一文徹底搞懂智能體Agent基于ReAct的工具調用
作者：AI大模型應用開發
AI智能體是指具備一定自主性、能感知環境并通過智能決策執行特定任務的軟件或硬件實體。它結合了人工智能技術，能夠獨立或協作完成目標。本文包含ReAct基礎，基于ReAct的工具調用流程和涉及的交互消息，手搓Agent代碼實現基于ReAct的工具調用。
前言
AI智能體是指具備一定自主性、能感知環境并通過智能決策執行特定任務的軟件或硬件實體。它結合了人工智能技術（如機器學習、自然語言處理、計算機視覺等），能夠獨立或協作完成目標。
基于大語言模型（LLM）的Function Calling可以令智能體實現有效的工具使用和與外部API的交互。支持Function Calling的模型(如gpt-4，qwen-plus等)能夠檢測何時需要調用函數，并輸出調用函數的函數名和所需參數的JSON格式結構化數據。
但并非所有的LLM模型都支持Function Calling(如deepseek-v3)。對于不支持Function Calling的模型，可通過ReAct的相對較為復雜的提示詞工程，要求模型返回特定格式的響應，以便區分不同的階段（思考、行動、觀察）。
工具調用主要有兩個用途：
獲取數據:例如根據關鍵字從知識庫檢索內容、通過特定API接口獲取業務數據執行行動:例如通過API接口修改業務狀態數據、執行預定業務操作
本文包含如下內容：
- ReAct基礎
- 詳細介紹基于ReAct的工具調用流程和涉及的交互消息
- 手搓Agent代碼實現基于ReAct的工具調用
ReAct基礎
ReAct源于經典論文: REACT: SYNERGIZING REASONING AND ACTING IN LANGUAGE MODELS (鏈接：https://arxiv.org/pdf/2210.03629)
基于ReAct的智能體為了解決問題，需要經過幾個階段
- Thought: 思考推理
- Action：作出行動，決定要調用的工具和參數
- Observation：行動的結果(工具輸出)
以上3個階段可能迭代多次，直到問題得到解決或者達到迭代次數上限。
基于ReAct的工具調用依賴于復雜的提示詞工程。系統提示詞參考langchain的模板:
Answer the following questions as best you can. You have access to the following tools:
{tool_strings}
The way you use the tools is by specifying a json blob.
Specifically, this json should have a `action` key (with the name of the tool to use) and a `action_input` key (with the input to the tool going here).
The only values that should be in the "action" field are: {tool_names}
The $JSON_BLOB should only contain a SINGLE action, do NOT return a list of multiple actions. Here is an example of a valid $JSON_BLOB:
```
{{{{
"action": $TOOL_NAME,
"action_input": $INPUT
}}}}
```
ALWAYS use the following format:
Question: the input question you must answer
Thought: you should always think about what to do
Action:
```
$JSON_BLOB
```
Observation: the result of the action
... (this Thought/Action/Observation can repeat N times)
Thought: I now know the final answer
Final Answer: the final answer to the original input question
Begin! Reminder to always use the exact characters `Final Answer` when responding.基于ReAct的工具調用流程和交互消息
我們以查詢北京和廣州天氣為例，LLM采用阿里云的deepseek-v3。查詢天氣的流程如下圖：
1. 發起查詢請求
向LLM發起查詢時，messages列表有2條messages：
- 第1條role為
system，定義了系統提示詞(含工具定義) - 第2條role為
user，包含如下內容：
Question: 北京和廣州天氣怎么樣
我們用curl發起POST請求，body的JSON結構可參考https://platform.openai.com/docs/api-reference/chat/create 。
請求里的
stop字段需要設置為Observation:，否則LLM會直接輸出整個Thought/Action/Observation流程并給出虛構的最終答案。我們僅需要LLM輸出Thought/Action即可
#!/bin/bash
export OPENAI_BASE_URL="https://dashscope.aliyuncs.com/compatible-mode/v1"
export OPENAI_API_KEY="sk-xxx" # 替換為你的key
curl ${OPENAI_BASE_URL}/chat/completions \
-H "Content-Type: application/json" \
-H "Authorization: Bearer $OPENAI_API_KEY" \
-d '{
"model": "deepseek-v3",
"messages": [
{
"role": "system",
