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lansan69
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app/services/document/__pycache__/prompt_builder.cpython-312.pyc
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app/services/document/__pycache__/prompt_builder.cpython-312.pyc
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app/services/document/evaluations_adapters.py
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app/services/document/evaluations_adapters.py
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"""
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Gateway de IA de Qualidot - Módulo de Adaptadores de Evaluación de Imágenes
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Propósito:
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Este módulo contiene funciones de adaptadores que permiten evaluar imágenes usando diferentes proveedores de IA.
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Cada función de adaptador se encarga de interactuar con un proveedor específico (como OpenAI, AssemblyAI, Deepgram, etc.)
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y de convertir la respuesta del proveedor al formato estándar de evaluación de imágenes de Qualidot.
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"""
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import json
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import mimetypes
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import mimetypes
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import tempfile
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import os
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from fastapi import HTTPException
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from matplotlib import image
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from openai import OpenAI, AsyncOpenAI
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import assemblyai as aai
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from pyparsing.common import Any
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from app.core.config import settings
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from app.schemas.document_standard import DocumentRequestFile, StandardDocumentAnalysisResult
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from app.core import config
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from app.utilities.document_utilities import json_to_rubric, encode_document_from_bytes
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from app.services.document.prompt_builder import build_document_evaluation_prompt
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from anthropic import Anthropic
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from app.utilities.image_utilities import encode_image_from_bytes
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# Función de adaptador principal que infiere el proveedor y llama al adaptador específico
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async def evaluate_document_with_provider(document_request: DocumentRequestFile) -> StandardDocumentAnalysisResult:
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"""
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Función de adaptador para evaluar documentos usando el proveedor de IA configurado.
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"""
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provider = document_request.provider.lower()
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content = await document_request.rubric.read()
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rubric_dict = json.loads(content)
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rubric = json_to_rubric(rubric_dict)
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prompt = build_document_evaluation_prompt(rubric)
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match provider:
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case "openai":
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return await evaluate_with_openai(document_request, prompt)
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case "claude":
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return await evaluate_with_claude(document_request, prompt)
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case "gemini":
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return await evaluate_with_gemini(document_request, prompt)
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case _:
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raise ValueError(f"Proveedor de IA no soportado: {document_request.provider}")
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# Función de adaptador para evaluar documentos usando OpenAI
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async def evaluate_with_openai(document_request: DocumentRequestFile, prompt: str) -> StandardDocumentAnalysisResult:
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"""
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Función de adaptador para evaluar documentos usando OpenAI.
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"""
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client = AsyncOpenAI(api_key=settings.OPENAI_API_KEY)
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document_bytes = await document_request.file.read()
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base64_document = encode_document_from_bytes(document_bytes)
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media_type = document_request.file.content_type
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try:
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response = await client.chat.completions.create(
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model=document_request.model,
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messages=[
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{"role": "user", "content": [
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{"type": "text", "text": prompt},
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{
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"type": "file",
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"file": {
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"file_data": f"data:{media_type};base64,{base64_document}",
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"filename": document_request.file.filename
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}
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}
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]}
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],
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response_format={"type": "json_object"}
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)
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resultado = json.loads(response.choices[0].message.content)
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return StandardDocumentAnalysisResult(
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status="success",
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original_filename=document_request.file.filename,
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provider_used="openai",
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model_used=document_request.model,
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**resultado
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)
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except Exception as e:
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raise HTTPException(
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status_code=500,
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detail=f"Error evaluando el documento: {str(e)}"
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)
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# Función de adaptador para evaluar documentos usando Claude
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async def evaluate_with_claude(document_request: DocumentRequestFile, prompt: str) -> StandardDocumentAnalysisResult:
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"""
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Función de adaptador para evaluar documentos usando Claude.
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"""
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client = Anthropic(api_key=os.environ.get("ANTHROPIC_API_KEY"))
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document_bytes = await document_request.file.read()
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base64_document = encode_document_from_bytes(document_bytes)
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media_type = document_request.file.content_type
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if media_type not in ["application/pdf", "application/msword", "application/vnd.openxmlformats-officedocument.wordprocessingml.document"]:
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raise ValueError(f"Tipo de documento no soportado por Anthropic: {media_type}")
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try:
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messages = [
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{
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"role": "user",
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"content": [
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{
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"type": "document",
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"source": {
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"type": "base64",
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"media_type": media_type,
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"data": base64_document
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},
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},
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{"type": "text", "text": prompt}
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],
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}
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]
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response = client.messages.create(
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model=document_request.model,
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max_tokens=4096,
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messages=messages,
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)
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json_string = response.content[0].text
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parsed_data = json.loads(json_string)
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return StandardDocumentAnalysisResult(
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status="success",
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original_filename=document_request.file.filename,
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provider_used="Claude",
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model_used=document_request.model,
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**parsed_data
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)
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except Exception as e:
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raise HTTPException(
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status_code=500,
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detail=f"Error evaluando el documento: {str(e)}"
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)
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# Función de adaptador para evaluar documentos usando Gemini
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async def evaluate_with_gemini(document_request: DocumentRequestFile, prompt: str) -> StandardDocumentAnalysisResult:
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"""
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Función de adaptador para evaluar documentos usando Gemini.
