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これはインタラクティブなノートブックです。ローカルで実行するか、以下のリンクを使用してください：
マルチエージェントシステムのための構造化出力
OpenAIはリリースしましたStructured Outputsを使用することで、強い言葉遣いのプロンプトなしでも、モデルが常にユーザーが提供したJSONスキーマに準拠した応答を生成することを保証できます。Structured Outputsを使用すれば、不正な形式の応答を検証したり再試行したりする必要はありません。 新しいパラメータstrict: trueを使用することで、応答が提供されたスキーマに従うことを保証できます。
マルチエージェントシステムにおける構造化出力の使用は、エージェント間の一貫性のある、簡単に処理できるデータを確保することでコミュニケーションを強化します。また、明示的な拒否を可能にすることで安全性を向上させ、再試行や検証の必要性を排除することでパフォーマンスを向上させます。これにより、相互作用が簡素化され、システム全体の効率が向上します。
このチュートリアルでは、マルチエージェントシステムで構造化出力を活用し、それらをWeaveでトレースする方法を示します。
ソース：このクックブックはOpenAIの構造化出力のサンプルコードに基づいており、Weaveを使用した視覚化を改善するためにいくつかの修正が加えられています。
依存関係のインストール
このチュートリアルには以下のライブラリが必要です：!pip install -qU openai weave wandb
python
%%capture
# Temporary workaround to fix bug in openai:
# TypeError: Client.__init__() got an unexpected keyword argument 'proxies'
# See https://community.openai.com/t/error-with-openai-1-56-0-client-init-got-an-unexpected-keyword-argument-proxies/1040332/15
!pip install "httpx<0.28"
WANDB_API_KEYを設定して、wandb.login()で簡単にログインできるようにします（これはcolabにシークレットとして与えられるべきです）。
ログを記録したいW&Bのプロジェクトをname_of_wandb_projectに設定します。
注意:name_of_wandb_projectは{team_name}/{project_name}の形式でも指定でき、トレースをログに記録するチームを指定できます。
その後、weave.init()を呼び出してweaveクライアントを取得します
私たちはOpenAI APIを使用するので、OpenAI APIキーも必要になります。サインアップしてOpenAIプラットフォームで独自のAPIキーを取得できます。（これもcolabにシークレットとして与えられるべきです。）
import base64
import json
import os
from io import BytesIO, StringIO
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import wandb
from google.colab import userdata
from openai import OpenAI
import weave
python
os.environ["WANDB_API_KEY"] = userdata.get("WANDB_API_KEY")
os.environ["OPENAI_API_KEY"] = userdata.get("OPENAI_API_KEY")
wandb.login()
name_of_wandb_project = "multi-agent-structured-output"
weave.init(name_of_wandb_project)
client = OpenAI()
MODEL = "gpt-4o-2024-08-06"
エージェントのセットアップ
取り組むユースケースはデータ分析タスクです。 まず、4つのエージェントシステムをセットアップしましょう：- トリアージエージェント：どのエージェントを呼び出すかを決定します
- データ前処理エージェント：データをクリーニングするなど、分析のためにデータを準備します
- データ分析エージェント：データの分析を実行します
- データ可視化エージェント：分析の出力を視覚化して洞察を抽出します まず、これらの各エージェントのシステムプロンプトを定義します。
triaging_system_prompt = """You are a Triaging Agent. Your role is to assess the user's query and route it to the relevant agents. The agents available are:
- Data Processing Agent: Cleans, transforms, and aggregates data.
- Analysis Agent: Performs statistical, correlation, and regression analysis.
- Visualization Agent: Creates bar charts, line charts, and pie charts.
Use the send_query_to_agents tool to forward the user's query to the relevant agents. Also, use the speak_to_user tool to get more information from the user if needed."""
