mirror of
https://github.com/hwchase17/langchain
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76 lines
2.3 KiB
Python
76 lines
2.3 KiB
Python
from langchain_community.chat_models import ChatOpenAI
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from langchain_community.utilities import DuckDuckGoSearchAPIWrapper
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from langchain_core.output_parsers import StrOutputParser
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from langchain_core.prompts import ChatPromptTemplate, FewShotChatMessagePromptTemplate
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from langchain_core.runnables import RunnableLambda
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search = DuckDuckGoSearchAPIWrapper(max_results=4)
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def retriever(query):
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return search.run(query)
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# Few Shot Examples
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examples = [
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{
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"input": "Could the members of The Police perform lawful arrests?",
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"output": "what can the members of The Police do?",
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},
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{
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"input": "Jan Sindel’s was born in what country?",
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"output": "what is Jan Sindel’s personal history?",
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},
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]
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# We now transform these to example messages
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example_prompt = ChatPromptTemplate.from_messages(
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[
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("human", "{input}"),
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("ai", "{output}"),
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]
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)
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few_shot_prompt = FewShotChatMessagePromptTemplate(
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example_prompt=example_prompt,
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examples=examples,
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)
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prompt = ChatPromptTemplate.from_messages(
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[
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(
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"system",
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"You are an expert at world knowledge. Your task is to step back "
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"and paraphrase a question to a more generic step-back question, which "
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"is easier to answer. Here are a few examples:",
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),
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# Few shot examples
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few_shot_prompt,
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# New question
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("user", "{question}"),
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]
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)
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question_gen = prompt | ChatOpenAI(temperature=0) | StrOutputParser()
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response_prompt_template = """You are an expert of world knowledge. I am going to ask you a question. Your response should be comprehensive and not contradicted with the following context if they are relevant. Otherwise, ignore them if they are not relevant.
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{normal_context}
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{step_back_context}
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Original Question: {question}
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Answer:""" # noqa: E501
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response_prompt = ChatPromptTemplate.from_template(response_prompt_template)
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chain = (
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{
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# Retrieve context using the normal question
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"normal_context": RunnableLambda(lambda x: x["question"]) | retriever,
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# Retrieve context using the step-back question
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"step_back_context": question_gen | retriever,
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# Pass on the question
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"question": lambda x: x["question"],
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}
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| response_prompt
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| ChatOpenAI(temperature=0)
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| StrOutputParser()
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)
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