From 7c6e8c485863ba51378fcdffdb58d6377bb0fc7d Mon Sep 17 00:00:00 2001 From: Sebastian Date: Wed, 5 Nov 2025 19:18:11 +0000 Subject: [PATCH] Primera version de chunkeo completo crud --- .gitignore | 2 + backend/app/__pycache__/main.cpython-312.pyc | Bin 4578 -> 4990 bytes .../core/__pycache__/config.cpython-312.pyc | Bin 1695 -> 4297 bytes backend/app/core/config.py | 64 +- backend/app/main.py | 15 + backend/app/models/chunking_models.py | 128 ++ backend/app/models/vector_models.py | 184 +++ backend/app/routers/chunking.py | 183 +++ backend/app/routers/vectors.py | 277 ++++ backend/app/services/chunking_service.py | 316 +++++ backend/app/services/embedding_service.py | 127 ++ backend/app/services/vector_service.py | 442 +++++++ backend/app/utils/chunking/__init__.py | 18 + backend/app/utils/chunking/chunk_processor.py | 258 ++++ backend/app/utils/chunking/gemini_client.py | 91 ++ backend/app/utils/chunking/pdf_extractor.py | 299 +++++ backend/app/utils/chunking/pipeline.py | 65 + backend/app/utils/chunking/token_manager.py | 72 + backend/app/vector_db/__init__.py | 12 + backend/app/vector_db/base.py | 189 +++ backend/app/vector_db/factory.py | 76 ++ backend/app/vector_db/qdrant_client.py | 410 ++++++ backend/pyproject.toml | 13 + backend/uv.lock | 1170 ++++++++++++++++- frontend/package-lock.json | 323 +++++ frontend/package.json | 3 + frontend/src/components/ChunkPreviewPanel.tsx | 228 ++++ frontend/src/components/ChunkViewerModal.tsx | 205 +++ .../src/components/ChunkingConfigModal.tsx | 307 +++++ .../src/components/CollectionVerifier.tsx | 164 +++ frontend/src/components/Dashboard.tsx | 113 +- frontend/src/components/ui/select.tsx | 159 +++ frontend/src/components/ui/switch.tsx | 27 + frontend/src/components/ui/tabs.tsx | 53 + frontend/src/components/ui/textarea.tsx | 22 + frontend/src/services/api.ts | 232 ++++ 36 files changed, 6242 insertions(+), 5 deletions(-) create mode 100644 backend/app/models/chunking_models.py create mode 100644 backend/app/models/vector_models.py create mode 100644 backend/app/routers/chunking.py create mode 100644 backend/app/routers/vectors.py create mode 100644 backend/app/services/chunking_service.py create mode 100644 backend/app/services/embedding_service.py create mode 100644 backend/app/services/vector_service.py create mode 100644 backend/app/utils/chunking/__init__.py create mode 100644 backend/app/utils/chunking/chunk_processor.py create mode 100644 backend/app/utils/chunking/gemini_client.py create mode 100644 backend/app/utils/chunking/pdf_extractor.py create mode 100644 backend/app/utils/chunking/pipeline.py create mode 100644 backend/app/utils/chunking/token_manager.py create mode 100644 backend/app/vector_db/__init__.py create mode 100644 backend/app/vector_db/base.py create mode 100644 backend/app/vector_db/factory.py create mode 100644 backend/app/vector_db/qdrant_client.py create mode 100644 frontend/src/components/ChunkPreviewPanel.tsx create mode 100644 frontend/src/components/ChunkViewerModal.tsx create mode 100644 frontend/src/components/ChunkingConfigModal.tsx create mode 100644 frontend/src/components/CollectionVerifier.tsx create mode 100644 frontend/src/components/ui/select.tsx create mode 100644 frontend/src/components/ui/switch.tsx create mode 100644 frontend/src/components/ui/tabs.tsx create mode 100644 frontend/src/components/ui/textarea.tsx diff --git a/.gitignore b/.gitignore index 23baa75..5a8cb3a 100644 --- a/.gitignore +++ b/.gitignore @@ -350,6 +350,8 @@ docker-compose.override.yml .env.*.local config.json secrets.json +.secrets/ +**/.secrets/ # === ARCHIVOS DE RESPALDO === *.bak diff --git a/backend/app/__pycache__/main.cpython-312.pyc b/backend/app/__pycache__/main.cpython-312.pyc index 9750f6e260ecacdca2001e2a19903bdaa51f723d..8d210ba5a3ee517cca18e19c70c5d5b9941c2901 100644 GIT binary patch delta 915 zcmZuv-Ahwp7(eg%*yh>!+LyZgIMcbaEh#fuUurfAL^M-Vi9w9zIoOVR7TXk52nr$T zZaj2tm%+RUcF`XZ)TBZgO3_6Z(Z!-}BBn 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z2XR3J2p2aqOrF7)tn!eR?*hNUWmdzFOf0N|pP3ohj6Q%^-+7o=R1_AyupnBt9@RGBVy~;JnKqewRV+E`!z=HU&nxj+!qF SK= values["max_tokens"]: + raise ValueError("target_tokens debe ser menor que max_tokens") + return v + + +class ChunkingProcessRequest(BaseModel): + """Request para procesar PDF completo""" + file_name: str = Field(..., description="Nombre del archivo PDF") + tema: str = Field(..., description="Tema/carpeta del archivo") + collection_name: str = Field(..., description="Nombre de la colección en Qdrant") + max_tokens: int = Field(default=950, ge=100, le=2000, description="Límite máximo de tokens por chunk") + target_tokens: int = Field(default=800, ge=100, le=2000, description="Tokens objetivo") + chunk_size: int = Field(default=1000, ge=100, le=3000, description="Tamaño del chunk") + chunk_overlap: int = Field(default=200, ge=0, le=1000, description="Solapamiento entre chunks") + use_llm: bool = Field(default=True, description="Usar LLM (Gemini) para procesamiento inteligente") + custom_instructions: str = Field(default="", description="Instrucciones personalizadas (solo si use_llm=True)") + + @validator("custom_instructions") + def validate_custom_instructions(cls, v, values): + """Valida que custom_instructions solo se use con LLM habilitado""" + if v and not values.get("use_llm", True): + raise ValueError("custom_instructions solo puede usarse cuando use_llm=True") + return v + + @validator("target_tokens") + def validate_target_tokens(cls, v, values): + """Valida que target_tokens sea menor que max_tokens""" + if "max_tokens" in values and v >= values["max_tokens"]: + raise ValueError("target_tokens debe ser menor que max_tokens") + return v + + +# Response Models + +class ChunkPreview(BaseModel): + """Modelo para un chunk de preview""" + index: int = Field(..., description="Índice del chunk") + text: str = Field(..., description="Contenido del chunk") + page: int = Field(..., description="Número de página") + file_name: str = Field(..., description="Nombre del archivo") + tokens: int = Field(..., description="Número aproximado de tokens") + + +class ChunkingPreviewResponse(BaseModel): + """Response para preview de chunks""" + success: bool = Field(default=True, description="Indica si la operación fue exitosa") + file_name: str = Field(..., description="Nombre del archivo procesado") + tema: str = Field(..., description="Tema del archivo") + chunks: List[ChunkPreview] = Field(..., description="Lista de chunks de preview (hasta 3)") + message: str = Field(default="Preview generado exitosamente", description="Mensaje descriptivo") + + @validator("chunks") + def validate_chunk_count(cls, v): + """Valida que haya al menos 1 chunk y máximo 3 chunks en el preview""" + if len(v) < 1: + raise ValueError("El preview debe contener al menos 1 chunk") + if len(v) > 3: + raise ValueError("El preview no puede contener más de 3 chunks") + return v + + +class ChunkingProcessResponse(BaseModel): + """Response para procesamiento completo""" + success: bool = Field(..., description="Indica si la operación fue exitosa") + collection_name: str = Field(..., description="Nombre de la colección") + file_name: str = Field(..., description="Nombre del archivo procesado") + total_chunks: int = Field(..., description="Total de chunks generados") + chunks_added: int = Field(..., description="Chunks agregados a Qdrant") + message: str = Field(..., description="Mensaje descriptivo") + + +# Profile Models + +class ChunkingProfile(BaseModel): + """Perfil de configuración predefinido para chunking""" + id: str = Field(..., description="ID del perfil") + name: str = Field(..., description="Nombre del perfil") + description: str = Field(..., description="Descripción del perfil") + max_tokens: int = Field(..., description="Límite máximo de tokens") + target_tokens: int = Field(..., description="Tokens objetivo") + chunk_size: int = Field(..., description="Tamaño del chunk") + chunk_overlap: int = Field(..., description="Solapamiento") + use_llm: bool = Field(..., description="Si usa LLM") + + +class ChunkingProfilesResponse(BaseModel): + """Response con perfiles disponibles""" + profiles: List[ChunkingProfile] = Field(..., description="Lista de perfiles disponibles") + + +# Progress Models (para WebSockets) + +class ChunkingProgress(BaseModel): + """Modelo para reportar progreso de chunking""" + status: str = Field(..., description="Estado actual: downloading, chunking, embedding, uploading, completed, error") + progress: int = Field(..., ge=0, le=100, description="Progreso en porcentaje") + message: Optional[str] = Field(None, description="Mensaje adicional") + error: Optional[str] = Field(None, description="Mensaje de error si status=error") diff --git a/backend/app/models/vector_models.py b/backend/app/models/vector_models.py new file mode 100644 index 0000000..304dcef --- /dev/null +++ b/backend/app/models/vector_models.py @@ -0,0 +1,184 @@ +""" +Modelos Pydantic para operaciones con bases de datos vectoriales. + +Este módulo define todos los modelos de datos para requests y responses +relacionados con la gestión de colecciones y chunks en bases de datos vectoriales. +""" + +from typing import List, Optional, Dict, Any +from pydantic import BaseModel, Field, validator + + +# ============================================================================ +# Modelos para Colecciones +# ============================================================================ + +class CollectionExistsRequest(BaseModel): + """Request para verificar si una colección existe.""" + collection_name: str = Field(..., description="Nombre de la colección a verificar") + + +class CollectionExistsResponse(BaseModel): + """Response de verificación de existencia de colección.""" + exists: bool = Field(..., description="True si la colección existe") + collection_name: str = Field(..., description="Nombre de la colección") + + +class CollectionCreateRequest(BaseModel): + """Request para crear una nueva colección.""" + collection_name: str = Field(..., description="Nombre de la colección a crear") + vector_size: int = Field(default=3072, description="Dimensión de los vectores") + distance: str = Field(default="Cosine", description="Métrica de distancia") + + @validator("distance") + def validate_distance(cls, v): + """Valida que la métrica de distancia sea válida.""" + allowed = ["Cosine", "Euclid", "Dot"] + if v not in allowed: + raise ValueError(f"Métrica de distancia debe ser una de: {allowed}") + return v + + @validator("vector_size") + def validate_vector_size(cls, v): + """Valida que el tamaño del vector sea positivo.""" + if v <= 0: + raise ValueError("El tamaño del vector debe ser mayor a 0") + return v + + +class CollectionCreateResponse(BaseModel): + """Response de creación de colección.""" + success: bool = Field(..., description="True si se creó exitosamente") + collection_name: str = Field(..., description="Nombre de la colección creada") + message: str = Field(..., description="Mensaje descriptivo") + + +class CollectionDeleteResponse(BaseModel): + """Response de eliminación de colección.""" + success: bool = Field(..., description="True si se eliminó exitosamente") + collection_name: str = Field(..., description="Nombre de la colección eliminada") + message: str = Field(..., description="Mensaje descriptivo") + + +class CollectionInfoResponse(BaseModel): + """Response con información de una colección.""" + name: str = Field(..., description="Nombre de la colección") + vectors_count: int = Field(..., description="Número total de vectores") + vectors_config: Dict[str, Any] = Field(..., description="Configuración de vectores") + status: str = Field(..., description="Estado de la colección") + + +# ============================================================================ +# Modelos para Archivos en Colecciones +# ============================================================================ + +class FileExistsInCollectionRequest(BaseModel): + """Request para verificar si un archivo existe en una colección.""" + collection_name: str = Field(..., description="Nombre de la colección") + file_name: str = Field(..., description="Nombre del archivo a verificar") + + +class FileExistsInCollectionResponse(BaseModel): + """Response de verificación de existencia de archivo.""" + exists: bool = Field(..., description="True si el archivo existe") + collection_name: str = Field(..., description="Nombre de la colección") + file_name: str = Field(..., description="Nombre del archivo") + chunk_count: Optional[int] = Field(None, description="Número de chunks del archivo si existe") + + +# ============================================================================ +# Modelos para Chunks +# ============================================================================ + +class ChunkMetadata(BaseModel): + """Metadata de un chunk.""" + file_name: str = Field(..., description="Nombre del archivo") + page: int = Field(..., description="Número de página") + text: Optional[str] = Field(None, description="Texto del chunk") + # Se pueden agregar más campos según necesidad + + +class ChunkData(BaseModel): + """Datos completos de un chunk.""" + id: str = Field(..., description="ID único del chunk") + payload: ChunkMetadata = Field(..., description="Metadata del chunk") + vector: Optional[List[float]] = Field(None, description="Vector de embeddings") + + +class GetChunksByFileRequest(BaseModel): + """Request para obtener chunks de un archivo.""" + collection_name: str = Field(..., description="Nombre de la colección") + file_name: str = Field(..., description="Nombre del archivo") + limit: Optional[int] = Field(None, description="Límite de chunks a retornar") + + @validator("limit") + def validate_limit(cls, v): + """Valida que el límite sea positivo si está presente.""" + if v is not None and v <= 0: + raise ValueError("El límite debe ser mayor a 0") + return v + + +class GetChunksByFileResponse(BaseModel): + """Response con los chunks de un archivo.""" + collection_name: str = Field(..., description="Nombre de la colección") + file_name: str = Field(..., description="Nombre del archivo") + chunks: List[Dict[str, Any]] = Field(..., description="Lista de chunks") + total_chunks: int = Field(..., description="Número total de chunks") + + +class DeleteFileFromCollectionRequest(BaseModel): + """Request para eliminar un archivo de una colección.""" + collection_name: str = Field(..., description="Nombre de la colección") + file_name: str = Field(..., description="Nombre del archivo a eliminar") + + +class DeleteFileFromCollectionResponse(BaseModel): + """Response de eliminación de archivo.""" + success: bool = Field(..., description="True si se eliminó exitosamente") + collection_name: str = Field(..., description="Nombre de la colección") + file_name: str = Field(..., description="Nombre del archivo eliminado") + chunks_deleted: int = Field(..., description="Número de chunks eliminados") + message: str = Field(..., description="Mensaje descriptivo") + + +class