(level=logging.DEBUG, format='%(asctime)s - %(levelname)s - %(message)s')
-log = logging.getLogger(__name__)
-
-class CollectionHandler:
-    def __init__(self, collection_name: str, vector: Any, vector_size: int, payload: Dict,
-    id: int=None,
-    link: str=None,
-    asin: str=None,
-    category: str=None,
-    brand: str=None,
-    client: AsyncQdrantClient=None
-    ):
-        self.collection_name = collection_name
-        self.vector = vector
-        self.id = id
-        self.vector_size = vector_size
-        self.payload = payload
-        self.link = link
-        self.asin = asin
-        self.category = category
-        self.brand = brand
-        self.client = client if client else AsyncQdrantClient("localhost", port=6333)
-
-    async def create_collection(self):
-        try:
-            if await self.client.collection_exists(self.collection_name):
-                return {"message": "Collection already exists"}
-            
-            await self.client.create_collection(
-                collection_name=self.collection_name,
-                vectors_config=models.VectorParams(size=self.vector_size, distance=models.Distance.EUCLID),
-                optimizers_config=models.OptimizersConfigDiff(indexing_threshold=20000)
-            )
-            
-            # Creating payload indexes as per project logic
-
-            await self.client.create_payload_index(
-                collection_name=self.collection_name,
-                field_name="link",
-                field_schema=models.PayloadSchemaType.KEYWORD
-            )
-            await self.client.create_payload_index(
-                collection_name=self.collection_name,
-                field_name="title",
-                field_schema=models.PayloadSchemaType.KEYWORD
-            )
-
-            await self.client.create_payload_index(
-                collection_name=self.collection_name,
-                field_name="brand",
-                field_schema=models.PayloadSchemaType.KEYWORD
-            )
-            await self.client.create_payload_index(
-                collection_name=self.collection_name,
-                field_name="asin",
-                field_schema=models.PayloadSchemaType.KEYWORD
-            )
-
-            
-            return {"message": f"Collection {self.collection_name} created successfully"}
-        except Exception as e:
-            return {"message": str(e)}
-
-    async def insertion(self):
-        try:
-            await self.client.upsert(
-                collection_name=self.collection_name,
-                points=[
-                    PointStruct(id=self.id, vector=self.vector, payload=self.payload)
-                ]
-            )
-            return True
-        except Exception as e:
-            # Note: In a real app we'd use a logger here
-            print(f"Insertion failed for ID {self.id}: {e}")
-            return False
-    
-    async def upsert_point(self):
-        return await self.insertion()
-
-    async def search(self, query_vector, score_threshold: float = 0.3, limit: int = 10):
-        try:
-            result = await self.client.query_points(
-                collection_name=self.collection_name,
-                query=query_vector,
-                score_threshold=score_threshold,
-                limit=limit
-            )
-            return result.points
-        except Exception as e:
-            log.error(f"Search failed: {e}")
-            return None
-
-    async def update_collection(self):
-        """Update is implemented as an upsert of the point data."""
-        return await self.upsert_point()
-
-    async def delete_collection(self):
-        try:
-            await self.client.delete_collection(collection_name=self.collection_name)
-            return {"message": f"Collection {self.collection_name} deleted successfully"}
-        except Exception as e:
-            return {"message": str(e)}
-    

