feat: Add Vertex AI support with ADC authentication

api/config.py

@@ -11,6 +11,7 @@
 from api.openrouter_client import OpenRouterClient
 from api.bedrock_client import BedrockClient
 from api.google_embedder_client import GoogleEmbedderClient
+from api.vertexai_embedder_client import VertexAIEmbedderClient
 from api.azureai_client import AzureAIClient
 from api.dashscope_client import DashscopeClient
 from adalflow import GoogleGenAIClient, OllamaClient
@@ -55,6 +56,7 @@
 CLIENT_CLASSES = {
     "GoogleGenAIClient": GoogleGenAIClient,
     "GoogleEmbedderClient": GoogleEmbedderClient,
+    "VertexAIEmbedderClient": VertexAIEmbedderClient,
     "OpenAIClient": OpenAIClient,
     "OpenRouterClient": OpenRouterClient,
     "OllamaClient": OllamaClient,
@@ -149,7 +151,7 @@ def load_embedder_config():
     embedder_config = load_json_config("embedder.json")
 
     # Process client classes
-    for key in ["embedder", "embedder_ollama", "embedder_google"]:
+    for key in ["embedder", "embedder_ollama", "embedder_google", "embedder_vertex"]:
         if key in embedder_config and "client_class" in embedder_config[key]:
             class_name = embedder_config[key]["client_class"]
             if class_name in CLIENT_CLASSES:
@@ -169,6 +171,8 @@ def get_embedder_config():
         return configs.get("embedder_google", {})
     elif embedder_type == 'ollama' and 'embedder_ollama' in configs:
         return configs.get("embedder_ollama", {})
+    elif embedder_type == 'vertex' and 'embedder_vertex' in configs:
+        return configs.get("embedder_vertex", {})
     else:
         return configs.get("embedder", {})
 
@@ -212,15 +216,37 @@ def is_google_embedder():
     client_class = embedder_config.get("client_class", "")
     return client_class == "GoogleEmbedderClient"
 
+def is_vertex_embedder():
+    """
+    Check if the current embedder configuration uses VertexAIEmbedderClient.
+
+    Returns:
+        bool: True if using VertexAIEmbedderClient, False otherwise
+    """
+    embedder_config = get_embedder_config()
+    if not embedder_config:
+        return False
+
+    # Check if model_client is VertexAIEmbedderClient
+    model_client = embedder_config.get("model_client")
+    if model_client:
+        return model_client.__name__ == "VertexAIEmbedderClient"
+
+    # Fallback: check client_class string
+    client_class = embedder_config.get("client_class", "")
+    return client_class == "VertexAIEmbedderClient"
+
 def get_embedder_type():
     """
     Get the current embedder type based on configuration.
-    
+
     Returns:
-        str: 'ollama', 'google', or 'openai' (default)
+        str: 'ollama', 'google', 'vertex', or 'openai' (default)
     """
     if is_ollama_embedder():
         return 'ollama'
+    elif is_vertex_embedder():
+        return 'vertex'
     elif is_google_embedder():
         return 'google'
     else:
@@ -316,7 +342,7 @@ def load_lang_config():
 
 # Update embedder configuration
 if embedder_config:
-    for key in ["embedder", "embedder_ollama", "embedder_google", "retriever", "text_splitter"]:
+    for key in ["embedder", "embedder_ollama", "embedder_google", "embedder_vertex", "retriever", "text_splitter"]:
         if key in embedder_config:
             configs[key] = embedder_config[key]
 

api/config/embedder.json

@@ -22,6 +22,19 @@
       "task_type": "SEMANTIC_SIMILARITY"
     }
   },
+  "embedder_vertex": {
+    "client_class": "VertexAIEmbedderClient",
+    "initialize_kwargs": {
+      "project_id": "${GOOGLE_CLOUD_PROJECT}",
+      "location": "${GOOGLE_CLOUD_LOCATION}"
+    },
+    "batch_size": 15,
+    "model_kwargs": {
+      "model": "text-embedding-005",
+      "task_type": "SEMANTIC_SIMILARITY",
+      "auto_truncate": true
+    }
+  },
   "retriever": {
     "top_k": 20
   },

api/pyproject.toml

@@ -13,6 +13,8 @@ fastapi = ">=0.95.0"
 uvicorn = { extras = ["standard"], version = ">=0.21.1" }
 pydantic = ">=2.0.0"
 google-generativeai = ">=0.3.0"
+google-cloud-aiplatform = ">=1.38.0"
+google-auth = ">=2.23.0"
 tiktoken = ">=0.5.0"
 adalflow = ">=0.1.0"
 numpy = ">=1.24.0"

api/tools/embedder.py

@@ -5,12 +5,12 @@
 
 def get_embedder(is_local_ollama: bool = False, use_google_embedder: bool = False, embedder_type: str = None) -> adal.Embedder:
     """Get embedder based on configuration or parameters.
-    
+
     Args:
         is_local_ollama: Legacy parameter for Ollama embedder
-        use_google_embedder: Legacy parameter for Google embedder  
-        embedder_type: Direct specification of embedder type ('ollama', 'google', 'openai')
-    
+        use_google_embedder: Legacy parameter for Google embedder
+        embedder_type: Direct specification of embedder type ('ollama', 'google', 'vertex', 'openai')
+
     Returns:
         adal.Embedder: Configured embedder instance
     """
@@ -20,6 +20,8 @@ def get_embedder(is_local_ollama: bool = False, use_google_embedder: bool = Fals
             embedder_config = configs["embedder_ollama"]
         elif embedder_type == 'google':
             embedder_config = configs["embedder_google"]
+        elif embedder_type == 'vertex':
+            embedder_config = configs["embedder_vertex"]
         else:  # default to openai
             embedder_config = configs["embedder"]
     elif is_local_ollama:
@@ -33,6 +35,8 @@ def get_embedder(is_local_ollama: bool = False, use_google_embedder: bool = Fals
             embedder_config = configs["embedder_ollama"]
         elif current_type == 'google':
             embedder_config = configs["embedder_google"]
+        elif current_type == 'vertex':
+            embedder_config = configs["embedder_vertex"]
         else:
             embedder_config = configs["embedder"]