"content": "\nAnswer the following questions as best you can. You have access to the following tools:\n{\"name\": \"get_weather\", \"description\": \"Get weather\", \"parameters\": {\"type\": \"object\", \"properties\": {\"location\": {\"type\": \"string\", \"description\": \"the name of the location\"}}, \"required\": [\"location\"]}}\n\n\nThe way you use the tools is by specifying a json blob.\nSpecifically, this json should have a `action` key (with the name of the tool to use) and a `action_input` key (with the input to the tool going here).\n\nThe only values that should be in the \"action\" field are: get_weather\n\nThe $JSON_BLOB should only contain a SINGLE action, do NOT return a list of multiple actions. Here is an example of a valid $JSON_BLOB:\n\n```\n{{\n\"action\": $TOOL_NAME,\n\"action_input\": $INPUT\n}}\n```\n\nALWAYS use the following format:\n\nQuestion: the input question you must answer\nThought: you should always think about what to do\nAction:\n```\n$JSON_BLOB\n```\nObservation: the result of the action\n... (this Thought/Action/Observation can repeat N times)\nThought: I now know the final answer\nFinal Answer: the final answer to the original input question\n\n\nBegin! Reminder to always use the exact characters `Final Answer` when responding. \n"
},
{
"role": "user",
"content": "Question: 北京和廣州天氣怎么樣\n\n"
}
],
"stop": "Observation:"
}'2. LLM返回Action獲取北京天氣
LLM經過推理，發現需要先調用函數獲取北京天氣。
Thought: 我需要獲取北京和廣州的天氣信息。首先，我將獲取北京的天氣。
Action:
```
{
"action": "get_weather",
"action_input": {
"location": "北京"
}
}
```完整的JSON響應如下：
{
"choices": [
{
"message": {
"content": "Thought: 我需要獲取北京和廣州的天氣信息。首先，我將獲取北京的天氣。\n\nAction:\n```\n{\n \"action\": \"get_weather\",\n \"action_input\": {\n \"location\": \"北京\"\n }\n}\n```",
"role": "assistant"
},
"finish_reason": "stop",
"index": 0,
"logprobs": null
}
],
"object": "chat.completion",
"usage": {
"prompt_tokens": 305,
"completion_tokens": 49,
"total_tokens": 354
},
"created": 1745651748,
"system_fingerprint": null,
"model": "deepseek-v3",
"id": "chatcmpl-697b0627-4fca-975b-954c-7304386ac224"
}3. 處理函數調用獲取北京天氣
解析處理LLM的Action獲得函數名和參數列表，調用相應的API接口獲得結果。
例如：通過http://weather.cma.cn/api/now/54511可獲得北京的天氣情況。
完整的JSON響應如下：
{
"msg": "success",
"code": 0,
"data": {
"location": {
"id": "54511",
"name": "北京",
"path": "中國, 北京, 北京"
},
"now": {
"precipitation": 0.0,
"temperature": 23.4,
"pressure": 1005.0,
"humidity": 43.0,
"windDirection": "西南風",
"windDirectionDegree": 216.0,
"windSpeed": 2.7,
"windScale": "微風",
"feelst": 23.1
},
"alarm": [],
"jieQi": "",
"lastUpdate": "2025/04/26 15:00"
}
}4. 把上下文信息以及函數調用結果發給LLM
發給LLM的messages列表有2條messages：
- 第1條role為
system，定義了系統提示詞(含工具定義) - 第2條role為
user，包含如下內容：
Question: 北京和廣州天氣怎么樣
Thought: 我需要獲取北京和廣州的天氣信息。首先，我將獲取北京的天氣
Action: {"action":"get_weather","action_input":{"location":"北京"}}
Observation: 工具調用get_weather('北京')的結果
#!/bin/bash
export OPENAI_BASE_URL="https://dashscope.aliyuncs.com/compatible-mode/v1"
export OPENAI_API_KEY="sk-xxx" # 替換為你的key
curl ${OPENAI_BASE_URL}/chat/completions \
-H "Content-Type: application/json" \
-H "Authorization: Bearer $OPENAI_API_KEY" \
-d '{
"model": "deepseek-v3",
"messages": [
{
"role": "system",
