From c03d0e27c4b2d2ec8b07b7106085c1788befbecc Mon Sep 17 00:00:00 2001 From: Sebastian Date: Thu, 6 Nov 2025 13:29:43 +0000 Subject: [PATCH] Landing AI integrado --- .../app/__pycache__/__init__.cpython-312.pyc | Bin 146 -> 117 bytes backend/app/__pycache__/main.cpython-312.pyc | Bin 4990 -> 5209 bytes .../core/__pycache__/__init__.cpython-312.pyc | Bin 151 -> 122 bytes .../core/__pycache__/config.cpython-312.pyc | Bin 4297 -> 4790 bytes backend/app/core/config.py | 14 + backend/app/main.py | 8 + .../__pycache__/__init__.cpython-312.pyc | Bin 153 -> 124 bytes .../__pycache__/file_models.cpython-312.pyc | Bin 13436 -> 13407 bytes backend/app/models/schema_models.py | 96 + backend/app/repositories/__init__.py | 4 + backend/app/repositories/schema_repository.py | 243 +++ .../__pycache__/__init__.cpython-312.pyc | Bin 154 -> 125 bytes .../routers/__pycache__/files.cpython-312.pyc | Bin 25673 -> 25673 bytes backend/app/routers/chunking_landingai.py | 396 ++++ backend/app/routers/schemas.py | 288 +++ .../__pycache__/__init__.cpython-312.pyc | Bin 155 -> 126 bytes .../__pycache__/azure_service.cpython-312.pyc | Bin 15578 -> 15578 bytes .../__pycache__/file_service.cpython-312.pyc | Bin 9434 -> 9409 bytes backend/app/services/landingai_service.py | 353 ++++ .../app/services/schema_builder_service.py | 215 +++ backend/pyproject.toml | 2 + backend/uv.lock | 19 + frontend/package-lock.json | 1615 ++++++++++------- .../ChunkingConfigModalLandingAI.tsx | 345 ++++ frontend/src/components/Dashboard.tsx | 106 +- frontend/src/components/Layout.tsx | 41 +- frontend/src/components/Sidebar.tsx | 121 +- .../src/components/schemas/SchemaBuilder.tsx | 340 ++++ .../src/components/schemas/SchemaList.tsx | 151 ++ frontend/src/pages/SchemaManagement.tsx | 127 ++ frontend/src/services/api.ts | 92 + frontend/src/types/schema.ts | 60 + 32 files changed, 3908 insertions(+), 728 deletions(-) create mode 100644 backend/app/models/schema_models.py create mode 100644 backend/app/repositories/__init__.py create mode 100644 backend/app/repositories/schema_repository.py create mode 100644 backend/app/routers/chunking_landingai.py create mode 100644 backend/app/routers/schemas.py create mode 100644 backend/app/services/landingai_service.py create mode 100644 backend/app/services/schema_builder_service.py create mode 100644 frontend/src/components/ChunkingConfigModalLandingAI.tsx create mode 100644 frontend/src/components/schemas/SchemaBuilder.tsx create mode 100644 frontend/src/components/schemas/SchemaList.tsx create mode 100644 frontend/src/pages/SchemaManagement.tsx create mode 100644 frontend/src/types/schema.ts diff --git a/backend/app/__pycache__/__init__.cpython-312.pyc b/backend/app/__pycache__/__init__.cpython-312.pyc index 8fe1914b2f7a8fbf2826a53d2362829ec01c9fba..d8d7e5b7fbeeb4ef59da56d1463df3bc8cfb4402 100644 GIT binary patch delta 45 vcmbQlSjuynmzRqH2-@R#GAHsFiHYbZ78HPSe0*kJW=VX!UP0xpiG_*)3Dga$ delta 74 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z8Uj2(H4O|47?*P| str: + """Valida que el nombre del campo sea snake_case válido""" + if not v.replace('_', '').isalnum(): + raise ValueError("El nombre del campo debe ser snake_case alfanumérico") + if v[0].isdigit(): + raise ValueError("El nombre del campo no puede empezar con número") + return v.lower() + + @field_validator('min_value', 'max_value') + @classmethod + def validate_numeric_constraints(cls, v: Optional[float], info) -> Optional[float]: + """Valida que min/max solo se usen con tipos numéricos""" + if v is not None: + field_type = info.data.get('type') + if