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提交 0234a95f 作者: imClumsyPanda
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update README.md and README_en.md

上级 abb1bc89
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agent/__main__.py deleted 100644 → 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 deleted 100644 → 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 deleted 100644 → 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
# """)
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