Merge dev into main for testing

routes/model_routes.py

@@ -17,6 +17,7 @@ from fastapi import APIRouter, HTTPException, Form, Query, Body, Request, Respon
 from pydantic import BaseModel
 from fastapi.responses import StreamingResponse
 from core.database import SessionLocal, ModelEndpoint, Session as DbSession
+from core.log_safety import redact_url as _redact_url_for_log
 from core.middleware import require_admin
 from src.constants import COOKBOOK_STATE_FILE
 from src.llm_core import _detect_provider, _host_match, ANTHROPIC_MODELS
@@ -606,6 +607,10 @@ _NON_CHAT_EXACT_PREFIXES = (
 
 def _is_chat_model(model_id: str) -> bool:
     """Return True if the model ID looks like a chat/completions-capable model."""
+    if not isinstance(model_id, str):
+        # Non-compliant upstreams can return non-string IDs (e.g. int/None);
+        # treat them as chat-capable rather than crashing on .lower().
+        return True
     mid = model_id.lower()
     for prefix in _NON_CHAT_PREFIXES:
         if mid.startswith(prefix):
@@ -666,18 +671,6 @@ def _safe_build_headers(api_key: Optional[str], base_url: str) -> dict:
         return {"Authorization": f"Bearer {api_key}"} if api_key else {}
 
 
-def _redact_url_for_log(url: str) -> str:
-    """Return a URL safe for logs by removing userinfo and query/fragment."""
-    try:
-        parsed = urlparse(url or "")
-        host = parsed.hostname or ""
-        if parsed.port:
-            host = f"{host}:{parsed.port}"
-        return urlunparse((parsed.scheme, host, parsed.path, "", "", ""))
-    except Exception:
-        return "<endpoint>"
-
-
 def _is_discovery_only_provider(provider: str) -> bool:
     return provider == "chatgpt-subscription"
 
@@ -821,6 +814,41 @@ def _is_loading_model_response(resp: Any) -> bool:
 
 
 
+def _openai_model_ids(data: Any) -> List[str]:
+    """Extract OpenAI-style model IDs.
+
+    Accepts both standard ``{"data": [{"id": ...}]}`` responses and bare
+    ``[{"id": ...}]`` lists returned by some OpenAI-compatible providers.
+    Tolerates non-dict/non-list bodies and non-string IDs, returning only
+    non-empty string IDs.
+    """
+    if isinstance(data, list):
+        items = data
+    elif isinstance(data, dict):
+        items = data.get("data")
+    else:
+        items = None
+    return [m["id"] for m in (items or [])
+            if isinstance(m, dict) and isinstance(m.get("id"), str) and m["id"]]
+
+
+def _ollama_model_names(data: Any) -> List[str]:
+    """Extract native-Ollama model names (``{"models": [{"name"|"model": ...}]}``).
+
+    Same tolerance as :func:`_openai_model_ids`: a non-dict body or non-string
+    value is skipped rather than crashing, preserving name-then-model precedence.
+    """
+    items = data.get("models") if isinstance(data, dict) else None
+    out: List[str] = []
+    for m in (items or []):
+        if not isinstance(m, dict):
+            continue
+        v = m.get("name") or m.get("model")
+        if isinstance(v, str) and v:
+            out.append(v)
+    return out
+
+
 def _probe_endpoint(base_url: str, api_key: str = None, timeout: int = 5) -> List[str]:
     """Probe a base URL's /models endpoint and return list of model IDs.
     For Anthropic, queries their /v1/models API, falling back to hardcoded list."""
@@ -843,7 +871,7 @@ def _probe_endpoint(base_url: str, api_key: str = None, timeout: int = 5) -> Lis
             r = httpx.get(url, headers=headers, timeout=timeout, verify=llm_verify())
             r.raise_for_status()
             data = r.json()
-            models = [m.get("id") for m in (data.get("data") or []) if m.get("id")]
+            models = _openai_model_ids(data)
             if models:
                 return models
         except httpx.HTTPStatusError as e:
@@ -865,10 +893,10 @@ def _probe_endpoint(base_url: str, api_key: str = None, timeout: int = 5) -> Lis
         r.raise_for_status()
         data = r.json()
         # OpenAI format: {"data": [{"id": "model-name"}]}
-        models = [m.get("id") for m in (data.get("data") or []) if m.get("id")]
+        models = _openai_model_ids(data)
         # Ollama format: {"models": [{"name": "model-name"}]}
         if not models:
-            models = [m.get("name") or m.get("model") for m in (data.get("models") or []) if m.get("name") or m.get("model")]
+            models = _ollama_model_names(data)
         if models:
             # Z.AI coding plan omits some working models from /models;
             # append curated-only entries for that endpoint only.
@@ -894,9 +922,9 @@ def _probe_endpoint(base_url: str, api_key: str = None, timeout: int = 5) -> Lis
         logger.warning("Failed to probe %s: %s", _redact_url_for_log(url), e)
     except Exception as e:
         if api_key:
-            logger.warning(f"Failed to probe {url} with API key: {e}")
+            logger.warning("Failed to probe %s with API key: %s", _redact_url_for_log(url), e)
             return []
-        logger.warning(f"Failed to probe {url}: {e}")
+        logger.warning("Failed to probe %s: %s", _redact_url_for_log(url), e)
 
     # Older Ollama builds and some proxies expose native /api/tags even when
     # the OpenAI-compatible /v1/models path is unavailable.
@@ -907,7 +935,7 @@ def _probe_endpoint(base_url: str, api_key: str = None, timeout: int = 5) -> Lis
             r = httpx.get(root + "/api/tags", timeout=timeout, verify=llm_verify())
             r.raise_for_status()
             data = r.json()
-            models = [m.get("name") or m.get("model") for m in (data.get("models") or []) if m.get("name") or m.get("model")]
+            models = _ollama_model_names(data)
             if models:
                 return [m for m in models if _is_chat_model(m)]
     except Exception as e:
@@ -2169,6 +2197,16 @@ def setup_model_routes(model_discovery):
             ep_id = (_user_prefs.get("default_endpoint_id") or "").strip()
             model = (_user_prefs.get("default_model") or "").strip()
             _fallbacks = _user_prefs.get("default_model_fallbacks") or []
+            # If user has no personal default, fall back to global default
+            # But only based on the "share_defaults_with_users" flag
+            # (only if share_defaults_with_users is enabled)
+            if settings.get("share_defaults_with_users", False):
+                if not ep_id:
+                    ep_id = settings.get("default_endpoint_id", "")
+                if not model:
+                    model = settings.get("default_model", "")
+                if not _fallbacks:
+                    _fallbacks = settings.get("default_model_fallbacks") or []
         else:
             ep_id = settings.get("default_endpoint_id", "")
             model = settings.get("default_model", "")