wehub-resource-sync
129 lines
3.9 KiB
Python
129 lines
3.9 KiB
Python
"""Interview preparation generation service."""
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import json
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from typing import Any
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from app.llm import (
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complete_json,
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get_llm_config,
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get_model_name,
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get_safe_max_tokens,
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)
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from app.prompts import INTERVIEW_PREP_PROMPT, get_language_name
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from app.schemas import InterviewPrepData
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_JOB_DESCRIPTION_PROMPT_CHAR_LIMIT = 12_000
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_RESUME_DATA_PROMPT_CHAR_LIMIT = 30_000
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_TRUNCATION_NOTICE = (
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"[Content truncated for prompt length. Use only the visible evidence; "
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"do not infer or invent omitted details.]"
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)
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def _truncate_text_for_prompt(value: str, max_chars: int) -> str:
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"""Bound unstructured prompt input while making omissions explicit."""
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if len(value) <= max_chars:
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return value
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return f"{value[:max_chars].rstrip()}\n\n{_TRUNCATION_NOTICE}"
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def _truncate_json_value(
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value: Any,
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*,
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max_string_chars: int,
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max_list_items: int,
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) -> Any:
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if isinstance(value, str):
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return _truncate_text_for_prompt(value, max_string_chars)
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if isinstance(value, list):
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truncated = [
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_truncate_json_value(
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item,
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max_string_chars=max_string_chars,
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max_list_items=max_list_items,
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)
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for item in value[:max_list_items]
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]
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if len(value) > max_list_items:
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truncated.append(
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{
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"_prompt_truncation_notice": (
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f"{len(value) - max_list_items} additional items omitted. "
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"Do not infer omitted details."
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)
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}
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)
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return truncated
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if isinstance(value, dict):
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return {
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key: _truncate_json_value(
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item,
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max_string_chars=max_string_chars,
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max_list_items=max_list_items,
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)
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for key, item in value.items()
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}
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return value
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def _serialize_resume_data_for_prompt(resume_data: dict[str, Any]) -> str:
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resume_json = json.dumps(resume_data, ensure_ascii=False)
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if len(resume_json) <= _RESUME_DATA_PROMPT_CHAR_LIMIT:
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return resume_json
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for max_string_chars, max_list_items in ((2_000, 30), (1_000, 20), (500, 10)):
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bounded = _truncate_json_value(
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resume_data,
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max_string_chars=max_string_chars,
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max_list_items=max_list_items,
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)
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bounded_json = json.dumps(bounded, ensure_ascii=False)
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if len(bounded_json) <= _RESUME_DATA_PROMPT_CHAR_LIMIT:
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return bounded_json
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compact_snapshot = json.dumps(
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_truncate_json_value(resume_data, max_string_chars=250, max_list_items=5),
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ensure_ascii=False,
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)
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return json.dumps(
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{
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"_prompt_truncation_notice": _TRUNCATION_NOTICE,
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"limited_resume_snapshot": _truncate_text_for_prompt(
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compact_snapshot,
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_RESUME_DATA_PROMPT_CHAR_LIMIT - 500,
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),
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},
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ensure_ascii=False,
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)
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async def generate_interview_prep(
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resume_data: dict[str, Any],
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job_description: str,
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language: str = "en",
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) -> InterviewPrepData:
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"""Generate structured interview preparation for a tailored resume."""
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prompt = INTERVIEW_PREP_PROMPT.format(
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job_description=_truncate_text_for_prompt(
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job_description,
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_JOB_DESCRIPTION_PROMPT_CHAR_LIMIT,
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),
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resume_data=_serialize_resume_data_for_prompt(resume_data),
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output_language=get_language_name(language),
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)
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config = get_llm_config()
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max_tokens = get_safe_max_tokens(get_model_name(config), requested=8192)
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result = await complete_json(
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prompt=prompt,
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system_prompt=(
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"You are a career interview coach. Output truthful, resume-grounded "
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"interview preparation as JSON only."
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),
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max_tokens=max_tokens,
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schema_type="interview_prep",
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)
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return InterviewPrepData.model_validate(result)
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