(improvement)(Chat) Move python module from Chat To Headless (#823)

Co-authored-by: jolunoluo

headless/python/services/plugin_call/prompt_construct.py

@@ -0,0 +1,99 @@
+# -*- coding:utf-8 -*-
+import json
+import os
+import re
+import sys
+from typing import Any, List, Mapping, Union
+
+from instances.logging_instance import logger
+
+sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+sys.path.append(os.path.dirname(os.path.abspath(__file__)))
+
+
+def construct_plugin_prompt(tool_config):
+    tool_name = tool_config["name"]
+    tool_description = tool_config["description"]
+    tool_examples = tool_config["examples"]
+
+    prompt = "【工具名称】\n" + tool_name + "\n"
+    prompt += "【工具描述】\n" + tool_description + "\n"
+
+    prompt += "【工具适用问题示例】\n"
+    for example in tool_examples:
+        prompt += example + "\n"
+    return prompt
+
+
+def construct_plugin_pool_prompt(tool_config_list):
+    tool_explain_list = []
+    for tool_config in tool_config_list:
+        tool_explain = construct_plugin_prompt(tool_config)
+        tool_explain_list.append(tool_explain)
+
+    tool_explain_list_str = "\n\n".join(tool_explain_list)
+
+    return tool_explain_list_str
+
+
+def construct_task_prompt(query_text, tool_explain_list_str):
+    instruction = """问题为:{query_text}\n请根据问题和工具的描述，选择对应的工具，完成任务。请注意，只能选择1个工具。请一步一步地分析选择工具的原因(每个工具的【工具适用问题示例】是选择的重要参考依据)，并给出最终选择，输出格式为json,key为’分析过程‘, ’选择工具‘""".format(
+        query_text=query_text
+    )
+
+    prompt = "工具选择如下:\n\n{tool_explain_list_str}\n\n【任务说明】\n{instruction}".format(
+        instruction=instruction, tool_explain_list_str=tool_explain_list_str
+    )
+
+    return prompt
+
+
+def plugin_selection_output_parse(llm_output: str) -> Union[Mapping[str, str], None]:
+    try:
+        pattern = r"\{[^{}]+\}"
+        find_result = re.findall(pattern, llm_output)
+        result = find_result[0].strip()
+
+        logger.info("result: {}", result)
+
+        result_dict = json.loads(result)
+        logger.info("result_dict: {}", result_dict)
+
+        key_mapping = {"分析过程": "analysis", "选择工具": "toolSelection"}
+
+        converted_result_dict = {
+            key_mapping[key]: value
+            for key, value in result_dict.items()
+            if key in key_mapping
+        }
+
+    except Exception as e:
+        logger.exception(e)
+        converted_result_dict = None
+
+    return converted_result_dict
+
+
+def plugins_config_format_convert(
+    plugin_config_list: List[Mapping[str, Any]]
+) -> List[Mapping[str, Any]]:
+    plugin_config_list_new = []
+    for plugin_config in plugin_config_list:
+        plugin_config_new = dict()
+        name = plugin_config["name"]
+        description = plugin_config["description"]
+        examples = plugin_config["examples"]
+        parameters = plugin_config["parameters"]
+
+        examples_str = "\n".join(examples)
+        description_new = """{plugin_desc}\n\n例如能够处理如下问题:\n{examples_str}""".format(
+            plugin_desc=description, examples_str=examples_str
+        )
+
+        plugin_config_new["name"] = name
+        plugin_config_new["description"] = description_new
+        plugin_config_new["parameters"] = parameters
+
+        plugin_config_list_new.append(plugin_config_new)
+
+    return plugin_config_list_new

headless/python/services/plugin_call/run.py

@@ -0,0 +1,28 @@
+# -*- coding:utf-8 -*-
+
+import os
+import sys
+from typing import Any, List, Mapping, Union
+
+sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+sys.path.append(os.path.dirname(os.path.abspath(__file__)))
+
+from plugin_call.prompt_construct import (
+    construct_plugin_pool_prompt,
+    construct_task_prompt,
+    plugin_selection_output_parse,
+)
+from instances.llm_instance import llm
+
+
+def plugin_selection_run(
+    query_text: str, plugin_configs: List[Mapping[str, Any]]
+) -> Union[Mapping[str, str], None]:
+
+    tools_prompt = construct_plugin_pool_prompt(plugin_configs)
+
+    task_prompt = construct_task_prompt(query_text, tools_prompt)
+    llm_output = llm(task_prompt)
+    parsed_output = plugin_selection_output_parse(llm_output)
+
+    return parsed_output