traceAI-vertexai 0.1.10

OpenTelemetry instrumentation for Vertex AI

VertexAI OpenTelemetry Integration

Overview

This integration provides support for using OpenTelemetry with the VertexAI framework. It enables tracing and monitoring of applications built with VertexAI.

Installation

1. Install traceAI VertexAI

pip install traceAI-vertexai

2. Install VertexAI SDK

pip install vertexai

Set Environment Variables

Set up your environment variables to authenticate with FutureAGI

import os

os.environ["FI_API_KEY"] = FI_API_KEY
os.environ["FI_SECRET_KEY"] = FI_SECRET_KEY
os.environ["GOOGLE_APPLICATION_CREDENTIALS"] = GOOGLE_APPLICATION_CREDENTIALS

Quickstart

Register Tracer Provider

Set up the trace provider to establish the observability pipeline. The trace provider:

from fi_instrumentation import register
from fi_instrumentation.fi_types import ProjectType

trace_provider = register(
    project_type=ProjectType.OBSERVE,
    project_name="vertexai_app"
)

Configure VertexAI Instrumentation

Instrument the VertexAI client to enable telemetry collection. This step ensures that all interactions with the VertexAI SDK are tracked and monitored.

from traceai_vertexai import VertexAIInstrumentor

VertexAIInstrumentor().instrument(tracer_provider=trace_provider)

Create VertexAI Components

Set up your VertexAI client with built-in observability.

import vertexai
from vertexai.generative_models import FunctionDeclaration, GenerativeModel, Part, Tool

vertexai.init(
    project="project_name",
)

# Describe a function by specifying its schema (JsonSchema format)
get_current_weather_func = FunctionDeclaration(
    name="get_current_weather",
    description="Get the current weather in a given location",
    parameters={
        "type": "object",
        "properties": {
            "location": {
                "type": "string",
                "description": "The city and state, e.g. San Francisco, CA",
            },
            "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]},
        },
        "required": ["location"],
    },
)

# Tool is a collection of related functions
weather_tool = Tool(function_declarations=[get_current_weather_func])

# Use tools in chat
chat = GenerativeModel("gemini-1.5-flash", tools=[weather_tool]).start_chat()

if __name__ == "__main__":
    for response in chat.send_message(
        "What is the weather like in Boston?", stream=True
    ):
        print(response)
    for response in chat.send_message(
        Part.from_function_response(
            name="get_current_weather",
            response={"content": {"weather": "super nice"}},
        ),
        stream=True,
    ):
        print(response)