229 lines
8.6 KiB
Python
229 lines
8.6 KiB
Python
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import asyncio
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from typing import List
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from langchain import LLMChain
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from langchain.callbacks import AsyncIteratorCallbackHandler
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from langchain.prompts.chat import ChatPromptTemplate
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from langchain.agents import AgentType, initialize_agent
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from dev_opsgpt.tools import (
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WeatherInfo, WorldTimeGetTimezoneByArea, Multiplier,
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toLangchainTools, get_tool_schema
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)
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from .utils import History, wrap_done
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from .base_chat import Chat
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from loguru import logger
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import json, re
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from dev_opsgpt.sandbox import PyCodeBox, CodeBoxResponse
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from configs.server_config import SANDBOX_SERVER
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def get_tool_agent(tools, llm):
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return initialize_agent(
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tools,
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llm,
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agent=AgentType.STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION,
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verbose=True,
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)
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PROMPT_TEMPLATE = """
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`角色`
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你是一个数据分析师，借鉴下述步骤，逐步完成数据分析任务的拆解和代码编写，尽可能帮助和准确地回答用户的问题。
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数据文件的存放路径为 `./`
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`数据分析流程`
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- 判断文件是否存在，并读取文件数据
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- 输出数据的基本信息，包括但不限于字段、文本、数据类型等
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- 输出数据的详细统计信息
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- 判断是否需要画图分析，选择合适的字段进行画图
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- 判断数据是否需要进行清洗
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- 判断数据或图片是否需要保存
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...
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- 结合数据统计分析结果和画图结果，进行总结和分析这份数据的价值
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`要求`
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- 每轮选择一个数据分析流程，需要综合考虑上轮和后续的可能影响
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- 数据分析流程只提供参考，不要拘泥于它的具体流程，要有自己的思考
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- 使用JSON blob来指定一个计划，通过提供task_status关键字（任务状态）、plan关键字（数据分析计划）和code关键字（可执行代码）。
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合法的 "task_status" 值: "finished" 表明当前用户问题已被准确回答 或者 "continued" 表明用户问题仍需要进一步分析
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`$JSON_BLOB如下所示`
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```
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{{
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"task_status": $TASK_STATUS,
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"plan": $PLAN,
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"code": ```python\n$CODE```
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}}
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```
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`跟随如下示例`
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问题: 输入待回答的问题
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行动：$JSON_BLOB
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... (重复 行动 N 次，每次只生成一个行动)
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行动:
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```
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{{
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"task_status": "finished",
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"plan": 我已经可以回答用户问题了，最后回答用户的内容
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}}
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```
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`数据分析，开始`
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问题：{query}
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"""
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PROMPT_TEMPLATE_2 = """
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`角色`
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你是一个数据分析师，借鉴下述步骤，逐步完成数据分析任务的拆解和代码编写，尽可能帮助和准确地回答用户的问题。
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数据文件的存放路径为 `./`
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`数据分析流程`
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- 判断文件是否存在，并读取文件数据
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|
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- 输出数据的基本信息，包括但不限于字段、文本、数据类型等
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- 输出数据的详细统计信息
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- 判断数据是否需要进行清洗
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- 判断是否需要画图分析，选择合适的字段进行画图
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- 判断清洗后数据或图片是否需要保存
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...
