langchain/chains/question_answering/refine_prompts.py

# flake8: noqa
from langchain.prompts.prompt import PromptTemplate
from langchain.prompts.chat import (
    SystemMessagePromptTemplate,
    HumanMessagePromptTemplate,
    ChatPromptTemplate,
    AIMessagePromptTemplate,
)
from langchain.chains.prompt_selector import (
    ConditionalPromptSelector,
    is_chat_model,
)


DEFAULT_REFINE_PROMPT_TMPL = (
    "The original question is as follows: {question}\n"
    "We have provided an existing answer: {existing_answer}\n"
    "We have the opportunity to refine the existing answer"
    "(only if needed) with some more context below.\n"
    "------------\n"
    "{context_str}\n"
    "------------\n"
    "Given the new context, refine the original answer to better "
    "answer the question. "
    "If the context isn't useful, return the original answer."
)
DEFAULT_REFINE_PROMPT = PromptTemplate(
    input_variables=["question", "existing_answer", "context_str"],
    template=DEFAULT_REFINE_PROMPT_TMPL,
)
refine_template = (
    "We have the opportunity to refine the existing answer"
    "(only if needed) with some more context below.\n"
    "------------\n"
    "{context_str}\n"
    "------------\n"
    "Given the new context, refine the original answer to better "
    "answer the question. "
    "If the context isn't useful, return the original answer."
)
messages = [
    HumanMessagePromptTemplate.from_template("{question}"),
    AIMessagePromptTemplate.from_template("{existing_answer}"),
    HumanMessagePromptTemplate.from_template(refine_template),
]
CHAT_REFINE_PROMPT = ChatPromptTemplate.from_messages(messages)
REFINE_PROMPT_SELECTOR = ConditionalPromptSelector(
    default_prompt=DEFAULT_REFINE_PROMPT,
    conditionals=[(is_chat_model, CHAT_REFINE_PROMPT)],
)


DEFAULT_TEXT_QA_PROMPT_TMPL = (
    "Context information is below. \n"
    "---------------------\n"
    "{context_str}"
    "\n---------------------\n"
    "Given the context information and not prior knowledge, "
    "answer the question: {question}\n"
)
DEFAULT_TEXT_QA_PROMPT = PromptTemplate(
    input_variables=["context_str", "question"], template=DEFAULT_TEXT_QA_PROMPT_TMPL
)
chat_qa_prompt_template = (
    "Context information is below. \n"
    "---------------------\n"
    "{context_str}"
    "\n---------------------\n"
    "Given the context information and not prior knowledge, "
    "answer any questions"
)
messages = [
    SystemMessagePromptTemplate.from_template(chat_qa_prompt_template),
    HumanMessagePromptTemplate.from_template("{question}"),
]
CHAT_QUESTION_PROMPT = ChatPromptTemplate.from_messages(messages)
QUESTION_PROMPT_SELECTOR = ConditionalPromptSelector(
    default_prompt=DEFAULT_TEXT_QA_PROMPT,
    conditionals=[(is_chat_model, CHAT_QUESTION_PROMPT)],
)