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fix: Remove ModelRes references from resume modules #328

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Merged
shijinpjlab merged 2 commits into from
Dec 31, 2025
Merged

fix: Remove ModelRes references from resume modules #328

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c7bdcbe
fix: Remove ModelRes references from resume modules
Kylie-dot-s Dec 31, 2025
8df42d4
🎨 Auto-format code with pre-commit
actions-user Dec 31, 2025
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80 changes: 22 additions & 58 deletions dingo/model/llm/llm_keyword_matcher.py
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Original file line number Diff line number Diff line change
Expand Up @@ -9,19 +9,12 @@
from typing import List

from dingo.io import Data
from dingo.io.output.eval_detail import EvalDetail, QualityLabel
from dingo.model import Model
from dingo.model.llm.base_openai import BaseOpenAI
from dingo.utils import log
from dingo.utils.exception import ConvertJsonError

# Import EvalDetail for dev branch compatibility, fallback to ModelRes for main branch
try:
from dingo.io.output.eval_detail import EvalDetail, QualityLabel
USE_EVAL_DETAIL = True
except ImportError:
from dingo.model.modelres import ModelRes
USE_EVAL_DETAIL = False

# Complete synonym mapping for keyword normalization
SYNONYM_MAP = {
"k8s": "Kubernetes",
Expand Down Expand Up @@ -185,7 +178,7 @@ def _build_prompt(jd_text: str, resume_text: str) -> str:

@classmethod
def process_response(cls, response: str):
"""Process LLM response. Returns EvalDetail (dev) or ModelRes (main)."""
"""Process LLM response. Returns EvalDetail."""
log.info(f"Raw LLM response: {response}")

# Extract think content and clean response
Expand Down Expand Up @@ -213,29 +206,16 @@ def process_response(cls, response: str):

log.info(f"Keyword match score: {score:.1%}, threshold: {cls.threshold:.0%}")

# Return appropriate result type based on branch
if USE_EVAL_DETAIL:
result = EvalDetail(metric=cls.__name__)
result.score = score
result.reason = [reason]
if score >= cls.threshold:
result.status = False
result.label = [QualityLabel.QUALITY_GOOD]
else:
result.status = True
result.label = [f"QUALITY_BAD.{cls.__name__}"]
# Return EvalDetail result
result = EvalDetail(metric=cls.__name__)
result.score = score
result.reason = [reason]
if score >= cls.threshold:
result.status = False
result.label = [QualityLabel.QUALITY_GOOD]
else:
result = ModelRes()
result.score = score
result.reason = [reason]
if score >= cls.threshold:
result.error_status = False
result.type = "KEYWORD_MATCH_GOOD"
result.name = "MATCH_GOOD"
else:
result.error_status = True
result.type = "KEYWORD_MATCH_LOW"
result.name = "MATCH_LOW"
result.status = True
result.label = [f"QUALITY_BAD.{cls.__name__}"]

return result

Expand Down Expand Up @@ -346,38 +326,22 @@ def _generate_reason(cls, jd_analysis: dict, keyword_analysis: List[dict], score

@classmethod
def eval(cls, input_data: Data):
"""Override eval to validate inputs. Returns EvalDetail (dev) or ModelRes (main)."""
"""Override eval to validate inputs. Returns EvalDetail."""
# Validate that content (resume) is provided
if not input_data.content:
if USE_EVAL_DETAIL:
result = EvalDetail(metric=cls.__name__)
result.status = True
result.label = [f"QUALITY_BAD.{cls.__name__}"]
result.reason = ["Resume text (content) is required but was not provided"]
return result
else:
return ModelRes(
error_status=True,
type="KEYWORD_MATCH_ERROR",
name="MISSING_RESUME",
reason=["Resume text (content) is required but was not provided"]
)
result = EvalDetail(metric=cls.__name__)
result.status = True
result.label = [f"QUALITY_BAD.{cls.__name__}"]
result.reason = ["Resume text (content) is required but was not provided"]
return result