processing_system_prompt = """You are a Data Processing Agent. Your role is to clean, transform, and aggregate data using the following tools:
- clean_data
- transform_data
- aggregate_data"""
analysis_system_prompt = """You are an Analysis Agent. Your role is to perform statistical, correlation, and regression analysis using the following tools:
- stat_analysis
- correlation_analysis
- regression_analysis"""
visualization_system_prompt = """You are a Visualization Agent. Your role is to create bar charts, line charts, and pie charts using the following tools:
- create_bar_chart
- create_line_chart
- create_pie_chart"""
triage_tools = [
{
"type": "function",
"function": {
"name": "send_query_to_agents",
"description": "Sends the user query to relevant agents based on their capabilities.",
"parameters": {
"type": "object",
"properties": {
"agents": {
"type": "array",
"items": {"type": "string"},
"description": "An array of agent names to send the query to.",
},
"query": {
"type": "string",
"description": "The user query to send.",
},
},
"required": ["agents", "query"],
},
},
"strict": True,
}
]
preprocess_tools = [
{
"type": "function",
"function": {
"name": "clean_data",
"description": "Cleans the provided data by removing duplicates and handling missing values.",
"parameters": {
"type": "object",
"properties": {
"data": {
"type": "string",
"description": "The dataset to clean. Should be in a suitable format such as JSON or CSV.",
}
},
"required": ["data"],
"additionalProperties": False,
},
},
"strict": True,
},
{
"type": "function",
"function": {
"name": "transform_data",
"description": "Transforms data based on specified rules.",
"parameters": {
"type": "object",
"properties": {
"data": {
"type": "string",
"description": "The data to transform. Should be in a suitable format such as JSON or CSV.",
},
"rules": {
"type": "string",
"description": "Transformation rules to apply, specified in a structured format.",
},
},
"required": ["data", "rules"],
"additionalProperties": False,
},
},
"strict": True,
},
{
"type": "function",
"function": {
"name": "aggregate_data",
"description": "Aggregates data by specified columns and operations.",
"parameters": {
"type": "object",
"properties": {
"data": {
"type": "string",
"description": "The data to aggregate. Should be in a suitable format such as JSON or CSV.",
},
"group_by": {
"type": "array",
"items": {"type": "string"},
"description": "Columns to group by.",
},
"operations": {
"type": "string",
"description": "Aggregation operations to perform, specified in a structured format.",
},
},
"required": ["data", "group_by", "operations"],
"additionalProperties": False,
},
},
"strict": True,
},
]
analysis_tools = [
{
"type": "function",
"function": {
"name": "stat_analysis",
"description": "Performs statistical analysis on the given dataset.",
"parameters": {
"type": "object",
"properties": {
"data": {
"type": "string",
"description": "The dataset to analyze. Should be in a suitable format such as JSON or CSV.",
}
},
"required": ["data"],
"additionalProperties": False,
},
},
"strict": True,
},
{
"type": "function",
"function": {
"name": "correlation_analysis",
"description": "Calculates correlation coefficients between variables in the dataset.",
"parameters": {
"type": "object",
"properties": {
"data": {
"type": "string",
"description": "The dataset to analyze. Should be in a suitable format such as JSON or CSV.",
},
"variables": {
"type": "array",
"items": {"type": "string"},
"description": "List of variables to calculate correlations for.",
},
},
"required": ["data", "variables"],
"additionalProperties": False,
},
},
"strict": True,
},
{
"type": "function",
"function": {
"name": "regression_analysis",
"description": "Performs regression analysis on the dataset.",
"parameters": {
"type": "object",
"properties": {
"data": {
"type": "string",
"description": "The dataset to analyze. Should be in a suitable format such as JSON or CSV.",
},
"dependent_var": {
"type": "string",
"description": "The dependent variable for regression.",
},
"independent_vars": {
"type": "array",
"items": {"type": "string"},
"description": "List of independent variables.",
},
},
"required": ["data", "dependent_var", "independent_vars"],
"additionalProperties": False,
},
},
"strict": True,
},
]
visualization_tools = [
{
"type": "function",
"function": {
"name": "create_bar_chart",
"description": "Creates a bar chart from the provided data.",
"parameters": {
"type": "object",
"properties": {
"data": {
"type": "string",
"description": "The data for the bar chart. Should be in a suitable format such as JSON or CSV.",
},
"x": {"type": "string", "description": "Column for the x-axis."},
"y": {"type": "string", "description": "Column for the y-axis."},
},
"required": ["data", "x", "y"],
"additionalProperties": False,
},
},
"strict": True,
},
{
"type": "function",
"function": {
"name": "create_line_chart",
"description": "Creates a line chart from the provided data.",
"parameters": {
"type": "object",
"properties": {
"data": {
"type": "string",
"description": "The data for the line chart. Should be in a suitable format such as JSON or CSV.",
},
"x": {"type": "string", "description": "Column for the x-axis."},
"y": {"type": "string", "description": "Column for the y-axis."},
},
"required": ["data", "x", "y"],
"additionalProperties": False,
},
},
"strict": True,
},
{
"type": "function",
"function": {
"name": "create_pie_chart",
"description": "Creates a pie chart from the provided data.",
"parameters": {
"type": "object",
"properties": {
"data": {
"type": "string",
"description": "The data for the pie chart. Should be in a suitable format such as JSON or CSV.",
},
"labels": {
"type": "string",
"description": "Column for the labels.",
},
"values": {
"type": "string",
"description": "Column for the values.",
},
},
"required": ["data", "labels", "values"],
"additionalProperties": False,
},
},
"strict": True,
},
]
Weaveを使用したマルチエージェントの追跡を有効にする
以下のコードロジックを記述する必要があります：- ユーザークエリをマルチエージェントシステムに渡す処理
- マルチエージェントシステムの内部動作の処理
- ツール呼び出しの実行
# Example query
user_query = """
Below is some data. I want you to first remove the duplicates then analyze the statistics of the data as well as plot a line chart.
