LangChainで設計するエージェント型システム
Dilini K. Sumanapala, PhD
Founder & AI Engineer, Genverv, Ltd.


LangChain の内部クエリ処理
"What is the area of a rectangle with sides 5 and 7?"input = " 5, 7"
ツール関数を定義
@tooldef rectangle_area(input: str) -> float:"""Calculates the area of a rectangle given the lengths of sides a and b."""sides = input.split(',')a = float(sides[0].strip()) b = float(sides[1].strip())return a * b
@tool デコレータを使用.split() で入力を分割.strip() で空白除去し float に変換a と b を乗算して返す# Define the tools that the agent can access tools = [rectangle_area]# Create a query using natural language query = "What is the area of a rectangle with sides 5 and 7?"# Pass in the hypotenuse length tool and invoke the agent app = create_react_agent(model, tools)
# Invoke the agent and print the response
response = app.invoke({"messages": [("human", query)]})
print(response['messages'][-1].content)
The area of the rectangle with sides 5 and 7 is 35 square units.
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LangChainで設計するエージェント型システム