"content": "\nAnswer the following questions as best you can. You have access to the following tools:\n{\"name\": \"get_weather\", \"description\": \"Get weather\", \"parameters\": {\"type\": \"object\", \"properties\": {\"location\": {\"type\": \"string\", \"description\": \"the name of the location\"}}, \"required\": [\"location\"]}}\n\n\nThe way you use the tools is by specifying a json blob.\nSpecifically, this json should have a `action` key (with the name of the tool to use) and a `action_input` key (with the input to the tool going here).\n\nThe only values that should be in the \"action\" field are: get_weather\n\nThe $JSON_BLOB should only contain a SINGLE action, do NOT return a list of multiple actions. Here is an example of a valid $JSON_BLOB:\n\n```\n{{\n\"action\": $TOOL_NAME,\n\"action_input\": $INPUT\n}}\n```\n\nALWAYS use the following format:\n\nQuestion: the input question you must answer\nThought: you should always think about what to do\nAction:\n```\n$JSON_BLOB\n```\nObservation: the result of the action\n... (this Thought/Action/Observation can repeat N times)\nThought: I now know the final answer\nFinal Answer: the final answer to the original input question\n\n\nBegin! Reminder to always use the exact characters `Final Answer` when responding. \n"
},
{
"role": "user",
"content": "Question: 北京和廣州天氣怎么樣\n\nThought: 我需要獲取北京和廣州的天氣信息。首先，我將獲取北京的天氣。\n\nAction:\n```\n{\n\"action\": \"get_weather\",\n\"action_input\": {\n\"location\": \"北京\"\n}\n}\n```\nObservation: {\"msg\":\"success\",\"code\":0,\"data\":{\"location\":{\"id\":\"54511\",\"name\":\"北京\",\"path\":\"中國, 北京, 北京\"},\"now\":{\"precipitation\":0.0,\"temperature\":23.4,\"pressure\":1005.0,\"humidity\":43.0,\"windDirection\":\"西南風\",\"windDirectionDegree\":216.0,\"windSpeed\":2.7,\"windScale\":\"微風\",\"feelst\":23.1},\"alarm\":[],\"jieQi\":\"\",\"lastUpdate\":\"2025/04/26 15:00\"}}\n"
}
],
"stop": "Observation:"
}'5. LLM返回Action獲取廣州天氣
LLM經過推理，發現還需要調用函數獲取廣州天氣。
Thought: 我已經獲取了北京的天氣信息。接下來，我將獲取廣州的天氣信息。
Action:
```
{
"action": "get_weather",
"action_input": {
"location": "廣州"
}
}
```完整的JSON響應如下：
{
"choices": [
{
"message": {
"content": "Thought: 我已經獲取了北京的天氣信息。接下來，我將獲取廣州的天氣信息。\n\nAction:\n```\n{\n\"action\": \"get_weather\",\n\"action_input\": {\n\"location\": \"廣州\"\n}\n}\n```\nObservation",
"role": "assistant"
},
"finish_reason": "stop",
"index": 0,
"logprobs": null
}
],
"object": "chat.completion",
"usage": {
"prompt_tokens": 472,
"completion_tokens": 46,
"total_tokens": 518
},
"created": 1745651861,
"system_fingerprint": null,
"model": "deepseek-v3",
"id": "chatcmpl-a822b8d7-9105-9dc2-8e98-4327afb50b3a"
}6. 處理函數調用獲取廣州天氣
解析處理LLM的Action獲得函數名和參數列表，調用相應的API接口獲得結果。
例如：通過http://weather.cma.cn/api/now/59287可獲得廣州的天氣情況。
完整的JSON響應如下：
{
"msg": "success",
"code": 0,
"data": {
"location": {
"id": "59287",
"name": "廣州",
"path": "中國, 廣東, 廣州"
},
"now": {
"precipitation": 0.0,
"temperature": 24.2,
"pressure": 1005.0,
"humidity": 79.0,
"windDirection": "東北風",
"windDirectionDegree": 31.0,
"windSpeed": 1.3,
"windScale": "微風",
"feelst": 27.1
},
"alarm": [],
"jieQi": "",
"lastUpdate": "2025/04/26 15:00"
}
}7. 把上下文信息以及函數調用結果發給LLM
發給LLM的messages列表有2條messages：
- 第1條role為
system，定義了系統提示詞(含工具定義) - 第2條role為
user，包含如下內容：
Question: 北京和廣州天氣怎么樣
Thought: 我需要獲取北京和廣州的天氣信息。首先，我將獲取北京的天氣
Action: {"action":"get_weather","action_input":{"location":"北京"}}
Observation: 工具調用get_weather('北京')的結果
Thought: 現在我已經獲取了北京的天氣信息，接下來我將獲取廣州的天氣信息。
Action: {"action":"get_weather","action_input":{"location":"廣州"}}
Observation: 工具調用get_weather('廣州')的結果
