prompts.py

from datetime import datetime

summary_prompt = (
    "Summarize the excerpt below to help answer a question.\n\nExcerpt from"
    " {citation}\n\n------------\n\n{text}\n\n------------"
    "\n\nQuestion: {question}\n\nDo not directly"
    " answer the question, instead summarize to give evidence to help answer the"
    " question. Stay detailed; report specific numbers, equations, or direct quotes"
    ' (marked with quotation marks). Reply "Not applicable" if the excerpt is'
    " irrelevant. At the end of your response,"
    "provide an integer score from 1-10 on a newline indicating relevance to question."  # Don't use 0-10 since we mention "not applicable" instead  # noqa: E501
    " Do not explain your score."
    "\n\nRelevant Information Summary ({summary_length}):"
)
# This prompt template integrates with `text` variable of the above `summary_prompt`
text_with_tables_prompt_template = (
    "{text}\n\n------------\n\nMarkdown tables from {citation}."
    " If the markdown is poorly formatted, defer to the images"
    "\n\n{tables}"
)

summary_json_prompt = (
    "Excerpt from {citation}\n\n------------\n\n{text}\n\n------------"
    "\n\nQuestion: {question}\n\n"
)

# The below "cannot answer" sentinel phrase should:
# 1. Lead to complete tool being called with has_successful_answer=False
# 2. Can be used for unit testing
CANNOT_ANSWER_PHRASE = "I cannot answer"

answer_iteration_prompt_template = (
    "You are iterating on a prior answer, with a potentially different context:\n\n"
    "{prior_answer}\n\n"
    "Create a new answer only using context keys and data from the included context."
    " You can not use context keys from the prior answer which are not "
    "also included in the above context.\n\n"
)

CITATION_KEY_CONSTRAINTS = (
    "## Valid citation examples, only use comma/space delimited parentheticals: \n"
    "- (pqac-d79ef6fa, pqac-0f650d59) \n"
    "- (pqac-d79ef6fa) \n"
    "## Invalid citation examples: \n"
    "- (pqac-d79ef6fa and pqac-0f650d59) \n"
    "- (pqac-d79ef6fa;pqac-0f650d59) \n"
    "- (pqac-d79ef6fa-pqac-0f650d59) \n"
    "- pqac-d79ef6fa and pqac-0f650d59 \n"
    "- Example's work (pqac-d79ef6fa) \n"
    "- (pages pqac-d79ef6fa) \n"
)

qa_prompt = (
    "Answer the question below with the context.\n\n"
    "Context:\n\n{context}\n\n------------\n\n"
    "Question: {question}\n\n"
    "Write an answer based on the context. "
    "If the context provides insufficient information reply "
    f'"{CANNOT_ANSWER_PHRASE}." '
    "For each part of your answer, indicate which sources most support "
    "it via citation keys at the end of sentences, like {example_citation}. "
    "Only cite from the context above and only use the citation keys from the context. "
    f"{CITATION_KEY_CONSTRAINTS}"
    "Do not concatenate citation keys, just use them as is. "
    "Write in the style of a scientific article, with concise sentences and "
    "coherent paragraphs. This answer will be used directly, "
    "so do not add any extraneous information.\n\n"
    "{prior_answer_prompt}"
    "Answer ({answer_length}):"
)

select_paper_prompt = (
    "Select papers that may help answer the question below. "
    "Papers are listed as $KEY: $PAPER_INFO. "
    "Return a list of keys, separated by commas. "
    'Return "None", if no papers are applicable. '
    "Choose papers that are relevant, from reputable sources, and timely "
    "(if the question requires timely information).\n\n"
    "Question: {question}\n\n"
    "Papers: {papers}\n\n"
    "Selected keys:"
)

citation_prompt = (
    "Provide the citation for the following text in MLA Format. "
    "Do not write an introductory sentence. "
    "Do not fabricate a DOI such as '10.xxxx' if one cannot be found,"
    " just leave it out of the citation. "
    f"If reporting date accessed, the current year is {datetime.now().year}\n\n"
    "{text}\n\n"
    "Citation:"
)

structured_citation_prompt = (
    "Extract the title, authors, and doi as a JSON from this MLA citation. "
    "If any field can not be found, return it as null. "
    "Use title, authors, and doi as keys, author's value should be a list of authors. "
    "{citation}\n\n"
    "Citation JSON:"
)

default_system_prompt = (
    "Answer in a direct and concise tone. "
    "Your audience is an expert, so be highly specific. "
    "If there are ambiguous terms or acronyms, first define them."
)

# NOTE: we use double curly braces here so it's not considered an f-string template
summary_json_system_prompt = (
    "Provide a summary of the relevant information"
    " that could help answer the question based on the excerpt."
    " Your summary, combined with many others,"
    " will be given to the model to generate an answer."
    " Respond with the following JSON format:"
    '\n\n{{\n  "summary": "...",\n  "relevance_score": 0-10\n}}'
    "\n\nwhere `summary` is relevant information from the text - {summary_length} words."
    " `relevance_score` is an integer 0-10 for the relevance of `summary` to the question."
    "\n\nThe excerpt may or may not contain relevant information."
    " If not, leave `summary` empty, and make `relevance_score` be 0."
)
summary_json_multimodal_system_prompt = (
    "Provide a summary of the relevant information"
    " that could help answer the question based on the excerpt."
    " Your summary, combined with many others,"
    " will be given to the model to generate an answer."
    " Respond with the following JSON format:"
    '\n\n{{\n  "summary": "...",\n  "relevance_score": 0-10,\n  "used_images": "..."\n}}'
    "\n\nwhere `summary` is relevant information from the text - {summary_length} words."
    " `relevance_score` is an integer 0-10 for the relevance of `summary` to the question."
    " `used_images` is a boolean flag indicating"
    " if any images present in a multimodal message were used,"
    " and if no images were present it should be false."
    "\n\nThe excerpt may or may not contain relevant information."
    " If not, leave `summary` empty, and make `relevance_score` be 0."
)

env_system_prompt = (
    # Matching https://github.com/langchain-ai/langchain/blob/langchain%3D%3D0.2.3/libs/langchain/langchain/agents/openai_functions_agent/base.py#L213-L215
    "You are a helpful AI assistant."
)
env_reset_prompt = (
    "Use the tools to answer the question: {question}"
    "\n\nWhen the answer looks sufficient,"
    " you can terminate by calling the {complete_tool_name} tool."
    " If the answer does not look sufficient,"
    " and you have already tried to answer several times with different evidence,"
    " terminate by calling the {complete_tool_name} tool."
    " The current status of evidence/papers/cost is {status}"
)

# Prompt templates for use with LitQA
QA_PROMPT_TEMPLATE = "Q: {question}\n\nOptions:\n{options}"
EVAL_PROMPT_TEMPLATE = (
    "Given the following question and a proposed answer to the question, return the"
    " single-letter choice in the question that matches the proposed answer."
    " If the proposed answer is blank or an empty string,"
    " or multiple options are matched, respond with '0'."
    "\n\nQuestion: {qa_prompt}"
    "\n\nProposed Answer: {qa_answer}"
    "\n\nSingle Letter Answer:"
)

CONTEXT_OUTER_PROMPT = "{context_str}\n\nValid Keys: {valid_keys}"
EMPTY_CONTEXTS = len(CONTEXT_OUTER_PROMPT.format(context_str="", valid_keys="").strip())
CONTEXT_INNER_PROMPT_NOT_DETAILED = "{name}: {text}"
CONTEXT_INNER_PROMPT = f"{CONTEXT_INNER_PROMPT_NOT_DETAILED}\nFrom {{citation}}"