AddChunksRequest(BaseModel): + """Request para agregar chunks a una colección.""" + collection_name: str = Field(..., description="Nombre de la colección") + chunks: List[Dict[str, Any]] = Field(..., description="Lista de chunks a agregar") + + @validator("chunks") + def validate_chunks(cls, v): + """Valida que la lista de chunks no esté vacía.""" + if not v: + raise ValueError("La lista de chunks no puede estar vacía") + return v + + +class AddChunksResponse(BaseModel): + """Response de agregado de chunks.""" + success: bool = Field(..., description="True si se agregaron exitosamente") + collection_name: str = Field(..., description="Nombre de la colección") + chunks_added: int = Field(..., description="Número de chunks agregados") + message: str = Field(..., description="Mensaje descriptivo") + + +# ============================================================================ +# Modelos para Health Check +# ============================================================================ + +class VectorDBHealthResponse(BaseModel): + """Response del health check de la base de datos vectorial.""" + status: str = Field(..., description="Estado de la conexión") + db_type: str = Field(..., description="Tipo de base de datos vectorial") + message: str = Field(..., description="Mensaje descriptivo") + + +# ============================================================================ +# Modelos para Errores +# ============================================================================ + +class VectorDBErrorResponse(BaseModel): + """Response genérico de error.""" + error: str = Field(..., description="Descripción del error") + detail: Optional[str] = Field(None, description="Detalle adicional del error") diff --git a/backend/app/routers/chunking.py b/backend/app/routers/chunking.py new file mode 100644 index 0000000..cfbfefc --- /dev/null +++ b/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)}" + ) diff --git a/backend/app/routers/vectors.py b/backend/app/routers/vectors.py new file mode 100644 index 0000000..e8175ee --- /dev/null +++ b/backend/app/routers/vectors.py @@ -0,0 +1,277 @@ +""" +Router para endpoints de operaciones con bases de datos vectoriales. + +Este módulo define todos los endpoints de la API relacionados con +la gestión de colecciones y chunks en bases de datos vectoriales. +""" + +import logging +from fastapi import APIRouter, HTTPException, status, Query +from typing import Optional + +from app.services.vector_service import vector_service +from app.models.vector_models import ( + CollectionExistsRequest, + CollectionExistsResponse, + CollectionCreateRequest, + CollectionCreateResponse, + CollectionDeleteResponse, + CollectionInfoResponse, + FileExistsInCollectionRequest, + FileExistsInCollectionResponse, + GetChunksByFileRequest, + GetChunksByFileResponse, + DeleteFileFromCollectionRequest, + DeleteFileFromCollectionResponse, + AddChunksRequest, + AddChunksResponse, + VectorDBHealthResponse, + VectorDBErrorResponse +) + +logger = logging.getLogger(__name__) + +router = APIRouter( + prefix="/vectors", + tags=["Vectors"], + responses={ + 500: {"model": VectorDBErrorResponse, "description": "Error interno del servidor"} + } +) + + +# ============================================================================ +# Endpoints de Health Check +# ============================================================================ + +@router.get( + "/health", + response_model=VectorDBHealthResponse, + summary="Verificar estado de la base de datos vectorial", + description="Verifica que la conexión con la base de datos vectorial esté funcionando correctamente" +) +async def health_check(): + """Health check de la base de datos vectorial.""" + try: + return await vector_service.health_check() + except Exception as e: + logger.error(f"Error en health check: {e}") + raise HTTPException( + status_code=status.HTTP_500_INTERNAL_SERVER_ERROR, + detail=f"Error al verificar estado de la base de datos: {str(e)}" + ) + + +# ============================================================================ +# Endpoints de Colecciones +# ============================================================================ + +@router.post( + "/collections/exists", + response_model=CollectionExistsResponse, + summary="Verificar si una colección existe", + description="Verifica si existe una colección con el nombre especificado" +) +async def check_collection_exists(request: CollectionExistsRequest): + """Verifica si una colección existe.""" + try: + return await vector_service.check_collection_exists(request.collection_name) + except Exception as e: + logger.error(f"Error al verificar colección: {e}") + raise HTTPException( + status_code=status.HTTP_500_INTERNAL_SERVER_ERROR, + detail=f"Error al verificar colección: {str(e)}" + ) + + +@router.post( + "/collections/create", + response_model=CollectionCreateResponse, + status_code=status.HTTP_201_CREATED, + summary="Crear una nueva colección", + description="Crea una nueva colección en la base de datos vectorial" +) +async def create_collection(request: CollectionCreateRequest): + """Crea una nueva colección.""" + try: + return await vector_service.create_collection(request) + except ValueError as e: + logger.warning(f"Error de validación al crear colección: {e}") + raise HTTPException( + status_code=status.HTTP_400_BAD_REQUEST, + detail=str(e) + ) + except Exception as e: + logger.error(f"Error al crear colección: {e}") + raise HTTPException( + status_code=status.HTTP_500_INTERNAL_SERVER_ERROR, + detail=f"Error al crear colección: {str(e)}" + ) + + +@router.delete( + "/collections/{collection_name}", + response_model=CollectionDeleteResponse, + summary="Eliminar una colección", + description="Elimina completamente una colección y todos sus datos" +) +async def delete_collection(collection_name: str): + """Elimina una colección completa.""" + try: + return await vector_service.delete_collection(collection_name) + except ValueError as e: + logger.warning(f"Error de validación al eliminar colección: {e}") + raise HTTPException( + status_code=status.HTTP_404_NOT_FOUND, + detail=str(e) + ) + except Exception as e: + logger.error(f"Error al eliminar colección: {e}") + raise HTTPException( + status_code=status.HTTP_500_INTERNAL_SERVER_ERROR, + detail=f"Error al eliminar colección: {str(e)}" + ) + + +@router.get( + "/collections/{collection_name}/info", + response_model=CollectionInfoResponse, + summary="Obtener información de una colección", + description="Obtiene información detallada sobre una colección" +) +async def get_collection_info(collection_name: str): + """Obtiene información de una colección.""" + try: + info = await vector_service.get_collection_info(collection_name) + + if info is None: + raise HTTPException( + status_code=status.HTTP_404_NOT_FOUND, + detail=f"Colección '{collection_name}' no encontrada" + ) + + return info + except HTTPException: + raise + except Exception as e: + logger.error(f"Error al obtener info de colección: {e}") + raise HTTPException( + status_code=status.HTTP_500_INTERNAL_SERVER_ERROR, + detail=f"Error al obtener información de colección: {str(e)}" + ) + + +# ============================================================================ +# Endpoints de Archivos en Colecciones +# ============================================================================ + +@router.post( + "/files/exists", + response_model=FileExistsInCollectionResponse, + summary="Verificar si un archivo existe en una colección", + description="Verifica si un archivo específico existe en una colección" +) +async def check_file_exists_in_collection(request: FileExistsInCollectionRequest): + """Verifica si un archivo existe en una colección.""" + try: + return await vector_service.check_file_exists_in_collection( + request.collection_name, + request.file_name + ) + except Exception as e: + logger.error(f"Error al verificar archivo en colección: {e}") + raise HTTPException( + status_code=status.HTTP_500_INTERNAL_SERVER_ERROR, + detail=f"Error al verificar archivo: {str(e)}" + ) + + +@router.get( + "/collections/{collection_name}/files/{file_name}/chunks", + response_model=GetChunksByFileResponse, + summary="Obtener chunks de un archivo", + description="Obtiene todos los chunks de un archivo específico en una colección" +) +async def get_chunks_by_file( + collection_name: str, + file_name: str, + limit: Optional[int] = Query(None, description="Límite de chunks a retornar") +): + """Obtiene todos los chunks de un archivo.""" + try: + return await vector_service.get_chunks_by_file( + collection_name, + file_name, + limit + ) + except ValueError as e: + logger.warning(f"Error de validación al obtener chunks: {e}") + raise HTTPException( + status_code=status.HTTP_404_NOT_FOUND, + detail=str(e) + ) + except Exception as e: + logger.error(f"Error al obtener chunks: {e}") + raise HTTPException( + status_code=status.HTTP_500_INTERNAL_SERVER_ERROR, + detail=f"Error al obtener chunks: {str(e)}" + ) + + +@router.delete( + "/collections/{collection_name}/files/{file_name}", + response_model=DeleteFileFromCollectionResponse, + summary="Eliminar un archivo de una colección", + description="Elimina todos los chunks de un archivo de una colección" +) +async def delete_file_from_collection(collection_name: str, file_name: str): + """Elimina todos los chunks de un archivo.""" + try: + return await vector_service.delete_file_from_collection( + collection_name, + file_name + ) + except ValueError as e: + logger.warning(f"Error de validación al eliminar archivo: {e}") + raise HTTPException( + status_code=status.HTTP_404_NOT_FOUND, + detail=str(e) + ) + except Exception as e: + logger.error(f"Error al eliminar archivo: {e}") + raise HTTPException( + status_code=status.HTTP_500_INTERNAL_SERVER_ERROR, + detail=f"Error al eliminar archivo: {str(e)}" + ) + + +# ============================================================================ +# Endpoints de Chunks +# ============================================================================ + +@router.post( + "/chunks/add", + response_model=AddChunksResponse, + status_code=status.HTTP_201_CREATED, + summary="Agregar chunks a una colección", + description="Agrega múltiples chunks a una colección existente" +) +async def add_chunks(request: AddChunksRequest): + """Agrega chunks a una colección.""" + try: + return await vector_service.add_chunks( + request.collection_name, + request.chunks + ) + except ValueError as e: + logger.warning(f"Error de validación al agregar chunks: {e}") + raise HTTPException( + status_code=status.HTTP_400_BAD_REQUEST, + detail=str(e) + ) + except Exception as e: + logger.error(f"Error al agregar chunks: {e}") + raise HTTPException( + status_code=status.HTTP_500_INTERNAL_SERVER_ERROR, + detail=f"Error al agregar chunks: {str(e)}" + ) diff --git a/backend/app/services/chunking_service.py b/backend/app/services/chunking_service.py new file mode 100644 index 0000000..1c54836 --- /dev/null +++ b/backend/app/services/chunking_service.py @@ -0,0 +1,316 @@ +""" +Servicio de chunking que orquesta todo el proceso: +- Descarga PDF desde Azure Blob +- Procesa con pipeline de chunking +- Genera embeddings con Azure OpenAI +- Sube a Qdrant con IDs determinísticos +""" +import logging +import uuid +from typing import List, Dict, Any, Optional +from io import BytesIO + +from azure.storage.blob import BlobServiceClient +from langchain_core.documents import Document + +from ..core.config import settings +from ..utils.chunking import ( + process_pdf_with_token_control, + get_gemini_client, + GeminiClient +) +from ..services.embedding_service import get_embedding_service +from ..vector_db.factory import get_vector_db + +logger = logging.getLogger(__name__) + + +class ChunkingService: + """Servicio para procesar PDFs y subirlos a Qdrant""" + + def __init__(self): + """Inicializa el servicio con conexiones a Azure Blob y clientes""" + self.blob_service = BlobServiceClient.from_connection_string( + settings.AZURE_STORAGE_CONNECTION_STRING + ) + self.container_name = settings.AZURE_CONTAINER_NAME + self.embedding_service = get_embedding_service() + self.vector_db = get_vector_db() + + def _generate_deterministic_id( + self, + file_name: str, + page: int, + chunk_index: int + ) -> str: + """ + Genera un ID determinístico para un chunk usando UUID v5. + + Args: + file_name: Nombre del archivo + page: Número de página + chunk_index: Índice del chunk dentro de la página + + Returns: + ID en formato UUID válido para Qdrant + """ + id_string = f"{file_name}_{page}_{chunk_index}" + # Usar UUID v5 con namespace DNS para generar UUID determinístico + return str(uuid.uuid5(uuid.NAMESPACE_DNS, id_string)) + + async def download_pdf_from_blob( + self, + file_name: str, + tema: str + ) -> bytes: + """ + Descarga un PDF desde Azure Blob Storage. + + Args: + file_name: Nombre del archivo + tema: Tema/carpeta del archivo + + Returns: + Contenido del PDF en bytes + + Raises: + Exception: Si hay error descargando el archivo + """ + try: + blob_path = f"{tema}/{file_name}" + logger.info(f"Descargando PDF: {blob_path}") + + blob_client = self.blob_service.get_blob_client( + container=self.container_name, + blob=blob_path + ) + + pdf_bytes = blob_client.download_blob().readall() + logger.info(f"PDF descargado: {len(pdf_bytes)} bytes") + return pdf_bytes + + except Exception as e: + logger.error(f"Error descargando PDF: {e}") + raise + + async def process_pdf_preview( + self, + file_name: str, + tema: str, + max_tokens: int = 950, + target_tokens: int = 800, + chunk_size: int = 1000, + chunk_overlap: int = 200, + use_llm: bool = True, + custom_instructions: str = "" + ) -> List[Dict[str, Any]]: + """ + Procesa un PDF y genera exactamente 3 chunks de preview. + + Args: + file_name: Nombre del archivo PDF + tema: Tema/carpeta del archivo + max_tokens: Límite máximo de tokens por chunk + target_tokens: Tokens objetivo + chunk_size: Tamaño del chunk + chunk_overlap: Solapamiento + use_llm: Si True, usa Gemini para procesamiento inteligente + custom_instructions: Instrucciones personalizadas (solo si use_llm=True) + + Returns: + Lista con exactamente 3 chunks de preview con metadata + """ + try: + logger.info(f"Generando preview para {file_name} (tema: {tema})") + + # Descargar PDF + pdf_bytes = await self.download_pdf_from_blob(file_name, tema) + + # Configurar cliente Gemini si está habilitado + gemini_client = get_gemini_client() if use_llm else None + + # Si LLM está deshabilitado, ignorar custom_instructions + instructions = custom_instructions if use_llm else "" + + # Procesar PDF + chunks = process_pdf_with_token_control( + pdf_bytes=pdf_bytes, + file_name=file_name, + max_tokens=max_tokens, + target_tokens=target_tokens, + chunk_size=chunk_size, + chunk_overlap=chunk_overlap, + merge_related=True, + gemini_client=gemini_client, + custom_instructions=instructions, + extract_images=False # Deshabilitado según requerimientos + ) + + # Tomar los primeros chunks para preview (máximo 