generated from kgod/ai-review-template
117 lines
5.0 KiB
Python
117 lines
5.0 KiB
Python
"""File persistence, extraction, and parser injection for resume imports."""
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from __future__ import annotations
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import hashlib
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import time
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from collections import OrderedDict
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from pathlib import Path
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from typing import Protocol
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from uuid import uuid4
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from .document_extractors import extract_text, normalize_upload_name, validate_upload
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from .import_parser_fast import slim_parser
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from .llm_services import log_ai_event
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from .resume_import_models import ParsedResumeDraft
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from .resume_import_rules import parse_resume_text
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MAX_IMPORT_BYTES = 10 * 1024 * 1024
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_PARSE_CACHE_SIZE = 64
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class ResumeImportParser(Protocol):
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def parse(self, *, text: str, source_name: str) -> ParsedResumeDraft: ...
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class RuleBasedResumeImportParser:
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"""Local structured fallback when the model parser is unavailable."""
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def parse(self, *, text: str, source_name: str) -> ParsedResumeDraft:
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return parse_resume_text(text=text, source_name=source_name)
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class ResumeImportService:
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def __init__(self, *, storage_root: str | Path, parser: ResumeImportParser | None = None) -> None:
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self.storage_root = Path(storage_root)
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# OpenAI parsers are wrapped in the slim-schema variant (no model-emitted
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# evidence quotes; roughly half the output tokens and latency).
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self.parser = slim_parser(parser) if parser is not None else RuleBasedResumeImportParser()
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# Re-uploading an unchanged file must not re-run the LLM parse; keyed by
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# content hash so the cache works across sessions. Process-local by design.
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self._parse_cache: OrderedDict[str, ParsedResumeDraft] = OrderedDict()
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def prepare(self, *, file_name: str, declared_mime: str | None, content: bytes) -> dict:
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total_started = time.perf_counter()
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if len(content) > MAX_IMPORT_BYTES:
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raise ValueError("import_file_too_large")
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safe_name, extension = normalize_upload_name(file_name)
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mime_type = validate_upload(extension=extension, declared_mime=declared_mime, content=content)
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sha256 = hashlib.sha256(content).hexdigest()
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draft = self._parse_cache.get(sha256)
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cache_hit = draft is not None
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extract_ms = 0
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parse_ms = 0
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validate_ms = 0
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text_characters: int | None = None
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if draft is None:
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extract_started = time.perf_counter()
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text = extract_text(extension=extension, content=content)
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extract_ms = round((time.perf_counter() - extract_started) * 1000)
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text_characters = len(text)
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parse_started = time.perf_counter()
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draft = self.parser.parse(text=text, source_name=safe_name)
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parse_ms = round((time.perf_counter() - parse_started) * 1000)
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validate_started = time.perf_counter()
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self._validate_document(draft.document)
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validate_ms = round((time.perf_counter() - validate_started) * 1000)
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self._parse_cache[sha256] = draft
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self._parse_cache.move_to_end(sha256)
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while len(self._parse_cache) > _PARSE_CACHE_SIZE:
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self._parse_cache.popitem(last=False)
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else:
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self._parse_cache.move_to_end(sha256)
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draft = draft.model_copy(deep=True)
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object_key = f"{sha256[:2]}/{uuid4().hex}{extension}"
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target = self.storage_root / object_key
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storage_started = time.perf_counter()
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target.parent.mkdir(parents=True, exist_ok=True)
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target.write_bytes(content)
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storage_ms = round((time.perf_counter() - storage_started) * 1000)
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log_ai_event(
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"resume_import_prepared",
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extract_ms=extract_ms,
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parse_ms=parse_ms,
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validate_ms=validate_ms,
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storage_ms=storage_ms,
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total_ms=round((time.perf_counter() - total_started) * 1000),
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cache_hit=cache_hit,
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file_extension=extension,
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size_bytes=len(content),
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text_characters=text_characters,
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)
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return {
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"file_name": safe_name,
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"mime_type": mime_type,
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"size_bytes": len(content),
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"sha256": sha256,
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"object_key": object_key,
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"document": draft.document,
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"field_reviews": [item.model_dump(mode="json") for item in draft.field_reviews],
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}
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def remove(self, object_key: str | None) -> None:
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if not object_key:
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return
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path = (self.storage_root / object_key).resolve()
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root = self.storage_root.resolve()
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if root not in path.parents:
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return
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path.unlink(missing_ok=True)
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@staticmethod
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def _validate_document(document: dict) -> None:
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required = {"schema_version", "basics", "target", "sections", "skill_groups"}
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if document.get("schema_version") != 3 or not required.issubset(document):
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raise ValueError("invalid_import_document")
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if not isinstance(document["sections"], list) or not isinstance(document["skill_groups"], list):
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raise ValueError("invalid_import_document")
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