fix: vertexai authentication via service account

[nicoloboschi]

currently with vertexai embeddings and llm you can upload the json file but the auth is not well initialized and this makes those components unusable without setting the global environment variable

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[ogabrielluiz]

LGTM

[rodgermoore]

Great was waiting for this one!

🥇

Edit:
Hmm I was hoping to use the Anthropic Claude 3.5 model via VertexAI but I can't get it to work. Did you @nicoloboschi or @ogabrielluiz manage to use the Anthropic models via VertexAI? Ref: https://console.cloud.google.com/vertex-ai/publishers/anthropic/model-garden/claude-3-5-sonnet

[rodgermoore]

@ogabrielluiz @nicoloboschi I created a custom component that enables Anthropic models on Vertex AI. Maybe you can add it to the regular VertexAI component or make a new Model? Here is the code:

import os
from langflow.custom import Component
from langflow.inputs import MessageTextInput
from langflow.inputs import StrInput, IntInput, FileInput, FloatInput
from langflow.template import Output
from langflow.schema.message import Message
from anthropic import AnthropicVertex
from google.oauth2 import service_account
from google.auth.transport.requests import Request

class AnthropicVertexComponent(Component):
    display_name = "Anthropic Vertex Component"
    description = "A component that interacts with the Anthropic Vertex API using Google Cloud authentication."
    icon = "VertexAI"
    name = "AnthropicVertexComponent"

    inputs = [
        MessageTextInput(
            name="message",
            display_name="Message",
            info="The message to send to the model.",
            value="Describe the number 42 in a few words."
        ),
        FileInput(
            name="service_account_key",
            display_name="Service Account Key File",
            info="JSON service account credentials file.",
            file_types=["json"],
        ),
        StrInput(
            name="model",
            display_name="Model",
            info="The Anthropic model to use.",
            value="claude-3-5-sonnet@20240620"
        ),        
        StrInput(
            name="region",
            display_name="Region",
            info="The Google Cloud region to use.",
            value="europe-west1"
        ),
        StrInput(
            name="project_id",
            display_name="Project ID",
            info="Your Google Cloud Project ID.",
            value="Project ID is mandatory"
        ),
        StrInput(
            name="system_message",
            display_name="System message",
            info="The system message to send to the model.",
            value="You are a helpful AI assistant.",
        ),
        IntInput(
            name="max_tokens",
            display_name="Max Tokens",
            info="The maximum number of tokens to generate.",
            value=256,
        ),
        FloatInput(
            name="temperature", 
            value=0.1, 
            display_name="Temperature",
            info="Controls randomness in the output. Lower values make the output more deterministic."
        ),
        FloatInput(
            name="top_p", 
            display_name="Top P", 
            value=0.95, 
            advanced=True,
            info="Controls diversity of the output. Lower values make the output more focused."
        ),
    ]

    outputs = [
        Output(display_name="Response", name="response", method="get_response"),
    ]

    def get_response(self) -> Message:
        try:
            # Load the service account key with the required scope
            credentials = service_account.Credentials.from_service_account_file(
                self.service_account_key,
                scopes=['https://www.googleapis.com/auth/cloud-platform']
            )

            # Ensure the credentials are valid and refreshed
            if credentials.expired:
                credentials.refresh(Request())

            # Initialize the AnthropicVertex client
            client = AnthropicVertex(
                project_id=self.project_id,
                region=self.region,
                credentials=credentials
            )

            # Create the message
            message = client.messages.create(
                model=self.model,
                max_tokens=self.max_tokens,
                temperature=self.temperature,
                top_p=self.top_p,
                system=self.system_message,
                messages=[
                    {
                        "role": "user",
                        "content": self.message,
                    }
                ],
            )
            
            # Extract the text content from the message object
            response_text = message.content[0].text if message.content else "No response generated"
            # response_text = message.model_dump_json(indent=2)
            self.status = "Message processed successfully"
            return Message(text=response_text)
        
        except Exception as e:
            error_message = f"Error processing message: {str(e)}"
            self.status = error_message
            return Message(text=error_message)

Langflow should support all imports, I didn't need to install requirements.