3, mínimo 1) + preview_chunks = chunks[:min(3, len(chunks))] if chunks else [] + + # Formatear para respuesta + result = [] + for idx, chunk in enumerate(preview_chunks): + result.append({ + "index": idx, + "text": chunk.page_content, + "page": chunk.metadata.get("page", 0), + "file_name": chunk.metadata.get("file_name", file_name), + "tokens": len(chunk.page_content.split()) # Aproximación + }) + + logger.info(f"Preview generado: {len(result)} chunks") + return result + + except Exception as e: + logger.error(f"Error generando preview: {e}") + raise + + async def process_pdf_full( + self, + file_name: str, + tema: str, + collection_name: str, + max_tokens: int = 950, + target_tokens: int = 800, + chunk_size: int = 1000, + chunk_overlap: int = 200, + use_llm: bool = True, + custom_instructions: str = "", + progress_callback: Optional[callable] = None + ) -> Dict[str, Any]: + """ + Procesa un PDF completo y lo sube a Qdrant. + + Args: + file_name: Nombre del archivo PDF + tema: Tema/carpeta del archivo + collection_name: Nombre de la colección en Qdrant + max_tokens: Límite máximo de tokens por chunk + target_tokens: Tokens objetivo + chunk_size: Tamaño del chunk + chunk_overlap: Solapamiento + use_llm: Si True, usa Gemini para procesamiento inteligente + custom_instructions: Instrucciones personalizadas (solo si use_llm=True) + progress_callback: Callback para reportar progreso + + Returns: + Diccionario con resultados del procesamiento + """ + try: + logger.info(f"Procesando PDF completo: {file_name} (tema: {tema})") + + if progress_callback: + await progress_callback({"status": "downloading", "progress": 0}) + + # 1. Descargar PDF + pdf_bytes = await self.download_pdf_from_blob(file_name, tema) + + if progress_callback: + await progress_callback({"status": "chunking", "progress": 20}) + + # 2. Configurar cliente Gemini + gemini_client = get_gemini_client() if use_llm else None + instructions = custom_instructions if use_llm else "" + + # 3. Procesar PDF + chunks = process_pdf_with_token_control( + pdf_bytes=pdf_bytes, + file_name=file_name, + max_tokens=max_tokens, + target_tokens=target_tokens, + chunk_size=chunk_size, + chunk_overlap=chunk_overlap, + merge_related=True, + gemini_client=gemini_client, + custom_instructions=instructions, + extract_images=False + ) + + if progress_callback: + await progress_callback({"status": "embedding", "progress": 50}) + + # 4. Generar embeddings + texts = [chunk.page_content for chunk in chunks] + logger.info(f"Generando embeddings para {len(texts)} chunks") + embeddings = await self.embedding_service.generate_embeddings_batch(texts) + logger.info(f"Embeddings generados: {len(embeddings)} vectores de dimensión {len(embeddings[0]) if embeddings else 0}") + + if progress_callback: + await progress_callback({"status": "uploading", "progress": 80}) + + # 5. Preparar chunks para Qdrant con IDs determinísticos + qdrant_chunks = [] + page_chunk_count = {} # Contador de chunks por página + + logger.info(f"Preparando {len(chunks)} chunks con {len(embeddings)} embeddings para subir") + for chunk, embedding in zip(chunks, embeddings): + page = chunk.metadata.get("page", 0) + + # Incrementar contador para esta página + if page not in page_chunk_count: + page_chunk_count[page] = 0 + chunk_index = page_chunk_count[page] + page_chunk_count[page] += 1 + + # Generar ID determinístico + chunk_id = self._generate_deterministic_id( + file_name=file_name, + page=page, + chunk_index=chunk_index + ) + + qdrant_chunks.append({ + "id": chunk_id, + "vector": embedding, + "payload": { + "page_content": chunk.page_content, + "metadata": { + "page": page, + "file_name": file_name + } + } + }) + + # 6. Subir a Qdrant + logger.info(f"Subiendo {len(qdrant_chunks)} chunks a Qdrant colección '{collection_name}'") + result = await self.vector_db.add_chunks(collection_name, qdrant_chunks) + logger.info(f"Resultado de upsert: {result}") + + if progress_callback: + await progress_callback({"status": "completed", "progress": 100}) + + logger.info(f"Procesamiento completo: {result['chunks_added']} chunks subidos") + + return { + "success": True, + "collection_name": collection_name, + "file_name": file_name, + "total_chunks": len(chunks), + "chunks_added": result['chunks_added'], + "message": "PDF procesado y subido exitosamente" + } + + except Exception as e: + logger.error(f"Error procesando PDF completo: {e}") + if progress_callback: + await progress_callback({"status": "error", "progress": 0, "error": str(e)}) + raise + + +# Instancia global singleton +_chunking_service: ChunkingService | None = None + + +def get_chunking_service() -> ChunkingService: + """ + Obtiene la instancia singleton del servicio de chunking. + + Returns: + Instancia de ChunkingService + """ + global _chunking_service + if _chunking_service is None: + _chunking_service = ChunkingService() + return _chunking_service diff --git a/backend/app/services/embedding_service.py b/backend/app/services/embedding_service.py new file mode 100644 index 0000000..7581584 --- /dev/null +++ b/backend/app/services/embedding_service.py @@ -0,0 +1,127 @@ +""" +Servicio de embeddings usando Azure OpenAI. +Genera embeddings para chunks de texto usando text-embedding-3-large (3072 dimensiones). +""" +import logging +from typing import List +from openai import AzureOpenAI +from ..core.config import settings + +logger = logging.getLogger(__name__) + + +class EmbeddingService: + """Servicio para generar embeddings usando Azure OpenAI""" + + def __init__(self): + """Inicializa el cliente de Azure OpenAI""" + try: + self.client = AzureOpenAI( + api_key=settings.AZURE_OPENAI_API_KEY, + api_version=settings.AZURE_OPENAI_API_VERSION, + azure_endpoint=settings.AZURE_OPENAI_ENDPOINT + ) + self.model = settings.AZURE_OPENAI_EMBEDDING_DEPLOYMENT + self.embedding_dimension = 3072 + logger.info(f"EmbeddingService inicializado con modelo {self.model}") + except Exception as e: + logger.error(f"Error inicializando EmbeddingService: {e}") + raise + + async def generate_embedding(self, text: str) -> List[float]: + """ + Genera un embedding para un texto individual. + + Args: + text: Texto para generar embedding + + Returns: + Vector de embedding (3072 dimensiones) + + Raises: + Exception: Si hay error al generar el embedding + """ + try: + response = self.client.embeddings.create( + input=[text], + model=self.model + ) + embedding = response.data[0].embedding + + if len(embedding) != self.embedding_dimension: + raise ValueError( + f"Dimensión incorrecta: esperada {self.embedding_dimension}, " + f"obtenida {len(embedding)}" + ) + + return embedding + + except Exception as e: + logger.error(f"Error generando embedding: {e}") + raise + + async def generate_embeddings_batch( + self, + texts: List[str], + batch_size: int = 100 + ) -> List[List[float]]: + """ + Genera embeddings para múltiples textos en lotes. + + Args: + texts: Lista de textos para generar embeddings + batch_size: Tamaño del lote para procesamiento (default: 100) + + Returns: + Lista de vectores de embeddings + + Raises: + Exception: Si hay error al generar los embeddings + """ + try: + embeddings = [] + + for i in range(0, len(texts), batch_size): + batch = texts[i:i + batch_size] + logger.info(f"Procesando lote {i//batch_size + 1}/{(len(texts)-1)//batch_size + 1}") + + response = self.client.embeddings.create( + input=batch, + model=self.model + ) + + batch_embeddings = [item.embedding for item in response.data] + + # Validar dimensiones + for idx, emb in enumerate(batch_embeddings): + if len(emb) != self.embedding_dimension: + raise ValueError( + f"Dimensión incorrecta en índice {i + idx}: " + f"esperada {self.embedding_dimension}, obtenida {len(emb)}" + ) + + embeddings.extend(batch_embeddings) + + logger.info(f"Generados {len(embeddings)} embeddings exitosamente") + return embeddings + + except Exception as e: + logger.error(f"Error generando embeddings en lote: {e}") + raise + + +# Instancia global singleton +_embedding_service: EmbeddingService | None = None + + +def get_embedding_service() -> EmbeddingService: + """ + Obtiene la instancia singleton del servicio de embeddings. + + Returns: + Instancia de EmbeddingService + """ + global _embedding_service + if _embedding_service is None: + _embedding_service = EmbeddingService() + return _embedding_service diff --git a/backend/app/services/vector_service.py b/backend/app/services/vector_service.py new file mode 100644 index 0000000..81b7009 --- /dev/null +++ b/backend/app/services/vector_service.py @@ -0,0 +1,442 @@ +""" +Servicio de lógica de negocio para operaciones con bases de datos vectoriales. + +Este módulo contiene toda la lógica de negocio relacionada con la gestión +de colecciones y chunks en bases de datos vectoriales. +""" + +import logging +from typing import List, Dict, Any, Optional + +from app.vector_db import get_vector_db +from app.models.vector_models import ( + CollectionCreateRequest, + CollectionCreateResponse, + CollectionDeleteResponse, + CollectionExistsResponse, + CollectionInfoResponse, + FileExistsInCollectionResponse, + GetChunksByFileResponse, + DeleteFileFromCollectionResponse, + AddChunksResponse, + VectorDBHealthResponse, + VectorDBErrorResponse +) + +logger = logging.getLogger(__name__) + + +class VectorService: + """ + Servicio para gestionar operaciones con bases de datos vectoriales. + + Este servicio actúa como una capa intermedia entre los routers y + la implementación de la base de datos vectorial. + """ + + def __init__(self): + """Inicializa el servicio con la instancia de la base de datos vectorial.""" + self.vector_db = get_vector_db() + + async def check_collection_exists(self, collection_name: str) -> CollectionExistsResponse: + """ + Verifica si una colección existe. + + Args: + collection_name: Nombre de la colección + + Returns: + CollectionExistsResponse: Response con el resultado + """ + try: + exists = await self.vector_db.collection_exists(collection_name) + logger.info(f"Verificación de colección '{collection_name}': {exists}") + + return CollectionExistsResponse( + exists=exists, + collection_name=collection_name + ) + + except Exception as e: + logger.error(f"Error al verificar colección '{collection_name}': {e}") + raise + + async def create_collection( + self, + request: CollectionCreateRequest + ) -> CollectionCreateResponse: + """ + Crea una nueva colección. + + Args: + request: Request con los datos de la colección + + Returns: + CollectionCreateResponse: Response con el resultado + + Raises: + ValueError: Si la colección ya existe + """ + try: + # Verificar si ya existe + exists = await self.vector_db.collection_exists(request.collection_name) + + if exists: + logger.warning(f"Intento de crear colección existente: '{request.collection_name}'") + raise ValueError(f"La colección '{request.collection_name}' ya existe") + + # Crear la colección + success = await self.vector_db.create_collection( + collection_name=request.collection_name, + vector_size=request.vector_size, + distance=request.distance + ) + + if success: + logger.info(f"Colección '{request.collection_name}' creada exitosamente") + return CollectionCreateResponse( + success=True, + collection_name=request.collection_name, + message=f"Colección '{request.collection_name}' creada exitosamente" + ) + else: + logger.error(f"Fallo al crear colección '{request.collection_name}'") + raise Exception(f"No se pudo crear la colección '{request.collection_name}'") + + except ValueError: + raise + except Exception as e: + logger.error(f"Error al crear colección '{request.collection_name}': {e}") + raise + + async def delete_collection(self, collection_name: str) -> CollectionDeleteResponse: + """ + Elimina una colección completa. + + Args: + collection_name: Nombre de la colección + + Returns: + CollectionDeleteResponse: Response con el resultado + + Raises: + ValueError: Si la colección no existe + """ + try: + # Verificar que existe + exists = await self.vector_db.collection_exists(collection_name) + + if not exists: + logger.warning(f"Intento de eliminar colección inexistente: '{collection_name}'") + raise ValueError(f"La colección '{collection_name}' no existe") + + # Eliminar la colección + success = await self.vector_db.delete_collection(collection_name) + + if success: + logger.info(f"Colección '{collection_name}' eliminada exitosamente") + return CollectionDeleteResponse( + success=True, + collection_name=collection_name, + message=f"Colección '{collection_name}' eliminada exitosamente" + ) + else: + logger.error(f"Fallo al eliminar colección '{collection_name}'") + raise Exception(f"No se pudo eliminar la colección '{collection_name}'") + + except ValueError: + raise + except Exception as e: + logger.error(f"Error al eliminar colección '{collection_name}': {e}") + raise + + async def get_collection_info(self, collection_name: str) -> Optional[CollectionInfoResponse]: + """ + Obtiene información de una colección. + + Args: + collection_name: Nombre de la colección + + Returns: + Optional[CollectionInfoResponse]: Información de la colección o None + """ + try: + info = await self.vector_db.get_collection_info(collection_name) + + if info is None: + logger.warning(f"Colección '{collection_name}' no encontrada") + return None + + return CollectionInfoResponse(**info) + + except Exception as e: + logger.error(f"Error al obtener info de colección '{collection_name}': {e}") + raise + + async def check_file_exists_in_collection( + self, + collection_name: str, + file_name: str + ) -> FileExistsInCollectionResponse: + """ + Verifica si un archivo existe en una colección. + + Args: + collection_name: Nombre de la colección + file_name: Nombre del archivo + + Returns: + FileExistsInCollectionResponse: Response con el resultado + """ + try: + # Primero verificar que la colección existe + collection_exists = await self.vector_db.collection_exists(collection_name) + + if not collection_exists: + logger.warning(f"Colección '{collection_name}' no existe") + return FileExistsInCollectionResponse( + exists=False, + collection_name=collection_name, + file_name=file_name, + chunk_count=0 + ) + + # Verificar si el archivo existe + file_exists = await self.vector_db.file_exists_in_collection( + collection_name, + file_name + ) + + chunk_count = None + if file_exists: + chunk_count = await self.vector_db.count_chunks_in_file( + collection_name, + file_name + ) + + logger.info( + f"Archivo '{file_name}' en colección '{collection_name}': " + f"existe={file_exists}, chunks={chunk_count}" + ) + + return FileExistsInCollectionResponse( + exists=file_exists, + collection_name=collection_name, + file_name=file_name, + chunk_count=chunk_count + ) + + except Exception as e: + logger.error( + f"Error al verificar archivo '{file_name}' " + f"en colección '{collection_name}': {e}" + ) + raise + + async def get_chunks_by_file( + self, + collection_name: str, + file_name: str, + limit: Optional[int] = None + ) -> GetChunksByFileResponse: + """ + Obtiene