field_type not in [FieldType.INTEGER, FieldType.FLOAT, FieldType.ARRAY_INTEGER, FieldType.ARRAY_FLOAT]: + raise ValueError(f"min_value/max_value solo aplican a campos numéricos, no a {field_type}") + return v + + +class CustomSchema(BaseModel): + """Schema personalizable por el usuario para extracción de datos""" + schema_id: Optional[str] = Field(None, description="ID único del schema (generado automáticamente si no se provee)") + schema_name: str = Field(..., description="Nombre descriptivo del schema", min_length=1, max_length=100) + description: str = Field(..., description="Descripción de qué extrae este schema", min_length=1, max_length=500) + fields: List[SchemaField] = Field(..., description="Lista de campos a extraer", min_items=1, max_items=50) + + # Metadata + created_at: Optional[str] = Field(None, description="Timestamp de creación ISO") + updated_at: Optional[str] = Field(None, description="Timestamp de última actualización ISO") + tema: Optional[str] = Field(None, description="Tema asociado (si es específico de un tema)") + is_global: bool = Field(default=False, description="¿Disponible para todos los temas?") + + @field_validator('fields') + @classmethod + def validate_unique_field_names(cls, v: List[SchemaField]) -> List[SchemaField]: + """Valida que no haya nombres de campos duplicados""" + field_names = [field.name for field in v] + if len(field_names) != len(set(field_names)): + raise ValueError("Los nombres de campos deben ser únicos en el schema") + return v + + @field_validator('schema_name') + @classmethod + def validate_schema_name(cls, v: str) -> str: + """Limpia y valida el nombre del schema""" + return v.strip() + + +class SchemaListResponse(BaseModel): + """Response para listar schemas""" + schemas: List[CustomSchema] + total: int + + +class SchemaValidationResponse(BaseModel): + """Response para validación de schema""" + valid: bool + message: str + json_schema: Optional[dict] = None + errors: Optional[List[str]] = None diff --git a/backend/app/repositories/__init__.py b/backend/app/repositories/__init__.py new file mode 100644 index 0000000..be584c6 --- /dev/null +++ b/backend/app/repositories/__init__.py @@ -0,0 +1,4 @@ +""" +Repositories for data persistence. +Implementa patrón Repository para abstraer la capa de datos. +""" diff --git a/backend/app/repositories/schema_repository.py b/backend/app/repositories/schema_repository.py new file mode 100644 index 0000000..7f89db7 --- /dev/null +++ b/backend/app/repositories/schema_repository.py @@ -0,0 +1,243 @@ +""" +Schema Repository - Patrón Repository +Abstrae la persistencia de schemas, actualmente usando archivos JSON. +Fácil migrar a base de datos después. +""" +import logging +import json +from pathlib import Path +from typing import List, Optional +from datetime import datetime + +from ..models.schema_models import CustomSchema + +logger = logging.getLogger(__name__) + + +class SchemaRepository: + """ + Repository para gestión de schemas. + Implementa patrón Repository para abstraer almacenamiento. + + Actualmente usa archivos JSON en disco. + Para migrar a DB: solo cambiar esta clase, resto del código no cambia. + """ + + def __init__(self, schemas_dir: Path): + """ + Inicializa el repositorio. + + Args: + schemas_dir: Directorio donde se guardan los schemas + """ + self.schemas_dir = Path(schemas_dir) + self.schemas_dir.mkdir(parents=True, exist_ok=True) + logger.info(f"SchemaRepository inicializado en: {self.schemas_dir}") + + def save(self, schema: CustomSchema) -> CustomSchema: + """ + Guarda o actualiza un schema. + + Args: + schema: Schema a guardar + + Returns: + Schema guardado con timestamps actualizados + + Raises: + IOError: Si hay error escribiendo el archivo + """ + try: + # Actualizar timestamps + now = datetime.utcnow().isoformat() + if not schema.created_at: + schema.created_at = now + schema.updated_at = now + + # Guardar archivo + file_path = self._get_file_path(schema.schema_id) + with open(file_path, 'w', encoding='utf-8') as f: + json.dump(schema.model_dump(), f, indent=2, ensure_ascii=False) + + logger.info(f"Schema guardado: {schema.schema_id} - {schema.schema_name}") + return schema + + except Exception as e: + logger.error(f"Error guardando schema {schema.schema_id}: {e}") + raise IOError(f"No se pudo guardar el schema: {str(e)}") + + def get_by_id(self, schema_id: str) -> Optional[CustomSchema]: + """ + Obtiene un schema por su ID. + + Args: + schema_id: ID del schema + + Returns: + Schema si existe, None si no + + Raises: + ValueError: Si el archivo está corrupto + """ + try: + file_path = self._get_file_path(schema_id) + if not file_path.exists(): + logger.debug(f"Schema no encontrado: {schema_id}") + return None + + with open(file_path, 'r', encoding='utf-8') as f: + data = json.load(f) + + schema = CustomSchema(**data) + logger.debug(f"Schema cargado: {schema_id}") + return schema + + except json.JSONDecodeError as e: + logger.error(f"Archivo JSON corrupto para schema {schema_id}: {e}") + raise ValueError(f"Schema corrupto: {schema_id}") + except Exception as e: + logger.error(f"Error cargando schema {schema_id}: {e}") + return None + + def list_all(self) -> List[CustomSchema]: + """ + Lista todos los schemas disponibles. + + Returns: + Lista de schemas ordenados por fecha de creación (más reciente primero) + """ + schemas = [] + + try: + for file_path in self.schemas_dir.glob("*.json"): + try: + with open(file_path, 'r', encoding='utf-8') as f: + data = json.load(f) + schema = CustomSchema(**data) + schemas.append(schema) + except Exception as e: + logger.warning(f"Error cargando schema desde {file_path}: {e}") + continue + + # Ordenar por fecha de creación (más reciente primero) + schemas.sort(key=lambda s: s.created_at or "", reverse=True) + + logger.info(f"Listados {len(schemas)} schemas") + return schemas + + except Exception as e: + logger.error(f"Error listando schemas: {e}") + return [] + + def list_by_tema(self, tema: str) -> List[CustomSchema]: + """ + Lista schemas disponibles para un tema específico. + Incluye schemas del tema + schemas globales. + + Args: + tema: Nombre del tema + + Returns: + Lista de schemas aplicables al tema + """ + all_schemas = self.list_all() + + filtered = [ + schema for schema in all_schemas + if schema.tema == tema or schema.is_global + ] + + logger.info(f"Encontrados {len(filtered)} schemas para tema '{tema}'") + return filtered + + def delete(self, schema_id: str) -> bool: + """ + Elimina un schema. + + Args: + schema_id: ID del schema a eliminar + + Returns: + True si se eliminó, False si no existía + """ + try: + file_path = self._get_file_path(schema_id) + if not file_path.exists(): + logger.warning(f"Intento de eliminar schema inexistente: {schema_id}") + return False + + file_path.unlink() + logger.info(f"Schema eliminado: {schema_id}") + return True + + except Exception as e: + logger.error(f"Error eliminando schema {schema_id}: {e}") + raise IOError(f"No se pudo eliminar el schema: {str(e)}") + + def exists(self, schema_id: str) -> bool: + """ + Verifica si un schema existe. + + Args: + schema_id: ID del schema + + Returns: + True si existe, False si no + """ + file_path = self._get_file_path(schema_id) + return file_path.exists() + + def count(self) -> int: + """ + Cuenta el número total de schemas. + + Returns: + Número de schemas + """ + return len(list(self.schemas_dir.glob("*.json"))) + + def _get_file_path(self, schema_id: str) -> Path: + """ + Obtiene la ruta del archivo para un schema. + + Args: + schema_id: ID del schema + + Returns: + Path del archivo + """ + # Sanitizar schema_id para evitar path traversal + safe_id = schema_id.replace("/", "_").replace("\\", "_") + return self.schemas_dir / f"{safe_id}.json" + + +# Singleton factory pattern +_schema_repository: Optional[SchemaRepository] = None + + +def get_schema_repository() -> SchemaRepository: + """ + Factory para obtener instancia singleton del repositorio. + + Returns: + Instancia única de SchemaRepository + + Raises: + RuntimeError: Si la configuración no está disponible + """ + global _schema_repository + + if _schema_repository is None: + try: + from ..core.config import settings + + schemas_dir = getattr(settings, 'SCHEMAS_DIR', None) or "./data/schemas" + _schema_repository = SchemaRepository(Path(schemas_dir)) + + logger.info("SchemaRepository singleton inicializado") + + except Exception as e: + logger.error(f"Error inicializando SchemaRepository: {e}") + raise RuntimeError(f"No se pudo inicializar SchemaRepository: {str(e)}") + + return _schema_repository diff --git a/backend/app/routers/__pycache__/__init__.cpython-312.pyc b/backend/app/routers/__pycache__/__init__.cpython-312.pyc index e2585dc6ce850aecb55a7bf30a1b234b2f987b4a..739d0bb98b1a152dd05c686bcd5d5eca146e8b17 100644 GIT binary patch delta 33 ncmbQmSj%&omzRqH2-@R#GAHsFvC8Nt78FcOkYKzuu~HELg}?~i delta 82 zcmb=O#mIA-mzRqH2r4%1$e74uWNoRRk)NBYU!0nhSX`2sn5XZOAC#z{l3$dlpOl!K kotl@TpIA_!UzA^3l3G-(A0MBYmst`YuUAm{YhsKd08m04^Z)<= diff --git a/backend/app/routers/__pycache__/files.cpython-312.pyc b/backend/app/routers/__pycache__/files.cpython-312.pyc index 4acffca3a2727871acab4f3d6ab4dec7529c770e..1b5b46a0e6e3ca955750c9a7bacc79354c92500f 100644 GIT binary patch delta 22 ccmX?kg7M@DM()$Ryj%=G&>qLLk=rf>09Q5!ZU6uP delta 22 ccmX?kg7M@DM()$Ryj%=GaCblBMsB+l09&628UO$Q diff --git a/backend/app/routers/chunking_landingai.py b/backend/app/routers/chunking_landingai.py new file mode 100644 index 0000000..d04e8f1 --- /dev/null +++ b/backend/app/routers/chunking_landingai.py @@ -0,0 +1,396 @@ +""" +Router para procesamiento de PDFs con LandingAI. +Soporta dos modos: rápido (solo parse) y extracción (parse + extract con schema). +""" +import logging +import time +from fastapi import APIRouter, HTTPException +from pydantic import BaseModel, Field +from typing import Optional, List, Literal + +from langchain_core.documents import Document + +from ..services.landingai_service import get_landingai_service +from ..services.chunking_service import get_chunking_service +from ..repositories.schema_repository import get_schema_repository +from ..utils.chunking.token_manager import TokenManager + +logger = logging.getLogger(__name__) + +router = APIRouter(prefix="/api/v1/chunking-landingai", tags=["chunking-landingai"]) + + +class ProcessLandingAIRequest(BaseModel): + """Request para procesar PDF con LandingAI""" + file_name: str = Field(..., description="Nombre del archivo PDF") + tema: str = Field(..., description="Tema/carpeta del archivo") + collection_name: str = Field(..., description="Colección de Qdrant") + + # Modo de procesamiento + mode: