Enhance Image Search Example - Live Update

README.md

@@ -137,7 +137,7 @@ It defines an index flow like this:
 | [Embeddings to Qdrant](examples/text_embedding_qdrant) | Index documents in a Qdrant collection for semantic search |
 | [FastAPI Server with Docker](examples/fastapi_server_docker) | Run the semantic search server in a Dockerized FastAPI setup | 
 | [Product_Taxonomy_Knowledge_Graph](examples/product_taxonomy_knowledge_graph) | Build knowledge graph for product recommendations | 
-| [Image Search with Vision API](examples/image_search_example) | Generates detailed captions for images using a vision model, embeds them, enables semantic search via FastAPI and served on a React frontend.|
+| [Image Search with Vision API](examples/image_search_example) | Generates detailed captions for images using a vision model, embeds them, enables live-updating semantic search via FastAPI and served on a React frontend|
 
 More coming and stay tuned 👀!
 

examples/image_search_example/README.md

@@ -5,6 +5,7 @@
 - QDrant for Vector Storage
 - Ollama Gemma3 (Image to Text)
 - CLIP ViT-L/14 - Embeddings Model
+- Live Update
 
 ## Make sure Postgres and Qdrant are running
 ```
@@ -42,12 +43,12 @@ pip install -r requirements.txt
 ```
 
 ### Place your images in the `img` directory.
+- No need to update manually. CocoIndex will automatically update the index as new images are added to the directory.
 
 
 ## Run Backend
 ```
 python main.py cocoindex setup
-python main.py cocoindex update
 uvicorn main:app --reload --host 0.0.0.0 --port 8000
 ```
 
@@ -59,3 +60,4 @@ npm run dev
 ```
 
 Go to `http://localhost:5174` to search.
+

examples/image_search_example/main.py

@@ -1,5 +1,6 @@
 from dotenv import load_dotenv
 import cocoindex
+import datetime
 import os
 import requests
 import base64
@@ -14,7 +15,7 @@
 OLLAMA_MODEL = "gemma3"
 
 # 1. Extract caption from image using Ollama vision model
-@cocoindex.op.function()
+@cocoindex.op.function(cache=True, behavior_version=1)
 def get_image_caption(img_bytes: bytes) -> str:
     """
     Use Ollama's gemma3 model to extract a detailed caption from an image.
@@ -55,7 +56,8 @@ def caption_to_embedding(caption: cocoindex.DataSlice) -> cocoindex.DataSlice:
 @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)
+        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()
     with data_scope["images"].row() as img:
@@ -101,6 +103,8 @@ def startup_event():
         query_transform_flow=caption_to_embedding,
         default_similarity_metric=cocoindex.VectorSimilarityMetric.COSINE_SIMILARITY,
     )
+    app.state.live_updater = cocoindex.FlowLiveUpdater(image_object_embedding_flow)
+    app.state.live_updater.start()
 
 @app.get("/search")
 def search(q: str = Query(..., description="Search query"), limit: int = Query(5, description="Number of results")):