todos los chunks de un archivo. + + Args: + collection_name: Nombre de la colección + file_name: Nombre del archivo + limit: Límite opcional de chunks + + Returns: + GetChunksByFileResponse: Response con los chunks + + Raises: + ValueError: Si la colección no existe + """ + try: + # Verificar que la colección existe + exists = await self.vector_db.collection_exists(collection_name) + + if not exists: + logger.warning(f"Colección '{collection_name}' no existe") + raise ValueError(f"La colección '{collection_name}' no existe") + + # Obtener chunks + chunks = await self.vector_db.get_chunks_by_file( + collection_name, + file_name, + limit + ) + + logger.info( + f"Obtenidos {len(chunks)} chunks del archivo '{file_name}' " + f"de la colección '{collection_name}'" + ) + + return GetChunksByFileResponse( + collection_name=collection_name, + file_name=file_name, + chunks=chunks, + total_chunks=len(chunks) + ) + + except ValueError: + raise + except Exception as e: + logger.error( + f"Error al obtener chunks del archivo '{file_name}' " + f"de la colección '{collection_name}': {e}" + ) + raise + + async def delete_file_from_collection( + self, + collection_name: str, + file_name: str + ) -> DeleteFileFromCollectionResponse: + """ + Elimina todos los chunks de un archivo de una colección. + + Args: + collection_name: Nombre de la colección + file_name: Nombre del archivo + + Returns: + DeleteFileFromCollectionResponse: Response con el resultado + + Raises: + ValueError: Si la colección no existe o el archivo no está en la colección + """ + try: + # Verificar que la colección existe + collection_exists = await self.vector_db.collection_exists(collection_name) + + if not collection_exists: + logger.warning(f"Colección '{collection_name}' no existe") + raise ValueError(f"La colección '{collection_name}' no existe") + + # Verificar que el archivo existe en la colección + file_exists = await self.vector_db.file_exists_in_collection( + collection_name, + file_name + ) + + if not file_exists: + logger.warning( + f"Archivo '{file_name}' no existe en colección '{collection_name}'" + ) + raise ValueError( + f"El archivo '{file_name}' no existe en la colección '{collection_name}'" + ) + + # Eliminar el archivo + chunks_deleted = await self.vector_db.delete_file_from_collection( + collection_name, + file_name + ) + + logger.info( + f"Eliminados {chunks_deleted} chunks del archivo '{file_name}' " + f"de la colección '{collection_name}'" + ) + + return DeleteFileFromCollectionResponse( + success=True, + collection_name=collection_name, + file_name=file_name, + chunks_deleted=chunks_deleted, + message=f"Archivo '{file_name}' eliminado exitosamente ({chunks_deleted} chunks)" + ) + + except ValueError: + raise + except Exception as e: + logger.error( + f"Error al eliminar archivo '{file_name}' " + f"de la colección '{collection_name}': {e}" + ) + raise + + async def add_chunks( + self, + collection_name: str, + chunks: List[Dict[str, Any]] + ) -> AddChunksResponse: + """ + Agrega chunks a una colección. + + Args: + collection_name: Nombre de la colección + chunks: Lista de chunks a agregar + + Returns: + AddChunksResponse: Response con el resultado + + Raises: + ValueError: Si la colección no existe + """ + try: + # Verificar que la colección existe + exists = await self.vector_db.collection_exists(collection_name) + + if not exists: + logger.warning(f"Colección '{collection_name}' no existe") + raise ValueError(f"La colección '{collection_name}' no existe") + + # Agregar chunks + success = await self.vector_db.add_chunks(collection_name, chunks) + + if success: + logger.info( + f"Agregados {len(chunks)} chunks a la colección '{collection_name}'" + ) + return AddChunksResponse( + success=True, + collection_name=collection_name, + chunks_added=len(chunks), + message=f"Se agregaron {len(chunks)} chunks exitosamente" + ) + else: + logger.error(f"Fallo al agregar chunks a '{collection_name}'") + raise Exception(f"No se pudieron agregar los chunks a '{collection_name}'") + + except ValueError: + raise + except Exception as e: + logger.error(f"Error al agregar chunks a '{collection_name}': {e}") + raise + + async def health_check(self) -> VectorDBHealthResponse: + """ + Verifica el estado de la conexión con la base de datos vectorial. + + Returns: + VectorDBHealthResponse: Response con el estado + """ + try: + is_healthy = await self.vector_db.health_check() + + if is_healthy: + return VectorDBHealthResponse( + status="healthy", + db_type="qdrant", + message="Conexión exitosa con la base de datos vectorial" + ) + else: + return VectorDBHealthResponse( + status="unhealthy", + db_type="qdrant", + message="No se pudo conectar con la base de datos vectorial" + ) + + except Exception as e: + logger.error(f"Error en health check: {e}") + return VectorDBHealthResponse( + status="error", + db_type="qdrant", + message=f"Error al verificar conexión: {str(e)}" + ) + + +# Instancia global del servicio +vector_service = VectorService() diff --git a/backend/app/utils/chunking/__init__.py b/backend/app/utils/chunking/__init__.py new file mode 100644 index 0000000..286a965 --- /dev/null +++ b/backend/app/utils/chunking/__init__.py @@ -0,0 +1,18 @@ +""" +Utilidades de chunking para procesamiento de PDFs. +Refactorización modular del pipeline de chunking_token.py +""" +from .gemini_client import GeminiClient, get_gemini_client +from .token_manager import TokenManager +from .chunk_processor import OptimizedChunkProcessor +from .pdf_extractor import OptimizedPDFExtractor +from .pipeline import process_pdf_with_token_control + +__all__ = [ + "GeminiClient", + "get_gemini_client", + "TokenManager", + "OptimizedChunkProcessor", + "OptimizedPDFExtractor", + "process_pdf_with_token_control", +] diff --git a/backend/app/utils/chunking/chunk_processor.py b/backend/app/utils/chunking/chunk_processor.py new file mode 100644 index 0000000..fbff4c1 --- /dev/null +++ b/backend/app/utils/chunking/chunk_processor.py @@ -0,0 +1,258 @@ +""" +Procesador optimizado de chunks con soporte para LLM (Gemini). +Permite merge inteligente y mejora de chunks usando IA. +""" +import logging +import time +import hashlib +from typing import List, Optional +from langchain_core.documents import Document + +from .token_manager import TokenManager +from .gemini_client import GeminiClient + +logger = logging.getLogger(__name__) + + +class OptimizedChunkProcessor: + """Procesador de chunks con optimización mediante LLM""" + + def __init__( + self, + max_tokens: int = 1000, + target_tokens: int = 800, + chunks_per_batch: int = 5, + gemini_client: Optional[GeminiClient] = None, + model_name: str = "gpt-3.5-turbo", + custom_instructions: str = "" + ): + """ + Inicializa el procesador de chunks. + + Args: + max_tokens: Límite máximo de tokens por chunk + target_tokens: Tokens objetivo para chunks optimizados + chunks_per_batch: Chunks a procesar por lote + gemini_client: Cliente de Gemini para procesamiento (opcional) + model_name: Modelo para cálculo de tokens + custom_instructions: Instrucciones adicionales para el prompt de optimización + """ + self.client = gemini_client + self.chunks_per_batch = chunks_per_batch + self.max_tokens = max_tokens + self.target_tokens = target_tokens + self.token_manager = TokenManager(model_name) + self.custom_instructions = custom_instructions + + # Caché para evitar reprocesamiento + self._merge_cache = {} + self._enhance_cache = {} + + def _get_cache_key(self, text: str) -> str: + """Genera una clave de caché para el texto""" + combined = text + self.custom_instructions + return hashlib.md5(combined.encode()).hexdigest()[:16] + + def should_merge_chunks(self, chunk1: str, chunk2: str) -> bool: + """ + Determina si dos chunks deben unirse basándose en continuidad semántica. + + Args: + chunk1: Primer chunk + chunk2: Segundo chunk + + Returns: + True si los chunks deben unirse + """ + cache_key = f"{self._get_cache_key(chunk1)}_{self._get_cache_key(chunk2)}" + if cache_key in self._merge_cache: + return self._merge_cache[cache_key] + + try: + combined_text = f"{chunk1}\n\n{chunk2}" + combined_tokens = self.token_manager.count_tokens(combined_text) + + if combined_tokens > self.max_tokens: + self._merge_cache[cache_key] = False + return False + + if self.client: + base_prompt = f"""Analiza estos dos fragmentos de texto y determina si deben unirse. + +LÍMITES ESTRICTOS: +- Tokens combinados: {combined_tokens}/{self.max_tokens} +- Solo unir si hay continuidad semántica clara + +Criterios de unión: +1. El primer fragmento termina abruptamente +2. El segundo fragmento continúa la misma idea/concepto +3. La unión mejora la coherencia del contenido +4. Exceder {self.max_tokens} tokens, SOLAMENTE si es necesario para mantener el contexto + +Responde SOLO 'SI' o 'NO'. + +Fragmento 1 ({self.token_manager.count_tokens(chunk1)} tokens): +{chunk1[:500]}... + +Fragmento 2 ({self.token_manager.count_tokens(chunk2)} tokens): +{chunk2[:500]}...""" + + response = self.client.generate_content(base_prompt) + result = response.strip().upper() == 'SI' + self._merge_cache[cache_key] = result + return result + + # Heurística simple si no hay cliente LLM + result = ( + chunk1.rstrip().endswith(('.', '!', '?')) == False and + combined_tokens <= self.target_tokens + ) + self._merge_cache[cache_key] = result + return result + + except Exception as e: + logger.error(f"Error analizando chunks para merge: {e}") + self._merge_cache[cache_key] = False + return False + + def enhance_chunk(self, chunk_text: str) -> str: + """ + Mejora un chunk usando LLM o truncamiento. + + Args: + chunk_text: Texto del chunk a mejorar + + Returns: + Texto del chunk mejorado + """ + cache_key = self._get_cache_key(chunk_text) + if cache_key in self._enhance_cache: + return self._enhance_cache[cache_key] + + current_tokens = self.token_manager.count_tokens(chunk_text) + + try: + if self.client and current_tokens < self.max_tokens: + base_prompt = f"""Optimiza este texto siguiendo estas reglas ESTRICTAS: + +LÍMITES DE TOKENS: +- Actual: {current_tokens} tokens +- Máximo permitido: {self.max_tokens} tokens +- Objetivo: {self.target_tokens} tokens + +REGLAS FUNDAMENTALES: +NO exceder {self.max_tokens} tokens bajo ninguna circunstancia +Mantener TODA la información esencial y metadatos +NO cambiar términos técnicos o palabras clave +Asegurar oraciones completas y coherentes +Optimizar claridad y estructura sin añadir contenido +SOLO devuelve el texto no agregues conclusiones NUNCA + +Si el texto está cerca del límite, NO expandir. Solo mejorar estructura.""" + + if self.custom_instructions.strip(): + base_prompt += f"\n\nINSTRUCCIONES ADICIONALES:\n{self.custom_instructions}" + + base_prompt += f"\n\nTexto a optimizar:\n{chunk_text}" + + response = self.client.generate_content(base_prompt) + enhanced_text = response.strip() + + enhanced_tokens = self.token_manager.count_tokens(enhanced_text) + if enhanced_tokens > self.max_tokens: + logger.warning( + f"Texto optimizado excede límite ({enhanced_tokens} > {self.max_tokens}), truncando" + ) + enhanced_text = self.token_manager.truncate_to_tokens(enhanced_text, self.max_tokens) + + self._enhance_cache[cache_key] = enhanced_text + return enhanced_text + else: + # Sin LLM o ya en límite, solo truncar si es necesario + if current_tokens > self.max_tokens: + truncated = self.token_manager.truncate_to_tokens(chunk_text, self.max_tokens) + self._enhance_cache[cache_key] = truncated + return truncated + + self._enhance_cache[cache_key] = chunk_text + return chunk_text + + except Exception as e: + logger.error(f"Error procesando chunk: {e}") + if current_tokens > self.max_tokens: + truncated = self.token_manager.truncate_to_tokens(chunk_text, self.max_tokens) + self._enhance_cache[cache_key] = truncated + return truncated + + self._enhance_cache[cache_key] = chunk_text + return chunk_text + + def process_chunks_batch( + self, + chunks: List[Document], + merge_related: bool = False + ) -> List[Document]: + """ + Procesa un lote de chunks, aplicando merge y mejoras. + + Args: + chunks: Lista de documentos a procesar + merge_related: Si True, intenta unir chunks relacionados + + Returns: + Lista de documentos procesados + """ + processed_chunks = [] + total_chunks = len(chunks) + + logger.info(f"Procesando {total_chunks} chunks en lotes de {self.chunks_per_batch}") + if self.custom_instructions: + logger.info(f"Con instrucciones personalizadas: {self.custom_instructions[:100]}...") + + i = 0 + while i < len(chunks): + batch_start = time.time() + current_chunk = chunks[i] + merged_content = current_chunk.page_content + original_tokens = self.token_manager.count_tokens(merged_content) + + # Intentar merge si está habilitado + if merge_related and i < len(chunks) - 1: + merge_count = 0 + while ( + i + merge_count < len(chunks) - 1 and + self.should_merge_chunks( + merged_content, + chunks[i + merge_count + 1].page_content + ) + ): + merge_count += 1 + merged_content += "\n\n" + chunks[i + merge_count].page_content + logger.info(f" Uniendo chunk {i + 1} con chunk {i + merge_count + 1}") + + i += merge_count + + logger.info(f"\nProcesando chunk {i + 1}/{total_chunks}") + logger.info(f" Tokens originales: {original_tokens}") + + # Mejorar chunk + enhanced_content = self.enhance_chunk(merged_content) + final_tokens = self.token_manager.count_tokens(enhanced_content) + + processed_chunks.append(Document( + page_content=enhanced_content, + metadata={ + **current_chunk.metadata, + } + )) + + logger.info(f" Tokens finales: {final_tokens}") + logger.info(f" Tiempo de procesamiento: {time.time() - batch_start:.2f}s") + + i += 1 + + if i % self.chunks_per_batch == 0 and i < len(chunks): + logger.info(f"\nCompletados {i}/{total_chunks} chunks") + time.sleep(0.1) + + return processed_chunks diff --git a/backend/app/utils/chunking/gemini_client.py b/backend/app/utils/chunking/gemini_client.py new file mode 100644 index 0000000..83a05a0 --- /dev/null +++ b/backend/app/utils/chunking/gemini_client.py @@ -0,0 +1,91 @@ +""" +Cliente para interactuar con Gemini (Google Vertex AI). +Usado para procesamiento inteligente de chunks con LLM. +""" +import logging +import os +import google.oauth2.service_account as sa +import vertexai.generative_models as gm +import vertexai +from ...core.config import settings + +logger = logging.getLogger(__name__) + + +class GeminiClient: + """Cliente para generar contenido usando Gemini via Vertex AI""" + + def __init__( + self, + account_file: str | None = None, + project: str | None = None, + model: str | None = None + ) -> None: + """ + Inicializa el cliente de Gemini. + + Args: + account_file: Ruta al archivo de credenciales de servicio (default: desde settings) + project: ID del proyecto de Google Cloud (default: desde settings) + model: Modelo de Gemini a usar (default: desde settings) + """ + # Usar configuración de settings si no se proporciona + account_file = account_file or settings.GOOGLE_APPLICATION_CREDENTIALS + project = project or