dev_backend/vector_db_router/plugins.py

@@ -1,90 +0,0 @@
-import os
-import requests
-from urllib.parse import urlparse
-from pathlib import Path
-from PIL import Image
-
-def download_image(url: str, filename: str = None) -> str:
-    """
-    Download an image from URL and save it in data/temp/ folder.
-    
-    Args:
-        url (str): Image URL
-        filename (str, optional): Custom filename. If None, extracted from URL.
-    
-    Returns:
-        str: Full path to the downloaded image
-    """
-    try:
-        # Get project root directory (where your main script is)
-        root_dir = Path(os.path.dirname(os.path.abspath(__file__))).parent
-        
-        # Create data/temp folder structure
-        temp_dir = root_dir / "data" / "temp"
-        temp_dir.mkdir(parents=True, exist_ok=True)
-        
-        # Generate filename if not provided
-        if not filename:
-            parsed_url = urlparse(url)
-            filename = os.path.basename(parsed_url.path)
-            if not filename or "." not in filename:
-                # Fallback filename
-                ext = filename.split('.')[-1] if '.' in filename else 'jpg'
-                filename = f"image_{hash(url) % 100000}.{ext}"
-        
-        # Ensure filename has extension
-        if '.' not in filename:
-            filename += ".jpg"
-        
-        file_path = temp_dir / filename
-        
-        # Download the image
-        response = requests.get(url, stream=True, timeout=30)
-        response.raise_for_status()
-        
-        # Save image
-        with open(file_path, 'wb') as f:
-            for chunk in response.iter_content(chunk_size=8192):
-                f.write(chunk)
-        
-        print(f"✅ Image downloaded: {file_path}")
-        return str(file_path)
-        
-    except Exception as e:
-        print(f"❌ Failed to download image: {e}")
-        raise
-
-def read_image(image_path: str) -> Image.Image:
-    """
-    Read an image from the given path and return a PIL Image object.
-    
-    Args:
-        image_path (str): Path to the image file
-    
-    Returns:
-        PIL.Image.Image: Loaded image
-    
-    Raises:
-        FileNotFoundError: If image doesn't exist
-        Exception: For other image loading errors
-    """
-    try:
-        if not os.path.exists(image_path):
-            raise FileNotFoundError(f"Image not found at path: {image_path}")
-        
-        # Open the image
-        image = Image.open(image_path)
-        
-        # Convert to RGB (important for DINOv2 and most models)
-        if image.mode != "RGB":
-            image = image.convert("RGB")
-        
-        print(f"✅ Image loaded successfully: {image_path} | Size: {image.size}")
-        return image
-        
-    except FileNotFoundError as e:
-        print(f"❌ File not found: {e}")
-        raise
-    except Exception as e:
-        print(f"❌ Failed to read image: {e}")
-        raise

dev_backend/vector_db_router/serializers.py

@@ -1,24 +0,0 @@
-from pydantic import BaseModel
-from typing import Dict, List, Any
-
-class CreateCollectionSerializer(BaseModel):
-    collection_name: str
-    vector: List[float]
-    vector_size: int
-    payload: Dict[str, Any]
-    id: int
-
-class QueryCollectionSerializer(BaseModel):
-    collection_name: str
-    url: str
-    score_threshold: float = 0.3  # Euclidean distance — lower = more similar. 0.3 = very tight match
-    limit: int = 10
-
-class UpdateCollectionSerializer(BaseModel):
-    collection_name: str
-    vector: List[float]
-    payload: Dict[str, Any]
-    id: int
-
-class DeleteCollectionSerializer(BaseModel):
-    collection_name: str

