update README.md and README_en.md

agent/__main__.py

@@ -1,18 +0,0 @@
-
-from .chatglm_with_shared_memory_openai_llm import *
-
-if __name__ == "__main__":
-    # 创建 ChatglmWithSharedMemoryOpenaiLLM 类的实例
-    chatglm_instance = ChatglmWithSharedMemoryOpenaiLLM()
-
-    # 使用代理链运行一些示例输入
-    chatglm_instance.agent_chain.run(input="我跟露露聊了什么?")
-    chatglm_instance.agent_chain.run(input="她开心吗?")
-    chatglm_instance.agent_chain.run(input="她有表达意见吗?")
-    chatglm_instance.agent_chain.run(input="根据历史对话总结下?")
-    chatglm_instance.agent_chain.run(input="""可以拓展下吗?，比如写个小作文。
-    大纲：游戏的美好回忆，触不可及的距离，不在乎得失
-    主题：露露的陪伴无比珍贵
-    背景：游戏，通话，当下
-    开篇需要以游戏相识你挑逗的话语讲起
-    """)

agent/chatglm_with_shared_memory_openai_llm.py

@@ -1,128 +0,0 @@
-import torch
-from langchain.agents import ZeroShotAgent, Tool, AgentExecutor
-from langchain.llms import OpenAI
-from langchain.memory import ConversationBufferMemory, ReadOnlySharedMemory
-from langchain.chains import LLMChain, RetrievalQA
-from langchain.embeddings.huggingface import HuggingFaceEmbeddings
-from langchain.prompts import PromptTemplate
-from langchain.text_splitter import CharacterTextSplitter
-from langchain.vectorstores import Chroma
-from langchain.document_loaders import TextLoader
-from models import ChatGLM
-import sentence_transformers
-import os
-import readline
-from pathlib import Path
-
-
-class ChatglmWithSharedMemoryOpenaiLLM:
-
-    def __init__(self, params: dict = None):
-        params = params or {}
-        self.embedding_model = params.get('embedding_model', 'text2vec')
-        self.vector_search_top_k = params.get('vector_search_top_k', 6)
-        self.llm_model = params.get('llm_model', 'chatglm-6b')
-        self.llm_history_len = params.get('llm_history_len', 10)
-        self.device = 'cuda' if params.get('use_cuda', False) else 'cpu'
-        self._embedding_model_dict = {
-            "ernie-tiny": "nghuyong/ernie-3.0-nano-zh",
-            "ernie-base": "nghuyong/ernie-3.0-base-zh",
-            "text2vec": "GanymedeNil/text2vec-large-chinese",
-        }
-        self._llm_model_dict = {
-            "chatglm-6b-int4-qe": "THUDM/chatglm-6b-int4-qe",
-            "chatglm-6b-int4": "THUDM/chatglm-6b-int4",
-            "chatglm-6b": "THUDM/chatglm-6b",
-        }
-        self.init_cfg()
-        self.init_docsearch()
-        self.init_state_of_history()
-        self.summry_chain, self.memory = self.agents_answer()
-        self.agent_chain = self.create_agent_chain()
-
-    def init_cfg(self):
-        self.chatglm = ChatGLM()
-        self.chatglm.load_model(model_name_or_path=self._llm_model_dict[self.llm_model])
-        self.chatglm.history_len = self.llm_history_len
-        self.embeddings = HuggingFaceEmbeddings(model_name=self._embedding_model_dict[self.embedding_model],)
-        self.embeddings.client = sentence_transformers.SentenceTransformer(self.embeddings.model_name,
-                                                                           device=self.device)
-
-    def init_docsearch(self):
-        doc_path = str(Path.cwd() / "content/state_of_the_search.txt")
-        loader = TextLoader(doc_path)
-        documents = loader.load()
-        text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
-        texts = text_splitter.split_documents(documents)
-        docsearch = Chroma.from_documents(texts, self.embeddings, collection_name="state-of-search")
-        self.state_of_search = RetrievalQA.from_chain_type(llm=self.chatglm, chain_type="stuff", retriever=docsearch.as_retriever())