house_size (m3), house_price ($)
90, 100
80, 90
100, 120
90, 100
"""
clean_data、start_analysisおよびuse_line_chartであることがわかります。
まず、ツール呼び出しの実行を担当する実行関数を定義します。
Pythonの関数を@weave.op()でデコレートすることで、言語モデルの入力、出力、およびトレースをログに記録してデバッグできます。
マルチエージェントシステムを作成する際には多くの関数が登場しますが、単に@weave.op()をその上に追加するだけで十分です。
@weave.op()
def clean_data(data):
data_io = StringIO(data)
df = pd.read_csv(data_io, sep=",")
df_deduplicated = df.drop_duplicates()
return df_deduplicated
@weave.op()
def stat_analysis(data):
data_io = StringIO(data)
df = pd.read_csv(data_io, sep=",")
return df.describe()
@weave.op()
def plot_line_chart(data):
data_io = StringIO(data)
df = pd.read_csv(data_io, sep=",")
x = df.iloc[:, 0]
y = df.iloc[:, 1]
coefficients = np.polyfit(x, y, 1)
polynomial = np.poly1d(coefficients)
y_fit = polynomial(x)
plt.figure(figsize=(10, 6))
plt.plot(x, y, "o", label="Data Points")
plt.plot(x, y_fit, "-", label="Best Fit Line")
plt.title("Line Chart with Best Fit Line")
plt.xlabel(df.columns[0])
plt.ylabel(df.columns[1])
plt.legend()
plt.grid(True)
# Save the plot to a BytesIO buffer before showing it
buf = BytesIO()
plt.savefig(buf, format="png")
buf.seek(0)
# Display the plot
plt.show()
# Encode the image in base64 for the data URL
image_data = buf.getvalue()
base64_encoded_data = base64.b64encode(image_data)
base64_string = base64_encoded_data.decode("utf-8")
data_url = f"data:image/png;base64,{base64_string}"
return data_url
# Define the function to execute the tools
@weave.op()
def execute_tool(tool_calls, messages):
for tool_call in tool_calls:
tool_name = tool_call.function.name
tool_arguments = json.loads(tool_call.function.arguments)
if tool_name == "clean_data":
# Simulate data cleaning
cleaned_df = clean_data(tool_arguments["data"])
cleaned_data = {"cleaned_data": cleaned_df.to_dict()}
messages.append(
{"role": "tool", "name": tool_name, "content": json.dumps(cleaned_data)}
)
print("Cleaned data: ", cleaned_df)
elif tool_name == "transform_data":
# Simulate data transformation
transformed_data = {"transformed_data": "sample_transformed_data"}
messages.append(
{
"role": "tool",
"name": tool_name,
"content": json.dumps(transformed_data),
}
)
elif tool_name == "aggregate_data":
# Simulate data aggregation
aggregated_data = {"aggregated_data": "sample_aggregated_data"}
messages.append(
{
"role": "tool",
"name": tool_name,
"content": json.dumps(aggregated_data),
}
)
elif tool_name == "stat_analysis":
# Simulate statistical analysis
stats_df = stat_analysis(tool_arguments["data"])
stats = {"stats": stats_df.to_dict()}
messages.append(
{"role": "tool", "name": tool_name, "content": json.dumps(stats)}
)
print("Statistical Analysis: ", stats_df)
elif tool_name == "correlation_analysis":
# Simulate correlation analysis
correlations = {"correlations": "sample_correlations"}
messages.append(
{"role": "tool", "name": tool_name, "content": json.dumps(correlations)}
)
elif tool_name == "regression_analysis":
# Simulate regression analysis
regression_results = {"regression_results": "sample_regression_results"}
messages.append(
{
"role": "tool",
"name": tool_name,
"content": json.dumps(regression_results),
}
)
elif tool_name == "create_bar_chart":
# Simulate bar chart creation
bar_chart = {"bar_chart": "sample_bar_chart"}
messages.append(
{"role": "tool", "name": tool_name, "content": json.dumps(bar_chart)}
)
elif tool_name == "create_line_chart":
# Simulate line chart creation
line_chart = {"line_chart": plot_line_chart(tool_arguments["data"])}
messages.append(
{"role": "tool", "name": tool_name, "content": json.dumps(line_chart)}
)
elif tool_name == "create_pie_chart":
# Simulate pie chart creation
pie_chart = {"pie_chart": "sample_pie_chart"}
messages.append(
{"role": "tool", "name": tool_name, "content": json.dumps(pie_chart)}
)
return messages
# Define the functions to handle each agent's processing
@weave.op()