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(Plantilla para futuras implementaciones)
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"""
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# Aquí iría la implementación específica para Gemini
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pass
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58
app/services/document/prompt_builder.py
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app/services/document/prompt_builder.py
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"""
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Módulo encargado de construir el prompt estandarizado para la evaluación de imágenes,
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inyectando la rúbrica, el contexto de especialidad y el esquema JSON esperado.
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Este módulo es crucial para asegurar que los evaluadores expertos reciban instrucciones
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claras y consistentes, y que sus respuestas se ajusten al formato requerido para su posterior
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procesamiento y análisis.
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"""
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import json
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from app.schemas.document_standard import DocumentEvaluationRubric, StandardDocumentAnalysis
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def build_document_evaluation_prompt(rubric: DocumentEvaluationRubric) -> str:
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"""
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Construye el prompt estandarizado inyectando la rúbrica,
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el contexto de especialidad y el esquema JSON esperado.
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"""
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# Extraemos el JSON de la rúbrica de forma limpia (ignorando nulos)
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rubric_json = rubric.model_dump_json(exclude_none=True, indent=2)
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# Extraemos el esquema dinámico de salida basado en Pydantic
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expected_output_schema = json.dumps(StandardDocumentAnalysis.model_json_schema(), indent=2)
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# Obtenemos el path de especialización
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# Si por alguna razón viene vacío, le damos un rol genérico por defecto
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specialization_path = rubric.category if rubric.category else "General Document Analysis"
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# Ensamblamos el prompt con f-strings
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prompt = f"""
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# ROLE
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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.
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# TASK
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I will provide you with an input and a JSON object representing an `EvaluationRubric`.
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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.
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# DOMAIN CONTEXT
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Specialization Path: {specialization_path}
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# RUBRIC
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{rubric_json}
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# EVALUATION RULES
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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.
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2. **Strict Adherence:** Evaluate ONLY the criteria and subcriteria listed in the rubric. Do not invent new metrics.
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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.
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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.
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5. **Subcriteria Handling:** If a criterion has `subcriteria`, evaluate and score each subcriterion individually. The parent criterion's `score` MUST be the exact mathematical **sum** of its subcriteria scores.
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6. **Final Score Calculation:** The overall final `score` of the evaluation MUST be the exact mathematical **sum** of all the main criteria scores.
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# OUTPUT FORMAT
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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.
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# EXPECTED JSON SCHEMA
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{expected_output_schema}
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"""
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return prompt
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Binary file not shown.
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@@ -24,6 +24,12 @@ from app.core import config
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from app.utilities.image_utilities import json_to_rubric, encode_image_from_bytes
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from app.services.image.prompt_builder import build_image_evaluation_prompt
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from anthropic import Anthropic
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import re
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# Importaciones de Clarifai
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from clarifai_grpc.channel.clarifai_channel import ClarifaiChannel
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from clarifai_grpc.grpc.api import resources_pb2, service_pb2, service_pb2_grpc
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from clarifai_grpc.grpc.api.status import status_code_pb2
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# Función de adaptador principal que infiere el proveedor y llama al adaptador específico
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async def evaluate_image_with_provider(image_request: ImageRequestFile) -> StandardImageAnalysisResult:
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@@ -91,62 +97,77 @@ async def evaluate_with_openai(image_request: ImageRequestFile, prompt: str) ->
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detail=f"Error evaluando la imagen: {str(e)}"
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)
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async def evaluate_with_clarifai(image_request: ImageRequestFile, rubric: ImageEvaluationRubric, prompt: str) -> StandardImageAnalysisResult:
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async def evaluate_with_clarifai(image_request: ImageRequestFile, prompt: str) -> StandardImageAnalysisResult:
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"""
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Función de adaptador para evaluar imágenes usando Clarifai con un modelo Multimodal.
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Función de adaptador para evaluar imágenes usando Clarifai.
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"""
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try:
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# 1. Obtener el token de configuración (PAT)
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pat = settings.CLARIFAI_API_KEY
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if not pat:
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raise ValueError("La clave CLARIFAI_API_KEY no está configurada en el entorno.")
