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9f72826143
* adding some logging * better var name --------- Co-authored-by: Richard Kuo (Onyx) <rkuo@onyx.app>
104 lines
3.5 KiB
Python
104 lines
3.5 KiB
Python
from collections.abc import Callable
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from onyx.configs.chat_configs import DISABLE_LLM_DOC_RELEVANCE
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from onyx.llm.interfaces import LLM
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from onyx.llm.utils import dict_based_prompt_to_langchain_prompt
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from onyx.llm.utils import message_to_string
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from onyx.prompts.llm_chunk_filter import NONUSEFUL_PAT
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from onyx.prompts.llm_chunk_filter import SECTION_FILTER_PROMPT
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from onyx.utils.logger import setup_logger
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from onyx.utils.threadpool_concurrency import run_functions_tuples_in_parallel
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logger = setup_logger()
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def llm_eval_section(
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query: str,
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section_content: str,
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llm: LLM,
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title: str,
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metadata: dict[str, str | list[str]],
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) -> bool:
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def _get_metadata_str(metadata: dict[str, str | list[str]]) -> str:
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metadata_str = "\nMetadata:\n"
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for key, value in metadata.items():
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value_str = ", ".join(value) if isinstance(value, list) else value
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metadata_str += f"{key} - {value_str}\n"
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return metadata_str
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def _get_usefulness_messages() -> list[dict[str, str]]:
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metadata_str = _get_metadata_str(metadata) if metadata else ""
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messages = [
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{
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"role": "user",
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"content": SECTION_FILTER_PROMPT.format(
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title=title.replace("\n", " "),
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chunk_text=section_content,
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user_query=query,
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optional_metadata=metadata_str,
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),
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},
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]
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return messages
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def _extract_usefulness(model_output: str) -> bool:
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"""Default useful if the LLM doesn't match pattern exactly
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This is because it's better to trust the (re)ranking if LLM fails"""
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if model_output.strip().strip('"').lower() == NONUSEFUL_PAT.lower():
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return False
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return True
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messages = _get_usefulness_messages()
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filled_llm_prompt = dict_based_prompt_to_langchain_prompt(messages)
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model_output = message_to_string(llm.invoke(filled_llm_prompt))
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# NOTE(rkuo): all this does is print "Yes useful" or "Not useful"
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# disabling becuase it's spammy, restore and give more context if this is needed
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# logger.debug(model_output)
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return _extract_usefulness(model_output)
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def llm_batch_eval_sections(
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query: str,
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section_contents: list[str],
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llm: LLM,
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titles: list[str],
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metadata_list: list[dict[str, str | list[str]]],
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use_threads: bool = True,
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) -> list[bool]:
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answer: list[bool]
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if DISABLE_LLM_DOC_RELEVANCE:
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raise RuntimeError(
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"LLM Doc Relevance is globally disabled, "
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"this should have been caught upstream."
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)
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if use_threads:
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functions_with_args: list[tuple[Callable, tuple]] = [
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(llm_eval_section, (query, section_content, llm, title, metadata))
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for section_content, title, metadata in zip(
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section_contents, titles, metadata_list
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)
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]
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logger.debug(
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"Running LLM usefulness eval in parallel (following logging may be out of order)"
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)
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parallel_results = run_functions_tuples_in_parallel(
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functions_with_args, allow_failures=True
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)
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# In case of failure/timeout, don't throw out the section
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answer = [True if item is None else item for item in parallel_results]
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return answer
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answer = [
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llm_eval_section(query, section_content, llm, title, metadata)
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for section_content, title, metadata in zip(
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section_contents, titles, metadata_list
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)
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]
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return answer
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