feat: use OpenRouter for embeddings with text-embedding-3-small (768 dims)

- Ollama Cloud has no embedding endpoint, OpenRouter does
- text-embedding-3-small with dimensions=768 matches DB vector(768) column
- API_KEY_OPENROUTER env var is primary, DB provider is fallback
- Added dimensions parameter for text-embedding-3 models

app/plugins/builtins/unified_search/embedding.py

@@ -1,4 +1,4 @@
-"""Embedding pipeline using LiteLLM with configurable Ollama Cloud provider."""
+"""Embedding pipeline using LiteLLM with OpenRouter for embeddings."""
 
 from __future__ import annotations
 
@@ -16,17 +16,28 @@ logger = logging.getLogger(__name__)
 
 MAX_INPUT_CHARS = 8000
 
-DEFAULT_EMBEDDING_MODEL = os.environ.get('SEARCH_EMBEDDING_MODEL', 'ollama/nomic-embed-text')
+# OpenRouter for embeddings (Ollama Cloud has no embedding endpoint)
+OPENROUTER_API_KEY = os.environ.get('API_KEY_OPENROUTER', '')
+OPENROUTER_BASE_URL = 'https://openrouter.ai/api/v1'
+OPENROUTER_EMBEDDING_MODEL = os.environ.get('SEARCH_EMBEDDING_MODEL', 'openai/text-embedding-3-small')
+EMBEDDING_DIMENSIONS = 768  # Must match DB column vector(768)
 
 
 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.
+    """Get API key, base_url and provider_type for embeddings.
 
-    Falls back to API_KEY_OLLAMA_CLOUD env var.
+    Priority:
-    Returns (api_key, base_url, provider_type).
+    1. OpenRouter env var (API_KEY_OPENROUTER) – dedicated embedding provider
+    2. Default AI provider from DB (fallback)
+    3. API_KEY_OLLAMA_CLOUD env var (last resort)
     """
+    # OpenRouter is the primary embedding provider
+    if OPENROUTER_API_KEY:
+        return OPENROUTER_API_KEY, OPENROUTER_BASE_URL, 'openai'
+
+    # Fallback to DB provider
     if db and tenant_id:
         try:
             from app.plugins.builtins.ai_assistant.services import get_default_provider
@@ -55,16 +66,18 @@ async def generate_embedding(
 ) -> list[float]:
     """Generate a single embedding via LiteLLM.
 
+    Uses OpenRouter with text-embedding-3-small (768 dimensions).
+
     Args:
         text: Input text (truncated to 8000 chars).
-        model: Embedding model name (default: ollama/nomic-embed-text).
+        model: Embedding model name (default: openai/text-embedding-3-small).
         db: Optional DB session for API key lookup.
         tenant_id: Optional tenant ID for API key lookup.
 
     Returns:
         Embedding vector as list of floats.
     """
-    model = model or DEFAULT_EMBEDDING_MODEL
+    model = model or OPENROUTER_EMBEDDING_MODEL
     truncated = text[:MAX_INPUT_CHARS]
     try:
         api_key, api_base, provider_type = await _get_api_credentials(db, tenant_id)
@@ -79,10 +92,14 @@ async def generate_embedding(
         if api_base:
             litellm_kwargs["api_base"] = api_base
 
+        # Request 768 dimensions to match DB vector(768) column
+        if 'text-embedding-3' in litellm_model:
+            litellm_kwargs['dimensions'] = EMBEDDING_DIMENSIONS
+
         response = await litellm.aembedding(**litellm_kwargs)
         return response.data[0]["embedding"]
     except Exception:
-        logger.warning("Failed to generate embedding (Ollama Cloud may not support embeddings)", exc_info=True)
+        logger.warning("Failed to generate embedding", exc_info=True)
         return []
 
 
@@ -96,14 +113,14 @@ async def generate_embeddings_batch(
 
     Args:
         texts: List of input texts.
-        model: Embedding model name (default: ollama/nomic-embed-text).
+        model: Embedding model name (default: openai/text-embedding-3-small).
         db: Optional DB session for API key lookup.
         tenant_id: Optional tenant ID for API key lookup.
 
     Returns:
         List of embedding vectors.
     """
-    model = model or DEFAULT_EMBEDDING_MODEL
+    model = model or OPENROUTER_EMBEDDING_MODEL
     truncated = [t[:MAX_INPUT_CHARS] for t in texts]
     try:
         api_key, api_base, provider_type = await _get_api_credentials(db, tenant_id)
@@ -118,6 +135,9 @@ async def generate_embeddings_batch(
         if api_base:
             litellm_kwargs["api_base"] = api_base
 
+        if 'text-embedding-3' in litellm_model:
+            litellm_kwargs['dimensions'] = EMBEDDING_DIMENSIONS
+
         response = await litellm.aembedding(**litellm_kwargs)
         return [d["embedding"] for d in response.data]
     except Exception: