added google api support

src/backend/base/langflow/components/agents/agent.py

@@ -1,4 +1,5 @@
 import json
+import os
 import re
 
 from langchain_core.tools import StructuredTool
@@ -525,9 +526,18 @@ async def update_build_config(
                 # Add provider-specific fields
                 build_config.update(fields_to_add)
 
-                # Apply provider-specific defaults (only for Azure OpenAI currently)
+                # Apply provider-specific defaults
                 if field_value == "Azure OpenAI":
                     build_config = apply_provider_defaults(field_value, build_config)
+                elif field_value == "Google Generative AI":
+                    # Prefill Google API Key from environment variable
+                    if "api_key" in build_config:
+                        build_config["api_key"]["value"] = os.getenv("GOOGLE_API_KEY", "")
+                    # Prefill Google Model from environment variable
+                    if "model_name" in build_config:
+                        google_model = os.getenv("GOOGLE_GENAI_MODEL", "")
+                        if google_model:
+                            build_config["model_name"]["value"] = google_model
 
                 # Reset input types for agent_llm
                 build_config["agent_llm"]["input_types"] = []
@@ -617,4 +627,4 @@ async def _get_tools(self) -> list[Tool]:
         )
         if hasattr(self, "tools_metadata"):
             tools = component_toolkit(component=self, metadata=self.tools_metadata).update_tools_metadata(tools=tools)
-        return tools
+        return tools

src/backend/base/langflow/components/azure/azure_openai_embeddings.py

@@ -1,3 +1,5 @@
+import os
+
 from langchain_openai import AzureOpenAIEmbeddings
 
 from langflow.base.models.model import LCModelComponent
@@ -35,11 +37,13 @@ class AzureOpenAIEmbeddingsComponent(LCModelComponent):
             display_name="Azure Endpoint",
             required=True,
             info="Your Azure endpoint, including the resource. Example: `https://example-resource.azure.openai.com/`",
+            value=os.getenv("AZURE_OPENAI_EMBEDDING_ENDPOINT", ""),
         ),
         MessageTextInput(
             name="azure_deployment",
             display_name="Deployment Name",
             required=True,
+            value=os.getenv("AZURE_OPENAI_EMBEDDING_DEPLOYMENT_NAME", ""),
         ),
         DropdownInput(
             name="api_version",
@@ -51,7 +55,10 @@ class AzureOpenAIEmbeddingsComponent(LCModelComponent):
         SecretStrInput(
             name="api_key",
             display_name="Azure OpenAI API Key",
-            required=True,
+            required=False,
+            show=True,
+            real_time_refresh=True,
+            value=os.getenv("AZURE_OPENAI_EMBEDDING_API_KEY", ""),
         ),
         IntInput(
             name="dimensions",
@@ -67,17 +74,22 @@ class AzureOpenAIEmbeddingsComponent(LCModelComponent):
     ]
 
     def build_embeddings(self) -> Embeddings:
+        # Add fallback to environment variables if fields are empty
+        azure_endpoint = self.azure_endpoint or os.getenv("AZURE_OPENAI_EMBEDDING_ENDPOINT")
+        azure_deployment = self.azure_deployment or os.getenv("AZURE_OPENAI_EMBEDDING_DEPLOYMENT_NAME")
+        api_key = self.api_key or os.getenv("AZURE_OPENAI_EMBEDDING_API_KEY")
+
         try:
             embeddings = AzureOpenAIEmbeddings(
                 model=self.model,
-                azure_endpoint=self.azure_endpoint,
-                azure_deployment=self.azure_deployment,
+                azure_endpoint=azure_endpoint,
+                azure_deployment=azure_deployment,
                 api_version=self.api_version,
-                api_key=self.api_key,
+                api_key=api_key,
                 dimensions=self.dimensions or None,
             )
         except Exception as e:
             msg = f"Could not connect to AzureOpenAIEmbeddings API: {e}"
             raise ValueError(msg) from e
 
-        return embeddings
+        return embeddings

src/backend/base/langflow/components/models/embedding_model.py

@@ -112,6 +112,6 @@ def update_build_config(self, build_config: dotdict, field_value: Any, field_nam
             build_config["model"]["options"] = OPENAI_EMBEDDING_MODEL_NAMES
             build_config["model"]["value"] = OPENAI_EMBEDDING_MODEL_NAMES[0]
             build_config["api_key"]["display_name"] = "OpenAI API Key"
-            build_config["api_key"]["value"] = os.getenv("OPENAI_API_KEY", "")
+            build_config["api_key"]["value"] = os.getenv("AZURE_OPENAI_API_KEY", "")
             build_config["api_base"]["display_name"] = "OpenAI API Base URL"
         return build_config

src/backend/base/langflow/components/models/language_model.py

@@ -128,7 +128,7 @@ def build_model(self) -> LanguageModel:
         if provider == "Azure OpenAI":
             api_key = self.api_key or os.getenv("AZURE_OPENAI_API_KEY")
             azure_endpoint = self.azure_endpoint or os.getenv("AZURE_OPENAI_ENDPOINT")
-            azure_deployment = self.azure_deployment or os.getenv("AZURE_OPENAI_DEPLOYMENT_NAME", "gpt-4o")
+            azure_deployment = self.azure_deployment or os.getenv("AZURE_DEPLOYMENT_NAME")
             api_version = self.api_version or os.getenv("AZURE_API_VERSION", "2024-06-01")
 
