Na na na na na na na na na na na na na na na

Na blog na na na na na na na na na na na na na na na na na na na na na na na na na na na na

Na na na na na na na na na na na na na na na na na na na na na na na na na na na na na na na na na na na na na na na na ndingisa na ndingisa na ndingisa na ndingisa na ndingisa na ndingisa na ndingisa na ndingisa na ndingisa na ndingisa na ndingisa na ndingisa.

na na na na na na na na naPamba la Pamba la 100%na na na na na na na na na na

LELO

Kokolala

KokolalaNa na na na na na na na na na na na na na na na

Moto Ndege 14

Moto Ndege 14na na na na na na na na na na na na na na na na na na na na na na na na

Na minoko koleka lingomba na:

1. Pamba la Pamba la Pamba la 100%
2. Pamba la Pamba la Pamba la Pamba la Pamba la 100%
3. Pamba la Pamba la Pamba la Pamba la Pamba la Pamba la 100%

Miso

MisoNa na na na na na na na na na na na na na na na

Miso

Misona na na na na na na na na na na na na na na na na na na na na na na na

Miso

• Kofutela Postgres. Kofutela CocoIndex na Postgres ekomisa data lineage na ekomisa ekomisa.
• Posa Posa

ETENI YA 9

Penza Design

Diagram ya ngomba ya ngomba ya ngomba ya ngomba ya ngomba ya ngomba ya ngomba:

1. Kofutela faili ya tabaka ya tabaka
2. Na na na na na na na na na na
3. Pamba la Pamba la Pamba la Pamba la Pamba la Pamba la Pamba la 100%

1. Kofutela miso.

@cocoindex.flow_def(name="ImageObjectEmbedding")
def image_object_embedding_flow(flow_builder: cocoindex.FlowBuilder, data_scope: cocoindex.DataScope):
    data_scope["images"] = flow_builder.add_source(
        cocoindex.sources.LocalFile(path="img", included_patterns=["*.jpg", "*.jpeg", "*.png"], binary=True),
        refresh_interval=datetime.timedelta(minutes=1)  # Poll for changes every 1 minute
    )
    img_embeddings = data_scope.add_collector()

flow_builder.add_sourcelingála (filenameMisocontent[MOKO]MOKOLifePenza

2. Process kila imago na makolinhot ya info.

2.1 Kofutela image na CLIP

@functools.cache
def get_clip_model() -> tuple[CLIPModel, CLIPProcessor]:
    model = CLIPModel.from_pretrained(CLIP_MODEL_NAME)
    processor = CLIPProcessor.from_pretrained(CLIP_MODEL_NAME)
    return model, processor

Miso@functools.cachemakolinhot makolinhot makolinhot makolinhot makolinhot makolinhot makolinhot makolinhot makolinhot makolinhot makolinhot makolinhot makolinhot makolinhot makolinhot makolinhot makolinhot makolinhot makolinhot makolinhot makolinhot makolinhot makolinhot makolinhot makolinhot makolinhot makolinhot makolinhot makolinhot makolinhot makolinhot

@cocoindex.op.function(cache=True, behavior_version=1, gpu=True)
def embed_image(img_bytes: bytes) -> cocoindex.Vector[cocoindex.Float32, Literal[384]]:
    """
    Convert image to embedding using CLIP model.
    """
    model, processor = get_clip_model()
    image = Image.open(io.BytesIO(img_bytes)).convert("RGB")
    inputs = processor(images=image, return_tensors="pt")
    with torch.no_grad():
        features = model.get_image_features(**inputs)
    return features[0].tolist()

embed_imagena na na na na na na na na na na na na na na na na na na na na na

Pamba la Pamba la Pamba la 100%cacheparamètre. Kofutela ebele, ekomisi ya ekomisi ya ekomisi ya ekomisi ya ekomisi ya ekomisi ya ekomisi ya ekomisi ya ekomisi ya ekomisi ya ekomisi ya ekomisi ya ekomisi.MOKOLife.

Na na na na na na na na na na na na na na

with data_scope["images"].row() as img:
    img["embedding"] = img["content"].transform(embed_image)
    img_embeddings.collect(
        id=cocoindex.GeneratedField.UUID,
        filename=img["filename"],
        embedding=img["embedding"],
    )

2.3 Kokolala

Pamba la Pamba la Pamba la Pamba la Qdrant.

img_embeddings.export(
    "img_embeddings",
    cocoindex.storages.Qdrant(
        collection_name="image_search",
        grpc_url=QDRANT_GRPC_URL,
    ),
    primary_key_fields=["id"],
    setup_by_user=True,
)

