1、langchain agent简单使用

参考：https://zhuanlan.zhihu.com/p/643868934?utm_id=0

from langchain.agents.agent_toolkits import create_python_agent
from langchain.agents import load_tools, initialize_agent
from langchain.agents import AgentType
from langchain.tools.python.tool import PythonREPLTool
from langchain.python import PythonREPL
from langchain.chat_models import ChatOpenAI
 

#创建llm；llm调用本地模型
api_base_url = "http://localhost:10860/v1" 
api_key= "EMPTY"
LLM_MODEL = "/mnt/data/loong/AquilaChat2-7B"
llm = ChatOpenAI(
    streaming=True,
    verbose=True,
    # callbacks=[callback],
    openai_api_key=api_key,
    openai_api_base=api_base_url,
    model_name=LLM_MODEL
)

#定义工具
tools = load_tools(["llm-math"], llm=llm)
 
#创建代理； ReAct框架默认
agent= initialize_agent(
    tools, 
    llm, 
    agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
    handle_parsing_errors=True,
    verbose = True)
print(agent.agent.llm_chain.prompt.template)

##使用
agent("300的25%是多少？")

agent("2的10次方？")

2、文档总结load_summarize_chain

参考：https://zhuanlan.zhihu.com/p/641384348

测试下来总结效果一般，速度也比较慢

 
##加载数据
from langchain.document_loaders import UnstructuredFileLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter

loader = UnstructuredFileLoader(r"C:\Users\loo***资料.txt")
document = loader.load()

text_splitter = RecursiveCharacterTextSplitter(
    chunk_size = 500,
    chunk_overlap = 0
)

split_documents = text_splitter.split_documents(document)
print(f'documents:{len(split_documents)}')


#创建llm
from langchain.chains.summarize import load_summarize_chain
from langchain.chat_models import ChatOpenAI


api_base_url = "http://localhost:10860/v1" 
api_key= "EMPTY"
LLM_MODEL = "/mnt/data/loong/AquilaChat2-7B"
llm = ChatOpenAI(
    streaming=True,
    verbose=True,
    # callbacks=[callback],
    openai_api_key=api_key,
    openai_api_base=api_base_url,
    model_name=LLM_MODEL
)

chain = load_summarize_chain(llm, chain_type="refine", verbose=True)  

 ##运行
import textwrap


output_summary = chain.run(split_documents[:5])
wrapped_text = textwrap.fill(output_summary, width=100)
print(wrapped_text)