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# 2. Obtener la URL del modelo enviada en la petición
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model_url = image_request.model
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CLARIFAI_API_KEY = settings.CLARIFAI_API_KEY
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if not CLARIFAI_API_KEY:
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raise HTTPException(
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status_code=500,
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detail="No se encontró CLARIFAI_API_KEY en las variables de entorno"
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)
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# Inicializar el modelo de Clarifai
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model = Model(url=model_url, pat=pat)
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# 3. Leer los bytes de la imagen subida
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USER_ID = "openai"
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APP_ID = "chat-completion"
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try:
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image_bytes = await image_request.file.read()
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if not image_bytes:
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raise ValueError("El archivo de imagen recibido está vacío.")
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# 2. Preparamos y ejecutamos la llamada a Clarifai (gRPC)
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channel = ClarifaiChannel.get_grpc_channel()
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stub = service_pb2_grpc.V2Stub(channel)
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metadata = (('authorization', 'Key ' + CLARIFAI_API_KEY),)
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userDataObject = resources_pb2.UserAppIDSet(user_id=USER_ID, app_id=APP_ID)
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# 4. Preparar el input multimodal para Clarifai combinando la imagen y el prompt de evaluación
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multimodal_input = Inputs.get_multimodal_input(
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input_id="image_evaluation",
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image_bytes=image_bytes,
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raw_text=prompt
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request = service_pb2.PostModelOutputsRequest(
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user_app_id=userDataObject,
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model_id=image_request.model,
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inputs=[
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resources_pb2.Input(
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data=resources_pb2.Data(
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image=resources_pb2.Image(base64=image_bytes),
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text=resources_pb2.Text(raw=prompt)
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)
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)
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]
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)
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# 5. Llamar a la API de Clarifai para evaluar la imagen
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predict_response = model.predict([multimodal_input])
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# Extraer el texto crudo de la respuesta del modelo
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raw_output = predict_response.outputs[0].data.text.raw
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# 6. Limpiar la respuesta y convertirla a JSON
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# Los LLMs suelen devolver el JSON envuelto en bloques de markdown (```json ... ```)
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clean_json = raw_output.replace("```json", "").replace("```", "").strip()
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response = stub.PostModelOutputs(request, metadata=metadata)
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# Convertir el string limpio a un diccionario de Python
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parsed_data = json.loads(clean_json)
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if response.status.code != status_code_pb2.SUCCESS:
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raise Exception(f"Clarifai Error: {response.status.description}")
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# 3. Extraemos la respuesta cruda
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raw_output = response.outputs[0].data.text.raw
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# 7. Retornar el resultado validado contra tu esquema estándar de Pydantic
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return StandardImageAnalysisResult(**parsed_data)
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except json.JSONDecodeError as e:
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# Error específico si el modelo alucinó texto extra y no devolvió un JSON válido
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raise HTTPException(
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status_code=500,
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detail=f"El modelo de Clarifai no devolvió un JSON válido. Error: {str(e)} | Respuesta cruda: {raw_output if 'raw_output' in locals() else 'N/A'}"
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# 4. Limpiamos y parseamos el JSON devuelto
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json_match = re.search(r'\{.*\}', raw_output, re.DOTALL)
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if json_match:
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clean_json = json_match.group(0)
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parsed_data = json.loads(clean_json)
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else:
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# Respaldo en caso de que el modelo devuelva texto plano en lugar de JSON
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parsed_data = {"raw_response": raw_output}
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# 5. Retornamos el modelo estandarizado
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return StandardImageAnalysisResult(
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status="success",
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original_filename=image_request.file.filename,
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provider_used="Clarifai",
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model_used=image_request.model,
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**parsed_data
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)
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except Exception as e:
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# Captura cualquier otro error (problemas de red, token inválido, URL incorrecta, etc.)
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raise HTTPException(
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status_code=500,
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detail=f"Error interno al evaluar con Clarifai: {str(e)}"
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status_code=500,
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detail=f"Error evaluando la imagen con Clarifai: {str(e)}"
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)
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async def evaluate_with_claude(image_request: ImageRequestFile, prompt: str) -> StandardImageAnalysisResult:
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@@ -44,9 +44,10 @@ Specialization Path: {specialization_path}
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# EVALUATION RULES
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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.
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2. **Strict Adherence:** Evaluate ONLY the criteria and subcriteria listed in the rubric. Do not invent new metrics.
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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.
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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.
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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.
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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.
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5. **Subcriteria Handling:** If a criterion has `subcriteria`, evaluate and score each subcriterion individually. The parent criterion's `score` MUST be the exact mathematical **sum** of its subcriteria scores.
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6. **Final Score Calculation:** The overall final `score` of the evaluation MUST be the exact mathematical **sum** of all the main criteria scores.
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# OUTPUT FORMAT
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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.
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