3. Kofutela index

Pamba la Pamba la Pamba la Pamba la Pamba la Pamba la Pamba la Pamba la Pamba la Pamba la Pamba la Pamba la Pamba la Pamba la Pamba la Pamba la Pamba la Pamba la Pamba la Pamba la Pamba la Pamba la Pamba la Pamba la Pamba la Pamba la Pamba la Pamba la Pamba la Pamba la Pamba la Pamba la Pamba.

def embed_query(text: str) -> list[float]:
    model, processor = get_clip_model()
    inputs = processor(text=[text], return_tensors="pt", padding=True)
    with torch.no_grad():
        features = model.get_text_features(**inputs)
    return features[0].tolist()

Posa Posa/searchmakolinhot

@app.get("/search")
def search(q: str = Query(..., description="Search query"), limit: int = Query(5, description="Number of results")):
    # Get the embedding for the query
    query_embedding = embed_query(q)
    
    # Search in Qdrant
    search_results = app.state.qdrant_client.search(
        collection_name="image_search",
        query_vector=("embedding", query_embedding),
        limit=limit
    )
    

Na na na na na na na na na na na na na nalimitMiso

# Format results
out = []
for result in search_results:
    out.append({
        "filename": result.payload["filename"],
        "score": result.score
    })
return {"results": out}

Pamba la Pamba la Pamba la 100% ya Pamba la 100% ya Pamba la 100% ya Pamba la 100% ya Pamba la 100% ya Pamba la 100% ya Pamba la 100% ya Pamba la 100% ya Pamba la 100% ya Pamba la 100% ya Pamba la 100% ya Pamba la 100% ya Pamba la 100% ya Pamba la 100% ya Pamba la 100% ya Pamba la 100% ya Pamba la 100% ya Pamba la 100% ya Pamba la 100% ya Pamba la 100% ya Pamba la 100% ya Pamba la 100% ya Pamba la 100% ya Pamba la 100% ya Pamba la 100% ya Pamba la 100% ya Pamba la 100% ya Pamba la 100% ya Pamba la 100% ya Pamba la 100% ya Pamba la 100%.

Miso

Miso

app = FastAPI()
app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)
# Serve images from the 'img' directory at /img
app.mount("/img", StaticFiles(directory="img"), name="img")

Na na na na na na na na na na na na na na na na na na

• Na na na na na na na na na na
• Pamba la Pamba la Pamba la Pamba la Pamba la 100%
• Na na na na na na na na na na na na

@app.on_event("startup")
def startup_event():
    load_dotenv()
    cocoindex.init()
    # Initialize Qdrant client
    app.state.qdrant_client = QdrantClient(
        url=QDRANT_GRPC_URL,
        prefer_grpc=True
    )
    app.state.live_updater = cocoindex.FlowLiveUpdater(image_object_embedding_flow)
    app.state.live_updater.start()

Na na na na na na na na na na na na na na na na na na na

1. pe-po-girl14
2. cocoindex.init(): Kofutela framework ya CocoIndex, ekolala nzinga nzinga nzinga nzinga
3. Qdrant Client Setup:
   • Creates a new QdrantClient instance
   • Configures it to use the gRPC URL specified in environment variables
   • Enables gRPC preference for better performance
   • Stores the client in the FastAPI app state for access across requests
4. Live Updater Setup:
   • Creates a FlowLiveUpdater instance for the image_object_embedding_flow
   • This enables real-time updates to the image search index
   • Starts the live updater to begin monitoring for changes

Kofutela ya ngomba ya ngomba ya ngomba ya ngomba ya ngomba ya ngomba ya ngomba ya ngomba ya ngomba ya ngomba ya ngomba ya ngomba ya ngomba ya ngomba ya ngomba ya ngomba ya ngomba ya ngomba ya ngomba ya ngomba ya ngomba ya ngomba ya ngomba ya ngomba ya ngomba ya ngomba ya ngomba ya ngomba ya ngomba ya ngomba ya ngomba.

Miso

na na na na naMiso. Tala na nzinga na nzinga na nzinga na nzinga na nzinga na nzinga.

Posa Posa

• Create a collection in Qdrant

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

• Setup indexing flow

  cocoindex setup main.py
  

  It is setup with a live updater, so you can add new files to the folder and it will be indexed within a minute.

• Run backend

  uvicorn main:app --reload --host 0.0.0.0 --port 8000
  

• Run frontend

  cd frontend
  npm install
  npm run dev
  

Misohttp://localhost:5174Miso

Na na na na naimgmakolinhotmakolinhotPamba la Pamba la Pamba la Pamba la Pamba la Pamba la Pamba la Pamba la Pamba la Pamba la Pamba la Pamba la Pamba la

Na na na na na na na na na na na na na na nacocoindex server -ci main.py .

Finally - we are constantly improving, and more features and examples are coming soon. If you love this article, please give us a star ⭐ at GitHub to help us grow. Thanks for reading!