MLflow 集成

使用 MLflow 快速开始监控你的智能体。

MLflow 概览

MLflow 是一个开源平台，帮助机器学习从业者和团队处理机器学习流程的复杂性。

它提供追踪功能，通过捕获应用程序服务执行的详细信息，增强生成式 AI 应用中的 LLM 可观测性。追踪提供了一种记录请求每个中间步骤相关输入、输出和元数据的方式，使你能够轻松定位错误和意外行为的来源。

特性

追踪仪表盘：使用包含 span 输入、输出和元数据的详细仪表盘监控你的 crewAI 智能体活动。
自动追踪：与 crewAI 的全自动集成，可通过运行 mlflow.crewai.autolog() 启用。
少量工作量即可手动进行追踪插桩：通过 MLflow 的高层流畅 API（如装饰器、函数包装器和上下文管理器）自定义追踪插桩。
OpenTelemetry 兼容性：MLflow 追踪支持将追踪导出到 OpenTelemetry Collector，然后可使用它将追踪导出到各种后端，例如 Jaeger、Zipkin 和 AWS X-Ray。
打包并部署智能体：将你的 crewAI 智能体打包并部署到具有多种部署目标的推理服务器。
安全托管 LLM：通过 MFflow 网关在一个统一端点中托管来自不同服务商的多个 LLM。
评估：使用便捷的 API mlflow.evaluate()，通过广泛的指标评估你的 crewAI 智能体。

设置说明

Install MLflow package

shell

# The crewAI integration is available in mlflow>=2.19.0
pip install mlflow

Start MFflow tracking server

shell

# This process is optional, but it is recommended to use MLflow tracking server for better visualization and broader features.
mlflow server

Initialize MLflow in Your Application

将以下两行添加到你的应用代码中：

python

import mlflow

mlflow.crewai.autolog()

# Optional: Set a tracking URI and an experiment name if you have a tracking server
mlflow.set_tracking_uri("http://localhost:5000")
mlflow.set_experiment("CrewAI")

追踪 CrewAI 智能体的示例用法：

python

from crewai import Agent, Crew, Task
from crewai.knowledge.source.string_knowledge_source import StringKnowledgeSource
from crewai_tools import SerperDevTool, WebsiteSearchTool

from textwrap import dedent

content = "Users name is John. He is 30 years old and lives in San Francisco."
string_source = StringKnowledgeSource(
    content=content, metadata={"preference": "personal"}
)

search_tool = WebsiteSearchTool()

class TripAgents:
    def city_selection_agent(self):
        return Agent(
            role="City Selection Expert",
            goal="Select the best city based on weather, season, and prices",
            backstory="An expert in analyzing travel data to pick ideal destinations",
            tools=[
                search_tool,
            ],
            verbose=True,
        )

    def local_expert(self):
        return Agent(
            role="Local Expert at this city",
            goal="Provide the BEST insights about the selected city",
            backstory="""A knowledgeable local guide with extensive information
        about the city, it's attractions and customs""",
            tools=[search_tool],
            verbose=True,
        )

class TripTasks:
    def identify_task(self, agent, origin, cities, interests, range):
        return Task(
            description=dedent(
                f"""
                Analyze and select the best city for the trip based
                on specific criteria such as weather patterns, seasonal
                events, and travel costs. This task involves comparing
                multiple cities, considering factors like current weather
                conditions, upcoming cultural or seasonal events, and
                overall travel expenses.
                Your final answer must be a detailed
                report on the chosen city, and everything you found out
                about it, including the actual flight costs, weather
                forecast and attractions.

                Traveling from: {origin}
                City Options: {cities}
                Trip Date: {range}
                Traveler Interests: {interests}
            """
            ),
            agent=agent,
            expected_output="Detailed report on the chosen city including flight costs, weather forecast, and attractions",
        )

    def gather_task(self, agent, origin, interests, range):
        return Task(
            description=dedent(
                f"""
                As a local expert on this city you must compile an
                in-depth guide for someone traveling there and wanting
                to have THE BEST trip ever!
                Gather information about key attractions, local customs,
                special events, and daily activity recommendations.
                Find the best spots to go to, the kind of place only a
                local would know.
                This guide should provide a thorough overview of what
                the city has to offer, including hidden gems, cultural
                hotspots, must-visit landmarks, weather forecasts, and
                high level costs.
                The final answer must be a comprehensive city guide,
                rich in cultural insights and practical tips,
                tailored to enhance the travel experience.

                Trip Date: {range}
                Traveling from: {origin}
                Traveler Interests: {interests}
            """
            ),
            agent=agent,
            expected_output="Comprehensive city guide including hidden gems, cultural hotspots, and practical travel tips",
        )

class TripCrew:
    def __init__(self, origin, cities, date_range, interests):
        self.cities = cities
        self.origin = origin
        self.interests = interests
        self.date_range = date_range

    def run(self):
        agents = TripAgents()
        tasks = TripTasks()

        city_selector_agent = agents.city_selection_agent()
        local_expert_agent = agents.local_expert()

        identify_task = tasks.identify_task(
            city_selector_agent,
            self.origin,
            self.cities,
            self.interests,
            self.date_range,
        )
        gather_task = tasks.gather_task(
            local_expert_agent, self.origin, self.interests, self.date_range
        )

        crew = Crew(
            agents=[city_selector_agent, local_expert_agent],
            tasks=[identify_task, gather_task],
            verbose=True,
            memory=True,
            knowledge={
                "sources": [string_source],
                "metadata": {"preference": "personal"},
            },
        )

        result = crew.kickoff()
        return result

trip_crew = TripCrew("California", "Tokyo", "Dec 12 - Dec 20", "sports")
result = trip_crew.run()

print(result)

有关更多配置和用例，请参阅 MLflow 追踪文档。

Visualize Activities of Agents

现在，你的 crewAI 智能体的追踪会被 MLflow 捕获。让我们访问 MLflow 追踪服务器以查看追踪并深入了解你的智能体。

在浏览器中打开 127.0.0.1:5000 以访问 MLflow 追踪服务器。

MLflow tracing example with crewai — MLflow Tracing Dashboard

MLflow 集成

MLflow 概览

特性

设置说明

友情链接