fix: unified_search + ai_proactive get API key from DB, fix model names for Ollama Cloud

- query_understanding.py: get API key/base_url/provider_type from ai_providers DB
- embedding.py: get API key from DB, pass db+tenant_id through call chain
- routes.py: pass db+tenant_id to llm_analyze_query and llm_aggregate_results
- search_engine.py: pass db+tenant_id to generate_embedding
- unified_search/jobs.py: pass db+tenant_id to generate_embedding
- Fix all default model names: ollama/deepseek-v4 -> ollama/deepseek-v4-flash
- Ollama Cloud has no embedding endpoint; embedding calls fail gracefully

app/plugins/builtins/unified_search/embedding.py

@@ -5,7 +5,7 @@ from __future__ import annotations
 import os
 import logging
 import uuid
-from typing import TYPE_CHECKING
+from typing import Any, TYPE_CHECKING
 
 import litellm
 
@@ -17,15 +17,49 @@ logger = logging.getLogger(__name__)
 MAX_INPUT_CHARS = 8000
 
 DEFAULT_EMBEDDING_MODEL = os.environ.get('SEARCH_EMBEDDING_MODEL', 'ollama/nomic-embed-text')
-OLLAMA_API_KEY = os.environ.get('API_KEY_OLLAMA_CLOUD', '')
 
 
-async def generate_embedding(text: str, model: str | None = None) -> list[float]:
+async def _get_api_credentials(
+    db: "AsyncSession | None", tenant_id: "uuid.UUID | None"
+) -> tuple[str | None, str | None, str | None]:
+    """Get API key, base_url and provider_type from the default AI provider in DB.
+
+    Falls back to API_KEY_OLLAMA_CLOUD env var.
+    Returns (api_key, base_url, provider_type).
+    """
+    if db and tenant_id:
+        try:
+            from app.plugins.builtins.ai_assistant.services import get_default_provider
+            provider = await get_default_provider(db, tenant_id)
+            if provider and provider.api_key:
+                return provider.api_key, provider.base_url, provider.provider_type
+        except Exception:
+            logger.debug("Failed to get provider from DB, falling back to env")
+    env_key = os.environ.get('API_KEY_OLLAMA_CLOUD', '')
+    return (env_key if env_key else None), None, None
+
+
+def _build_model(model: str, provider_type: str | None) -> str:
+    """Build litellm model string with provider prefix."""
+    if provider_type:
+        model_parts = model.split("/", 1)
+        return f"{provider_type}/{model_parts[-1]}"
+    return model
+
+
+async def generate_embedding(
+    text: str,
+    model: str | None = None,
+    db: "AsyncSession | None" = None,
+    tenant_id: "uuid.UUID | None" = None,
+) -> list[float]:
     """Generate a single embedding via LiteLLM.
 
     Args:
         text: Input text (truncated to 8000 chars).
         model: Embedding model name (default: ollama/nomic-embed-text).
+        db: Optional DB session for API key lookup.
+        tenant_id: Optional tenant ID for API key lookup.
 
     Returns:
         Embedding vector as list of floats.
@@ -33,25 +67,38 @@ async def generate_embedding(text: str, model: str | None = None) -> list[float]
     model = model or DEFAULT_EMBEDDING_MODEL
     truncated = text[:MAX_INPUT_CHARS]
     try:
-        response = await litellm.aembedding(
-            model=model,
+        api_key, api_base, provider_type = await _get_api_credentials(db, tenant_id)
+        litellm_model = _build_model(model, provider_type)
+
+        litellm_kwargs: dict[str, Any] = dict(
+            model=litellm_model,
             input=truncated,
-            api_key=OLLAMA_API_KEY,
         )
+        if api_key:
+            litellm_kwargs["api_key"] = api_key
+        if api_base:
+            litellm_kwargs["api_base"] = api_base
+
+        response = await litellm.aembedding(**litellm_kwargs)
         return response.data[0]["embedding"]
     except Exception:
-        logger.exception("Failed to generate embedding")
+        logger.warning("Failed to generate embedding (Ollama Cloud may not support embeddings)", exc_info=True)
         return []
 
 
 async def generate_embeddings_batch(
-    texts: list[str], model: str | None = None
+    texts: list[str],
+    model: str | None = None,
+    db: "AsyncSession | None" = None,
+    tenant_id: "uuid.UUID | None" = None,
 ) -> list[list[float]]:
     """Generate embeddings for multiple texts in a single API call.
 
     Args:
         texts: List of input texts.
         model: Embedding model name (default: ollama/nomic-embed-text).
+        db: Optional DB session for API key lookup.
+        tenant_id: Optional tenant ID for API key lookup.
 
     Returns:
         List of embedding vectors.
@@ -59,14 +106,22 @@ async def generate_embeddings_batch(
     model = model or DEFAULT_EMBEDDING_MODEL
     truncated = [t[:MAX_INPUT_CHARS] for t in texts]
     try:
-        response = await litellm.aembedding(
-            model=model,
+        api_key, api_base, provider_type = await _get_api_credentials(db, tenant_id)
+        litellm_model = _build_model(model, provider_type)
+
+        litellm_kwargs: dict[str, Any] = dict(
+            model=litellm_model,
             input=truncated,
-            api_key=OLLAMA_API_KEY,
         )
+        if api_key:
+            litellm_kwargs["api_key"] = api_key
+        if api_base:
+            litellm_kwargs["api_base"] = api_base
+
+        response = await litellm.aembedding(**litellm_kwargs)
         return [d["embedding"] for d in response.data]
     except Exception:
-        logger.exception("Failed to generate batch embeddings")
+        logger.warning("Failed to generate batch embeddings", exc_info=True)
         return [[] for _ in texts]
 
 
@@ -74,7 +129,7 @@ async def index_entity(
     entity_type: str,
     entity_id: uuid.UUID,
     tenant_id: uuid.UUID,
-    db: AsyncSession,
+    db: "AsyncSession",
 ) -> bool:
     """Generate and store embedding for a single entity.
 
@@ -97,7 +152,7 @@ async def index_entity(
             logger.debug("Empty embedding text for %s/%s", entity_type, entity_id)
             return False
 
-        embedding = await generate_embedding(text)
+        embedding = await generate_embedding(text, db=db, tenant_id=tenant_id)
         if not embedding:
             return False