| # ReAct Prompting 示例 | |
| 本文档将介绍如何用 ReAct Prompting 技术命令千问使用工具。 | |
| 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)。 | |
| 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)。 | |
| ## 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 （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： | |
|  | |
| ``` | |
| 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。 | |
| ## FAQ | |
| **怎么配置 "Observation" 这个 stop word？** | |
| 通过 chat 接口的 stop_words_ids 指定： | |
| ```py | |
| react_stop_words = [ | |
| # tokenizer.encode('Observation'), # [37763, 367] | |
| tokenizer.encode('Observation:'), # [37763, 367, 25] | |
| tokenizer.encode('Observation:\n'), # [37763, 367, 510] | |
| ] | |
| response, history = model.chat( | |
| tokenizer, query, history, | |
| stop_words_ids=react_stop_words # 此接口用于增加 stop words | |
| ) | |
| ``` | |
| 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。 | |
| 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。 | |
| **对 top_p 等推理参数有调参建议吗？** | |
| 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。 | |
| 可以按如下方式调整 top_p 为 0.5： | |
| ```py | |
| model.generation_config.top_p = 0.5 | |
| ``` | |
| 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： | |
| ```py | |
| model.generation_config.do_sample = False # greedy decoding | |
| ``` | |
| 此外，我们在 `model.chat()` 接口也提供了调整 top_p 等参数的接口。 | |
| **有解析Action、Action Input的参考代码吗？** | |
| 有的，可以参考： | |
| ```py | |
| def parse_latest_plugin_call(text: str) -> Tuple[str, str]: | |
| i = text.rfind('\nAction:') | |
| j = text.rfind('\nAction Input:') | |
| k = text.rfind('\nObservation:') | |
| if 0 <= i < j: # If the text has `Action` and `Action input`, | |
| if k < j: # but does not contain `Observation`, | |
| # then it is likely that `Observation` is ommited by the LLM, | |
| # because the output text may have discarded the stop word. | |
| text = text.rstrip() + '\nObservation:' # Add it back. | |
| k = text.rfind('\nObservation:') | |
| if 0 <= i < j < k: | |
| plugin_name = text[i + len('\nAction:'):j].strip() | |
| plugin_args = text[j + len('\nAction Input:'):k].strip() | |
| return plugin_name, plugin_args | |
| return '', '' | |
| ``` | |
| 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。 | |