Literal["quick", "extract"] = Field( + default="quick", + description="Modo: 'quick' (solo parse) o 'extract' (parse + datos estructurados)" + ) + + # Schema (obligatorio si mode='extract') + schema_id: Optional[str] = Field( + None, + description="ID del schema a usar (requerido si mode='extract')" + ) + + # Configuración de chunks + include_chunk_types: List[str] = Field( + default=["text", "table"], + description="Tipos de chunks a incluir: text, table, figure, etc." + ) + max_tokens_per_chunk: int = Field( + default=1500, + ge=500, + le=3000, + description="Tokens máximos por chunk (flexible para tablas/figuras)" + ) + merge_small_chunks: bool = Field( + default=True, + description="Unir chunks pequeños de la misma página y tipo" + ) + + +class ProcessLandingAIResponse(BaseModel): + """Response del procesamiento con LandingAI""" + success: bool + mode: str + processing_time_seconds: float + collection_name: str + file_name: str + total_chunks: int + chunks_added: int + schema_used: Optional[str] = None + extracted_data: Optional[dict] = None + parse_metadata: dict + message: str + + +@router.post("/process", response_model=ProcessLandingAIResponse) +async def process_with_landingai(request: ProcessLandingAIRequest): + """ + Procesa un PDF con LandingAI y sube a Qdrant. + + Flujo: + 1. Descarga PDF de Azure Blob + 2. Parse con LandingAI (siempre) + 3. Extract con schema (solo si mode='extract') + 4. Procesa chunks (filtrado, merge, control de tokens) + 5. Genera embeddings (Azure OpenAI) + 6. Sube a Qdrant con metadata rica + + Args: + request: Configuración del procesamiento + + Returns: + Resultado del procesamiento con estadísticas + + Raises: + HTTPException 400: Si mode='extract' y no se provee schema_id + HTTPException 404: Si el PDF o schema no existen + HTTPException 500: Si hay error en el procesamiento + """ + start_time = time.time() + + try: + logger.info(f"\n{'='*60}") + logger.info(f"INICIANDO PROCESAMIENTO CON LANDINGAI") + logger.info(f"{'='*60}") + logger.info(f"Archivo: {request.file_name}") + logger.info(f"Tema: {request.tema}") + logger.info(f"Modo: {request.mode}") + logger.info(f"Colección: {request.collection_name}") + + # 1. Validar schema si es modo extract + custom_schema = None + if request.mode == "extract": + if not request.schema_id: + raise HTTPException( + status_code=400, + detail="schema_id es requerido cuando mode='extract'" + ) + + schema_repo = get_schema_repository() + custom_schema = schema_repo.get_by_id(request.schema_id) + + if not custom_schema: + raise HTTPException( + status_code=404, + detail=f"Schema no encontrado: {request.schema_id}" + ) + + logger.info(f"Schema seleccionado: {custom_schema.schema_name}") + + # 2. Descargar PDF desde Azure Blob + logger.info("\n[1/5] Descargando PDF desde Azure Blob...") + chunking_service = get_chunking_service() + + try: + pdf_bytes = await chunking_service.download_pdf_from_blob( + request.file_name, + request.tema + ) + except Exception as e: + logger.error(f"Error descargando PDF: {e}") + raise HTTPException( + status_code=404, + detail=f"No se pudo descargar el PDF: {str(e)}" + ) + + # 3. Procesar con LandingAI + logger.info("\n[2/5] Procesando con LandingAI...") + landingai_service = get_landingai_service() + + try: + result = landingai_service.process_pdf( + pdf_bytes=pdf_bytes, + file_name=request.file_name, + custom_schema=custom_schema, + include_chunk_types=request.include_chunk_types + ) + except Exception as e: + logger.error(f"Error en LandingAI: {e}") + raise HTTPException( + status_code=500, + detail=f"Error