From 7c4fd767826467e57fc43dca179704e142bfd3ef Mon Sep 17 00:00:00 2001
From: LDLINGLINGLING <47373076+LDLINGLINGLING@users.noreply.github.com>
Date: Wed, 9 Oct 2024 17:01:56 +0800
Subject: [PATCH] Create build_react_prompt.py
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react的基本格式构建prompt
---
 agent_demo/build_react_prompt.py | 100 +++++++++++++++++++++++++++++++
 1 file changed, 100 insertions(+)
 create mode 100644 agent_demo/build_react_prompt.py

diff --git a/agent_demo/build_react_prompt.py b/agent_demo/build_react_prompt.py
new file mode 100644
index 0000000..e29d916
--- /dev/null
+++ b/agent_demo/build_react_prompt.py
@@ -0,0 +1,100 @@
+import json
+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}"""
+
+# ReAct prompting 的 instruction 模版，将包含插件的详细信息。
+PROMPT_REACT = """Answer the following questions as best you can. You have access to the following tools:
+
+{tools_text}
+
+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 [{tools_name_text}]
+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}"""
+
+def build_input_text(chat_history, list_of_plugin_info) -> str:
+    # 候选插件的详细信息
+    tools_text = []
+    for plugin_info in list_of_plugin_info:
+        tool = TOOL_DESC.format(
+            name_for_model=plugin_info["name_for_model"],
+            name_for_human=plugin_info["name_for_human"],
+            description_for_model=plugin_info["description_for_model"],
+            parameters=json.dumps(plugin_info["parameters"], ensure_ascii=False),
+        )
+        if plugin_info.get("args_format", "json") == "json":
+            tool += " Format the arguments as a JSON object."
+        elif plugin_info["args_format"] == "code":
+            tool += " Enclose the code within triple backticks (`) at the beginning and end of the code."
+        else:
+            raise NotImplementedError
+        tools_text.append(tool)
+    tools_text = "\n\n".join(tools_text)
+
+    # 候选插件的代号
+    tools_name_text = ", ".join(
+        [plugin_info["name_for_model"] for plugin_info in list_of_plugin_info]
+    )
+
+    im_start = "<|im_start|>"
+    im_end = "<|im_end|>"
+    prompt = f"{im_start}system\nYou are a helpful assistant.{im_end}"
+    for i, (query, response) in enumerate(chat_history):
+        if list_of_plugin_info:  # 如果有候选插件
+            # 倒数第一轮或倒数第二轮对话填入详细的插件信息，但具体什么位置填可以自行判断
+            if (len(chat_history) == 1) or (i == len(chat_history) - 2):
+                query = PROMPT_REACT.format(
+                    tools_text=tools_text,
+                    tools_name_text=tools_name_text,
+                    query=query,
+                )
+        query = query.lstrip(
+            "\n"
+        ).rstrip()  # 重要！若不 strip 会与训练时数据的构造方式产生差异。
+        response = response.lstrip(
+            "\n"
+        ).rstrip()  # 重要！若不 strip 会与训练时数据的构造方式产生差异。
+        # 使用续写模式（text completion）时，需要用如下格式区分用户和AI：
+        prompt += f"\n{im_start}user\n{query}{im_end}"
+        prompt += f"\n{im_start}assistant\n{response}"
+    return prompt
+
+def parse_latest_plugin_call(text):
+    plugin_name, plugin_args = "", ""
+    if text.startswith("\nAction:"):
+        i = text.rfind("\nAction:")
+        action_string = "\nAction:"
+    else:
+        i = text.find("Action:")
+        action_string = "Action:"
+    if "\nAction Input:" in text:
+        j = text.rfind("\nAction Input:")
+        action_input_string = "\nAction Input:"
+    else:
+        j = text.rfind("Action Input:")
+        action_input_string = "Action Input:"
+    if "\nObservation:" in text:
+        k = text.rfind("\nObservation:")
+        observation_string = "\nObservation:"
+    else:
+        k = text.rfind("Observation:")
+        observation_string = "Observation:"
+    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() + observation_string  # Add it back.
+        k = text.rfind(observation_string)
+        plugin_name = text[i + len(action_string) : j].strip()
+        plugin_args = text[j + len(action_input_string) : k].strip()
+        text = text[:k]
+    return plugin_name, plugin_args, text