#!/bin/bash
export OPENAI_BASE_URL="https://dashscope.aliyuncs.com/compatible-mode/v1"
export OPENAI_API_KEY="sk-xxx" # 替換為你的key
curl ${OPENAI_BASE_URL}/chat/completions \
-H "Content-Type: application/json" \
-H "Authorization: Bearer $OPENAI_API_KEY" \
-d '{
"model": "deepseek-v3",
"messages": [
{
"role": "system",
"content": "\nAnswer the following questions as best you can. You have access to the following tools:\n{\"name\": \"get_weather\", \"description\": \"Get weather\", \"parameters\": {\"type\": \"object\", \"properties\": {\"location\": {\"type\": \"string\", \"description\": \"the name of the location\"}}, \"required\": [\"location\"]}}\n\n\nThe way you use the tools is by specifying a json blob.\nSpecifically, this json should have a `action` key (with the name of the tool to use) and a `action_input` key (with the input to the tool going here).\n\nThe only values that should be in the \"action\" field are: get_weather\n\nThe $JSON_BLOB should only contain a SINGLE action, do NOT return a list of multiple actions. Here is an example of a valid $JSON_BLOB:\n\n```\n{{\n\"action\": $TOOL_NAME,\n\"action_input\": $INPUT\n}}\n```\n\nALWAYS use the following format:\n\nQuestion: the input question you must answer\nThought: you should always think about what to do\nAction:\n```\n$JSON_BLOB\n```\nObservation: the result of the action\n... (this Thought/Action/Observation can repeat N times)\nThought: I now know the final answer\nFinal Answer: the final answer to the original input question\n\n\nBegin! Reminder to always use the exact characters `Final Answer` when responding. \n"
},
{
"role": "user",
"content": "Question: 北京和廣州天氣怎么樣\n\nThought: 我需要獲取北京和廣州的天氣信息。首先，我將獲取北京的天氣。\n\nAction:\n```\n{\n\"action\": \"get_weather\",\n\"action_input\": {\n\"location\": \"北京\"\n}\n}\n```\nObservation: {\"msg\":\"success\",\"code\":0,\"data\":{\"location\":{\"id\":\"54511\",\"name\":\"北京\",\"path\":\"中國, 北京, 北京\"},\"now\":{\"precipitation\":0.0,\"temperature\":23.4,\"pressure\":1005.0,\"humidity\":43.0,\"windDirection\":\"西南風\",\"windDirectionDegree\":216.0,\"windSpeed\":2.7,\"windScale\":\"微風\",\"feelst\":23.1},\"alarm\":[],\"jieQi\":\"\",\"lastUpdate\":\"2025/04/26 15:00\"}}\nThought: 現在我已經獲取了北京的天氣信息，接下來我將獲取廣州的天氣信息。\n\nAction:\n```\n{\n\"action\": \"get_weather\",\n\"action_input\": {\n\"location\": \"廣州\"\n}\n}\n```\nObservation\nObservation: {\"msg\":\"success\",\"code\":0,\"data\":{\"location\":{\"id\":\"59287\",\"name\":\"廣州\",\"path\":\"中國, 廣東, 廣州\"},\"now\":{\"precipitation\":0.0,\"temperature\":24.2,\"pressure\":1005.0,\"humidity\":79.0,\"windDirection\":\"東北風\",\"windDirectionDegree\":31.0,\"windSpeed\":1.3,\"windScale\":\"微風\",\"feelst\":27.1},\"alarm\":[],\"jieQi\":\"\",\"lastUpdate\":\"2025/04/26 15:00\"}}\n"
}
],
"stop": "Observation:"
}'8. LLM生成最終回復
LLM生成最終的回復：
Thought: 我已經獲取了北京和廣州的天氣信息，現在可以回答用戶的問題了。
Final Answer: 北京的天氣溫度為23.4°C，濕度為43%，風向為西南風，風速為2.7米/秒。廣州 的天氣溫度為24.2°C，濕度為79%，風向為東北風，風速為1.3米/秒。完整的JSON響應如下：
{
"choices": [
{
"message": {
"content": "Thought: 我已經獲取了北京和廣州的天氣信息，現在可以回答用戶的問題了。\n\nFinal Answer: 北京的天氣溫度為23.4°C，濕度為43%，風向為西南風，風速為2.7米/秒。廣州 的天氣溫度為24.2°C，濕度為79%，風向為東北風，風速為1.3米/秒。",
