【GPT入门】第71课 autogen介绍与代码实现股票分析汇报多智能体
- [1. autogen介绍](#1. autogen介绍)
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- [1.1 autogen介绍](#1.1 autogen介绍)
- [1.2 特点](#1.2 特点)
- [2. 安装](#2. 安装)
- [3. 股票分析的多agent代码实践](#3. 股票分析的多agent代码实践)
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- [3.1 agent设计思路](#3.1 agent设计思路)
- [3.2 代码实现](#3.2 代码实现)
- [4. autogen studio](#4. autogen studio)
AutoGen是微软公司发布的一个开源编程框架，旨在通过多智能体协作来构建基于大语言模型的下一代应用程序。以下是其详细介绍和特点：
1. autogen介绍
1.1 autogen介绍
AutoGen的核心在于支持多个Agent之间的互动合作，以解决复杂的任务。每个Agent都可以被定制，以扮演不同的角色，例如程序员、公司高管或设计师等。通过这种方式，AutoGen使得大模型能够模拟人类间的对话和协作，从而高效地处理工作流。
除了AutoGen框架本身，微软还推出了AutoGen Studio，这是一个更为直观的工具，它为用户提供了一个可视化界面，用以定义、修改智能体参数，与智能体交互，添加技能，并管理会话。AutoGen的应用场景非常广泛，包括自动化文档生成、多智能体协作的客户服务、数据分析和报告，以及个性化学习等。
AUTOGEN 架构
1.2 特点
- 可定制的智能体：开发者可以定义不同角色和能力的智能体，如AssistantAgent、UserProxyAgent、TeachableAgent等，每个智能体都能根据需求被赋予特定的功能和行为模式。
- 多智能体协作：支持多种交互模式，如双人对话、顺序聊天、群聊等，群聊由管理者协调，能够模拟人类团队的协作方式，将复杂任务分解为多个子任务，由不同智能体分别处理，提高任务处理效率。
- 工具集成：智能体可以调用外部工具，如代码执行、API调用、数据库查询等，从而扩展了智能体的功能，使其能够更好地处理各种实际任务。
- 人类参与：支持"人在回路"，允许人类提供反馈或干预，使得系统在运行过程中能够结合人类的智慧和判断，提高结果的准确性和可靠性。
- 异步与可扩展架构：最新版本采用异步、事件驱动架构，支持分布式系统和跨语言开发，如Python和.NET，这使得AutoGen能够适应不同的开发环境和需求，具有良好的扩展性和性能表现。
- 低代码接口：通过AutoGen Studio提供无代码或低代码界面，降低了开发门槛，即使是非专业的开发者也能够轻松上手，快速构建基于智能体的应用程序。
2. 安装
参考官网：https://github.com/microsoft/autogen
官网建议python版本3.10及以上， 这里使用3.11，参考前面的文章安装conda环境
# Install AgentChat and OpenAI client from Extensions
pip install -U "autogen-agentchat" "autogen-ext[openai]"
# Install AutoGen Studio for no-code GUI
pip install -U "autogenstudio"3. 股票分析的多agent代码实践
3.1 agent设计思路
设计思路： 设计一个team: team由3个agent组成，分别是 股票分析agent、google搜索agent、股票分析汇报agent，执行顺序是依次。
3.2 代码实现
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导包
from autogen_agentchat.agents import AssistantAgent
from autogen_agentchat.conditions import TextMentionTermination
from autogen_agentchat.teams import RoundRobinGroupChat
from autogen_agentchat.ui import Console
from autogen_core.tools import FunctionTool
from autogen_ext.models.openai import OpenAIChatCompletionClient
import os
os.environ["GOOGLE_API_KEY"] = "AIzaSyChLbcLjXwSSssuzXzjchqA5E_t4aEDWp4"
os.environ["GOOGLE_SEARCH_ENGINE_ID"] = "961e69d5fd62145b2"-
编写function
#!pip install yfinance matplotlib pytz numpy pandas python-dotenv requests bs4
def google_search(query: str, num_results: int = 2, max_chars: int = 500) -> list: # type: ignore[type-arg]
import os
import timeimport requests from bs4 import BeautifulSoup from dotenv import load_dotenv load_dotenv() api_key = os.getenv("GOOGLE_API_KEY") search_engine_id = os.getenv("GOOGLE_SEARCH_ENGINE_ID") if not api_key or not search_engine_id: raise ValueError("API key or Search Engine ID not found in environment variables") url = "https://customsearch.googleapis.com/customsearch/v1" params = {"key": str(api_key), "cx": str(search_engine_id), "q": str(query), "num": str(num_results)} response = requests.get(url, params=params) if response.status_code != 200: print(response.json()) raise Exception(f"Error in API request: {response.status_code}") results = response.json().get("items", []) def get_page_content(url: str) -> str: try: response = requests.get(url, timeout=10) soup = BeautifulSoup(response.content, "html.parser") text = soup.get_text(separator=" ", strip=True) words = text.split() content = "" for word in words: if len(content) + len(word) + 1 > max_chars: break content += " " + word return content.strip() except Exception as e: print(f"Error fetching {url}: {str(e)}") return "" enriched_results = [] for item in results: body = get_page_content(item["link"]) enriched_results.append( {"title": item["title"], "link": item["link"], "snippet": item["snippet"], "body": body} ) time.sleep(1) # Be respectful to the servers return enriched_resultsdef analyze_stock(ticker: str) -> dict: # type: ignore[type-arg]
import os