settings.GOOGLE_CLOUD_PROJECT + model = model or settings.GEMINI_MODEL + + try: + # Cargar credenciales desde archivo + credentials = sa.Credentials.from_service_account_file(account_file) + + # Inicializar Vertex AI + vertexai.init( + project=project, + credentials=credentials, + location=settings.GOOGLE_CLOUD_LOCATION + ) + + # Inicializar modelo + self.model = gm.GenerativeModel(model) + logger.info(f"GeminiClient inicializado con modelo {model}") + + except Exception as e: + logger.error(f"Error inicializando GeminiClient: {e}") + raise + + def generate_content(self, prompt: str) -> str: + """ + Genera contenido usando Gemini. + + Args: + prompt: Prompt para el modelo + + Returns: + Texto generado por el modelo + + Raises: + Exception: Si hay error en la generación + """ + try: + response = self.model.generate_content(prompt) + return response.text + except Exception as e: + logger.error(f"Error en Gemini: {e}") + return "" + + +# Instancia global singleton +_gemini_client: GeminiClient | None = None + + +def get_gemini_client() -> GeminiClient: + """ + Obtiene la instancia singleton del cliente de Gemini. + + Returns: + Instancia de GeminiClient + """ + global _gemini_client + if _gemini_client is None: + _gemini_client = GeminiClient() + return _gemini_client diff --git a/backend/app/utils/chunking/pdf_extractor.py b/backend/app/utils/chunking/pdf_extractor.py new file mode 100644 index 0000000..6a93ef2 --- /dev/null +++ b/backend/app/utils/chunking/pdf_extractor.py @@ -0,0 +1,299 @@ +""" +Extractor optimizado de PDFs con soporte para BytesIO y procesamiento paralelo. +Adaptado para trabajar con Azure Blob Storage sin archivos temporales. +""" +import logging +import os +import time +import hashlib +from typing import List, Optional, Dict, BinaryIO +from io import BytesIO +from concurrent.futures import ThreadPoolExecutor + +from langchain_core.documents import Document +from pypdf import PdfReader +from langchain_text_splitters import RecursiveCharacterTextSplitter +from pdf2image import convert_from_bytes + +from .token_manager import TokenManager +from .chunk_processor import OptimizedChunkProcessor +from .gemini_client import GeminiClient + +logger = logging.getLogger(__name__) + + +class OptimizedPDFExtractor: + """Extractor optimizado de PDFs con soporte para BytesIO""" + + def __init__( + self, + max_tokens: int = 1000, + target_tokens: int = 800, + gemini_client: Optional[GeminiClient] = None, + custom_instructions: str = "", + extract_images: bool = False, # Por defecto deshabilitado según requerimientos + max_workers: int = 4 + ): + """ + Inicializa el extractor de PDFs. + + Args: + max_tokens: Límite máximo de tokens por chunk + target_tokens: Tokens objetivo para chunks + gemini_client: Cliente de Gemini (opcional) + custom_instructions: Instrucciones adicionales para optimización + extract_images: Si True, extrae páginas con formato especial como imágenes + max_workers: Número máximo de workers para procesamiento paralelo + """ + self.client = gemini_client + self.max_workers = max_workers + self.token_manager = TokenManager() + self.custom_instructions = custom_instructions + self.extract_images = extract_images + self._format_cache = {} + + self.chunk_processor = OptimizedChunkProcessor( + max_tokens=max_tokens, + target_tokens=target_tokens, + gemini_client=gemini_client, + custom_instructions=custom_instructions + ) + + def detect_special_format_batch(self, chunks: List[Document]) -> Dict[int, bool]: + """ + Detecta chunks con formatos especiales (tablas, diagramas, etc.) en lote. + + Args: + chunks: Lista de chunks a analizar + + Returns: + Diccionario con índices de chunks y si tienen formato especial + """ + results = {} + + chunks_to_process = [] + for i, chunk in enumerate(chunks): + cache_key = hashlib.md5(chunk.page_content.encode()).hexdigest()[:16] + if cache_key in self._format_cache: + results[i] = self._format_cache[cache_key] + else: + chunks_to_process.append((i, chunk, cache_key)) + + if not chunks_to_process: + return results + + logger.info(f"Analizando {len(chunks_to_process)} chunks para formatos especiales...") + + if self.client and len(chunks_to_process) > 1: + with ThreadPoolExecutor(max_workers=min(self.max_workers, len(chunks_to_process))) as executor: + futures = { + executor.submit(self._detect_single_format, chunk): (i, cache_key) + for i, chunk, cache_key in chunks_to_process + } + + for future in futures: + i, cache_key = futures[future] + try: + result = future.result() + results[i] = result + self._format_cache[cache_key] = result + except Exception as e: + logger.error(f"Error procesando chunk {i}: {e}") + results[i] = False + self._format_cache[cache_key] = False + else: + for i, chunk, cache_key in chunks_to_process: + result = self._detect_single_format(chunk) + results[i] = result + self._format_cache[cache_key] = result + + return results + + def _detect_single_format(self, chunk: Document) -> bool: + """Detecta formato especial en un chunk individual.""" + if not self.client: + content = chunk.page_content + table_indicators = ['│', '├', '┼', '┤', '┬', '┴', '|', '+', '-'] + has_table_chars = any(char in content for char in table_indicators) + has_multiple_columns = content.count('\t') > 10 or content.count(' ') > 20 + return has_table_chars or has_multiple_columns + + try: + prompt = f"""¿Contiene este texto tablas estructuradas, diagramas ASCII, o elementos que requieren formato especial? + +Responde SOLO 'SI' o 'NO'. + +Texto: +{chunk.page_content[:1000]}""" + + response = self.client.generate_content(prompt) + return response.strip().upper() == 'SI' + + except Exception as e: + logger.error(f"Error detectando formato: {e}") + return False + + def process_pdf_from_bytes( + self, + pdf_bytes: bytes, + file_name: str, + chunk_size: int = 1000, + chunk_overlap: int = 200, + merge_related: bool = True + ) -> List[Document]: + """ + Procesa un PDF desde bytes (BytesIO). + + Args: + pdf_bytes: Contenido del PDF en bytes + file_name: Nombre del archivo PDF + chunk_size: Tamaño del chunk + chunk_overlap: Solapamiento entre chunks + merge_related: Si True, intenta unir chunks relacionados + + Returns: + Lista de documentos procesados + """ + overall_start = time.time() + logger.info(f"\n=== Iniciando procesamiento optimizado de PDF: {file_name} ===") + logger.info(f"Configuración:") + logger.info(f" - Tokens máximos por chunk: {self.chunk_processor.max_tokens}") + logger.info(f" - Tokens objetivo: {self.chunk_processor.target_tokens}") + logger.info(f" - Chunk size: {chunk_size}") + logger.info(f" - Chunk overlap: {chunk_overlap}") + logger.info(f" - Merge relacionados: {merge_related}") + logger.info(f" - Extraer imágenes: {'✅' if self.extract_images else '❌'}") + if self.custom_instructions: + logger.info(f" - Instrucciones personalizadas: {self.custom_instructions[:100]}...") + + logger.info(f"\n1. Creando chunks del PDF...") + chunks = self._create_optimized_chunks_from_bytes( + pdf_bytes, + file_name, + chunk_size, + chunk_overlap + ) + logger.info(f" Total chunks creados: {len(chunks)}") + + # Nota: La extracción de imágenes desde bytes no se implementa por ahora + # ya que extract_images está deshabilitado por defecto según requerimientos + if self.extract_images: + logger.warning("Extracción de imágenes desde bytes no implementada aún") + + logger.info(f"\n2. Procesando y optimizando chunks...") + processed_chunks = self.chunk_processor.process_chunks_batch(chunks, merge_related) + + total_time = time.time() - overall_start + if processed_chunks: + avg_tokens = sum( + self.token_manager.count_tokens(chunk.page_content) + for chunk in processed_chunks + ) / len(processed_chunks) + else: + avg_tokens = 0 + + logger.info(f"\n=== Procesamiento completado ===") + logger.info(f" Tiempo total: {total_time:.2f}s") + logger.info(f" Chunks procesados: {len(processed_chunks)}") + logger.info(f" Tokens promedio por chunk: {avg_tokens:.1f}") + if self.custom_instructions: + logger.info(f" Custom instructions aplicadas: ✅") + + return processed_chunks + + def _create_optimized_chunks_from_bytes( + self, + pdf_bytes: bytes, + file_name: str, + chunk_size: int, + chunk_overlap: int + ) -> List[Document]: + """ + Crea chunks optimizados desde bytes del PDF. + + Args: + pdf_bytes: Contenido del PDF en bytes + file_name: Nombre del archivo + chunk_size: Tamaño del chunk + chunk_overlap: Solapamiento entre chunks + + Returns: + Lista de documentos con chunks + """ + logger.info(f" Leyendo PDF desde bytes: {file_name}") + + # Crear BytesIO para pypdf + pdf_buffer = BytesIO(pdf_bytes) + pdf = PdfReader(pdf_buffer) + chunks = [] + + text_splitter = RecursiveCharacterTextSplitter( + chunk_size=chunk_size, + chunk_overlap=chunk_overlap, + length_function=self.token_manager.count_tokens, + separators=["\n\n", "\n", ". ", " ", ""] + ) + + # Extraer todo el texto concatenado con tracking de páginas + full_text = "" + page_boundaries = [] # Lista de (char_position, page_num) + + for page_num, page in enumerate(pdf.pages, 1): + text = page.extract_text() + if text.strip(): + page_start = len(full_text) + full_text += text + # Agregar separador entre páginas (excepto después de la última) + if page_num < len(pdf.pages): + full_text += "\n\n" + page_end = len(full_text) + page_boundaries.append((page_start, page_end, page_num)) + + if not full_text.strip(): + return [] + + # Dividir el texto completo (esto permite overlap entre páginas) + text_chunks = text_splitter.split_text(full_text) + + logger.info(f" Total de chunks generados por splitter: {len(text_chunks)}") + if len(text_chunks) >= 2: + # Verificar overlap entre primer y segundo chunk + chunk0_end = text_chunks[0][-100:] if len(text_chunks[0]) > 100 else text_chunks[0] + chunk1_start = text_chunks[1][:100] if len(text_chunks[1]) > 100 else text_chunks[1] + logger.info(f" Chunk 0 termina con: ...{chunk0_end}") + logger.info(f" Chunk 1 empieza con: {chunk1_start}...") + + # Asignar página a cada chunk basándonos en su posición en el texto original + chunks = [] + current_search_pos = 0 + + for chunk_text in text_chunks: + # Buscar donde aparece este chunk en el texto completo + chunk_pos = full_text.find(chunk_text, current_search_pos) + + if chunk_pos == -1: + # Si no lo encontramos, usar la última posición conocida + chunk_pos = current_search_pos + + # Determinar la página basándonos en la posición del inicio del chunk + chunk_page = 1 + for start, end, page_num in page_boundaries: + if chunk_pos >= start and chunk_pos < end: + chunk_page = page_num + break + elif chunk_pos >= end: + # El chunk está después de esta página, continuar buscando + chunk_page = page_num # Guardar la última página vista + + chunks.append(Document( + page_content=chunk_text, + metadata={ + "page": chunk_page, + "file_name": file_name, + } + )) + + # Actualizar posición de búsqueda para el siguiente chunk + current_search_pos = chunk_pos + len(chunk_text) + + return chunks diff --git a/backend/app/utils/chunking/pipeline.py b/backend/app/utils/chunking/pipeline.py new file mode 100644 index 0000000..b9d22b0 --- /dev/null +++ b/backend/app/utils/chunking/pipeline.py @@ -0,0 +1,65 @@ +""" +Pipeline principal para procesar PDFs con control de tokens. +Función de alto nivel que orquesta el proceso completo de chunking. +""" +import logging +from typing import List, Optional +from langchain_core.documents import Document + +from .pdf_extractor import OptimizedPDFExtractor +from .gemini_client import GeminiClient + +logger = logging.getLogger(__name__) + + +def process_pdf_with_token_control( + pdf_bytes: bytes, + file_name: str, + max_tokens: int = 950, + target_tokens: int = 800, + chunk_size: int = 1000, + chunk_overlap: int = 200, + merge_related: bool = True, + gemini_client: Optional[GeminiClient] = None, + custom_instructions: str = "", + extract_images: bool = False +) -> List[Document]: + """ + Función principal para procesar PDFs con control completo de tokens. + + Args: + pdf_bytes: Contenido del PDF en bytes + file_name: Nombre del archivo PDF + max_tokens: Límite máximo de tokens por chunk + target_tokens: Tokens objetivo para optimización + chunk_size: Tamaño base de chunks + chunk_overlap: Solapamiento entre chunks + merge_related: Si unir chunks relacionados + gemini_client: Cliente de Gemini (opcional, para LLM processing) + custom_instructions: Instrucciones adicionales para optimización + extract_images: Si True, extrae páginas con formato especial como imágenes + + Returns: + Lista de documentos procesados con metadata simple (page, file_name) + """ + logger.info(f"Iniciando pipeline de chunking para {file_name}") + + extractor = OptimizedPDFExtractor( + max_tokens=max_tokens, + target_tokens=target_tokens, + gemini_client=gemini_client, + custom_instructions=custom_instructions, + extract_images=extract_images, + max_workers=4 + ) + + chunks = extractor.process_pdf_from_bytes( + pdf_bytes=pdf_bytes, + file_name=file_name, + chunk_size=chunk_size, + chunk_overlap=chunk_overlap, + merge_related=merge_related + ) + + logger.info(f"Pipeline completado: {len(chunks)} chunks generados") + return chunks diff --git a/backend/app/utils/chunking/token_manager.py b/backend/app/utils/chunking/token_manager.py new file mode 100644 index 0000000..09812d1 --- /dev/null +++ b/backend/app/utils/chunking/token_manager.py @@ -0,0 +1,72 @@ +""" +Gestor de tokens para contar y truncar texto basado en modelos de tokenización. +""" +import logging +import tiktoken + +logger = logging.getLogger(__name__) + + +class TokenManager: + """Gestor para contar y truncar tokens usando tiktoken""" + + def __init__(self, model_name: str = "gpt-3.5-turbo"): + """ + Inicializa el gestor de tokens. + + Args: + model_name: Nombre del modelo para la codificación de tokens + """ + try: + self.encoding = tiktoken.encoding_for_model(model_name) + except KeyError: + logger.warning( + f"Modelo {model_name} no encontrado, usando codificación por defecto cl100k_base" + ) + self.encoding = tiktoken.get_encoding("cl100k_base") + + def count_tokens(self, text: str) -> int: + """ + Cuenta el número de tokens en un texto. + + Args: + text: Texto a analizar + + Returns: + Número de tokens + """ + return len(self.encoding.encode(text)) + + def truncate_to_tokens( + self, + text: str, + max_tokens: int, + preserve_sentences: bool = True + ) -> str: + """ + Trunca texto a un número máximo de tokens. + + Args: + text: Texto a truncar + max_tokens: Número máximo de tokens + preserve_sentences: Si True, intenta mantener oraciones completas + + Returns: + Texto truncado + """ + tokens = self.encoding.encode(text) + + if len(tokens) <= max_tokens: + return text + + truncated_tokens = tokens[:max_tokens] + truncated_text = self.encoding.decode(truncated_tokens) + + if preserve_sentences: + # Intentar cortar en el último punto + last_period = truncated_text.rfind('.') + # Solo cortar si el punto está en el último 30% del texto + if last_period > len(truncated_text) * 0.7: + return