dev_backend/vector_db_router/views.py

@@ -1,233 +0,0 @@
-from db_setup import get_qdrant_client
-from typing import Annotated
-from fastapi import Depends, HTTPException, APIRouter
-from qdrant_client import AsyncQdrantClient
-from .plugins import download_image,read_image
-from fastapi.responses import JSONResponse
-from .serializers import (
-    CreateCollectionSerializer, 
-    QueryCollectionSerializer, 
-    UpdateCollectionSerializer, 
-    DeleteCollectionSerializer
-)
-from model_export.dino_image_matching import get_vectors,get_embedding
-from .models import CollectionHandler
-import os
-app_router = APIRouter()
-import logging
-logging.basicConfig(level=logging.DEBUG, format='%(asctime)s - %(levelname)s - %(message)s')
-log = logging.getLogger(__name__)
-
-import pandas as pd
-
-@app_router.get("/get_vectors")
-async def get_vectors_endpoint(
-    q: Annotated[AsyncQdrantClient, Depends(get_qdrant_client)],
-    image_path:str=os.getenv("DATASET")
-):
-    try:
-        # Construct path relative to this file
-        base_root = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
-        excel_path = os.path.join(base_root, "model_export", "listing_data.xlsx")
-        df = pd.read_excel(excel_path)
-        asin_list = df['ASIN'].dropna().astype(str).tolist()
-        log.info(f"Generating vectors and ingesting {len(asin_list)} ASINs into Qdrant from {excel_path}")
-        
-        # 1. Initialize/Create collection "Product"
-        # DINOv2 vitb14 vector size is 768
-        init_handler = CollectionHandler(
-            collection_name="Product",
-            vector=[],
-            vector_size=768,
-            payload={},
-            client=q
-        )
-        await init_handler.create_collection()
-        
-        result_lst = []
-        for index, row in df.iterrows():
-            asin = str(row['ASIN'])
-            if pd.isna(row['ASIN']):
-                continue
-                
-            title = str(row['Title'])
-            brand = str(row['Brand'])
-            link = str(row['Image'])
-            
-            # Call get_vectors
-            vector = get_vectors(image_input=image_path, item=asin)
-            
-            if vector is None:
-                log.warning(f"Skipping {asin} due to missing image/vector")
-                continue
-            
-            payload = {
-                "asin": asin,
-                "title": title,
-                "brand": brand,
-                "link": link
-            }
-            
-            # 2. Ingest into Qdrant using CollectionHandler
-            # Use the injected client 'q' and convert index to int
-            handler = CollectionHandler(
-                collection_name="Product",
-                vector=vector,
-                vector_size=768,
-                payload=payload,
-                id=int(index),
-                link=link,
-                asin=asin,
-                brand=brand,
-                client=q
-            )
-            success = await handler.upsert_point()
-            
-            if success:
-                result_lst.append({
-                    "item": asin, 
-                    "status": "ingested",
-                    "payload": payload
-                })
-                log.info(f"Vector ingested for {asin} (ID: {index})")
-            else:
-                log.error(f"Failed to ingest vector for {asin}")
-        
-        return JSONResponse({"status": "success", "message": f"Ingested {len(result_lst)} items into Product collection", "result": result_lst})
-    except Exception as e:
-        raise HTTPException(status_code=500, detail=str(e))
-
-@app_router.post("/create")
-async def create_collection_endpoint(
-    q: Annotated[AsyncQdrantClient, Depends(get_qdrant_client)],
-    body: CreateCollectionSerializer = None
-):
-    try:
-        if body is None:
-            raise HTTPException(status_code=400, detail="Collection name is required")
-        
-        print("collection_name: ", body.collection_name)
-        
-        handler = CollectionHandler(
-            collection_name=body.collection_name,
-            vector=body.vector,
-            vector_size=body.vector_size,
-            payload=body.payload,
-            id=body.id
-        )
-        
-        # 1. Create collection
-        result = await handler.create_collection()
-        
-        # 2. Automatically call upsert_point
-        await handler.upsert_point()
-
-        return JSONResponse(result)
-
-    except Exception as e:
-        raise HTTPException(status_code=500, detail=str(e))
-
-@app_router.get("/query")
-async def query_collection_endpoint(
-    q: Annotated[AsyncQdrantClient, Depends(get_qdrant_client)],
-    body: QueryCollectionSerializer
-):
-    try:
-        result = []
-        if isinstance(body.url, str):
-            # Handle semicolon-separated URLs by taking the first one
-            target_url = body.url.split(';')[0].strip() if ';' in body.url else body.url
-            log.info(f"Querying collection {body.collection_name} with URL: {target_url}")
-            downloaded_image_path = download_image(target_url)
-            query_vector = get_embedding(downloaded_image_path)
-            # get_embedding already returns a flat list of 768 floats
-            
-            handler = CollectionHandler(
-                collection_name=body.collection_name,
-                vector=query_vector,
-                vector_size=len(query_vector),
-                payload={},
-                id=0,
-                client=q
-            )
-            search_result = await handler.search(
-                query_vector,
-                score_threshold=body.score_threshold,
-                limit=body.limit
-            )
-            if search_result:
-                result = [
-                    {"id": p.id, "score": p.score, "payload": p.payload}
-                    for p in search_result
-                ]
-            else:
-                result = []  # No match within threshold
-            
-        elif isinstance(body.url, list):
-            result = []
-            for url in body.url:
-                downloaded_image_path = download_image(url)
-                query_vector = get_embedding(downloaded_image_path)
-                # get_embedding already returns a flat list of 768 floats
-                
-                handler = CollectionHandler(
-                    collection_name=body.collection_name,
-                    vector=query_vector,
-                    vector_size=len(query_vector),
-                    payload={},
-                    id=0,
-                    client=q
-                )       
-                search_result = await handler.search(
-                    query_vector,
-                    score_threshold=body.score_threshold,
-                    limit=body.limit
-                )
-                if search_result:
-                    result.append([
-                        {"id": p.id, "score": p.score, "payload": p.payload}
-                        for p in search_result
-                    ])
-        
-        return JSONResponse({"results": result})
-    except Exception as e:
-        log.error(f"Query failed: {e}")
-        raise HTTPException(status_code=500, detail=str(e))
-
-@app_router.put("/update")
-async def update_collection_endpoint(
-    q: Annotated[AsyncQdrantClient, Depends(get_qdrant_client)],
-    body: UpdateCollectionSerializer
-):
-    try:
-        handler = CollectionHandler(
-            collection_name=body.collection_name,
-            vector=body.vector,
-            vector_size=len(body.vector),
-            payload=body.payload,
-            id=body.id
-        )
-        result = await handler.update_collection()
-        return JSONResponse({"status": "success", "result": result})
-    except Exception as e:
-        raise HTTPException(status_code=500, detail=str(e))
-
-@app_router.delete("/delete")
-async def delete_collection_endpoint(
-    q: Annotated[AsyncQdrantClient, Depends(get_qdrant_client)],
-    body: DeleteCollectionSerializer
-):
-    try:
-        handler = CollectionHandler(
-            collection_name=body.collection_name,
-            vector=[],
-            vector_size=0,
-            payload={},
-            id=0
-        )
-        result = await handler.delete_collection()
-        return JSONResponse(result)
-    except Exception as e:
-        raise HTTPException(status_code=500, detail=str(e))
-        
-    

