from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import ChatPromptTemplate
from langchain_openai import ChatOpenAI

prompt = ChatPromptTemplate.from_template("给我讲一个关于 {topic}的笑话")
model = ChatOpenAI(model="gpt-4")
output_parser = StrOutputParser()

chain = prompt | model | output_parser

chain.invoke({"topic": "冰激凌"})

prompt_value = prompt.invoke({"topic": "刺猬"})

prompt_value
# ChatPromptValue(messages=[HumanMessage(content='给我讲一个关于 刺猬的笑话')])

prompt_value.to_messages()
# [HumanMessage(content='给我讲一个关于 刺猬的笑话')]

prompt_value.to_string()
# 'Human: 给我讲一个关于 刺猬的笑话'

message = model.invoke(prompt_value)
message
# AIMessage(content='一只兔子和一只刺猬赛跑，刺猬以微弱的优势赢了。\n\n兔子很不服气，说：“你只是运气好，我一定能赢你。”\n\n刺猬笑了笑，说：“好啊，那我们再比一次。”\n\n兔子问：“你为什么笑？”\n\n刺猬回答：“因为我知道，无论我跑多慢，你都会在我背后。” \n\n兔子：“为什么？”\n\n刺猬：“因为你不敢超过我，怕被我刺到。”', response_metadata={'finish_reason': 'stop', 'logprobs': None})

from langchain_openai.llms import OpenAI

llm = OpenAI(model="gpt-3.5-turbo-instruct")
llm.invoke(prompt_value)

# '\n\nRobot: 为什么刺猬的生日派对总是很失败？因为他们总是把蜡烛都弄灭了！'

output_parser.invoke(message)

#'一只兔子和一只刺猬赛跑，刺猬以微弱的优势赢了。\n\n兔子很不服气，说：“你只是运气好，我一定能赢你。”\n\n刺猬笑了笑，说：“好啊，那我们再比一次。”\n\n兔子问：“你为什么笑？”\n\n刺猬回答：“因为我知道，无论我跑多慢，你都会在我背后。” \n\n兔子：“为什么？”\n\n刺猬：“因为你不敢超过我，怕被我刺到。”'

from operator import itemgetter

from langchain_community.vectorstores import FAISS
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.runnables import RunnableLambda, RunnablePassthrough
from langchain_openai import ChatOpenAI, OpenAIEmbeddings

vectorstore = FAISS.from_texts(
    ["harrison worked at kensho"], embedding=OpenAIEmbeddings()
)
retriever = vectorstore.as_retriever()

template = """Answer the question based only on the following context:
{context}

Question: {question}
"""
prompt = ChatPromptTemplate.from_template(template)

model = ChatOpenAI()

chain = (
    {"context": retriever, "question": RunnablePassthrough()}
    | prompt
    | model
    | StrOutputParser()
)

chain.invoke("where did harrison work?")

template = """Answer the question based only on the following context:
{context}

Question: {question}

Answer in the following language: {language}
"""
prompt = ChatPromptTemplate.from_template(template)

chain = (
    {
        "context": itemgetter("question") | retriever,
        "question": itemgetter("question"),
        "language": itemgetter("language"),
    }
    | prompt
    | model
    | StrOutputParser()
)

chain.invoke({"question": "where did harrison work", "language": "chinese"})