# Validate that prompt (JD) is provided
if not input_data.prompt:
if USE_EVAL_DETAIL:
result = EvalDetail(metric=cls.__name__)
result.status = True
result.label = [f"QUALITY_BAD.{cls.__name__}"]
result.reason = ["Job description (prompt) is required but was not provided"]
return result
else:
return ModelRes(
error_status=True,
type="KEYWORD_MATCH_ERROR",
name="MISSING_JD",
reason=["Job description (prompt) is required but was not provided"]
)
result = EvalDetail(metric=cls.__name__)
result.status = True
result.label = [f"QUALITY_BAD.{cls.__name__}"]
result.reason = ["Job description (prompt) is required but was not provided"]
return result

# Call parent eval method
return super().eval(input_data)
54 changes: 15 additions & 39 deletions dingo/model/llm/llm_resume_optimizer.py
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Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -9,19 +9,12 @@
from typing import List, Tuple

from dingo.io import Data
from dingo.io.output.eval_detail import EvalDetail, QualityLabel
from dingo.model import Model
from dingo.model.llm.base_openai import BaseOpenAI
from dingo.utils import log
from dingo.utils.exception import ConvertJsonError

# Import EvalDetail for dev branch compatibility, fallback to ModelRes for main branch
try:
from dingo.io.output.eval_detail import EvalDetail, QualityLabel
USE_EVAL_DETAIL = True
except ImportError:
from dingo.model.modelres import ModelRes
USE_EVAL_DETAIL = False


@Model.llm_register("LLMResumeOptimizer")
class LLMResumeOptimizer(BaseOpenAI):
Expand Down Expand Up @@ -191,7 +184,7 @@ def _parse_match_report(cls, match_report) -> Tuple[List[str], List[str], List[s

@classmethod
def process_response(cls, response: str):
"""Process LLM response. Returns EvalDetail (dev) or ModelRes (main)."""
"""Process LLM response. Returns EvalDetail."""
log.info(f"Raw LLM response length: {len(response)} chars")

# Clean response
Expand All @@ -210,22 +203,13 @@ def process_response(cls, response: str):
# Generate reason text
reason = cls._generate_reason(optimization_summary, section_changes, overall_improvement)

# Return appropriate result type based on branch
if USE_EVAL_DETAIL:
result = EvalDetail(metric=cls.__name__)
result.status = False
result.label = [QualityLabel.QUALITY_GOOD]
result.reason = [reason]
# Store full response for downstream use (using extra field)
result.optimized_content = response_json
else:
result = ModelRes()
result.error_status = False
result.type = "RESUME_OPTIMIZED"
result.name = "OPTIMIZATION_COMPLETE"
result.reason = [reason]
# Store full response for downstream use
result.optimized_content = response_json
# Return EvalDetail result
result = EvalDetail(metric=cls.__name__)
result.status = False
result.label = [QualityLabel.QUALITY_GOOD]
result.reason = [reason]
# Store full response for downstream use (using extra field)
result.optimized_content = response_json

return result

Expand Down Expand Up @@ -287,22 +271,14 @@ def _generate_reason(cls, summary: dict, changes: List[dict], overall: str) -> s

@classmethod
def eval(cls, input_data: Data):
"""Override eval to validate inputs. Returns EvalDetail (dev) or ModelRes (main)."""
"""Override eval to validate inputs. Returns EvalDetail."""
# Validate that content (resume) is provided
if not input_data.content:
if USE_EVAL_DETAIL:
result = EvalDetail(metric=cls.__name__)
result.status = True
result.label = [f"QUALITY_BAD.{cls.__name__}"]
result.reason = ["Resume text (content) is required but was not provided"]
return result
else:
return ModelRes(
error_status=True,
type="RESUME_OPTIMIZER_ERROR",
name="MISSING_RESUME",
reason=["Resume text (content) is required but was not provided"]
)
result = EvalDetail(metric=cls.__name__)
result.status = True
result.label = [f"QUALITY_BAD.{cls.__name__}"]
result.reason = ["Resume text (content) is required but was not provided"]
return result

# Call parent eval method
return super().eval(input_data)
Expand Down