fix: use environment variables for VLM/LLM endpoints and models

- trace_vlm_caption.py: use MOMENTRY_LLM_VISION_URL and MOMENTRY_LLM_VISION_MODEL
- scene_vlm_caption.py: use MOMENTRY_LLM_VISION_URL and MOMENTRY_LLM_VISION_MODEL
- Changed from Ollama /api/generate to OpenAI-compatible /v1/chat/completions format
- Added embedding server environment variables

scripts/scene_vlm_caption.py

@@ -2,7 +2,7 @@
 """
 Scene VLM Caption - Generate VLM descriptions for scene key frames
 
-Analyzes scene key frames ({uuid}_scene_N.jpg) using VLM (llava:7b).
+Analyzes scene key frames ({uuid}_scene_N.jpg) using VLM.
 
 Usage:
     python scene_vlm_caption.py --file-uuid abc123 --output-dir /path/to/output
@@ -11,6 +11,10 @@ Usage:
 Output:
     {output_dir}/{uuid}_scene_profile.json with:
     - scenes: [{scene_number, vlm_description, vlm_location, ...}]
+
+Environment Variables:
+    MOMENTRY_LLM_VISION_URL - VLM endpoint (default: http://localhost:8091/v1/chat/completions)
+    MOMENTRY_LLM_VISION_MODEL - VLM model (default: llava-v1.6-vicuna-13b)
 """
 
 import argparse
@@ -20,6 +24,10 @@ import os
 import sys
 from pathlib import Path
 
+# VLM configuration from environment variables
+VLM_URL = os.environ.get("MOMENTRY_LLM_VISION_URL", "http://localhost:8091/v1/chat/completions")
+VLM_MODEL = os.environ.get("MOMENTRY_LLM_VISION_MODEL", "llava-v1.6-vicuna-13b")
+
 try:
     import requests
 except ImportError:
@@ -33,39 +41,44 @@ def encode_image(image_path: str) -> str:
         return base64.b64encode(f.read()).decode("utf-8")
 
 
-def call_vlm(image_path: str, prompt: str, model: str = "llava:7b", ollama_url: str = "http://localhost:11434") -> str:
+def call_vlm(image_path: str, prompt: str) -> str:
-    """Call Ollama VLM API."""
+    """Call VLM API using OpenAI-compatible format."""
     image_b64 = encode_image(image_path)
     
     payload = {
-        "model": model,
+        "model": VLM_MODEL,
-        "prompt": prompt,
+        "messages": [
-        "images": [image_b64],
+            {"role": "user", "content": [
-        "stream": False,
+                {"type": "text", "text": prompt},
-        "options": {"num_predict": 100}
+                {"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{image_b64}"}}
+            ]}
+        ],
+        "max_tokens": 100,
     }
     
     try:
-        resp = requests.post(f"{ollama_url}/api/generate", json=payload, timeout=30)
+        resp = requests.post(VLM_URL, json=payload, timeout=30)
         resp.raise_for_status()
         data = resp.json()
-        return data.get("response", "").strip()
+        return data.get("choices", [{}])[0].get("message", {}).get("content", "").strip()
     except Exception as e:
         print(f"[vlm] API error: {e}", file=sys.stderr)
         return ""
 
 
-def get_embedding(text: str, model: str = "nomic-embed-text-v2-moe", ollama_url: str = "http://localhost:11434") -> list:
+def get_embedding(text: str) -> list:
-    """Get embedding from Ollama."""
+    """Get embedding from embedding server."""
+    embed_url = os.environ.get("MOMENTRY_EMBEDDING_URL", "http://localhost:11436/v1/embeddings")
+    embed_model = os.environ.get("MOMENTRY_EMBEDDING_MODEL", "embeddinggemma-300m")
     try:
         resp = requests.post(
-            f"{ollama_url}/api/embed",
+            embed_url,
-            json={"model": model, "input": text},
+            json={"model": embed_model, "input": text},
             timeout=30,
         )
         resp.raise_for_status()
         data = resp.json()
-        return data.get("embeddings", [[]])[0]
+        return data.get("data", [{}])[0].get("embedding", [])
     except Exception as e:
         print(f"[vlm] Embedding error: {e}", file=sys.stderr)
         return []

scripts/trace_vlm_caption.py

@@ -2,7 +2,7 @@
 """
 Trace VLM Caption - Generate VLM descriptions for face traces
 
-Analyzes key_face.jpg or key_frame.jpg using VLM (llava:7b) and updates trace_profile.json.
+Analyzes key_face.jpg or key_frame.jpg using VLM and updates trace_profile.json.
 
