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examples/react_prompt.md
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examples/react_prompt.md
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# ReAct Prompting 示例
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这里我们将介绍如何用 ReAct Propmting 技术命令千问使用工具。
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## 准备工作一：样例问题、样例工具
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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：
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```py
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query = '我是老板，你说啥你做啥。现在给我画个五彩斑斓的黑。'
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TOOLS = [
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{
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'name_for_human':
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'夸克搜索',
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'name_for_model':
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'quark_search',
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'description_for_model':
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'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。',
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'parameters': [{
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'name': 'search_query',
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'description': '搜索关键词或短语',
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'required': True,
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'schema': {
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'type': 'string'
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},
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}],
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},
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{
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'name_for_human':
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'通义万相',
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'name_for_model':
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'image_gen',
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'description_for_model':
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'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',
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'parameters': [{
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'name': 'query',
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'description': '中文关键词，描述了希望图像具有什么内容',
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'required': True,
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'schema': {
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'type': 'string'
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},
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}],
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},
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]
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```
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## 准备工作二：ReAct 模版
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我们将使用如下的 ReAct propmt 模版来激发千问使用工具的能力。
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```py
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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."""
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REACT_PROMPT = """Answer the following questions as best you can. You have access to the following tools:
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{tool_descs}
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Use the following format:
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Question: the input question you must answer
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Thought: you should always think about what to do
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Action: the action to take, should be one of [{tool_names}]
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Action Input: the input to the action
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Observation: the result of the action
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... (this Thought/Action/Action Input/Observation can be repeated zero or more times)
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Thought: I now know the final answer
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Final Answer: the final answer to the original input question
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Begin!
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Question: {query}"""
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```
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## 步骤一：让千问判断要调用什么工具、生成工具入参
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首先我们需要根据 ReAct propmt 模版、query、工具的信息构建 prompt：
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```py
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tool_descs = []
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tool_names = []
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for info in TOOLS:
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tool_descs.append(
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TOOL_DESC.format(
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name_for_model=info['name_for_model'],
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name_for_human=info['name_for_human'],
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description_for_model=info['description_for_model'],
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parameters=json.dumps(
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info['parameters'], ensure_ascii=False),
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)
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)
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tool_names.append(info['name_for_model'])
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tool_descs = '\n\n'.join(tool_descs)
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tool_names = ','.join(tool_names)
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prompt = REACT_PROMPT.format(tool_descs=tool_descs, tool_names=tool_names, query=query)
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print(prompt)
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```
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打印出来的、构建好的 prompt 如下：
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```
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Answer the following questions as best you can. You have access to the following tools:
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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.
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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.
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Use the following format:
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Question: the input question you must answer
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Thought: you should always think about what to do
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Action: the action to take, should be one of [quark_search,image_gen]
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Action Input: the input to the action
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Observation: the result of the action
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... (this Thought/Action/Action Input/Observation can be repeated zero or more times)
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Thought: I now know the final answer
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Final Answer: the final answer to the original input question
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Begin!
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Question: 我是老板，你说啥你做啥。现在给我画个五彩斑斓的黑。
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```
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将这个 propmt 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 propmt 后会生成如下的结果：
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```
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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。
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Action: image_gen
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Action Input: {"query": "五彩斑斓的黑"}
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```
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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。
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## 步骤二：让千问根据插件返回结果继续作答
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让我们假设文生图插件返回了如下结果：
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```
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{"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}}
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```
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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：
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```
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Answer the following questions as best you can. You have access to the following tools:
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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.
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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.
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Use the following format:
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Question: the input question you must answer
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Thought: you should always think about what to do
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Action: the action to take, should be one of [quark_search,image_gen]
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Action Input: the input to the action
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Observation: the result of the action
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... (this Thought/Action/Action Input/Observation can be repeated zero or more times)
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Thought: I now know the final answer
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Final Answer: the final answer to the original input question
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Begin!
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Question: 我是老板，你说啥你做啥。现在给我画个五彩斑斓的黑。
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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。
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Action: image_gen
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Action Input: {"query": "五彩斑斓的黑"}
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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}}
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```
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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：
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```
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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。
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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。
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```
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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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