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1 day ago
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In this blog, we will build image search and query it with natural language. You can search for “a cute animal” or “a red car”, and the system returns visually relevant results — no manual tagging needed.
We are going to use multi-modal embedding model CLIP to understand and directly embed the image; and build a vector index for efficient retrieval. We are going use CocoIndex to build the indexing flow. It supports long running flow and only process changed files - we can keep adding new files to the folder and it will be indexed within a minute.
🚀 The entire project end to end is open sourced here.
Technologies
CLIP ViT-L/14
CLIP ViT-L/14 is a powerful vision-language model that can understand both images and texts. It's trained to align visual and textual representations in a shared embedding space, making it perfect for our image search use case.
In our project, we use CLIP to:
- Generate embeddings of the images directly
- Convert natural language search queries into the same embedding space
- Enable semantic search by comparing query embeddings with caption embeddings
Alternative: CLIP ViT-B/32 is a smaller model that is faster to run. While it may have slightly lower accuracy compared to ViT-L/14, it offers better performance and lower resource requirements, making it suitable for applications where speed and efficiency are priorities.
Qdrant
Qdrant is a high performance vector database. We use it to store and query the embeddings.
FastAPI
FastAPI is a modern, fast (high-performance), web framework for building APIs with Python 3.7+ based on standard Python type hints. We use it to build the web API for the image search.
Prerequisites
- Install Postgres. CocoIndex uses Postgres to keep track of data lineage for incremental processing.
- Install Qdrant.
Define Indexing Flow
Flow Design
The flow diagram illustrates how we'll process our codebase:
- Read image files from the local filesystem
- Use CLIP to understand and embed the image
- Store the embeddings in a vector database for retrieval
1. Ingest the images.
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)
)
img_embeddings = data_scope.add_collector()
flow_builder.add_source will create a table with sub fields (filename, content), we can refer to the documentation for more details.
2. Process each image and collect the information.
2.1 Embed the image with CLIP
def get_clip_model() -> tuple[CLIPModel, CLIPProcessor]:
model = CLIPModel.from_pretrained(CLIP_MODEL_NAME)
processor = CLIPProcessor.from_pretrained(CLIP_MODEL_NAME)
return model, processor
The @functools.cache decorator caches the results of a function call. In this case, it ensures that we only load the CLIP model and processor once.
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_image is a custom function that uses the CLIP model to convert an image into a vector embedding. It accepts image data in bytes format and returns a list of floating-point numbers representing the image's embedding.
The function supports caching through the cache parameter. When enabled, the executor will store the function's results for reuse during reprocessing, which is particularly useful for computationally intensive operations. For more information about custom function parameters, refer to the documentation.
Then we are going to process each image and collect the information.
img["embedding"] = img["content"].transform(embed_image)
img_embeddings.collect(
id=cocoindex.GeneratedField.UUID,
filename=img["filename"],
embedding=img["embedding"],
)
2.3 Collect the embeddings
Export the embeddings to a table in Qdrant.
"img_embeddings",
cocoindex.storages.Qdrant(
collection_name="image_search",
grpc_url=QDRANT_GRPC_URL,
),
primary_key_fields=["id"],
setup_by_user=True,
)
3. Query the index
Embed the query with CLIP, which maps both text and images into the same embedding space, allowing for cross-modal similarity search.
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()
Defines a FastAPI endpoint /search that performs semantic image search.
def search(q: str = Query(..., description="Search query"), limit: int = Query(5, description="Number of results")):
query_embedding = embed_query(q)
search_results = app.state.qdrant_client.search(
collection_name="image_search",
query_vector=("embedding", query_embedding),
limit=limit
)
This searches the Qdrant vector database for similar embeddings. Returns the top limit results
out = []
for result in search_results:
out.append({
"filename": result.payload["filename"],
"score": result.score
})
return {"results": out}
This endpoint enables semantic image search where users can find images by describing them in natural language, rather than using exact keyword matches.
Application
Fast API
app.add_middleware(
CORSMiddleware,
allow_origins=["*"],
allow_credentials=True,
allow_methods=["*"],
allow_headers=["*"],
)
app.mount("/img", StaticFiles(directory="img"), name="img")
FastAPI application setup with CORS middleware and static file serving The app is configured to:
- Allow cross-origin requests from any origin
- Serve static image files from the 'img' directory
- Handle API endpoints for image search functionality
def startup_event():
load_dotenv()
cocoindex.init()
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()
The startup event handler initializes the application when it first starts up. Here's what each part does:
-
load_dotenv(): Loads environment variables from a .env file, which is useful for configuration like API keys and URLs
-
cocoindex.init(): Initializes the CocoIndex framework, setting up necessary components and configurations
-
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
-
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
This initialization ensures that all necessary components are properly configured and running when the application starts.
Frontend
you can check the frontend code here. We intentionally kept it simple and minimalistic to focus on the image search functionality.
Time to have fun!
-
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
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
Go to http://localhost:5174 to search.
Now add another image in the img folder, for example, this cute squirrel, or any picture you like. Wait a minute for the new image to be processed and indexed.
If you want to monitor the indexing progress, you can view it in CocoInsight cocoindex server -ci main.py .
It's fascinating to see how modern open-source data stack has simplified building real-time image search systems. What once required a dedicated team of engineers and complex infrastructure can now be built with just a few key components: a powerful vision-language model, a vector database, and an indexing framework. This democratization of advanced search capabilities enables developers to create sophisticated image search applications in a fraction of the time and with significantly less complexity than before.
Acknowledgement
This image search project is largely contributed by @par4m - Param Arora ❤️. He is currently a open source developer at Google Summer of Code.
Support us
We are constantly improving, and more features and examples are coming soon. If this end to end project is helpful, we would appreciate a star ⭐ at GitHub to help us grow.
Thanks for reading!