             if not api_key:
@@ -214,7 +214,7 @@ def update_build_config(self, build_config: dotdict, field_value: Any, field_nam
                 # Prefill Azure fields from environment variables
                 build_config["api_key"]["value"] = os.getenv("AZURE_OPENAI_API_KEY", "")
                 build_config["azure_endpoint"]["value"] = os.getenv("AZURE_OPENAI_ENDPOINT", "")
-                build_config["azure_deployment"]["value"] = os.getenv("AZURE_OPENAI_DEPLOYMENT_NAME", "gpt-4o")
+                build_config["azure_deployment"]["value"] = os.getenv("AZURE_OPENAI_DEPLOYMENT_NAME", "gpt-4.1")
                 build_config["api_version"]["value"] = os.getenv("AZURE_API_VERSION", "2024-06-01")
             elif field_value == "Anthropic":
                 build_config["model_name"]["options"] = ANTHROPIC_MODELS
@@ -223,7 +223,9 @@ def update_build_config(self, build_config: dotdict, field_value: Any, field_nam
                 build_config["api_key"]["value"] = os.getenv("ANTHROPIC_API_KEY", "")
             elif field_value == "Google":
                 build_config["model_name"]["options"] = GOOGLE_GENERATIVE_AI_MODELS
-                build_config["model_name"]["value"] = GOOGLE_GENERATIVE_AI_MODELS[0]
+                # Prefill Google Model from environment variable if available, otherwise use default
+                google_model = os.getenv("GOOGLE_GENAI_MODEL", "")
+                build_config["model_name"]["value"] = google_model if google_model else GOOGLE_GENERATIVE_AI_MODELS[0]
                 build_config["api_key"]["display_name"] = "Google API Key"
                 build_config["api_key"]["value"] = os.getenv("GOOGLE_API_KEY", "")
         elif field_name == "model_name" and field_value.startswith("o1") and self.provider == "OpenAI":
@@ -232,4 +234,4 @@ def update_build_config(self, build_config: dotdict, field_value: Any, field_nam
                 build_config["system_message"]["show"] = False
         elif field_name == "model_name" and not field_value.startswith("o1") and "system_message" in build_config:
             build_config["system_message"]["show"] = True
-        return build_config
+        return build_config

src/backend/base/langflow/components/qdrant/qdrant.py

@@ -1,7 +1,9 @@
 from langchain.embeddings.base import Embeddings
 from langchain_community.vectorstores import Qdrant
+import os
 
 from langflow.base.vectorstores.model import LCVectorStoreComponent, check_cached_vector_store
+from typing import Dict
 from langflow.helpers.data import docs_to_data
 from langflow.inputs.inputs import MessageTextInput
 from langflow.io import (
@@ -10,6 +12,7 @@
     IntInput,
     SecretStrInput,
     StrInput,
+    BoolInput
 )
 from langflow.schema.data import Data
 import uuid
@@ -26,7 +29,7 @@ class QdrantVectorStoreComponent(LCVectorStoreComponent):
         StrInput(name="host", display_name="Host", value="localhost", advanced=True),
         IntInput(name="port", display_name="Port", value=6333, advanced=True),
         IntInput(name="grpc_port", display_name="gRPC Port", value=6334, advanced=True),
-        SecretStrInput(name="api_key", display_name="Qdrant API Key", advanced=True),
+        SecretStrInput(name="api_key", display_name="Qdrant API Key", required=False),
         StrInput(name="prefix", display_name="Prefix", advanced=True),
         IntInput(name="timeout", display_name="Timeout", advanced=True),
         StrInput(name="path", display_name="Path", advanced=True),
@@ -66,11 +69,13 @@ def build_vector_store(self) -> Qdrant:
             "metadata_payload_key": self.metadata_payload_key,
         }
 
+        api_key = self.api_key or os.getenv("QUADRANT_API_KEY", "")
+
         server_kwargs = {
             "host": self.host or None,
             "port": int(self.port),  # Ensure port is an integer
             "grpc_port": int(self.grpc_port),  # Ensure grpc_port is an integer
-            "api_key": self.api_key,
+            "api_key": api_key,
             "prefix": self.prefix,
             # Ensure timeout is an integer
             "timeout": int(self.timeout) if self.timeout else None,

src/backend/base/langflow/custom/default_providers.py

@@ -34,7 +34,7 @@ def _get_azure_openai_defaults(build_config: dict) -> Dict[str, Any]:
         },
         "azure_deployment": {
             **build_config.get("azure_deployment", {}),
-            "value": os.environ.get("AZURE_DEPLOYMENT_NAME", "gpt-4o")
+            "value": os.environ.get("AZURE_OPENAI_DEPLOYMENT_NAME", "gpt-4.1")
         },
         "azure_endpoint": {
             **build_config.get("azure_endpoint", {}),