forked from ericorps/ia-microservice
57 lines
3.3 KiB
Python
57 lines
3.3 KiB
Python
"""
|
|
Módulo encargado de construir el prompt estandarizado para la evaluación de imágenes,
|
|
inyectando la rúbrica, el contexto de especialidad y el esquema JSON esperado.
|
|
Este módulo es crucial para asegurar que los evaluadores expertos reciban instrucciones
|
|
claras y consistentes, y que sus respuestas se ajusten al formato requerido para su posterior
|
|
procesamiento y análisis.
|
|
"""
|
|
|
|
|
|
import json
|
|
from app.schemas.image_standard import StandardImageAnalysisResult, ImageEvaluationRubric
|
|
|
|
def build_image_evaluation_prompt(rubric: ImageEvaluationRubric) -> str:
|
|
"""
|
|
Construye el prompt estandarizado inyectando la rúbrica,
|
|
el contexto de especialidad y el esquema JSON esperado.
|
|
"""
|
|
|
|
# 1. Extraemos el JSON de la rúbrica de forma limpia (ignorando nulos)
|
|
rubric_json = rubric.model_dump_json(exclude_none=True, indent=2)
|
|
|
|
# 2. Extraemos el esquema dinámico de salida basado en Pydantic
|
|
expected_output_schema = json.dumps(StandardImageAnalysisResult.model_json_schema(), indent=2)
|
|
|
|
# 3. Obtenemos el path de especialización
|
|
# Si por alguna razón viene vacío, le damos un rol genérico por defecto
|
|
specialization_path = rubric.category if rubric.category else "General Image Analysis"
|
|
|
|
# 4. Ensamblamos el prompt con f-strings
|
|
prompt = f"""
|
|
# ROLE
|
|
You are a highly specialized Expert Evaluator with deep domain expertise in the following area: **{specialization_path}**. Your objective is to perform a highly accurate, objective, and domain-calibrated analysis of the provided input based STRICTLY on your specialization and the provided evaluation rubric.
|
|
|
|
# TASK
|
|
I will provide you with an input and a JSON object representing an `EvaluationRubric`.
|
|
Your task is to analyze the input and score it against every criterion and subcriterion defined in the rubric. You must justify your scores with objective feedback based on evidence.
|
|
|
|
# DOMAIN CONTEXT
|
|
Specialization Path: {specialization_path}
|
|
|
|
# RUBRIC
|
|
{rubric_json}
|
|
|
|
# EVALUATION RULES
|
|
1. **Domain Calibration (CRITICAL):** Calibrate your expectations, strictness, and feedback entirely according to the **{specialization_path}** context. Do not evaluate using generalized standards; apply the specific standards expected at this exact level and category.
|
|
2. **Strict Adherence:** Evaluate ONLY the criteria and subcriteria listed in the rubric. Do not invent new metrics.
|
|
3. **Scoring:** Assign a numeric `score` to each criterion and subcriterion. The score must reflect how well the input meets the description of that specific metric.
|
|
4. **Objective Feedback:** Generate constructive, evidence-grounded `feedback` for the overall evaluation. Mention specific elements or patterns observed in the input that justify the scores within the context of the specialization.
|
|
5. **Subcriteria Handling:** If a criterion has `subcriteria`, evaluate each subcriterion individually. The parent criterion's score should be a logical aggregate (e.g., average) of its subcriteria scores.
|
|
|
|
# OUTPUT FORMAT
|
|
You MUST return your response EXCLUSIVELY as a raw, valid JSON object that strictly adheres to the following JSON Schema definition. Do NOT include markdown blocks (```json), explanations, or any text outside the JSON object.
|
|
|
|
# EXPECTED JSON SCHEMA
|
|
{expected_output_schema}
|
|
"""
|
|
return prompt |