"role": "assistant"
},
"finish_reason": "stop",
"index": 0,
"logprobs": null
}
],
"object": "chat.completion",
"usage": {
"prompt_tokens": 641,
"completion_tokens": 79,
"total_tokens": 720
},
"created": 1745652025,
"system_fingerprint": null,
"model": "deepseek-v3",
"id": "chatcmpl-d9b85f31-589e-9c6f-8694-cf813344e464"
}手搓Agent代碼實現基于ReAct的工具調用
1. 創建python環境
uv init agent
cd agent
uv venv
.venv\Scripts\activate
uv add openai requests python-dotenv2. 設置API Key
創建.env，.env內容如下（注意修改OPENAI_API_KEY為您的key）
OPENAI_API_KEY=your_api_key_here
OPENAI_BASE_URL=https://dashscope.aliyuncs.com/compatible-mode/v1把.env添加到.gitignore
3. 實現Agent代碼
基于openai sdk實現ReAct agent的偽代碼主體邏輯如下：
maxIter = 5 # 最大迭代次數
agent_scratchpad = "" # agent思考過程(Thought/Action/Observation)
for iterSeq in range(1, maxIter+1):
構造chat completion請求
messages有2條
第1條為系統提示詞消息(含工具定義)
第2條為用戶消息：Question + agent思考過程(Thought/Action/Observation)
stop參數設置為"Observation:"
獲取chat completion結果
如果chat completion結果帶有"Final Answer:"
返回最終答案
如果chat completion結果帶有Action
解析并調用相應函數
更新agent思考過程：把本次LLM的輸出(Though/Action)和工具調用結果(Observation)添加到agent_scratchpad
繼續迭代完整的main.py代碼如下：
import json
import re
import requests
import urllib.parse
from typing import Iterable
from openai import OpenAI
from openai.types.chat.chat_completion_message_param import ChatCompletionMessageParam
from openai.types.chat.chat_completion_user_message_param import (
ChatCompletionUserMessageParam,
)
from openai.types.chat.chat_completion_system_message_param import (
ChatCompletionSystemMessageParam,
)
# 加載環境變量
from dotenv import load_dotenv
load_dotenv()
client = OpenAI()
model = "deepseek-v3"
# 工具定義
tools = [
{
"type": "function",
"function": {
"name": "get_weather",
"description": "Get weather",
"parameters": {
"type": "object",
"properties": {
"location": {"type": "string", "description": "location"}
},
"required": ["location"],
},
},
}
]
# 系統提示詞
def get_system_prompt():
tool_strings = "\n".join([json.dumps(tool["function"]) for tool in tools])
tool_names = ", ".join([tool["function"]["name"] for tool in tools])
systemPromptFormat = """
Answer the following questions as best you can. You have access to the following tools:
{tool_strings}
The way you use the tools is by specifying a json blob.
Specifically, this json should have a `action` key (with the name of the tool to use) and a `action_input` key (with the input to the tool going here).
The only values that should be in the "action" field are: {tool_names}
The $JSON_BLOB should only contain a SINGLE action, do NOT return a list of multiple actions. Here is an example of a valid $JSON_BLOB:
```
{{{{
"action": $TOOL_NAME,
"action_input": $INPUT
}}}}
```
ALWAYS use the following format:
Question: the input question you must answer
Thought: you should always think about what to do
Action:
```
$JSON_BLOB
```
Observation: the result of the action
... (this Thought/Action/Observation can repeat N times)
Thought: I now know the final answer
Final Answer: the final answer to the original input question
Begin! Reminder to always use the exact characters `Final Answer` when responding.