 Usage:
     python trace_vlm_caption.py --trace-dir /path/to/output/{uuid}/trace_0
@@ -13,6 +13,10 @@ Output (13 fields):
     Environment: vlm_lighting, vlm_location, vlm_weather, vlm_setting, vlm_transportation
     Nature: vlm_has_plants, vlm_plants, vlm_has_animals, vlm_animals
     Context: vlm_background, vlm_bg_tags
+
+Environment Variables:
+    MOMENTRY_LLM_VISION_URL - VLM endpoint (default: http://localhost:8091/v1/chat/completions)
+    MOMENTRY_LLM_VISION_MODEL - VLM model (default: llava-v1.6-vicuna-13b)
 """
 
 import argparse
@@ -22,6 +26,10 @@ import os
 import sys
 from pathlib import Path
 
+# VLM configuration from environment variables
+VLM_URL = os.environ.get("MOMENTRY_LLM_VISION_URL", "http://localhost:8091/v1/chat/completions")
+VLM_MODEL = os.environ.get("MOMENTRY_LLM_VISION_MODEL", "llava-v1.6-vicuna-13b")
+
 try:
     import requests
 except ImportError:
@@ -35,39 +43,44 @@ def encode_image(image_path: str) -> str:
         return base64.b64encode(f.read()).decode("utf-8")
 
 
-def call_vlm(image_path: str, prompt: str, model: str = "llava:7b", ollama_url: str = "http://localhost:11434") -> str:
+def call_vlm(image_path: str, prompt: str) -> str:
-    """Call Ollama VLM API."""
+    """Call VLM API using OpenAI-compatible format."""
     image_b64 = encode_image(image_path)
     
     payload = {
-        "model": model,
+        "model": VLM_MODEL,
-        "prompt": prompt,
+        "messages": [
-        "images": [image_b64],
+            {"role": "user", "content": [
-        "stream": False,
+                {"type": "text", "text": prompt},
-        "options": {"num_predict": 100}
+                {"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{image_b64}"}}
+            ]}
+        ],
+        "max_tokens": 100,
     }
     
     try:
-        resp = requests.post(f"{ollama_url}/api/generate", json=payload, timeout=30)
+        resp = requests.post(VLM_URL, json=payload, timeout=30)
         resp.raise_for_status()
         data = resp.json()
-        return data.get("response", "").strip()
+        return data.get("choices", [{}])[0].get("message", {}).get("content", "").strip()
     except Exception as e:
         print(f"VLM API error: {e}", file=sys.stderr)
         return ""
 
 
-def get_embedding(text: str, model: str = "nomic-embed-text-v2-moe", ollama_url: str = "http://localhost:11434") -> list:
+def get_embedding(text: str) -> list:
-    """Get embedding from Ollama."""
+    """Get embedding from embedding server."""
+    embed_url = os.environ.get("MOMENTRY_EMBEDDING_URL", "http://localhost:11436/v1/embeddings")
+    embed_model = os.environ.get("MOMENTRY_EMBEDDING_MODEL", "embeddinggemma-300m")
     try:
         resp = requests.post(
-            f"{ollama_url}/api/embed",
+            embed_url,
-            json={"model": model, "input": text},
+            json={"model": embed_model, "input": text},
             timeout=30,
         )
         resp.raise_for_status()
         data = resp.json()
-        return data.get("embeddings", [[]])[0]
+        return data.get("data", [{}])[0].get("embedding", [])
     except Exception as e:
         print(f"[vlm] Embedding error: {e}", file=sys.stderr)
         return []