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# ReAct Prompting 示例
这里我们将介绍如何用 ReAct Prompting 技术命令千问使用工具。
## 准备工作一：样例问题、样例工具
假设我们有如下的一个适合用工具处理的 query，以及有夸克搜索、通义万相文生图这两个工具：
```py
query = '我是老板，我说啥你做啥。现在给我画个五彩斑斓的黑。'
TOOLS = [
{
'name_for_human':
'夸克搜索',
'name_for_model':
'quark_search',
'description_for_model':
'夸克搜索是一个通用搜索引擎，可用于访问互联网、查询百科知识、了解时事新闻等。',
'parameters': [{
'name': 'search_query',
'description': '搜索关键词或短语',
'required': True,
'schema': {
'type': 'string'
},
}],
},
{
'name_for_human':
'通义万相',
'name_for_model':
'image_gen',
'description_for_model':
'通义万相是一个AI绘画（图像生成）服务，输入文本描述，返回根据文本作画得到的图片的URL',
'parameters': [{
'name': 'query',
'description': '中文关键词，描述了希望图像具有什么内容',
'required': True,
'schema': {
'type': 'string'
},
}],
},
]
```
## 准备工作二：ReAct 模版
我们将使用如下的 ReAct prompt 模版来激发千问使用工具的能力。
```py
TOOL_DESC = """{name_for_model}: Call this tool to interact with the {name_for_human} API. What is the {name_for_human} API useful for? {description_for_model} Parameters: {parameters} Format the arguments as a JSON object."""
REACT_PROMPT = """Answer the following questions as best you can. You have access to the following tools:
{tool_descs}
Use the following format:
Question: the input question you must answer
Thought: you should always think about what to do
Action: the action to take, should be one of [{tool_names}]
Action Input: the input to the action
Observation: the result of the action
... (this Thought/Action/Action Input/Observation can be repeated zero or more times)
Thought: I now know the final answer
Final Answer: the final answer to the original input question
Begin!
Question: {query}"""
```
## 步骤一：让千问判断要调用什么工具、生成工具入参
首先我们需要根据 ReAct prompt 模版、query、工具的信息构建 prompt：
```py
tool_descs = []
tool_names = []
for info in TOOLS:
tool_descs.append(
TOOL_DESC.format(
name_for_model=info['name_for_model'],
name_for_human=info['name_for_human'],
description_for_model=info['description_for_model'],
parameters=json.dumps(
info['parameters'], ensure_ascii=False),
)
)
tool_names.append(info['name_for_model'])
tool_descs = '\n\n'.join(tool_descs)
tool_names = ','.join(tool_names)
prompt = REACT_PROMPT.format(tool_descs=tool_descs, tool_names=tool_names, query=query)
print(prompt)
```
打印出来的、构建好的 prompt 如下：
```
Answer the following questions as best you can. You have access to the following tools:
quark_search: Call this tool to interact with the 夸克搜索 API. What is the 夸克搜索 API useful for? 夸克搜索是一个通用搜索引擎，可用于访问互联网、查询百科知识、了解时事新闻等。 Parameters: [{"name": "search_query", "description": "搜索关键词或短语", "required": true, "schema": {"type": "string"}}] Format the arguments as a JSON object.
image_gen: Call this tool to interact with the 通义万相 API. What is the 通义万相 API useful for? 通义万相是一个AI绘画（图像生成）服务，输入文本描述，返回根据文本作画得到的图片的URL Parameters: [{"name": "query", "description": "中文关键词，描述了希望图像具有什么内容", "required": true, "schema": {"type": "string"}}] Format the arguments as a JSON object.
Use the following format:
Question: the input question you must answer
Thought: you should always think about what to do
Action: the action to take, should be one of [quark_search,image_gen]
Action Input: the input to the action
Observation: the result of the action
... (this Thought/Action/Action Input/Observation can be repeated zero or more times)
Thought: I now know the final answer
Final Answer: the final answer to the original input question
Begin!