dino_image_matching.py

@@ -0,0 +1,54 @@
+import warnings
+
+import torch
+from PIL import Image
+from torchvision import transforms
+import torch.nn.functional as F
+
+# Optional dependency warnings from DINOv2 internals are non-critical.
+warnings.filterwarnings("ignore", message="xFormers is not available.*", category=UserWarning)
+
+# Load model
+model = torch.hub.load(
+    'facebookresearch/dinov2',
+    'dinov2_vitb14'
+)
+
+model.eval()
+
+# Device
+device = "cuda" if torch.cuda.is_available() else "cpu"
+model = model.to(device)
+
+# Image preprocessing
+transform = transforms.Compose([
+    transforms.Resize((518, 518)),  # DINOv2 recommended size
+    transforms.ToTensor(),
+])
+
+def get_embedding(image_path):
+    # Load image
+    image = Image.open(image_path).convert("RGB")
+
+    # Transform
+    tensor = transform(image).unsqueeze(0).to(device)
+
+    # Generate embedding
+    with torch.no_grad():
+        embedding = model(tensor)
+
+    # Normalize embedding (important for cosine similarity)
+    embedding = F.normalize(embedding, p=2, dim=1)
+
+    return embedding.cpu()
+
+# Example
+emb1 = get_embedding(r"data_images\B0B39FFJHF\03.jpg")
+emb2 = get_embedding(r"data_images\B09RWY127Q\03.jpg")
+
+# Cosine similarity
+similarity = torch.nn.functional.pdist( 
+    torch.cat([emb1, emb2])
+)
+
+print("Distance:", similarity.item())