"""
return systemPromptFormat.format(tool_strings=tool_strings, tool_names=tool_names)
# 實現獲取天氣
def get_weather(location: str) -> str:
url = "http://weather.cma.cn/api/autocomplete?q=" + urllib.parse.quote(location)
response = requests.get(url)
data = response.json()
if data["code"] != 0:
return "沒找到該位置的信息"
location_code = ""
for item in data["data"]:
str_array = item.split("|")
if (
str_array[1] == location
or str_array[1] + "市" == location
or str_array[2] == location
):
location_code = str_array[0]
break
if location_code == "":
return "沒找到該位置的信息"
url = f"http://weather.cma.cn/api/now/{location_code}"
return requests.get(url).text
# 實現工具調用
def invoke_tool(toolName: str, toolParamaters) -> str:
result = ""
if toolName == "get_weather":
result = get_weather(toolParamaters["location"])
else:
result = f"函數{toolName}未定義"
return result
def main():
query = "北京和廣州天氣怎么樣"
systemMsg = ChatCompletionSystemMessageParam(
role="system", cnotallow=get_system_prompt()
)
maxIter = 5 # 最大迭代次數
agent_scratchpad = "" # agent思考過程
action_pattern = re.compile(r"\nAction:\n`{3}(?:json)?\n(.*?)`{3}.*?$", re.DOTALL)
for iterSeq in range(1, maxIter + 1):
messages: Iterable[ChatCompletionMessageParam] = list()
messages.append(systemMsg)
messages.append(
ChatCompletionUserMessageParam(
role="user", cnotallow=f"Question: {query}\n\n{agent_scratchpad}"
)
)
print(f">> iterSeq:{iterSeq}")
print(f">>> messages: {json.dumps(messages)}")
# 向LLM發起請求，注意需要設置stop參數
chat_completion = client.chat.completions.create(
messages=messages,
model=model,
stop="Observation:",
)
content = chat_completion.choices[0].message.content
print(f">>> content:\n{content}")
final_answer_match = re.search(r"\nFinal Answer:\s*(.*)", content)
if final_answer_match:
final_answer = final_answer_match.group(1)
print(f">>> 最終答案: {final_answer}")
return
action_match = action_pattern.search(content)
if action_match:
obj = json.loads(action_match.group(1))
toolName = obj["action"]
toolParameters = obj["action_input"]
print(f">>> tool name:{toolName}")
print(f">>> tool parameters:{toolParameters}")
result = invoke_tool(toolName, toolParameters)
print(f">>> tool result: {result}")
# 把本次LLM的輸出(Though/Action)和工具調用結果(Observation)添加到agent_scratchpad
agent_scratchpad += content + f"\nObservation: {result}\n"
else:
print(">>> ERROR: detect invalid response")
return
print(">>> 迭代次數達到上限，我無法得到最終答案")
main()運行代碼：uv run .\main.py
輸出日志如下：
>> iterSeq:1
>>> messages: [{"role": "system", "content": "\nAnswer the following questions as best you can. You have access to the following tools:\n{\"name\": \"get_weather\", \"description\": \"Get weather\", \"parameters\": {\"type\": \"object\", \"properties\": {\"location\": {\"type\": \"string\", \"description\": \"the name of the location\"}}, \"required\": [\"location\"]}}\n\n\nThe way you use the tools is by specifying a json blob.\nSpecifically, this json should have a `action` key (with the name of the tool to use) and a `action_input` key (with the input to the tool going here).\n\nThe only values that should be in the \"action\" field are: get_weather\n\nThe $JSON_BLOB should only contain a SINGLE action, do NOT return a list of multiple actions. Here is an example of a valid $JSON_BLOB:\n\n```\n{{\n\"action\": $TOOL_NAME,\n\"action_input\": $INPUT\n}}\n```\n\nALWAYS use the following format:\n\nQuestion: the input question you must answer\nThought: you should always think about what to do\nAction:\n```\n$JSON_BLOB\n```\nObservation: the result of the action\n... (this Thought/Action/Observation can repeat N times)\nThought: I now know the final answer\nFinal Answer: the final answer to the original input question\n\n\nBegin! Reminder to always use the exact characters `Final Answer` when responding. \n"}, {"role": "user", "content": "Question: \u5317\u4eac\u548c\u5e7f\u5dde\u5929\u6c14\u600e\u4e48\u6837\n\n"}]