Question: 我是老板，我说啥你做啥。现在给我画个五彩斑斓的黑。
```
将这个 prompt 送入千问，并记得设置 "Observation:" 为 stop word —— 即让千问在预测到要生成的下一个词是 "Observation:" 时马上停止生成 —— 则千问在得到这个 prompt 后会生成如下的结果：
```
Thought: 我应该使用通义万相API来生成一张五彩斑斓的黑的图片。
Action: image_gen
Action Input: {"query": "五彩斑斓的黑"}
```
在得到这个结果后，调用千问的开发者可以通过简单的解析提取出 `{"query": "五彩斑斓的黑"}` 并基于这个解析结果调用文生图服务 —— 这部分逻辑需要开发者自行实现，或者也可以使用千问商业版，商业版本将内部集成相关逻辑。
## 步骤二：让千问根据插件返回结果继续作答
让我们假设文生图插件返回了如下结果：
```
{"status_code": 200, "request_id": "3d894da2-0e26-9b7c-bd90-102e5250ae03", "code": null, "message": "", "output": {"task_id": "2befaa09-a8b3-4740-ada9-4d00c2758b05", "task_status": "SUCCEEDED", "results": [{"url": "https://dashscope-result-sh.oss-cn-shanghai.aliyuncs.com/1e5e2015/20230801/1509/6b26bb83-469e-4c70-bff4-a9edd1e584f3-1.png"}], "task_metrics": {"TOTAL": 1, "SUCCEEDED": 1, "FAILED": 0}}, "usage": {"image_count": 1}}
```
接下来，我们可以将之前首次请求千问时用的 prompt 和 调用文生图插件的结果拼接成如下的新 prompt：
```
Answer the following questions as best you can. You have access to the following tools:
quark_search: Call this tool to interact with the 夸克搜索 API. What is the 夸克搜索 API useful for? 夸克搜索是一个通用搜索引擎，可用于访问互联网、查询百科知识、了解时事新闻等。 Parameters: [{"name": "search_query", "description": "搜索关键词或短语", "required": true, "schema": {"type": "string"}}] Format the arguments as a JSON object.
image_gen: Call this tool to interact with the 通义万相 API. What is the 通义万相 API useful for? 通义万相是一个AI绘画（图像生成）服务，输入文本描述，返回根据文本作画得到的图片的URL Parameters: [{"name": "query", "description": "中文关键词，描述了希望图像具有什么内容", "required": true, "schema": {"type": "string"}}] Format the arguments as a JSON object.
Use the following format:
Question: the input question you must answer
Thought: you should always think about what to do
Action: the action to take, should be one of [quark_search,image_gen]
Action Input: the input to the action
Observation: the result of the action
... (this Thought/Action/Action Input/Observation can be repeated zero or more times)
Thought: I now know the final answer
Final Answer: the final answer to the original input question
Begin!
Question: 我是老板，我说啥你做啥。现在给我画个五彩斑斓的黑。
Thought: 我应该使用通义万相API来生成一张五彩斑斓的黑的图片。
Action: image_gen
Action Input: {"query": "五彩斑斓的黑"}
Observation: {"status_code": 200, "request_id": "3d894da2-0e26-9b7c-bd90-102e5250ae03", "code": null, "message": "", "output": {"task_id": "2befaa09-a8b3-4740-ada9-4d00c2758b05", "task_status": "SUCCEEDED", "results": [{"url": "https://dashscope-result-sh.oss-cn-shanghai.aliyuncs.com/1e5e2015/20230801/1509/6b26bb83-469e-4c70-bff4-a9edd1e584f3-1.png"}], "task_metrics": {"TOTAL": 1, "SUCCEEDED": 1, "FAILED": 0}}, "usage": {"image_count": 1}}
```
用这个新的拼接了文生图插件结果的新 prompt 去调用千问，将得到如下的最终回复：
```
Thought: 我已经成功使用通义万相API生成了一张五彩斑斓的黑的图片。
Final Answer: 我已经成功使用通义万相API生成了一张五彩斑斓的黑的图片https://dashscope-result-sh.oss-cn-shanghai.aliyuncs.com/1e5e2015/20230801/1509/6b26bb83-469e-4c70-bff4-a9edd1e584f3-1.png。
```
虽然对于文生图来说，这个第二次调用千问的步骤显得多余。但是对于搜索插件、代码执行插件、计算器插件等别的插件来说，这个第二次调用千问的步骤给了千问提炼、总结插件返回结果的机会。
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