from datetime import datetime, timedeltaimport matplotlib.pyplot as plt import numpy as np import pandas as pd import yfinance as yf from pytz import timezone # type: ignore stock = yf.Ticker(ticker) # Get historical data (1 year of data to ensure we have enough for 200-day MA) end_date = datetime.now(timezone("UTC")) start_date = end_date - timedelta(days=365) hist = stock.history(start=start_date, end=end_date) # Ensure we have data if hist.empty: return {"error": "No historical data available for the specified ticker."} # Compute basic statistics and additional metrics current_price = stock.info.get("currentPrice", hist["Close"].iloc[-1]) year_high = stock.info.get("fiftyTwoWeekHigh", hist["High"].max()) year_low = stock.info.get("fiftyTwoWeekLow", hist["Low"].min()) # Calculate 50-day and 200-day moving averages ma_50 = hist["Close"].rolling(window=50).mean().iloc[-1] ma_200 = hist["Close"].rolling(window=200).mean().iloc[-1] # Calculate YTD price change and percent change ytd_start = datetime(end_date.year, 1, 1, tzinfo=timezone("UTC")) ytd_data = hist.loc[ytd_start:] # type: ignore[misc] if not ytd_data.empty: price_change = ytd_data["Close"].iloc[-1] - ytd_data["Close"].iloc[0] percent_change = (price_change / ytd_data["Close"].iloc[0]) * 100 else: price_change = percent_change = np.nan # Determine trend if pd.notna(ma_50) and pd.notna(ma_200): if ma_50 > ma_200: trend = "Upward" elif ma_50 < ma_200: trend = "Downward" else: trend = "Neutral" else: trend = "Insufficient data for trend analysis" # Calculate volatility (standard deviation of daily returns) daily_returns = hist["Close"].pct_change().dropna() volatility = daily_returns.std() * np.sqrt(252) # Annualized volatility # Create result dictionary result = { "ticker": ticker, "current_price": current_price, "52_week_high": year_high, "52_week_low": year_low, "50_day_ma": ma_50, "200_day_ma": ma_200, "ytd_price_change": price_change, "ytd_percent_change": percent_change, "trend": trend, "volatility": volatility, } # Convert numpy types to Python native types for better JSON serialization for key, value in result.items(): if isinstance(value, np.generic): result[key] = value.item() # Generate plot plt.figure(figsize=(12, 6)) plt.plot(hist.index, hist["Close"], label="Close Price") plt.plot(hist.index, hist["Close"].rolling(window=50).mean(), label="50-day MA") plt.plot(hist.index, hist["Close"].rolling(window=200).mean(), label="200-day MA") plt.title(f"{ticker} Stock Price (Past Year)") plt.xlabel("Date") plt.ylabel("Price ($)") plt.legend() plt.grid(True) # Save plot to file os.makedirs("coding", exist_ok=True) plot_file_path = f"coding/{ticker}_stockprice.png" plt.savefig(plot_file_path) print(f"Plot saved as {plot_file_path}") result["plot_file_path"] = plot_file_path return result -
定义function tool
google_search_tool = FunctionTool(
google_search, description="Search Google for information, returns results with a snippet and body content"
)
stock_analysis_tool = FunctionTool(analyze_stock, description="Analyze stock data and generate a plot") -
定义client
from autogen_core.models import ModelFamily
model_client = OpenAIChatCompletionClient(model="deepseek-chat",
base_url="https://api.deepseek.com/v1",
api_key="sk-改为自己的key",
model_info={
"vision": False,
"function_calling": True,
"json_output": False,
"family": ModelFamily.R1,
},
) -
组建team
model_client = OpenAIChatCompletionClient(model="gpt-4o")
search_agent = AssistantAgent(
name="Google_Search_Agent",
model_client=model_client,
tools=[google_search_tool],
description="Search Google for information, returns top 2 results with a snippet and body content",
system_message="You are a helpful AI assistant. Solve tasks using your tools.",
)stock_analysis_agent = AssistantAgent(
name="Stock_Analysis_Agent",
model_client=model_client,
tools=[stock_analysis_tool],
description="Analyze stock data and generate a plot",
system_message="Perform data analysis.",
)report_agent = AssistantAgent(
name="Report_Agent",
model_client=model_client,
description="Generate a report based the search and results of stock analysis",
system_message="You are a helpful assistant that can generate a comprehensive report on a given topic based on search and stock analysis. When you done with generating the report, reply with TERMINATE.",
)
team = RoundRobinGroupChat([stock_analysis_agent, search_agent, report_agent], max_turns=3) -
启动agent
stream = team.run_stream(task="Write a financial report on American airlines")
await Console(stream)await model_client.close()
4. autogen studio
可以使用可视化页面做上面代码的功能