Merge remote-tracking branch 'origin/dev' into authentication

src/backend/Dockerfile

@@ -11,4 +11,4 @@ RUN rm *.whl
 
 EXPOSE 80
 
-CMD [ "uvicorn", "--host", "0.0.0.0", "--port", "80", "langflow.backend.app:app" ]
+CMD [ "uvicorn", "--host", "0.0.0.0", "--port", "7860", "--factory", "langflow.main:create_app" ]

src/backend/langflow/config.yaml

@@ -104,6 +104,8 @@ embeddings:
     documentation: "https://python.langchain.com/docs/modules/data_connection/text_embedding/integrations/sentence_transformers"
   CohereEmbeddings:
     documentation: "https://python.langchain.com/docs/modules/data_connection/text_embedding/integrations/cohere"
+  VertexAIEmbeddings:
+    documentation: "https://python.langchain.com/docs/modules/data_connection/text_embedding/integrations/google_vertex_ai_palm"
 llms:
   OpenAI:
     documentation: "https://python.langchain.com/docs/modules/model_io/models/llms/integrations/openai"
@@ -127,8 +129,8 @@ llms:
   # There's a bug in this component deactivating until we get it sorted: _language_models.py", line 804, in send_message
   #     is_blocked=safety_attributes.get("blocked", False),
   # AttributeError: 'list' object has no attribute 'get'
-  # ChatVertexAI:
-  #   documentation: "https://python.langchain.com/docs/modules/model_io/models/chat/integrations/google_vertex_ai_palm"
+  ChatVertexAI:
+    documentation: "https://python.langchain.com/docs/modules/model_io/models/chat/integrations/google_vertex_ai_palm"
   ###
 memories:
   # https://github.com/supabase-community/supabase-py/issues/482

src/backend/langflow/interface/initialize/loading.py

@@ -88,7 +88,7 @@ def instantiate_based_on_type(class_object, base_type, node_type, params):
     elif base_type == "toolkits":
         return instantiate_toolkit(node_type, class_object, params)
     elif base_type == "embeddings":
-        return instantiate_embedding(class_object, params)
+        return instantiate_embedding(node_type, class_object, params)
     elif base_type == "vectorstores":
         return instantiate_vectorstore(class_object, params)
     elif base_type == "documentloaders":
@@ -147,7 +147,7 @@ def instantiate_llm(node_type, class_object, params: Dict):
     # This is a workaround so JinaChat works until streaming is implemented
     # if "openai_api_base" in params and "jina" in params["openai_api_base"]:
     # False if condition is True
-    if node_type == "VertexAI":
+    if "VertexAI" in node_type:
         return initialize_vertexai(class_object=class_object, params=params)
     # max_tokens sometimes is a string and should be an int
     if "max_tokens" in params:
@@ -261,9 +261,13 @@ def instantiate_toolkit(node_type, class_object: Type[BaseToolkit], params: Dict
     return loaded_toolkit
 
 
-def instantiate_embedding(class_object, params: Dict):
+def instantiate_embedding(node_type, class_object, params: Dict):
     params.pop("model", None)
     params.pop("headers", None)
+
+    if "VertexAI" in node_type:
+        return initialize_vertexai(class_object=class_object, params=params)
+
     try:
         return class_object(**params)
     except ValidationError:

src/backend/langflow/template/frontend_node/embeddings.py

@@ -5,6 +5,47 @@ from langflow.template.frontend_node.base import FrontendNode
 
 
 class EmbeddingFrontendNode(FrontendNode):
+    def add_extra_fields(self) -> None:
+        if "VertexAI" in self.template.type_name:
+            # Add credentials field which should of type file.
+            self.template.add_field(
+                TemplateField(
+                    field_type="file",
+                    required=False,
+                    show=True,
+                    name="credentials",
+                    value="",
+                    suffixes=[".json"],
+                    file_types=["json"],
+                )
+            )
+
+    @staticmethod
+    def format_vertex_field(field: TemplateField, name: str):
+        if "VertexAI" in name:
+            advanced_fields = [
+                "verbose",
+                "top_p",
+                "top_k",
+                "max_output_tokens",
+            ]
+            if field.name in advanced_fields:
+                field.advanced = True
+            show_fields = [
+                "verbose",
+                "project",
+                "location",
+                "credentials",
+                "max_output_tokens",
+                "model_name",
+                "temperature",
+                "top_p",
+                "top_k",
+            ]
+
+            if field.name in show_fields:
+                field.show = True
+
     @staticmethod
     def format_jina_fields(field: TemplateField):
         if "jina" in field.name:
@@ -41,10 +82,36 @@ class EmbeddingFrontendNode(FrontendNode):
     @staticmethod
     def format_field(field: TemplateField, name: Optional[str] = None) -> None:
         FrontendNode.format_field(field, name)
+        if name and "vertex" in name.lower():
+            EmbeddingFrontendNode.format_vertex_field(field, name)
         field.advanced = not field.required
         field.show = True
         if field.name == "headers":
             field.show = False
+        if field.name == "model_kwargs":
+            field.field_type = "code"
+            field.advanced = True
+            field.show = True
+        elif field.name in [
+            "model_name",
+            "temperature",
+            "model_file",
+            "model_type",
+            "deployment_name",
+            "credentials",
+        ]:
+            field.advanced = False
+            field.show = True
+        if field.name == "credentials":
+            field.field_type = "file"
+        if name == "VertexAI" and field.name not in [
+            "callbacks",
+            "client",
+            "stop",
+            "tags",
+            "cache",
+        ]:
+            field.show = True
 
         # Format Jina fields
         EmbeddingFrontendNode.format_jina_fields(field)