mirror of
https://github.com/RYDE-WORK/Langchain-Chatchat.git
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33bbb4779e
loader.py: 模型重载 定义 generatorAnswer 增加 AnswerResultStream 定义generate_with_callback收集器，在每次响应时将队列数据同步到AnswerResult requirements.txt 变更项目依赖
98 lines
3.0 KiB
Python
98 lines
3.0 KiB
Python
import sys
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import os
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sys.path.append(os.path.dirname(os.path.abspath(__file__)) + '/../')
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import asyncio
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from argparse import Namespace
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from models.loader.args import parser
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from models.loader import LoaderCheckPoint
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from langchain.agents import initialize_agent, Tool
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from langchain.agents import AgentType
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import models.shared as shared
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from langchain.chains import LLMChain
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from langchain.memory import ConversationBufferMemory, ReadOnlySharedMemory
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from langchain.prompts import PromptTemplate
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from langchain.agents import ZeroShotAgent, Tool, AgentExecutor
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from typing import List, Set
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class CustomLLMSingleActionAgent(ZeroShotAgent):
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allowed_tools: List[str]
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def __init__(self, *args, **kwargs):
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super(CustomLLMSingleActionAgent, self).__init__(*args, **kwargs)
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self.allowed_tools = kwargs['allowed_tools']
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def get_allowed_tools(self) -> Set[str]:
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return set(self.allowed_tools)
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async def dispatch(args: Namespace):
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args_dict = vars(args)
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shared.loaderCheckPoint = LoaderCheckPoint(args_dict)
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llm_model_ins = shared.loaderLLM()
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template = """This is a conversation between a human and a bot:
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{chat_history}
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Write a summary of the conversation for {input}:
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"""
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prompt = PromptTemplate(
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input_variables=["input", "chat_history"],
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template=template
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)
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memory = ConversationBufferMemory(memory_key="chat_history")
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readonlymemory = ReadOnlySharedMemory(memory=memory)
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summry_chain = LLMChain(
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llm=llm_model_ins,
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prompt=prompt,
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verbose=True,
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memory=readonlymemory, # use the read-only memory to prevent the tool from modifying the memory
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)
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tools = [
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Tool(
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name="Summary",
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func=summry_chain.run,
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description="useful for when you summarize a conversation. The input to this tool should be a string, representing who will read this summary."
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)
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]
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prefix = """Have a conversation with a human, answering the following questions as best you can. You have access to the following tools:"""
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suffix = """Begin!
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Question: {input}
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{agent_scratchpad}"""
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prompt = CustomLLMSingleActionAgent.create_prompt(
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tools,
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prefix=prefix,
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suffix=suffix,
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input_variables=["input", "agent_scratchpad"]
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)
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tool_names = [tool.name for tool in tools]
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llm_chain = LLMChain(llm=llm_model_ins, prompt=prompt)
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agent = CustomLLMSingleActionAgent(llm_chain=llm_chain, tools=tools, allowed_tools=tool_names)
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agent_chain = AgentExecutor.from_agent_and_tools(agent=agent, tools=tools)
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agent_chain.run(input="你好")
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agent_chain.run(input="你是谁?")
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agent_chain.run(input="我们之前聊了什么?")
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if __name__ == '__main__':
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args = None
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args = parser.parse_args(args=['--model-dir', '/media/checkpoint/', '--model', 'vicuna-13b-hf', '--no-remote-model', '--load-in-8bit'])
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loop = asyncio.new_event_loop()
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asyncio.set_event_loop(loop)
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loop.run_until_complete(dispatch(args))
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