84 lines
2.9 KiB
Python
84 lines
2.9 KiB
Python
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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import langchain
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from langchain.schema import (
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AgentAction
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)
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# langchain.debug = True
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from dev_opsgpt.tools import (
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TOOL_SETS, TOOL_DICT,
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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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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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return_intermediate_steps=True
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)
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class ToolChat(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.tools = toLangchainTools([TOOL_DICT[i] for i in TOOL_SETS if i in TOOL_DICT])
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def create_task(self, query: str, history: List[History], model, **kargs):
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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", "{query}")]))
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# chat_prompt = ChatPromptTemplate.from_messages(
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# [i.to_msg_tuple() for i in history] + [("human", "{query}")]
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# )
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tools = kargs.get("tool_sets", [])
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tools = toLangchainTools([TOOL_DICT[i] for i in tools if i in TOOL_DICT])
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agent = get_tool_agent(tools if tools else self.tools, model)
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content = agent(query)
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logger.debug(f"content: {content}")
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s = ""
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if isinstance(content, str):
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s = content
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else:
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for i in content["intermediate_steps"]:
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for j in i:
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if isinstance(j, AgentAction):
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s += j.log + "\n"
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else:
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s += "Observation: " + str(j) + "\n"
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s += "final answer:" + content["output"]
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# chain = LLMChain(prompt=chat_prompt, llm=model)
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# content = chain({"tools": tools, "input": query})
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return {"answer": "", "docs": ""}, {"text": s}
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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", self.tool_prompt)]
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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": ""} |