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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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本文档将介绍如何用 ReAct Prompting 技术命令千问使用工具。
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本文档主要基本的原理概念介绍，并在文末附上了一些具体实现相关的 FAQ，但不含被调用插件的实际实现。如果您更喜欢一边调试实际可执行的代码、一边理解原理，可以转而阅读整合了 LangChain 常用工具的这个 [ipython notebook](https://github.com/QwenLM/Qwen-7B/blob/main/examples/langchain_tooluse.ipynb)。
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此外，本文档和前述的 ipython notebook 都仅介绍单轮对话的实现。如果想了解多轮对话下的实现，可参见 [react_demo.py](https://github.com/QwenLM/Qwen-7B/blob/main/examples/react_demo.py)。
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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 = '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 prompt 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 prompt 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: 现在给我画个五彩斑斓的黑。
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```
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将这个 prompt 送入千问，并记得设置 "Observation" 为 stop word （见本文末尾的 FAQ）—— 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：
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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: 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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## FAQ
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**怎么配置 "Observation" 这个 stop word？**
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通过 chat 接口的 stop_words_ids 指定：
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```py
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react_stop_words = [
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# tokenizer.encode('Observation'), # [37763, 367]
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tokenizer.encode('Observation:'), # [37763, 367, 25]
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tokenizer.encode('Observation:\n'), # [37763, 367, 510]
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]
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response, history = model.chat(
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tokenizer, query, history,
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stop_words_ids=react_stop_words # 此接口用于增加 stop words
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)
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```
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如果报错称不存在 stop_words_ids 此参数，可能是因为您用了老的代码，请重新执行 from_pretrained 拉取新的代码和模型。
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需要注意的是，当前的 tokenizer 对 `\n` 有一系列较复杂的聚合操作。比如例子中的`:\n`这两个字符便被聚合成了一个 token。因此配置 stop words 需要非常细致地预估 tokenizer 的行为。
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**对 top_p 等推理参数有调参建议吗？**
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通常来讲，较低的 top_p 会有更高的准确度，但会牺牲回答的多样性、且更易出现重复某个词句的现象。
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可以按如下方式调整 top_p 为 0.5：
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```py
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model.generation_config.top_p = 0.5
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```
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特别的，可以用如下方式关闭 top-p sampling，改用 greedy sampling，效果上相当于 top_p=0 或 temperature=0：
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```py
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model.generation_config.do_sample = False # greedy decoding
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```
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此外，我们在 `model.chat()` 接口也提供了调整 top_p 等参数的接口。
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**有解析Action、Action Input的参考代码吗？**
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有的，可以参考：
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```py
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def parse_latest_plugin_call(text: str) -> Tuple[str, str]:
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i = text.rfind('\nAction:')
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j = text.rfind('\nAction Input:')
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k = text.rfind('\nObservation:')
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if 0 <= i < j: # If the text has `Action` and `Action input`,
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if k < j: # but does not contain `Observation`,
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# then it is likely that `Observation` is ommited by the LLM,
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# because the output text may have discarded the stop word.
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text = text.rstrip() + '\nObservation:' # Add it back.
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k = text.rfind('\nObservation:')
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if 0 <= i < j < k:
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plugin_name = text[i + len('\nAction:'):j].strip()
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plugin_args = text[j + len('\nAction Input:'):k].strip()
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return plugin_name, plugin_args
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return '', ''
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```
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此外，如果输出的 Action Input 内容是一段表示 JSON 对象的文本，我们建议使用 `json5` 包的 `json5.loads(...)` 方法加载。
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