update README.md and README_en.md
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imClumsyPanda
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from .chatglm_with_shared_memory_openai_llm import *
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if __name__ == "__main__":
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# 创建 ChatglmWithSharedMemoryOpenaiLLM 类的实例
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chatglm_instance = ChatglmWithSharedMemoryOpenaiLLM()
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# 使用代理链运行一些示例输入
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chatglm_instance.agent_chain.run(input="我跟露露聊了什么?")
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chatglm_instance.agent_chain.run(input="她开心吗?")
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chatglm_instance.agent_chain.run(input="她有表达意见吗?")
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chatglm_instance.agent_chain.run(input="根据历史对话总结下?")
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chatglm_instance.agent_chain.run(input="""可以拓展下吗?，比如写个小作文。
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大纲：游戏的美好回忆，触不可及的距离，不在乎得失
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主题：露露的陪伴无比珍贵
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背景：游戏，通话，当下
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开篇需要以游戏相识你挑逗的话语讲起
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""")
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import torch
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from langchain.agents import ZeroShotAgent, Tool, AgentExecutor
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from langchain.llms import OpenAI
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from langchain.memory import ConversationBufferMemory, ReadOnlySharedMemory
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from langchain.chains import LLMChain, RetrievalQA
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from langchain.embeddings.huggingface import HuggingFaceEmbeddings
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from langchain.prompts import PromptTemplate
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from langchain.text_splitter import CharacterTextSplitter
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from langchain.vectorstores import Chroma
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from langchain.document_loaders import TextLoader
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from models import ChatGLM
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import sentence_transformers
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import os
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import readline
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from pathlib import Path
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class ChatglmWithSharedMemoryOpenaiLLM:
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def __init__(self, params: dict = None):
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params = params or {}
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self.embedding_model = params.get('embedding_model', 'text2vec')
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self.vector_search_top_k = params.get('vector_search_top_k', 6)
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self.llm_model = params.get('llm_model', 'chatglm-6b')
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self.llm_history_len = params.get('llm_history_len', 10)
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self.device = 'cuda' if params.get('use_cuda', False) else 'cpu'
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self._embedding_model_dict = {
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"ernie-tiny": "nghuyong/ernie-3.0-nano-zh",
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"ernie-base": "nghuyong/ernie-3.0-base-zh",
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"text2vec": "GanymedeNil/text2vec-large-chinese",
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}
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self._llm_model_dict = {
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"chatglm-6b-int4-qe": "THUDM/chatglm-6b-int4-qe",
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"chatglm-6b-int4": "THUDM/chatglm-6b-int4",
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"chatglm-6b": "THUDM/chatglm-6b",
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}
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self.init_cfg()
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self.init_docsearch()
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self.init_state_of_history()
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self.summry_chain, self.memory = self.agents_answer()
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self.agent_chain = self.create_agent_chain()
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def init_cfg(self):
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self.chatglm = ChatGLM()
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self.chatglm.load_model(model_name_or_path=self._llm_model_dict[self.llm_model])
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self.chatglm.history_len = self.llm_history_len
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self.embeddings = HuggingFaceEmbeddings(model_name=self._embedding_model_dict[self.embedding_model],)
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self.embeddings.client = sentence_transformers.SentenceTransformer(self.embeddings.model_name,
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device=self.device)
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def init_docsearch(self):
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doc_path = str(Path.cwd() / "content/state_of_the_search.txt")
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loader = TextLoader(doc_path)
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documents = loader.load()
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text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
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texts = text_splitter.split_documents(documents)
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docsearch = Chroma.from_documents(texts, self.embeddings, collection_name="state-of-search")
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self.state_of_search = RetrievalQA.from_chain_type(llm=self.chatglm, chain_type="stuff", retriever=docsearch.as_retriever())
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def init_state_of_history(self):
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doc_path = str(Path.cwd() / "content/state_of_the_history.txt")
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loader = TextLoader(doc_path)
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documents = loader.load()
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text_splitter = CharacterTextSplitter(chunk_size=100, chunk_overlap=0)
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texts = text_splitter.split_documents(documents)
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docsearch = Chroma.from_documents(texts, self.embeddings, collection_name="state-of-history")
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self.state_of_history = RetrievalQA.from_chain_type(llm=self.chatglm, chain_type="stuff", retriever=docsearch.as_retriever())
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def agents_answer(self):
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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=self.chatglm,
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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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return summry_chain, memory
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def create_agent_chain(self):
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tools = [
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Tool(
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name="State of Search QA System",
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func=self.state_of_search.run,
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description="当您需要搜索有关问题时非常有用。输入应该是一个完整的问题。"
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),
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Tool(
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name="state-of-history-qa",
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func=self.state_of_history.run,
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description="跟露露的历史对话 - 当提出我们之间发生了什么事请时，这里面的回答是很有用的"
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),
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Tool(
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name="Summary",
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func=self.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 = """你需要充当一个倾听者,尽量回答人类的问题,你可以使用这里工具,它们非常有用:"""
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suffix = """Begin!
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{chat_history}
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Question: {input}
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{agent_scratchpad}"""
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prompt = ZeroShotAgent.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", "chat_history", "agent_scratchpad"]
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)
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llm_chain = LLMChain(llm=OpenAI(temperature=0), prompt=prompt)
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agent = ZeroShotAgent(llm_chain=llm_chain, tools=tools, verbose=True)
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agent_chain = AgentExecutor.from_agent_and_tools(agent=agent, tools=tools, verbose=True, memory=self.memory)
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return agent_chain
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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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from agent import ChatglmWithSharedMemoryOpenaiLLM
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if __name__ == "__main__":
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# 创建 ChatglmWithSharedMemoryOpenaiLLM 类的实例
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chatglm_instance = ChatglmWithSharedMemoryOpenaiLLM()
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# 使用代理链运行一些示例输入
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chatglm_instance.agent_chain.run(input="我跟露露聊了什么?")
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chatglm_instance.agent_chain.run(input="她开心吗?")
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chatglm_instance.agent_chain.run(input="她有表达意见吗?")
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chatglm_instance.agent_chain.run(input="根据历史对话总结下?")
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# chatglm_instance.agent_chain.run(input="""可以拓展下吗?，比如写个小作文。
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# 大纲：游戏的美好回忆，触不可及的距离，不在乎得失
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# 主题：露露的陪伴无比珍贵
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# 背景：游戏，通话，当下
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# 开篇需要以游戏相识你挑逗的话语讲起
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# """)
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