chatglm_with_shared_memory_openai_llm:这个demo,基于openai的任务处理，实现本地文…

…档调用链的示例，可以使用chatgml处理一部分任务事件，也可以使用chatgml来完成记忆共享（SharedMemory）,至于为什么使用openai原因主要有两个现象,第一点,因为大模型chatgml受限，导致很多langchain的demo只能部分实现，跟群主聊了下，这种问题只能通过微调模型，增加一部分指令集和状态术语的数据集进去，微调方案只能大家自己尝试了，对显卡要去较高.当然也可以考虑只训练一个任务格式化的gml,模型微调参考这个仓库的方式 https://github.com/liucongg/ChatGLM-Finetuning

.gitignore

 output/*
-__pycache__/*
+__pycache__
+*/**/__pycache__
 log/*
+*/**/state_of_the_history.txt
+.chroma
+.idea
 vector_store/*
\ No newline at end of file

agent/__init__.py

+from .chatglm_with_shared_memory_openai_llm import *
\ No newline at end of file

agent/__main__.py

+
+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

+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

models/__init__.py

+
+from .chatglm_llm import *
\ No newline at end of file

test/test_chatglm_with_shared_memory_openai_llm.py.py

+
+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
+    # """)