truncated_text[:last_period + 1] + + return truncated_text diff --git a/backend/app/vector_db/__init__.py b/backend/app/vector_db/__init__.py new file mode 100644 index 0000000..3c14fdd --- /dev/null +++ b/backend/app/vector_db/__init__.py @@ -0,0 +1,12 @@ +""" +Vector Database Module + +Este módulo proporciona una abstracción para trabajar con bases de datos vectoriales. +Utiliza el patrón Repository para permitir cambiar fácilmente entre diferentes +implementaciones (Qdrant, Pinecone, Weaviate, etc.). +""" + +from .base import VectorDBBase +from .factory import get_vector_db + +__all__ = ["VectorDBBase", "get_vector_db"] diff --git a/backend/app/vector_db/base.py b/backend/app/vector_db/base.py new file mode 100644 index 0000000..fa0bdba --- /dev/null +++ b/backend/app/vector_db/base.py @@ -0,0 +1,189 @@ +""" +Clase abstracta base para operaciones con bases de datos vectoriales. + +Este módulo define la interfaz que todas las implementaciones de bases de datos +vectoriales deben seguir, permitiendo cambiar fácilmente entre diferentes proveedores. +""" + +from abc import ABC, abstractmethod +from typing import List, Dict, Any, Optional + + +class VectorDBBase(ABC): + """ + Clase abstracta que define las operaciones básicas para una base de datos vectorial. + + Esta interfaz permite implementar el patrón Repository/Strategy para + abstraer la lógica de acceso a datos vectoriales. + """ + + @abstractmethod + async def collection_exists(self, collection_name: str) -> bool: + """ + Verifica si existe una colección con el nombre especificado. + + Args: + collection_name: Nombre de la colección a verificar + + Returns: + bool: True si la colección existe, False en caso contrario + """ + pass + + @abstractmethod + async def create_collection( + self, + collection_name: str, + vector_size: int = 3072, + distance: str = "Cosine" + ) -> bool: + """ + Crea una nueva colección en la base de datos vectorial. + + Args: + collection_name: Nombre de la colección a crear + vector_size: Dimensión de los vectores (por defecto 3072) + distance: Métrica de distancia ("Cosine", "Euclid", "Dot") + + Returns: + bool: True si se creó exitosamente, False en caso contrario + """ + pass + + @abstractmethod + async def delete_collection(self, collection_name: str) -> bool: + """ + Elimina una colección completa. + + Args: + collection_name: Nombre de la colección a eliminar + + Returns: + bool: True si se eliminó exitosamente, False en caso contrario + """ + pass + + @abstractmethod + async def file_exists_in_collection( + self, + collection_name: str, + file_name: str + ) -> bool: + """ + Verifica si un archivo ya existe en una colección. + + Args: + collection_name: Nombre de la colección + file_name: Nombre del archivo a buscar + + Returns: + bool: True si el archivo existe, False en caso contrario + """ + pass + + @abstractmethod + async def get_chunks_by_file( + self, + collection_name: str, + file_name: str, + limit: Optional[int] = None + ) -> List[Dict[str, Any]]: + """ + Obtiene todos los chunks de un archivo específico. + + Args: + collection_name: Nombre de la colección + file_name: Nombre del archivo + limit: Límite opcional de resultados + + Returns: + List[Dict]: Lista de chunks con su metadata + """ + pass + + @abstractmethod + async def delete_file_from_collection( + self, + collection_name: str, + file_name: str + ) -> int: + """ + Elimina todos los chunks de un archivo de una colección. + + Args: + collection_name: Nombre de la colección + file_name: Nombre del archivo a eliminar + + Returns: + int: Número de chunks eliminados + """ + pass + + @abstractmethod + async def add_chunks( + self, + collection_name: str, + chunks: List[Dict[str, Any]] + ) -> Dict[str, Any]: + """ + Agrega múltiples chunks a una colección. + + Args: + collection_name: Nombre de la colección + chunks: Lista de chunks con estructura: + { + "id": str, + "vector": List[float], + "payload": { + "text": str, + "file_name": str, + "page": int, + ...otros campos opcionales + } + } + + Returns: + Dict con 'success' (bool) y 'chunks_added' (int) + """ + pass + + @abstractmethod + async def get_collection_info(self, collection_name: str) -> Optional[Dict[str, Any]]: + """ + Obtiene información sobre una colección. + + Args: + collection_name: Nombre de la colección + + Returns: + Optional[Dict]: Información de la colección o None si no existe + """ + pass + + @abstractmethod + async def count_chunks_in_file( + self, + collection_name: str, + file_name: str + ) -> int: + """ + Cuenta el número de chunks de un archivo. + + Args: + collection_name: Nombre de la colección + file_name: Nombre del archivo + + Returns: + int: Número de chunks del archivo + """ + pass + + @abstractmethod + async def health_check(self) -> bool: + """ + Verifica que la conexión con la base de datos vectorial esté funcionando. + + Returns: + bool: True si la conexión es exitosa, False en caso contrario + """ + pass diff --git a/backend/app/vector_db/factory.py b/backend/app/vector_db/factory.py new file mode 100644 index 0000000..a8cfe0c --- /dev/null +++ b/backend/app/vector_db/factory.py @@ -0,0 +1,76 @@ +""" +Factory para crear instancias de bases de datos vectoriales. + +Este módulo implementa el patrón Factory para crear la instancia correcta +de base de datos vectorial según la configuración. +""" + +import logging +from typing import Optional + +from app.core.config import settings +from .base import VectorDBBase +from .qdrant_client import QdrantVectorDB + +logger = logging.getLogger(__name__) + +# Instancia global singleton +_vector_db_instance: Optional[VectorDBBase] = None + + +def get_vector_db() -> VectorDBBase: + """ + Factory function que retorna la instancia de base de datos vectorial configurada. + + Utiliza un patrón Singleton para mantener una sola instancia durante + el ciclo de vida de la aplicación. + + Returns: + VectorDBBase: Instancia de la base de datos vectorial configurada + + Raises: + ValueError: Si el tipo de base de datos no está soportado + """ + global _vector_db_instance + + # Si ya existe una instancia, retornarla + if _vector_db_instance is not None: + return _vector_db_instance + + # Crear nueva instancia según configuración + db_type = settings.VECTOR_DB_TYPE.lower() + + if db_type == "qdrant": + logger.info(f"Inicializando Qdrant con URL: {settings.QDRANT_URL}") + _vector_db_instance = QdrantVectorDB( + url=settings.QDRANT_URL, + api_key=settings.QDRANT_API_KEY + ) + + # Aquí se pueden agregar otros proveedores en el futuro + # elif db_type == "pinecone": + # _vector_db_instance = PineconeVectorDB(...) + # elif db_type == "weaviate": + # _vector_db_instance = WeaviateVectorDB(...) + + else: + raise ValueError( + f"Tipo de base de datos vectorial no soportado: {db_type}. " + f"Tipos soportados: qdrant" + ) + + logger.info(f"Base de datos vectorial '{db_type}' inicializada exitosamente") + return _vector_db_instance + + +def reset_vector_db() -> None: + """ + Resetea la instancia global de la base de datos vectorial. + + NOTA: Esta función solo cierra la conexión del cliente en memoria. + NO elimina ni modifica datos en Qdrant. + Útil principalmente para testing. + """ + global _vector_db_instance + _vector_db_instance = None + logger.info("Instancia de base de datos vectorial reseteada") diff --git a/backend/app/vector_db/qdrant_client.py b/backend/app/vector_db/qdrant_client.py new file mode 100644 index 0000000..99dfade --- /dev/null +++ b/backend/app/vector_db/qdrant_client.py @@ -0,0 +1,410 @@ +""" +Implementación de Qdrant para la interfaz VectorDBBase. + +Este módulo proporciona la implementación concreta de todas las operaciones +vectoriales utilizando Qdrant como base de datos. +""" + +import logging +from typing import List, Dict, Any, Optional +from qdrant_client import QdrantClient +from qdrant_client.models import ( + Distance, + VectorParams, + PointStruct, + Filter, + FieldCondition, + MatchValue +) +from qdrant_client.http.exceptions import UnexpectedResponse + +from .base import VectorDBBase + +logger = logging.getLogger(__name__) + + +class QdrantVectorDB(VectorDBBase): + """ + Implementación de VectorDBBase usando Qdrant como proveedor. + + Atributos: + client: Cliente de Qdrant + url: URL del servidor Qdrant + api_key: API key para autenticación + """ + + def __init__(self, url: str, api_key: str): + """ + Inicializa el cliente de Qdrant. + + Args: + url: URL del servidor Qdrant + api_key: API key para autenticación + """ + self.url = url + self.api_key = api_key + self.client = QdrantClient( + url=url, + api_key=api_key, + timeout=30 + ) + logger.info(f"QdrantVectorDB inicializado con URL: {url}") + + async def collection_exists(self, collection_name: str) -> bool: + """ + Verifica si existe una colección en Qdrant. + + Args: + collection_name: Nombre de la colección + + Returns: + bool: True si existe, False en caso contrario + """ + try: + collections = self.client.get_collections().collections + return any(col.name == collection_name for col in collections) + except Exception as e: + logger.error(f"Error al verificar colección '{collection_name}': {e}") + return False + + async def create_collection( + self, + collection_name: str, + vector_size: int = 3072, + distance: str = "Cosine" + ) -> bool: + """ + Crea una nueva colección en Qdrant. + + Args: + collection_name: Nombre de la colección + vector_size: Dimensión de los vectores (default: 3072) + distance: Métrica de distancia + + Returns: + bool: True si se creó exitosamente + """ + try: + # Mapear string a enum de Qdrant + distance_map = { + "Cosine": Distance.COSINE, + "Euclid": Distance.EUCLID, + "Dot": Distance.DOT + } + + distance_metric = distance_map.get(distance, Distance.COSINE) + + self.client.create_collection( + collection_name=collection_name, + vectors_config=VectorParams( + size=vector_size, + distance=distance_metric + ) + ) + + logger.info(f"Colección '{collection_name}' creada exitosamente") + return True + + except Exception as e: + logger.error(f"Error al crear colección '{collection_name}': {e}") + return False + + async def delete_collection(self, collection_name: str) -> bool: + """ + Elimina una colección completa de Qdrant. + + Args: + collection_name: Nombre de la colección + + Returns: + bool: True si se eliminó exitosamente + """ + try: + self.client.delete_collection(collection_name=collection_name) + logger.info(f"Colección '{collection_name}' eliminada exitosamente") + return True + except Exception as e: + logger.error(f"Error al eliminar colección '{collection_name}': {e}") + return False + + async def file_exists_in_collection( + self, + collection_name: str, + file_name: str + ) -> bool: + """ + Verifica si un archivo existe en una colección. + + Args: + collection_name: Nombre de la colección + file_name: Nombre del archivo + + Returns: + bool: True si el archivo existe + """ + try: + # Buscar un solo punto con el file_name en metadata + result = self.client.scroll( + collection_name=collection_name, + scroll_filter=Filter( + must=[ + FieldCondition( + key="metadata.file_name", + match=MatchValue(value=file_name) + ) + ] + ), + limit=1 + ) + + return len(result[0]) > 0 + + except Exception as e: + logger.error(f"Error al verificar archivo '{file_name}' en colección '{collection_name}': {e}") + return False + + async def get_chunks_by_file( + self, + collection_name: str, + file_name: str, + limit: Optional[int] = None + ) -> List[Dict[str, Any]]: + """ + Obtiene todos los chunks de un archivo. + + Args: + collection_name: Nombre de la colección + file_name: Nombre del archivo + limit: Límite opcional de resultados + + Returns: + List[Dict]: Lista de chunks con metadata + """ + try: + chunks = [] + offset = None + + while True: + result = self.client.scroll( + collection_name=collection_name, + scroll_filter=Filter( + must=[ + FieldCondition( + key="metadata.file_name", + match=MatchValue(value=file_name) + ) + ] + ), + limit=limit if limit else 100, + offset=offset + ) + + points, next_offset = result + + for point in points: + chunks.append({ + "id": str(point.id), + "payload": point.payload, + "vector": point.vector if hasattr(point, 'vector') else None + }) + + # Si hay límite y lo alcanzamos, salimos + if limit and len(chunks) >= limit: + break + + # Si no hay más resultados, salimos + if next_offset is None: + break + + offset = next_offset + + logger.info(f"Obtenidos {len(chunks)} chunks del archivo '{file_name}'") + return chunks + + except Exception as e: + logger.error(f"Error al obtener chunks del archivo '{file_name}': {e}") + return [] + + async def delete_file_from_collection( + self, + collection_name: str, + file_name: str + ) -> int: + """ + Elimina todos los chunks de un archivo. + + Args: + collection_name: Nombre de la colección + file_name: Nombre del archivo + + Returns: + int: Número de chunks eliminados + """ + try: + # Primero obtener todos los IDs del archivo + chunks = await self.get_chunks_by_file(collection_name, file_name) + + if not chunks: + logger.info(f"No se encontraron chunks para el archivo '{file_name}'") + return 0 + + # Extraer los IDs + point_ids = [chunk["id"] for chunk in chunks] + + # Eliminar por IDs + self.client.delete( + collection_name=collection_name, + points_selector=point_ids + ) + + logger.info(f"Eliminados {len(point_ids)} chunks del archivo '{file_name}'") + return len(point_ids) + + except Exception as e: + logger.error(f"Error al eliminar archivo '{file_name}': {e}") + return 0 + + async def add_chunks( + self, + collection_name: str, + chunks: List[Dict[str, Any]] + ) -> Dict[str, Any]: + """ + Agrega múltiples chunks a una colección. + + Args: + collection_name: Nombre de la colección + chunks: Lista de chunks con estructura: + { + "id": str, + "vector": List[float], + "payload": { + "page_content": str, + "metadata": { + "file_name": str, + "page": int + } + } + } + + Returns: + Dict con 'success' (bool) y 'chunks_added' (int) + """ + try: + points = [] + + for chunk in chunks: + point = PointStruct( + id=chunk["id"], + vector=chunk["vector"], + payload=chunk["payload"] + ) + points.append(point) + + self.client.upsert( + collection_name=collection_name, + points=points + ) + + logger.info(f"Agregados {len(points)} chunks a la colección '{collection_name}'") + return { + "success": True, + "chunks_added": len(points) + } + + except Exception as e: + logger.error(f"Error al agregar chunks a '{collection_name}': {e}") + return { + "success": False, + "chunks_added": 0 + } + + async def get_collection_info(self, collection_name: str) -> Optional[Dict[str, Any]]: + """ + Obtiene información sobre una colección. + + Args: + collection_name: Nombre de la colección + + Returns: + Optional[Dict]: Información de la colección o None + """ + try: + collection_info = self.client.get_collection(collection_name=collection_name) + + return { + "name": collection_name, + "vectors_count": collection_info.points_count, + "vectors_config": { + "size": collection_info.config.params.vectors.size, + "distance": collection_info.config.params.vectors.distance.name + }, + "status": collection_info.status.name + } + + except UnexpectedResponse as e: + if e.status_code == 404: + logger.warning(f"Colección '{collection_name}' no encontrada") + return None + logger.error(f"Error al obtener info de colección '{collection_name}': {e}") + return None + except Exception as e: + logger.error(f"Error inesperado al obtener info de colección '{collection_name}': {e}") + return None + + async def count_chunks_in_file( + self, + collection_name: str, + file_name: str + ) -> int: + """ + Cuenta el número de chunks de un archivo. + + Args: + collection_name: Nombre de la colección + file_name: Nombre del archivo + + Returns: + int: Número de chunks + """ + try: + result = self.client.scroll( + collection_name=collection_name, + scroll_filter=Filter( + must=[ + FieldCondition( + key="file_name", + match=MatchValue(value=file_name) + ) + ] + ), + limit=1, + with_payload=False, + with_vectors=False + ) + + # Qdrant scroll no retorna count directo, así que obtenemos todos + chunks = await self.get_chunks_by_file(collection_name, file_name) + count = len(chunks) + + logger.info(f"Archivo '{file_name}' tiene {count} chunks") + return count + + except Exception as e: + logger.error(f"Error al contar chunks del archivo '{file_name}': {e}") + return 0 + + async def health_check(self) -> bool: + """ + Verifica la conexión con Qdrant. + + Returns: + bool: True si la conexión es exitosa + """ + try: + self.client.get_collections() + logger.info("Health check de Qdrant exitoso") + return True + except Exception as e: + logger.error(f"Health check de Qdrant falló: {e}") + return False diff --git a/backend/pyproject.toml 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|| ^17.0 || ^18.0 || ^19.0 || ^19.0.0-rc" + }, + "peerDependenciesMeta": { + "@types/react": { + "optional": true + }, + "@types/react-dom": { + "optional": true + } + } + }, "node_modules/@radix-ui/react-checkbox": { "version": "1.3.3", "resolved": "https://registry.npmjs.org/@radix-ui/react-checkbox/-/react-checkbox-1.3.3.tgz", @@ -1240,6 +1310,32 @@ } } }, + "node_modules/@radix-ui/react-collection": { + "version": "1.1.7", + "resolved": "https://registry.npmjs.org/@radix-ui/react-collection/-/react-collection-1.1.7.tgz", + "integrity": "sha512-Fh9rGN0MoI4ZFUNyfFVNU4y9LUz93u9/0K+yLgA2bwRojxM8JU1DyvvMBabnZPBgMWREAJvU2jjVzq+LrFUglw==", + "license": "MIT", + "dependencies": { + "@radix-ui/react-compose-refs": "1.1.2", + "@radix-ui/react-context": "1.1.2", + "@radix-ui/react-primitive": "2.1.3", + "@radix-ui/react-slot": "1.2.3" + }, + "peerDependencies": { + "@types/react": "*", + "@types/react-dom": "*", + "react": "^16.8 || ^17.0 || ^18.0 || ^19.0 || ^19.0.0-rc", + "react-dom": "^16.8 || ^17.0 || ^18.0 || ^19.0 || ^19.0.0-rc" + }, + "peerDependenciesMeta": { + "@types/react": { + "optional": true + }, + "@types/react-dom": { + "optional": true + } + } + }, "node_modules/@radix-ui/react-compose-refs": { "version": "1.1.2", "resolved": "https://registry.npmjs.org/@radix-ui/react-compose-refs/-/react-compose-refs-1.1.2.tgz", @@ -1305,6 +1401,21 @@ } } }, + "node_modules/@radix-ui/react-direction": { + "version": "1.1.1", + "resolved": "https://registry.npmjs.org/@radix-ui/react-direction/-/react-direction-1.1.1.tgz", + "integrity": "sha512-1UEWRX6jnOA2y4H5WczZ44gOOjTEmlqv1uNW4GAJEO5+bauCBhv8snY65Iw5/VOS/ghKN9gr2KjnLKxrsvoMVw==", + "license": "MIT", + "peerDependencies": { + "@types/react": "*", + "react": "^16.8 || ^17.0 || ^18.0 || ^19.0 || ^19.0.0-rc" + }, + "peerDependenciesMeta": { + "@types/react": { + "optional": true + } + } + }, "node_modules/@radix-ui/react-dismissable-layer": { "version": "1.1.11", "resolved": "https://registry.npmjs.org/@radix-ui/react-dismissable-layer/-/react-dismissable-layer-1.1.11.tgz", @@ -1413,6 +1524,38 @@ } } }, + "node_modules/@radix-ui/react-popper": { + "version": "1.2.8", + "resolved": "https://registry.npmjs.org/@radix-ui/react-popper/-/react-popper-1.2.8.tgz", + "integrity": "sha512-0NJQ4LFFUuWkE7Oxf0htBKS6zLkkjBH+hM1uk7Ng705ReR8m/uelduy1DBo0PyBXPKVnBA6YBlU94MBGXrSBCw==", + "license": "MIT", + "dependencies": { + "@floating-ui/react-dom": "^2.0.0", + "@radix-ui/react-arrow": "1.1.7", + "@radix-ui/react-compose-refs": "1.1.2", + "@radix-ui/react-context": "1.1.2", + "@radix-ui/react-primitive": "2.1.3", + "@radix-ui/react-use-callback-ref": "1.1.1", + "@radix-ui/react-use-layout-effect": "1.1.1", + "@radix-ui/react-use-rect": "1.1.1", + "@radix-ui/react-use-size": "1.1.1", + "@radix-ui/rect": "1.1.1" + }, + "peerDependencies": { + "@types/react": "*", + "@types/react-dom": "*", + "react": "^16.8 || ^17.0 || ^18.0 || ^19.0 || ^19.0.0-rc", + "react-dom": "^16.8 || ^17.0 || ^18.0 || ^19.0 || ^19.0.0-rc" + }, + "peerDependenciesMeta": { + "@types/react": { + "optional": true + }, + "@types/react-dom": { + "optional": true + } + } + }, "node_modules/@radix-ui/react-portal": { "version": "1.1.9", "resolved": "https://registry.npmjs.org/@radix-ui/react-portal/-/react-portal-1.1.9.tgz", @@ -1484,6 +1627,80 @@ } } }, + "node_modules/@radix-ui/react-roving-focus": { + "version": "1.1.11", + "resolved": "https://registry.npmjs.org/@radix-ui/react-roving-focus/-/react-roving-focus-1.1.11.tgz", + "integrity": "sha512-7A6S9jSgm/S+7MdtNDSb+IU859vQqJ/QAtcYQcfFC6W8RS4IxIZDldLR0xqCFZ6DCyrQLjLPsxtTNch5jVA4lA==", + "license": "MIT", + "dependencies": { + "@radix-ui/primitive": "1.1.3", + "@radix-ui/react-collection": "1.1.7", + "@radix-ui/react-compose-refs": "1.1.2", + "@radix-ui/react-context": "1.1.2", + "@radix-ui/react-direction": "1.1.1", + "@radix-ui/react-id": "1.1.1", + "@radix-ui/react-primitive": "2.1.3", + "@radix-ui/react-use-callback-ref": "1.1.1", + "@radix-ui/react-use-controllable-state": "1.2.2" + }, + "peerDependencies": { + "@types/react": "*", + "@types/react-dom": "*", + "react": "^16.8 || ^17.0 || ^18.0 || ^19.0 || ^19.0.0-rc", + "react-dom": "^16.8 || ^17.0 || ^18.0 || ^19.0 || ^19.0.0-rc" + }, + "peerDependenciesMeta": { + "@types/react": { + "optional": true + }, + "@types/react-dom": { + "optional": true + } + } + }, + "node_modules/@radix-ui/react-select": { + "version": "2.2.6", + "resolved": "https://registry.npmjs.org/@radix-ui/react-select/-/react-select-2.2.6.tgz", + "integrity": "sha512-I30RydO+bnn2PQztvo25tswPH+wFBjehVGtmagkU78yMdwTwVf12wnAOF+AeP8S2N8xD+5UPbGhkUfPyvT+mwQ==", + "license": "MIT", + "dependencies": { + "@radix-ui/number": "1.1.1", + "@radix-ui/primitive": "1.1.3", + "@radix-ui/react-collection": "1.1.7", + "@radix-ui/react-compose-refs": "1.1.2", + "@radix-ui/react-context": "1.1.2", + "@radix-ui/react-direction": "1.1.1", + "@radix-ui/react-dismissable-layer": "1.1.11", + "@radix-ui/react-focus-guards": "1.1.3", + "@radix-ui/react-focus-scope": "1.1.7", + "@radix-ui/react-id": "1.1.1", + "@radix-ui/react-popper": "1.2.8", + "@radix-ui/react-portal": "1.1.9", + "@radix-ui/react-primitive": "2.1.3", + "@radix-ui/react-slot": "1.2.3", + "@radix-ui/react-use-callback-ref": "1.1.1", + "@radix-ui/react-use-controllable-state": "1.2.2", + "@radix-ui/react-use-layout-effect": "1.1.1", + "@radix-ui/react-use-previous": "1.1.1", + "@radix-ui/react-visually-hidden": "1.2.3", + "aria-hidden": "^1.2.4", + "react-remove-scroll": "^2.6.3" + }, + "peerDependencies": { + "@types/react": "*", + "@types/react-dom": "*", + "react": "^16.8 || ^17.0 || ^18.0 || ^19.0 || ^19.0.0-rc", + "react-dom": "^16.8 || ^17.0 || ^18.0 || ^19.0 || ^19.0.0-rc" + }, + "peerDependenciesMeta": { + "@types/react": { + "optional": true + }, + "@types/react-dom": { + "optional": true + } + } + }, "node_modules/@radix-ui/react-slot": { "version": "1.2.3", "resolved": "https://registry.npmjs.org/@radix-ui/react-slot/-/react-slot-1.2.3.tgz", @@ -1502,6 +1719,65 @@ } } }, + "node_modules/@radix-ui/react-switch": { + "version": "1.2.6", + "resolved": "https://registry.npmjs.org/@radix-ui/react-switch/-/react-switch-1.2.6.tgz", + "integrity": "sha512-bByzr1+ep1zk4VubeEVViV592vu2lHE2BZY5OnzehZqOOgogN80+mNtCqPkhn2gklJqOpxWgPoYTSnhBCqpOXQ==", + "license": "MIT", + "dependencies": { + "@radix-ui/primitive": "1.1.3", + "@radix-ui/react-compose-refs": "1.1.2", + "@radix-ui/react-context": "1.1.2", + "@radix-ui/react-primitive": "2.1.3", + "@radix-ui/react-use-controllable-state": "1.2.2", + "@radix-ui/react-use-previous": "1.1.1", + "@radix-ui/react-use-size": "1.1.1" + }, + "peerDependencies": { + "@types/react": "*", + "@types/react-dom": "*", + "react": "^16.8 || ^17.0 || ^18.0 || ^19.0 || ^19.0.0-rc", + "react-dom": "^16.8 || ^17.0 || ^18.0 || ^19.0 || ^19.0.0-rc" + }, + "peerDependenciesMeta": { + "@types/react": { + "optional": true + }, + "@types/react-dom": { + "optional": true + } + } + }, + "node_modules/@radix-ui/react-tabs": { + "version": "1.1.13", + "resolved": "https://registry.npmjs.org/@radix-ui/react-tabs/-/react-tabs-1.1.13.tgz", + "integrity": "sha512-7xdcatg7/U+7+Udyoj2zodtI9H/IIopqo+YOIcZOq1nJwXWBZ9p8xiu5llXlekDbZkca79a/fozEYQXIA4sW6A==", + "license": "MIT", + "dependencies": { + "@radix-ui/primitive": "1.1.3", + "@radix-ui/react-context": "1.1.2", + "@radix-ui/react-direction": "1.1.1", + "@radix-ui/react-id": "1.1.1", + "@radix-ui/react-presence": "1.1.5", + "@radix-ui/react-primitive": "2.1.3", + "@radix-ui/react-roving-focus": "1.1.11", + "@radix-ui/react-use-controllable-state": "1.2.2" + }, + "peerDependencies": { + "@types/react": "*", + "@types/react-dom": "*", + "react": "^16.8 || ^17.0 || ^18.0 || ^19.0 || ^19.0.0-rc", + "react-dom": "^16.8 || ^17.0 || ^18.0 || ^19.0 || ^19.0.0-rc" + }, + "peerDependenciesMeta": { + "@types/react": { + "optional": true + }, + "@types/react-dom": { + "optional": true + } + } + }, "node_modules/@radix-ui/react-use-callback-ref": { "version": "1.1.1", "resolved": "https://registry.npmjs.org/@radix-ui/react-use-callback-ref/-/react-use-callback-ref-1.1.1.tgz", @@ -1602,6 +1878,24 @@ } } }, + "node_modules/@radix-ui/react-use-rect": { + "version": "1.1.1", + "resolved": "https://registry.npmjs.org/@radix-ui/react-use-rect/-/react-use-rect-1.1.1.tgz", + "integrity": "sha512-QTYuDesS0VtuHNNvMh+CjlKJ4LJickCMUAqjlE3+j8w+RlRpwyX3apEQKGFzbZGdo7XNG1tXa+bQqIE7HIXT2w==", + "license": "MIT", + "dependencies": { + "@radix-ui/rect": "1.1.1" + }, + "peerDependencies": { + "@types/react": "*", + "react": "^16.8 || ^17.0 || ^18.0 || ^19.0 || ^19.0.0-rc" + }, + "peerDependenciesMeta": { + "@types/react": { + "optional": true + } + } + }, "node_modules/@radix-ui/react-use-size": { "version": "1.1.1", "resolved": "https://registry.npmjs.org/@radix-ui/react-use-size/-/react-use-size-1.1.1.tgz", @@ -1620,6 +1914,35 @@ } } }, + "node_modules/@radix-ui/react-visually-hidden": { + "version": "1.2.3", + "resolved": "https://registry.npmjs.org/@radix-ui/react-visually-hidden/-/react-visually-hidden-1.2.3.tgz", + "integrity": "sha512-pzJq12tEaaIhqjbzpCuv/OypJY/BPavOofm+dbab+MHLajy277+1lLm6JFcGgF5eskJ6mquGirhXY2GD/8u8Ug==", + "license": "MIT", + "dependencies": { + "@radix-ui/react-primitive": "2.1.3" + }, + "peerDependencies": { + "@types/react": "*", + "@types/react-dom": "*", + "react": "^16.8 || ^17.0 || ^18.0 || ^19.0 || ^19.0.0-rc", + "react-dom": "^16.8 || ^17.0 || ^18.0 || ^19.0 || ^19.0.0-rc" + }, + "peerDependenciesMeta": { + "@types/react": { + "optional": true + }, + "@types/react-dom": { + "optional": true + } + } + }, + "node_modules/@radix-ui/rect": { + "version": "1.1.1", + "resolved": "https://registry.npmjs.org/@radix-ui/rect/-/rect-1.1.1.tgz", + "integrity": "sha512-HPwpGIzkl28mWyZqG52jiqDJ12waP11Pa1lGoiyUkIEuMLBP0oeK/C89esbXrxsky5we7dfd8U58nm0SgAWpVw==", + "license": "MIT" + }, "node_modules/@rolldown/pluginutils": { "version": "1.0.0-beta.34", "resolved": "https://registry.npmjs.org/@rolldown/pluginutils/-/pluginutils-1.0.0-beta.34.tgz", diff --git a/frontend/package.json b/frontend/package.json index fe92cc4..3735414 100644 --- a/frontend/package.json +++ b/frontend/package.json @@ -13,7 +13,10 @@ "@radix-ui/react-checkbox": "^1.3.3", "@radix-ui/react-dialog": "^1.1.15", "@radix-ui/react-label": "^2.1.7", + "@radix-ui/react-select": "^2.2.6", "@radix-ui/react-slot": "^1.2.3", + "@radix-ui/react-switch": "^1.2.6", + "@radix-ui/react-tabs": "^1.1.13", "class-variance-authority": "^0.7.1", "clsx": "^2.1.1", "lucide-react": "^0.543.0", diff --git a/frontend/src/components/ChunkPreviewPanel.tsx b/frontend/src/components/ChunkPreviewPanel.tsx new file mode 100644 index 0000000..ce1037e --- /dev/null +++ b/frontend/src/components/ChunkPreviewPanel.tsx @@ -0,0 +1,228 @@ +import { useState, useEffect } from 'react' +import { api } from '../services/api' +import { + Dialog, + DialogContent, + DialogDescription, + DialogFooter, + DialogHeader, + DialogTitle, +} from './ui/dialog' +import { Button } from './ui/button' +import { AlertCircle, Loader2, FileText, CheckCircle2, XCircle } from 'lucide-react' +import type { ChunkingConfig } from './ChunkingConfigModal' + +interface ChunkPreviewPanelProps { + isOpen: boolean + onClose: () => void + config: ChunkingConfig | null + onAccept: (config: ChunkingConfig) => void + onCancel: () => void +} + +interface PreviewChunk { + index: number + text: string + page: number + file_name: string + tokens: number +} + +export function ChunkPreviewPanel({ + isOpen, + onClose, + config, + onAccept, + onCancel, +}: ChunkPreviewPanelProps) { + const [chunks, setChunks] = useState([]) + const [loading, setLoading] = useState(false) + const [processing, setProcessing] = useState(false) + const [error, setError] = useState(null) + const [success, setSuccess] = useState(false) + + // Auto-cargar preview cuando se abre el modal + useEffect(() => { + if (isOpen && config && chunks.length === 0) { + loadPreview() + } + }, [isOpen, config]) + + const loadPreview = async () => { + if (!config) return + + setLoading(true) + setError(null) + setSuccess(false) + + try { + const result = await api.generateChunkPreview(config) + setChunks(result.chunks) + } catch (err) { + console.error('Error loading preview:', err) + setError(err instanceof Error ? err.message : 'Error generando preview') + } finally { + setLoading(false) + } + } + + const handleAccept = async () => { + if (!config) return + + setProcessing(true) + setError(null) + + try { + await onAccept(config) + setSuccess(true) + + // Cerrar después de 2 segundos + setTimeout(() => { + handleClose() + }, 2000) + } catch (err) { + console.error('Error processing:', err) + setError(err instanceof Error ? err.message : 'Error procesando PDF') + } finally { + setProcessing(false) + } + } + + const handleCancel = () => { + onCancel() + handleClose() + } + + const handleClose = () => { + setChunks([]) + setError(null) + setSuccess(false) + onClose() + } + + if (!config) return null + + return ( + + + + + + Preview de Chunks + + + Vista previa de chunks para {config.file_name} + + + + {/* Contenido */} +
+ {loading ? ( +
+ + Generando preview... +
+ ) : error ? ( +
+ + {error} +
+ ) : success ? ( +
+ +