model_export/dino_image_matching.py

@@ -1,73 +0,0 @@
-import warnings
-from dotenv import load_dotenv
-import torch,glob
-from PIL import Image
-from torchvision import transforms
-import os
-from qdrant_client import AsyncQdrantClient, models
-from db_setup import get_qdrant_client
-from vector_db_router.models import CollectionHandler
-from vector_db_router.serializers import CreateCollectionSerializer
-import torch.nn.functional as F
-load_dotenv()
-
-# Optional dependency warnings from DINOv2 internals are non-critical.
-warnings.filterwarnings("ignore", message="xFormers is not available.*", category=UserWarning)
-
-# Load model
-model = torch.hub.load(
-    'facebookresearch/dinov2',
-    'dinov2_vitb14'
-)
-
-model.eval()
-
-# Device
-device = "cuda" if torch.cuda.is_available() else "cpu"
-model = model.to(device)
-
-# Image preprocessing
-transform = transforms.Compose([
-    transforms.Resize((518, 518)),  # DINOv2 recommended size
-    transforms.ToTensor(),
-])
-
-def get_embedding(image_path):
-    # Load image
-    image = Image.open(image_path).convert("RGB")
-
-    # Transform
-    tensor = transform(image).unsqueeze(0).to(device)
-
-    # Generate embedding
-    with torch.no_grad():
-        embedding = model(tensor)
-
-    # Normalize embedding (important for cosine similarity)
-    embedding = F.normalize(embedding, p=2, dim=1)
-
-    # Return flat list (squeeze batch dim) 
-    return embedding.squeeze(0).cpu().tolist()
-
-def get_vectors(image_input, item):
-    try:
-        base_dir = os.path.join(os.path.dirname(__file__), "downloaded_images")
-        path = image_input
-        
-        # If image_input is not a valid file, try to find one using the item (ASIN)
-        if not (path and os.path.isfile(path)):
-            # If path is a directory, use it as base_dir
-            search_base = path if (path and os.path.isdir(path)) else base_dir
-            glob_pattern = os.path.join(search_base, item, "*.jpg")
-            jpg_files = glob.glob(glob_pattern)
-            if jpg_files:
-                path = jpg_files[0]
-            else:
-                return None
-        
-        # Generate the vector for the identified image file
-        emb = get_embedding(path)
-        return emb.squeeze().tolist()
-    except Exception as e:
-        print(f"Error generating vector for {item}: {e}")
-        return None

req.txt

@@ -1,76 +0,0 @@
-annotated-doc==0.0.4
-annotated-types==0.7.0
-anyio==4.13.0
-asyncmy==0.2.11
-certifi==2026.4.22
-click==8.3.3
-contourpy==1.3.3
-cuda-bindings==13.2.0
-cuda-pathfinder==1.5.4
-cuda-toolkit==13.0.2
-cycler==0.12.1
-et_xmlfile==2.0.0
-fastapi==0.136.1
-filelock==3.29.0
-fonttools==4.62.1
-fsspec==2026.4.0
-greenlet==3.5.0
-grpcio==1.80.0
-h11==0.16.0
-h2==4.3.0
-hpack==4.1.0
-httpcore==1.0.9
-httpx==0.28.1
-hyperframe==6.1.0
-idna==3.13
-Jinja2==3.1.6
-joblib==1.5.3
-kiwisolver==1.5.0
-MarkupSafe==3.0.3
-matplotlib==3.10.9
-mpmath==1.3.0
-networkx==3.6.1
-numpy==2.4.4
-nvidia-cublas==13.1.0.3
-nvidia-cuda-cupti==13.0.85
-nvidia-cuda-nvrtc==13.0.88
-nvidia-cuda-runtime==13.0.96
-nvidia-cudnn-cu13==9.19.0.56
-nvidia-cufft==12.0.0.61
-nvidia-cufile==1.15.1.6
-nvidia-curand==10.4.0.35
-nvidia-cusolver==12.0.4.66
-nvidia-cusparse==12.6.3.3
-nvidia-cusparselt-cu13==0.8.0
-nvidia-nccl-cu13==2.28.9
-nvidia-nvjitlink==13.0.88
-nvidia-nvshmem-cu13==3.4.5
-nvidia-nvtx==13.0.85
-openpyxl==3.1.5
-packaging==26.2
-pandas==3.0.2
-pillow==12.2.0
-portalocker==3.2.0
-protobuf==7.34.1
-pydantic==2.13.4
-pydantic_core==2.46.4
-pyparsing==3.3.2
-python-dateutil==2.9.0.post0
-python-dotenv==1.2.2
-qdrant-client==1.17.1
-scikit-learn==1.8.0
-scipy==1.17.1
-setuptools==81.0.0
-six==1.17.0
-SQLAlchemy==2.0.49
-starlette==1.0.0
-sympy==1.14.0
-threadpoolctl==3.6.0
-torch==2.11.0
-torchvision==0.26.0
-tqdm==4.67.3
-triton==3.6.0
-typing-inspection==0.4.2
-typing_extensions==4.15.0
-urllib3==2.6.3
-uvicorn==0.46.0