>>> content:
Thought: 我需要獲取北京和廣州的天氣信息。首先，我將獲取北京的天氣。
Action:
```
{
"action": "get_weather",
"action_input": {
"location": "北京"
}
}
```
>>> tool name:get_weather
>>> tool parameters:{'location': '北京'}
>>> tool result: {"msg":"success","code":0,"data":{"location":{"id":"54511","name":"北京","path":"中國, 北京, 北京"},"now":{"precipitation":0.0,"temperature":23.4,"pressure":1005.0,"humidity":43.0,"windDirection":"西南風","windDirectionDegree":216.0,"windSpeed":2.7,"windScale":"微風","feelst":23.1},"alarm":[],"jieQi":"","lastUpdate":"2025/04/26 15:00"}}
>> iterSeq:2
>>> messages: [{"role": "system", "content": "\nAnswer the following questions as best you can. You have access to the following tools:\n{\"name\": \"get_weather\", \"description\": \"Get weather\", \"parameters\": {\"type\": \"object\", \"properties\": {\"location\": {\"type\": \"string\", \"description\": \"the name of the location\"}}, \"required\": [\"location\"]}}\n\n\nThe way you use the tools is by specifying a json blob.\nSpecifically, this json should have a `action` key (with the name of the tool to use) and a `action_input` key (with the input to the tool going here).\n\nThe only values that should be in the \"action\" field are: get_weather\n\nThe $JSON_BLOB should only contain a SINGLE action, do NOT return a list of multiple actions. Here is an example of a valid $JSON_BLOB:\n\n```\n{{\n\"action\": $TOOL_NAME,\n\"action_input\": $INPUT\n}}\n```\n\nALWAYS use the following format:\n\nQuestion: the input question you must answer\nThought: you should always think about what to do\nAction:\n```\n$JSON_BLOB\n```\nObservation: the result of the action\n... (this Thought/Action/Observation can repeat N times)\nThought: I now know the final answer\nFinal Answer: the final answer to the original input question\n\n\nBegin! Reminder to always use the exact characters `Final Answer` when responding. \n"}, {"role": "user", "content": "Question: \u5317\u4eac\u548c\u5e7f\u5dde\u5929\u6c14\u600e\u4e48\u6837\n\nThought: \u6211\u9700\u8981\u83b7\u53d6\u5317\u4eac\u548c\u5e7f\u5dde\u7684\u5929\u6c14\u4fe1\u606f\u3002\u9996\u5148\uff0c\u6211\u5c06\u83b7\u53d6\u5317\u4eac\u7684\u5929\u6c14\u3002\n\nAction:\n```\n{\n\"action\": \"get_weather\",\n\"action_input\": {\n\"location\": \"\u5317\u4eac\"\n}\n}\n```\nObservation: {\"msg\":\"success\",\"code\":0,\"data\":{\"location\":{\"id\":\"54511\",\"name\":\"\u5317\u4eac\",\"path\":\"\u4e2d\u56fd, \u5317\u4eac, \u5317\u4eac\"},\"now\":{\"precipitation\":0.0,\"temperature\":23.4,\"pressure\":1005.0,\"humidity\":43.0,\"windDirection\":\"\u897f\u5357\u98ce\",\"windDirectionDegree\":216.0,\"windSpeed\":2.7,\"windScale\":\"\u5fae\u98ce\",\"feelst\":23.1},\"alarm\":[],\"jieQi\":\"\",\"lastUpdate\":\"2025/04/26 15:00\"}}\n"}]
>>> content:
Thought: 現在我已經獲取了北京的天氣信息，接下來我將獲取廣州的天氣信息。
Action:
```
{
"action": "get_weather",
"action_input": {
"location": "廣州"
}
}
```
Observation
>>> tool name:get_weather
>>> tool parameters:{'location': '廣州'}
>>> tool result: {"msg":"success","code":0,"data":{"location":{"id":"59287","name":"廣州","path":"中國, 廣東, 廣州"},"now":{"precipitation":0.0,"temperature":24.2,"pressure":1005.0,"humidity":79.0,"windDirection":"東北風","windDirectionDegree":31.0,"windSpeed":1.3,"windScale":"微風","feelst":27.1},"alarm":[],"jieQi":"","lastUpdate":"2025/04/26 15:00"}}
