MultiPromptChain--精简版

from langchain_community.llms import Ollama
from langchain.chains.router import MultiPromptChain
from langchain.chains import ConversationChain
from langchain.chains.llm import LLMChain
from langchain.prompts import PromptTemplate

# physics_template = """You are a very smart physics professor. \
# You are great at answering questions about physics in a concise and easy to understand manner. \
# When you don't know the answer to a question you admit that you don't know.
#
# Here is a question:
# {input}"""
#
# math_template = """You are a very good mathematician. You are great at answering math questions. \
# You are so good because you are able to break down hard problems into their component parts, \
# answer the component parts, and then put them together to answer the broader question.
#
# Here is a question:
# {input}"""

# prompt_infos = [
#     {
#         "name": "physics",
#         "description": "Good for answering questions about physics",
#         "prompt_template": physics_template,
#     },
#     {
#         "name": "math",
#         "description": "Good for answering math questions",
#         "prompt_template": math_template,
#     },
# ]



# 以下是定义的提示模板
cigerate_template = """
如果问题是关于香烟的，请用英文回答问题
下面是需要你来回答的问题：
{input}
"""

conversion_template = """
你是一位聊天大师，擅长解答日常生活中的问题，把答复翻译成语句通顺的中文。
下面是需要你来回答的问题：
{input}
"""


# 构建提示信息
prompt_infos = [
    {
        "name": "cig",
        "description": "适合回答所有香烟的问题",
        "prompt_template": cigerate_template
    },
    {
        "name": "talk",
        "description": "适合回答日常问题",
        "prompt_template": conversion_template
    },
    # 历史模板被注释掉了，所以这里不包括它
    # {
    #     "name": "History",
    #     "description": "适合回答历史的问题",
    #     "prompt_template": history_template
    # }
]


MULTI_PROMPT_ROUTER_TEMPLATE = """
将原始文本输入到语言模型中，选择最合适输入的提示词。
你将获得最适合的提示词名称以及相应的描述。
<< FORMATTING >>
返回一个Markdown代码片段，其中包含格式化为JSON对象的内容：
```json
{{{{
"destination": string \\ 要使用的prompt的name，或者"DEFAULT"
"next_inputs": string \\ 原始的input，或者可能的原始输入的修改版本
}}}}
```
记住："destination"必须是下面指定的候选提示名之一，如果包含香烟二字"destination"是"cig"，或者如果输入不适合任何候选提示，则可以是"DEFAULT"
记住："next_inputs"如果你认为不需要进行任何修改，可以直接使用原始输入。

<< CANDIDATE PROMPTS >>
{destinations}

<<INPUT>>
{{input}}

<<OUTPUT(必须以```json开头作为回复)>>
<< OUTPUT (must end with ```) >>
"""

# 初始化语言模型
llm = Ollama(base_url='http://127.0.0.1:11434', model='phi3:3.8b')

# destination_chains = {}
# for p_info in prompt_infos:
#     name = p_info["name"]
#     prompt_template = p_info["prompt_template"]
#     prompt = PromptTemplate(template=prompt_template, input_variables=["input"])
#     chain = LLMChain(llm=llm, prompt=prompt)
#     # chain = llm | prompt
#     destination_chains[name] = chain

# default_chain = ConversationChain(llm=llm, output_key="text")

# from langchain.chains.router.llm_router import LLMRouterChain, RouterOutputParser

# from langchain.chains.router.multi_prompt_prompt import MULTI_PROMPT_ROUTER_TEMPLATE

# destinations = [f"{p['name']}: {p['description']}" for p in prompt_infos]

# destinations_str = "\n".join(destinations)

test_chain = MultiPromptChain.from_prompts(llm,prompt_infos=prompt_infos,verbose=True)


# router_template = MULTI_PROMPT_ROUTER_TEMPLATE.format(destinations=test_chain.destinations_str)

# router_prompt = PromptTemplate(
#     template=router_template,
#     input_variables=["input"],
#     output_parser=RouterOutputParser(),
# )

# router_chain = LLMRouterChain.from_llm(llm, router_prompt)

# chain = MultiPromptChain(
#     router_chain=router_chain,
#     destination_chains=destination_chains,
#     default_chain=default_chain,
#     verbose=True,
# )

res=test_chain.invoke({'input': "中国美女最多的省份是哪个省？"})

print(res.get('text'))