-
-    def init_state_of_history(self):
-        doc_path = str(Path.cwd() / "content/state_of_the_history.txt")
-        loader = TextLoader(doc_path)
-        documents = loader.load()
-        text_splitter = CharacterTextSplitter(chunk_size=100, chunk_overlap=0)
-        texts = text_splitter.split_documents(documents)
-        docsearch = Chroma.from_documents(texts, self.embeddings, collection_name="state-of-history")
-        self.state_of_history = RetrievalQA.from_chain_type(llm=self.chatglm, chain_type="stuff", retriever=docsearch.as_retriever())
-
-    def agents_answer(self):
-        template = """This is a conversation between a human and a bot:
-
-        {chat_history}
-
-        Write a summary of the conversation for {input}:
-        """
-
-        prompt = PromptTemplate(
-            input_variables=["input", "chat_history"],
-            template=template
-        )
-        memory = ConversationBufferMemory(memory_key="chat_history")
-        readonlymemory = ReadOnlySharedMemory(memory=memory)
-        summry_chain = LLMChain(
-            llm=self.chatglm,
-            prompt=prompt,
-            verbose=True,
-            memory=readonlymemory,  # use the read-only memory to prevent the tool from modifying the memory
-        )
-        return summry_chain, memory
-
-    def create_agent_chain(self):
-        tools = [
-            Tool(
-                name="State of Search QA System",
-                func=self.state_of_search.run,
-                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。"
-            ),
-            Tool(
-                name="state-of-history-qa",
-                func=self.state_of_history.run,
-                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"
-            ),
-            Tool(
-                name="Summary",
-                func=self.summry_chain.run,
-                description="useful for when you summarize a conversation. The input to this tool should be a string, representing who will read this summary."
-            )
-        ]
-
-        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:"""
-        suffix = """Begin!
-
-        {chat_history}
-        Question: {input}
-        {agent_scratchpad}"""
-
-        prompt = ZeroShotAgent.create_prompt(
-            tools,
-            prefix=prefix,
-            suffix=suffix,
-            input_variables=["input", "chat_history", "agent_scratchpad"]
-        )
-
-        llm_chain = LLMChain(llm=OpenAI(temperature=0), prompt=prompt)
-        agent = ZeroShotAgent(llm_chain=llm_chain, tools=tools, verbose=True)
-        agent_chain = AgentExecutor.from_agent_and_tools(agent=agent, tools=tools, verbose=True, memory=self.memory)
-
-        return agent_chain

test/test_chatglm_with_shared_memory_openai_llm.py.py

@@ -1,22 +0,0 @@
-
-import sys
-import os
-
-sys.path.append(os.path.dirname(os.path.abspath(__file__)) + '/../')
-from agent import ChatglmWithSharedMemoryOpenaiLLM
-
-if __name__ == "__main__":
-    # 创建 ChatglmWithSharedMemoryOpenaiLLM 类的实例
-    chatglm_instance = ChatglmWithSharedMemoryOpenaiLLM()
-
-    # 使用代理链运行一些示例输入
-    chatglm_instance.agent_chain.run(input="我跟露露聊了什么?")
-    chatglm_instance.agent_chain.run(input="她开心吗?")
-    chatglm_instance.agent_chain.run(input="她有表达意见吗?")
-    chatglm_instance.agent_chain.run(input="根据历史对话总结下?")
-    # chatglm_instance.agent_chain.run(input="""可以拓展下吗?，比如写个小作文。
-    # 大纲：游戏的美好回忆，触不可及的距离，不在乎得失
-    # 主题：露露的陪伴无比珍贵
-    # 背景：游戏，通话，当下
-    # 开篇需要以游戏相识你挑逗的话语讲起
-    # """)