>> iterSeq:3
>>> messages: [{"role": "system", "content": "\nAnswer the following questions as best you can. You have access to the following tools:\n{\"name\": \"get_weather\", \"description\": \"Get weather\", \"parameters\": {\"type\": \"object\", \"properties\": {\"location\": {\"type\": \"string\", \"description\": \"the name of the location\"}}, \"required\": [\"location\"]}}\n\n\nThe way you use the tools is by specifying a json blob.\nSpecifically, this json should have a `action` key (with the name of the tool to use) and a `action_input` key (with the input to the tool going here).\n\nThe only values that should be in the \"action\" field are: get_weather\n\nThe $JSON_BLOB should only contain a SINGLE action, do NOT return a list of multiple actions. Here is an example of a valid $JSON_BLOB:\n\n```\n{{\n\"action\": $TOOL_NAME,\n\"action_input\": $INPUT\n}}\n```\n\nALWAYS use the following format:\n\nQuestion: the input question you must answer\nThought: you should always think about what to do\nAction:\n```\n$JSON_BLOB\n```\nObservation: the result of the action\n... (this Thought/Action/Observation can repeat N times)\nThought: I now know the final answer\nFinal Answer: the final answer to the original input question\n\n\nBegin! Reminder to always use the exact characters `Final Answer` when responding. \n"}, {"role": "user", "content": "Question: \u5317\u4eac\u548c\u5e7f\u5dde\u5929\u6c14\u600e\u4e48\u6837\n\nThought: \u6211\u9700\u8981\u83b7\u53d6\u5317\u4eac\u548c\u5e7f\u5dde\u7684\u5929\u6c14\u4fe1\u606f\u3002\u9996\u5148\uff0c\u6211\u5c06\u83b7\u53d6\u5317\u4eac\u7684\u5929\u6c14\u3002\n\nAction:\n```\n{\n\"action\": \"get_weather\",\n\"action_input\": {\n\"location\": \"\u5317\u4eac\"\n}\n}\n```\nObservation: {\"msg\":\"success\",\"code\":0,\"data\":{\"location\":{\"id\":\"54511\",\"name\":\"\u5317\u4eac\",\"path\":\"\u4e2d\u56fd, \u5317\u4eac, \u5317\u4eac\"},\"now\":{\"precipitation\":0.0,\"temperature\":23.4,\"pressure\":1005.0,\"humidity\":43.0,\"windDirection\":\"\u897f\u5357\u98ce\",\"windDirectionDegree\":216.0,\"windSpeed\":2.7,\"windScale\":\"\u5fae\u98ce\",\"feelst\":23.1},\"alarm\":[],\"jieQi\":\"\",\"lastUpdate\":\"2025/04/26 15:00\"}}\nThought: \u73b0\u5728\u6211\u5df2\u7ecf\u83b7\u53d6\u4e86\u5317\u4eac\u7684\u5929\u6c14\u4fe1\u606f\uff0c\u63a5\u4e0b\u6765\u6211\u5c06\u83b7\u53d6\u5e7f\u5dde\u7684\u5929\u6c14\u4fe1\u606f\u3002\n\nAction:\n```\n{\n\"action\": \"get_weather\",\n\"action_input\": {\n\"location\": \"\u5e7f\u5dde\"\n}\n}\n```\nObservation\nObservation: {\"msg\":\"success\",\"code\":0,\"data\":{\"location\":{\"id\":\"59287\",\"name\":\"\u5e7f\u5dde\",\"path\":\"\u4e2d\u56fd, \u5e7f\u4e1c, \u5e7f\u5dde\"},\"now\":{\"precipitation\":0.0,\"temperature\":24.2,\"pressure\":1005.0,\"humidity\":79.0,\"windDirection\":\"\u4e1c\u5317\u98ce\",\"windDirectionDegree\":31.0,\"windSpeed\":1.3,\"windScale\":\"\u5fae\u98ce\",\"feelst\":27.1},\"alarm\":[],\"jieQi\":\"\",\"lastUpdate\":\"2025/04/26 15:00\"}}\n"}]
>>> content:
Thought: 我已經獲取了北京和廣州的天氣信息，現在可以回答用戶的問題了。
Final Answer: 北京的天氣情況為：溫度23.4°C，濕度43%，西南風，風速2.7米/秒，微風。廣州的天氣情況為：溫度24.2°C，濕度79%，東北風，風速1.3米/秒，微風。
>>> 最終答案: 北京的天氣情況為：溫度23.4°C，濕度43%，西南風，風速2.7米/秒，微風。廣州的天氣情況為：溫度24.2°C，濕度79%，東北風，風速1.3米/秒，微風。總結
基于Function Calling和基于ReAct的工具調用有各自的優缺點：
1. Function Calling
- 無需設定系統提示詞，LLM根據tools定義即可觸發工具調用，token消耗較少
- 模型參數量相對較大。模型的訓練數據必須包含Function Calling相關的內容，以確保模型能夠理解和生成結構化輸出，結構化輸出更穩定
- 輸出結果較為容易處理
- 隱藏了推理過程，缺乏可解釋性
2. ReAct
- 需要設置復雜的系統提示詞，token消耗較多
- 對模型參數要求較低
- 輸出結果處理比Function Calling復雜
- 推理過程可見，更高的可解釋性
責任編輯：龐桂玉
來源：
AI大模型應用開發
