Primera version de chunkeo completo crud

backend/app/routers/chunking.py

@@ -0,0 +1,183 @@
+"""
+Router para operaciones de chunking de PDFs.
+Endpoints para generar preview y procesar PDFs completos.
+"""
+import logging
+from fastapi import APIRouter, HTTPException, status
+from typing import List
+
+from ..models.chunking_models import (
+    ChunkingPreviewRequest,
+    ChunkingPreviewResponse,
+    ChunkingProcessRequest,
+    ChunkingProcessResponse,
+    ChunkingProfilesResponse,
+    ChunkingProfile,
+    ChunkPreview
+)
+from ..services.chunking_service import get_chunking_service
+
+logger = logging.getLogger(__name__)
+
+router = APIRouter(prefix="/chunking", tags=["chunking"])
+
+
+# Perfiles predefinidos
+CHUNKING_PROFILES = [
+    ChunkingProfile(
+        id="balanced",
+        name="Balanceado",
+        description="Configuración equilibrada entre velocidad y calidad",
+        max_tokens=950,
+        target_tokens=800,
+        chunk_size=1000,
+        chunk_overlap=200,
+        use_llm=True
+    ),
+    ChunkingProfile(
+        id="detailed",
+        name="Detallado",
+        description="Chunks más grandes para mantener más contexto",
+        max_tokens=1500,
+        target_tokens=1200,
+        chunk_size=1500,
+        chunk_overlap=300,
+        use_llm=True
+    ),
+    ChunkingProfile(
+        id="compact",
+        name="Compacto",
+        description="Chunks más pequeños para búsquedas precisas",
+        max_tokens=600,
+        target_tokens=500,
+        chunk_size=700,
+        chunk_overlap=150,
+        use_llm=True
+    ),
+    ChunkingProfile(
+        id="fast",
+        name="Rápido",
+        description="Sin LLM, solo procesamiento básico",
+        max_tokens=950,
+        target_tokens=800,
+        chunk_size=1000,
+        chunk_overlap=200,
+        use_llm=False
+    ),
+]
+
+
+@router.get("/profiles", response_model=ChunkingProfilesResponse)
+async def get_chunking_profiles():
+    """
+    Obtiene los perfiles de configuración predefinidos para chunking.
+
+    Returns:
+        Lista de perfiles disponibles
+    """
+    return ChunkingProfilesResponse(profiles=CHUNKING_PROFILES)
+
+
+@router.post("/preview", response_model=ChunkingPreviewResponse)
+async def generate_preview(request: ChunkingPreviewRequest):
+    """
+    Genera preview de chunks para un PDF (hasta 3 chunks).
+
+    Args:
+        request: Configuración de chunking y ubicación del archivo
+
+    Returns:
+        Preview con chunks de ejemplo (máximo 3, mínimo 1)
+
+    Raises:
+        HTTPException: Si hay error generando el preview
+    """
+    try:
+        logger.info(f"Generando preview para {request.file_name} (tema: {request.tema})")
+
+        chunking_service = get_chunking_service()
+
+        chunks = await chunking_service.process_pdf_preview(
+            file_name=request.file_name,
+            tema=request.tema,
+            max_tokens=request.max_tokens,
+            target_tokens=request.target_tokens,
+            chunk_size=request.chunk_size,
+            chunk_overlap=request.chunk_overlap,
+            use_llm=request.use_llm,
+            custom_instructions=request.custom_instructions
+        )
+
+        # Convertir a modelos Pydantic
+        chunk_previews = [
+            ChunkPreview(
+                index=chunk["index"],
+                text=chunk["text"],
+                page=chunk["page"],
+                file_name=chunk["file_name"],
+                tokens=chunk["tokens"]
+            )
+            for chunk in chunks
+        ]
+
+        return ChunkingPreviewResponse(
+            success=True,
+            file_name=request.file_name,
+            tema=request.tema,
+            chunks=chunk_previews,
+            message="Preview generado exitosamente"
+        )
+
+    except Exception as e:
+        logger.error(f"Error generando preview: {e}")
+        raise HTTPException(
+            status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
+            detail=f"Error generando preview: {str(e)}"
+        )
+
+
+@router.post("/process", response_model=ChunkingProcessResponse)
+async def process_pdf_full(request: ChunkingProcessRequest):
+    """
+    Procesa un PDF completo y lo sube a Qdrant.
+
+    Este endpoint:
+    1. Descarga el PDF desde Azure Blob
+    2. Lo procesa en chunks con control de tokens
+    3. Genera embeddings con Azure OpenAI
+    4. Sube los chunks a Qdrant con IDs determinísticos
+
+    Args:
+        request: Configuración de chunking y destino
+
+    Returns:
+        Resultado del procesamiento con estadísticas
+
+    Raises:
+        HTTPException: Si hay error procesando el PDF
+    """
+    try:
+        logger.info(f"Procesando PDF completo: {request.file_name} (tema: {request.tema})")
+
+        chunking_service = get_chunking_service()
+
+        result = await chunking_service.process_pdf_full(
+            file_name=request.file_name,
+            tema=request.tema,
+            collection_name=request.collection_name,
+            max_tokens=request.max_tokens,
+            target_tokens=request.target_tokens,
+            chunk_size=request.chunk_size,
+            chunk_overlap=request.chunk_overlap,
+            use_llm=request.use_llm,
+            custom_instructions=request.custom_instructions
+        )
+
+        return ChunkingProcessResponse(**result)
+
+    except Exception as e:
+        logger.error(f"Error procesando PDF: {e}", exc_info=True)
+        raise HTTPException(
+            status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
+            detail=f"Error procesando PDF: {str(e)}"
+        )