+ Procesamiento Completado +

+

+ El PDF ha sido procesado y subido a Qdrant exitosamente +

+
+ ) : chunks.length === 0 ? ( +
+ +

No hay chunks para mostrar

+
+ ) : ( +
+ {/* Información de configuración */} +
+

+ Configuración: Max {config.max_tokens} tokens, Target{' '} + {config.target_tokens} tokens + {config.use_llm && ' | LLM Habilitado'} +

+
+ + {/* Lista de chunks */} + {chunks.map((chunk) => ( +
+ {/* Header del chunk */} +
+
+ + Chunk #{chunk.index + 1} + + + Página {chunk.page} + + + ~{chunk.tokens} tokens + +
+
+ + {/* Texto del chunk */} +
+

+ {chunk.text} +

+
+ + {/* Indicador de longitud */} +
+ Longitud: {chunk.text.length} caracteres +
+
+ ))} + + {/* Información adicional */} +
+

+ Nota: Estos son chunks de ejemplo (hasta 3). El documento + completo generará más chunks según su tamaño. +

+
+
+ )} +
+ + {/* Footer con acciones */} + + + + + + + + ) +} diff --git a/frontend/src/components/ChunkViewerModal.tsx b/frontend/src/components/ChunkViewerModal.tsx new file mode 100644 index 0000000..d8d0007 --- /dev/null +++ b/frontend/src/components/ChunkViewerModal.tsx @@ -0,0 +1,205 @@ +import { useEffect, useState } from 'react' +import { api } from '../services/api' +import { + Dialog, + DialogContent, + DialogDescription, + DialogHeader, + DialogTitle, +} from './ui/dialog' +import { Button } from './ui/button' +import { AlertCircle, Loader2, FileText, Trash2 } from 'lucide-react' + +interface ChunkViewerModalProps { + isOpen: boolean + onClose: () => void + fileName: string + tema: string +} + +interface Chunk { + id: string + payload: { + page_content: string + metadata: { + file_name: string + page: number + [key: string]: any + } + [key: string]: any + } + vector?: number[] +} + +export function ChunkViewerModal({ isOpen, onClose, fileName, tema }: ChunkViewerModalProps) { + const [chunks, setChunks] = useState([]) + const [loading, setLoading] = useState(false) + const [error, setError] = useState(null) + const [deleting, setDeleting] = useState(false) + + useEffect(() => { + if (isOpen && fileName && tema) { + loadChunks() + } + }, [isOpen, fileName, tema]) + + const loadChunks = async () => { + setLoading(true) + setError(null) + + try { + const result = await api.getChunksByFile(tema, fileName) + setChunks(result.chunks) + } catch (err) { + console.error('Error loading chunks:', err) + setError(err instanceof Error ? err.message : 'Error al cargar chunks') + } finally { + setLoading(false) + } + } + + const handleDeleteFile = async () => { + if (!confirm(`¿Estás seguro de eliminar todos los chunks del archivo "${fileName}" de la colección "${tema}"?`)) { + return + } + + setDeleting(true) + setError(null) + + try { + await api.deleteFileFromCollection(tema, fileName) + alert('Archivo eliminado de la colección exitosamente') + onClose() + } catch (err) { + console.error('Error deleting file from collection:', err) + setError(err instanceof Error ? err.message : 'Error al eliminar archivo') + } finally { + setDeleting(false) + } + } + + const handleClose = () => { + setChunks([]) + setError(null) + onClose() + } + + return ( + + + + + + Chunks de "{fileName}" + + + Colección: {tema} + + + + {/* Contenido */} +
+ {loading ? ( +
+ + Cargando chunks... +
+ ) : error ? ( +
+ + {error} +
+ ) : chunks.length === 0 ? ( +
+ +

No se encontraron chunks para este archivo.

+

El archivo aún no ha sido procesado o no existe en la colección.

+
+ ) : ( +
+ {/* Estadísticas */} +
+

+ Total de chunks: {chunks.length} +

+
+ + {/* Lista de chunks */} + {chunks.map((chunk, index) => ( +
+ {/* Header del chunk */} +
+
+ + Chunk #{index + 1} + + + Página {chunk.payload.metadata.page} + +
+ + ID: {chunk.id.substring(0, 8)}... + +
+ + {/* Texto del chunk */} + {chunk.payload.page_content && ( +
+

+ {chunk.payload.page_content} +

+
+ Caracteres: {chunk.payload.page_content.length} +
+
+ )} + + {/* Metadata */} +
+ Metadata: + 
+                      {JSON.stringify(chunk.payload.metadata, null, 2)}
+
+
+ + {/* Información del vector (opcional) */} + {chunk.vector && ( +
+ Vector dimension: {chunk.vector.length} +
+ )} +
+ ))} +
+ )} +
+ + {/* Footer con acciones */} +
+ + + +
+ + + ) +} diff --git a/frontend/src/components/ChunkingConfigModal.tsx b/frontend/src/components/ChunkingConfigModal.tsx new file mode 100644 index 0000000..b3f9fd4 --- /dev/null +++ b/frontend/src/components/ChunkingConfigModal.tsx @@ -0,0 +1,307 @@ +import { useEffect, useState } from 'react' +import { api } from '../services/api' +import { + Dialog, + DialogContent, + DialogDescription, + DialogFooter, + DialogHeader, + DialogTitle, +} from './ui/dialog' +import { Button } from './ui/button' +import { Label } from './ui/label' +import { Input } from './ui/input' +import { Textarea } from './ui/textarea' +import { Select, SelectContent, SelectItem, SelectTrigger, SelectValue } from './ui/select' +import { Switch } from './ui/switch' +import { AlertCircle, Loader2, Settings, Sparkles } from 'lucide-react' +import { Tabs, TabsContent, TabsList, TabsTrigger } from './ui/tabs' + +interface ChunkingConfigModalProps { + isOpen: boolean + onClose: () => void + fileName: string + tema: string + collectionName: string + onPreview: (config: ChunkingConfig) => void +} + +export interface ChunkingConfig { + file_name: string + tema: string + collection_name: string + max_tokens: number + target_tokens: number + chunk_size: number + chunk_overlap: number + use_llm: boolean + custom_instructions: string +} + +interface ChunkingProfile { + id: string + name: string + description: string + max_tokens: number + target_tokens: number + chunk_size: number + chunk_overlap: number + use_llm: boolean +} + +export function ChunkingConfigModal({ + isOpen, + onClose, + fileName, + tema, + collectionName, + onPreview, +}: ChunkingConfigModalProps) { + const [profiles, setProfiles] = useState([]) + const [selectedProfile, setSelectedProfile] = useState('balanced') + const [loading, setLoading] = useState(false) + const [error, setError] = useState(null) + + // Custom configuration + const [maxTokens, setMaxTokens] = useState(950) + const [targetTokens, setTargetTokens] = useState(800) + const [chunkSize, setChunkSize] = useState(1000) + const [chunkOverlap, setChunkOverlap] = useState(200) + const [useLLM, setUseLLM] = useState(true) + const [customInstructions, setCustomInstructions] = useState('') + + useEffect(() => { + if (isOpen) { + loadProfiles() + } + }, [isOpen]) + + const loadProfiles = async () => { + setLoading(true) + setError(null) + + try { + const result = await api.getChunkingProfiles() + setProfiles(result.profiles) + } catch (err) { + console.error('Error loading profiles:', err) + setError(err instanceof Error ? err.message : 'Error cargando perfiles') + } finally { + setLoading(false) + } + } + + const handleProfileChange = (profileId: string) => { + setSelectedProfile(profileId) + const profile = profiles.find((p) => p.id === profileId) + if (profile) { + setMaxTokens(profile.max_tokens) + setTargetTokens(profile.target_tokens) + setChunkSize(profile.chunk_size) + setChunkOverlap(profile.chunk_overlap) + setUseLLM(profile.use_llm) + } + } + + const handlePreview = () => { + const config: ChunkingConfig = { + file_name: fileName, + tema: tema, + collection_name: collectionName, + max_tokens: maxTokens, + target_tokens: targetTokens, + chunk_size: chunkSize, + chunk_overlap: chunkOverlap, + use_llm: useLLM, + custom_instructions: useLLM ? customInstructions : '', + } + onPreview(config) + } + + const handleClose = () => { + setError(null) + onClose() + } + + return ( + + + + + + Configurar Chunking + + + Configura cómo se procesará el archivo {fileName} + + + + {loading ? ( +
+ + Cargando perfiles... +
+ ) : error ? ( +
+ + {error} +
+ ) : ( + + + Perfiles + Personalizado + + + {/* Tab de Perfiles */} + +
+ + +
+ + {/* Mostrar detalles del perfil seleccionado */} + {selectedProfile && ( +
+
+
+ Max Tokens: {maxTokens} +
+
+ Target Tokens: {targetTokens} +
+
+ Chunk Size: {chunkSize} +
+
+ Overlap: {chunkOverlap} +
+
+ LLM:{' '} + {useLLM ? '✅ Habilitado' : '❌ Deshabilitado'} +
+
+
+ )} + + + {/* Tab Personalizado */} + +
+
+ + setMaxTokens(Number(e.target.value))} + /> +
+ +
+ + setTargetTokens(Number(e.target.value))} + /> +
+ +
+ + setChunkSize(Number(e.target.value))} + /> +
+ +
+ + setChunkOverlap(Number(e.target.value))} + /> +
+
+ + {/* Toggle LLM */} +
+
+ +
+ +

+ Procesamiento inteligente con IA +

+
+
+ +
+ + {/* Custom Instructions (solo si LLM está habilitado) */} + {useLLM && ( +
+ +