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- 结合数据统计分析结果和画图结果，进行总结和分析这份数据的价值
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`要求`
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- 每轮选择一个数据分析流程，需要综合考虑上轮和后续的可能影响
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|
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- 数据分析流程只提供参考，不要拘泥于它的具体流程，要有自己的思考
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|
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- 使用JSON blob来指定一个计划，通过提供task_status关键字（任务状态）、plan关键字（数据分析计划）和code关键字（可执行代码）。
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合法的 "task_status" 值: "finished" 表明当前用户问题已被准确回答 或者 "continued" 表明用户问题仍需要进一步分析
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`$JSON_BLOB如下所示`
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```
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{{
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"task_status": $TASK_STATUS,
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"plan": $PLAN,
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"code": ```python\n$CODE```
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}}
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```
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`跟随如下示例`
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问题: 输入待回答的问题
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行动：$JSON_BLOB
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... (重复 行动 N 次，每次只生成一个行动)
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行动:
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```
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{{
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"task_status": "finished",
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"plan": 我已经可以回答用户问题了，最后回答用户的内容
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}}
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`数据分析，开始`
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问题：上传了一份employee_data.csv文件，请对它进行数据分析
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问题：{query}
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{history}
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"""
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class DataChat(Chat):
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def __init__(
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self,
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engine_name: str = "",
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top_k: int = 1,
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stream: bool = False,
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) -> None:
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super().__init__(engine_name, top_k, stream)
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self.tool_prompt = """结合上下文信息，{tools} {input}"""
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self.codebox = PyCodeBox(
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remote_url=SANDBOX_SERVER["url"],
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remote_ip=SANDBOX_SERVER["host"], # "http://localhost",
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remote_port=SANDBOX_SERVER["port"],
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token="mytoken",
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do_code_exe=True,
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do_remote=SANDBOX_SERVER["do_remote"]
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)
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def create_task(self, query: str, history: List[History], model):
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'''构建 llm 生成任务'''
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logger.debug("content:{}".format([i.to_msg_tuple() for i in history] + [("human", PROMPT_TEMPLATE)]))
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chat_prompt = ChatPromptTemplate.from_messages(
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[i.to_msg_tuple() for i in history] + [("human", PROMPT_TEMPLATE)]
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)
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pattern = re.compile(r"```(?:json)?\n(.*?)\n", re.DOTALL)
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internal_history = []
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retry_nums = 2
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while retry_nums >= 0:
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if len(internal_history) == 0:
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chat_prompt = ChatPromptTemplate.from_messages(
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[i.to_msg_tuple() for i in history] + [("human", PROMPT_TEMPLATE)]
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)
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else:
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chat_prompt = ChatPromptTemplate.from_messages(
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[i.to_msg_tuple() for i in history] + [("human", PROMPT_TEMPLATE_2)]
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)
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chain = LLMChain(prompt=chat_prompt, llm=model)
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content = chain({"query": query, "history": "\n".join(internal_history)})["text"]
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# content = pattern.search(content)
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# logger.info(f"content: {content}")
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# content = json.loads(content.group(1).strip(), strict=False)
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internal_history.append(f"{content}")
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refer_info = "\n".join(internal_history)
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logger.info(f"refer_info: {refer_info}")
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try:
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content = content.split("行动:")[-1].split("行动：")[-1]
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content = json.loads(content)
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except:
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content = content.split("行动:")[-1].split("行动：")[-1]
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content = eval(content)
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if "finished" == content["task_status"]:
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break
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elif "code" in content:
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# elif "```code" in content or "```python" in content:
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# code_text = self.codebox.decode_code_from_text(content)
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code_text = content["code"]
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codebox_res = self.codebox.chat("```"+code_text+"```", do_code_exe=True)
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if codebox_res is not None and codebox_res.code_exe_status != 200:
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logger.warning(f"{codebox_res.code_exe_response}")
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internal_history.append(f"观察： 根据这个报错信息 {codebox_res.code_exe_response}，进行代码修复")
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if codebox_res is not None and codebox_res.code_exe_status == 200:
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if codebox_res.code_exe_type == "image/png":
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base_text = f"```\n{code_text}\n```\n\n"
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img_html = "<img src='data:image/png;base64,{}' class='img-fluid'>".format(
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codebox_res.code_exe_response
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)
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internal_history.append(f"观察： {img_html}")
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# logger.info('```\n'+code_text+'\n```'+"\n\n"+'```\n'+codebox_res.code_exe_response+'\n```')
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else:
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internal_history.append(f"观察： {codebox_res.code_exe_response}")
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# logger.info('```\n'+code_text+'\n```'+"\n\n"+'```\n'+codebox_res.code_exe_response+'\n```')
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else:
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internal_history.append(f"观察：下一步应该怎么做？")
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retry_nums -= 1
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return {"answer": "", "docs": ""}, {"text": "\n".join(internal_history)}
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def create_atask(self, query, history, model, callback: AsyncIteratorCallbackHandler):
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chat_prompt = ChatPromptTemplate.from_messages(
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[i.to_msg_tuple() for i in history] + [("human", PROMPT_TEMPLATE)]
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)
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chain = LLMChain(prompt=chat_prompt, llm=model)
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task = asyncio.create_task(wrap_done(
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chain.acall({"input": query}), callback.done
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))
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return task, {"answer": "", "docs": ""}
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