feat: add optional envvar for checking Ollama model

Author: lemorage

examples/image_search/README.md

@@ -28,6 +28,7 @@ We appreciate a star ⭐ at [CocoIndex Github](https://github.com/cocoindex-io/c
 ```
 ollama pull gemma3
 ollama serve
+export OLLAMA_MODEL="gemma3"  # Optional, for caption generation
 ```
 
 ## Run the App

examples/image_search/main.py

@@ -6,7 +6,6 @@
 from typing import Any, Literal
 
 import cocoindex
-import requests
 import torch
 from dotenv import load_dotenv
 from fastapi import FastAPI, Query
@@ -24,15 +23,6 @@
 CLIP_MODEL_DIMENSION = 768
 
 
-def ollama_has_model(model) -> bool:
-    try:
-        r = requests.get(f"{OLLAMA_URL}/api/tags", timeout=1)
-        r.raise_for_status()
-        return any(m.get("name") == model for m in r.json().get("models", []))
-    except Exception:
-        return False
-
-
 @functools.cache
 def get_clip_model() -> tuple[CLIPModel, CLIPProcessor]:
     model = CLIPModel.from_pretrained(CLIP_MODEL_NAME)
@@ -81,8 +71,8 @@ def image_object_embedding_flow(
     )
     img_embeddings = data_scope.add_collector()
     with data_scope["images"].row() as img:
-        has_gemma3 = ollama_has_model(OLLAMA_MODEL)
-        if has_gemma3:
+        has_ollama_model = os.getenv("OLLAMA_MODEL") == OLLAMA_MODEL
+        if has_ollama_model:
             img["caption"] = flow_builder.transform(
                 cocoindex.functions.ExtractByLlm(
                     llm_spec=cocoindex.llm.LlmSpec(
@@ -115,7 +105,7 @@ def image_object_embedding_flow(
             "embedding": img["embedding"],
         }
 
-        if has_gemma3:
+        if has_ollama_model:
             print(
                 f"Ollama model '{OLLAMA_MODEL}' is available — captions will be extracted."
             )

examples/image_search/pyproject.toml

@@ -11,7 +11,6 @@ dependencies = [
     "transformers>=4.29.0",
     "qdrant-client>=1.14.2",
     "uvicorn>=0.34.3",
-    "requests>=2.32.4",
 ]
 
 [tool.setuptools]