def handle_data_processing_agent(query, conversation_messages):
messages = [{"role": "system", "content": processing_system_prompt}]
messages.append({"role": "user", "content": query})
response = client.chat.completions.create(
model=MODEL,
messages=messages,
temperature=0,
tools=preprocess_tools,
)
conversation_messages.append(
[tool_call.function for tool_call in response.choices[0].message.tool_calls]
)
execute_tool(response.choices[0].message.tool_calls, conversation_messages)
@weave.op()
def handle_analysis_agent(query, conversation_messages):
messages = [{"role": "system", "content": analysis_system_prompt}]
messages.append({"role": "user", "content": query})
response = client.chat.completions.create(
model=MODEL,
messages=messages,
temperature=0,
tools=analysis_tools,
)
conversation_messages.append(
[tool_call.function for tool_call in response.choices[0].message.tool_calls]
)
execute_tool(response.choices[0].message.tool_calls, conversation_messages)
@weave.op()
def handle_visualization_agent(query, conversation_messages):
messages = [{"role": "system", "content": visualization_system_prompt}]
messages.append({"role": "user", "content": query})
response = client.chat.completions.create(
model=MODEL,
messages=messages,
temperature=0,
tools=visualization_tools,
)
conversation_messages.append(
[tool_call.function for tool_call in response.choices[0].message.tool_calls]
)
execute_tool(response.choices[0].message.tool_calls, conversation_messages)
# Function to handle user input and triaging
@weave.op()
def handle_user_message(user_query, conversation_messages=None):
if conversation_messages is None:
conversation_messages = []
user_message = {"role": "user", "content": user_query}
conversation_messages.append(user_message)
messages = [{"role": "system", "content": triaging_system_prompt}]
messages.extend(conversation_messages)
response = client.chat.completions.create(
model=MODEL,
messages=messages,
temperature=0,
tools=triage_tools,
)
conversation_messages.append(
[tool_call.function for tool_call in response.choices[0].message.tool_calls]
)
for tool_call in response.choices[0].message.tool_calls:
if tool_call.function.name == "send_query_to_agents":
agents = json.loads(tool_call.function.arguments)["agents"]
query = json.loads(tool_call.function.arguments)["query"]
for agent in agents:
if agent == "Data Processing Agent":
handle_data_processing_agent(query, conversation_messages)
elif agent == "Analysis Agent":
handle_analysis_agent(query, conversation_messages)
elif agent == "Visualization Agent":
handle_visualization_agent(query, conversation_messages)
outputs = extract_tool_contents(conversation_messages)
return outputs
functions = [
"clean_data",
"transform_data",
"stat_analysis",
"aggregate_data",
"correlation_analysis",
"regression_analysis",
"create_bar_chart",
"create_line_chart",
"create_pie_chart",
]
@weave.op()
def extract_tool_contents(data):
contents = {}
contents["all"] = data
for element in data:
if (
isinstance(element, dict)
and element.get("role") == "tool"
and element.get("name") in functions
):
name = element["name"]
content_str = element["content"]
try:
content_json = json.loads(content_str)
if "chart" not in element.get("name"):
contents[name] = [content_json]
else:
first_key = next(iter(content_json))
second_level = content_json[first_key]
if isinstance(second_level, dict):
second_key = next(iter(second_level))
contents[name] = second_level[second_key]
else:
contents[name] = second_level
except json.JSONDecodeError:
print(f"Error decoding JSON for {name}")
contents[name] = None
return contents
マルチエージェントシステムの実行とWeaveでの可視化
最後に、ユーザーの入力を使用してプライマリhandle_user_message関数を実行し、結果を観察します。
handle_user_message(user_query)
analysis_agentの入力と出力を見ると、それが構造化出力形式であることがわかります。OpenAIの構造化出力はエージェント間のコラボレーションを容易にしますが、システムが複雑になるほど、これらの相互作用が行われる形式を把握することが難しくなります。Weaveを使用することで、これらの中間プロセスとその入出力を手に取るように理解することができます。