procesando con LandingAI: {str(e)}" + ) + + documents = result["chunks"] + + if not documents: + raise HTTPException( + status_code=400, + detail="No se generaron chunks después del procesamiento" + ) + + # 4. Aplicar control flexible de tokens + logger.info("\n[3/5] Aplicando control de tokens...") + documents = _apply_flexible_token_control( + documents, + max_tokens=request.max_tokens_per_chunk, + merge_small=request.merge_small_chunks + ) + + # 5. Generar embeddings + logger.info(f"\n[4/5] Generando embeddings para {len(documents)} chunks...") + texts = [doc.page_content for doc in documents] + + try: + embeddings = await chunking_service.embedding_service.generate_embeddings_batch(texts) + logger.info(f"Embeddings generados: {len(embeddings)} vectores") + except Exception as e: + logger.error(f"Error generando embeddings: {e}") + raise HTTPException( + status_code=500, + detail=f"Error generando embeddings: {str(e)}" + ) + + # 6. Preparar chunks para Qdrant con IDs determinísticos + logger.info("\n[5/5] Subiendo a Qdrant...") + qdrant_chunks = [] + + for idx, (doc, embedding) in enumerate(zip(documents, embeddings)): + # ID determinístico + chunk_id = chunking_service._generate_deterministic_id( + file_name=request.file_name, + page=doc.metadata.get("page", 1), + chunk_index=doc.metadata.get("chunk_id", str(idx)) + ) + + qdrant_chunks.append({ + "id": chunk_id, + "vector": embedding, + "payload": { + "page_content": doc.page_content, + "metadata": doc.metadata # Metadata rica de LandingAI + } + }) + + # 7. Subir a Qdrant + try: + upload_result = await chunking_service.vector_db.add_chunks( + request.collection_name, + qdrant_chunks + ) + logger.info(f"Subida completada: {upload_result['chunks_added']} chunks") + except Exception as e: + logger.error(f"Error subiendo a Qdrant: {e}") + raise HTTPException( + status_code=500, + detail=f"Error subiendo a Qdrant: {str(e)}" + ) + + # Tiempo total + processing_time = time.time() - start_time + + logger.info(f"\n{'='*60}") + logger.info(f"PROCESAMIENTO COMPLETADO") + logger.info(f"{'='*60}") + logger.info(f"Tiempo: {processing_time:.2f}s") + logger.info(f"Chunks procesados: {len(documents)}") + logger.info(f"Chunks subidos: {upload_result['chunks_added']}") + + return ProcessLandingAIResponse( + success=True, + mode=request.mode, + processing_time_seconds=round(processing_time, 2), + collection_name=request.collection_name, + file_name=request.file_name, + total_chunks=len(documents), + chunks_added=upload_result["chunks_added"], + schema_used=custom_schema.schema_id if custom_schema else None, + extracted_data=result.get("extracted_data"), + parse_metadata=result["parse_metadata"], + message=f"PDF procesado exitosamente en modo {request.mode}" + ) + + except HTTPException: + raise + except Exception as e: + logger.error(f"Error inesperado en procesamiento: {e}") + raise HTTPException( + status_code=500, + detail=f"Error inesperado: {str(e)}" + ) + + +def _apply_flexible_token_control( + documents: List[Document], + max_tokens: int, + merge_small: bool +) -> List[Document]: + """ + Aplica control flexible de tokens (Opción C del diseño). + + - Permite chunks más grandes para tablas/figuras (50% extra) + - Mergea chunks pequeños de misma página y tipo + - Divide chunks muy grandes en sub-chunks + + Args: + documents: Lista de Documents + max_tokens: Límite sugerido de tokens + merge_small: Si True, une chunks pequeños + + Returns: + Lista de Documents procesados + """ + token_manager = TokenManager() + processed = [] + i = 0 + + logger.info(f"Control de tokens: max={max_tokens}, merge={merge_small}") + + while i < len(documents): + doc = documents[i] + tokens = token_manager.count_tokens(doc.page_content) + chunk_type = doc.metadata.get("chunk_type", "text") + + # Límite flexible según tipo + if chunk_type in ["table", "figure"]: + max_allowed = int(max_tokens * 1.5) # 50% más para contenido estructurado + else: + max_allowed = max_tokens + + # Si excede mucho el límite, dividir + if tokens > max_allowed * 1.2: # 20% de tolerancia + logger.warning( + f"Chunk muy grande ({tokens} tokens), dividiendo... " + f"(tipo: {chunk_type})" + ) + sub_chunks = _split_large_chunk(doc, max_tokens, token_manager) + processed.extend(sub_chunks) + + else: + # Intentar merge si es pequeño + if ( + merge_small and + tokens < max_tokens * 0.5 and + i < len(documents) - 1 + ): + next_doc = documents[i + 1] + if _can_merge(doc, next_doc, max_tokens, token_manager): + logger.debug(f"Merging chunks {i} y {i+1}") + doc = _merge_documents(doc, next_doc) + i += 1 # Skip next + + processed.append(doc) + + i += 1 + + logger.info(f"Tokens aplicados: {len(documents)} → {len(processed)} chunks") + return processed + + +def _split_large_chunk( + doc: Document, + max_tokens: int, + token_manager: TokenManager +) -> List[Document]: + """Divide un chunk grande en sub-chunks""" + content = doc.page_content + words = content.split() + sub_chunks = [] + current_chunk = [] + current_tokens = 0 + + for word in words: + word_tokens = token_manager.count_tokens(word) + if current_tokens + word_tokens > max_tokens and current_chunk: + # Guardar chunk actual + sub_content = " ".join(current_chunk) + sub_doc = Document( + page_content=sub_content, + metadata={**doc.metadata, "is_split": True} + ) + sub_chunks.append(sub_doc) + current_chunk = [word] + current_tokens = word_tokens + else: + current_chunk.append(word) + current_tokens += word_tokens + + # Último chunk + if current_chunk: + sub_content = " ".join(current_chunk) + sub_doc = Document( + page_content=sub_content, + metadata={**doc.metadata, "is_split": True} + ) + sub_chunks.append(sub_doc) + + return sub_chunks + + +def _can_merge( + doc1: Document, + doc2: Document, + max_tokens: int, + token_manager: TokenManager +) -> bool: + """Verifica si dos docs se pueden mergear""" + # Misma página + if doc1.metadata.get("page") != doc2.metadata.get("page"): + return False + + # Mismo tipo + if doc1.metadata.get("chunk_type") != doc2.metadata.get("chunk_type"): + return False + + # No exceder límite + combined_text = f"{doc1.page_content}\n\n{doc2.page_content}" + combined_tokens = token_manager.count_tokens(combined_text) + + return combined_tokens <= max_tokens + + +def _merge_documents(doc1: Document, doc2: Document) -> Document: + """Mergea dos documentos""" + merged_content = f"{doc1.page_content}\n\n{doc2.page_content}" + return Document( + page_content=merged_content, + metadata={**doc1.metadata, "is_merged": True} + ) diff --git a/backend/app/routers/schemas.py b/backend/app/routers/schemas.py new file mode 100644 index 0000000..1790bf3 --- /dev/null +++ b/backend/app/routers/schemas.py @@ -0,0 +1,288 @@ +""" +Router para gestión de schemas personalizables. +Endpoints CRUD para crear, leer, actualizar y eliminar schemas. +""" +import logging +import uuid +from fastapi import APIRouter, HTTPException, Query +from typing import List, Optional + +from ..models.schema_models import ( + CustomSchema, + SchemaListResponse, + SchemaValidationResponse +) +from ..repositories.schema_repository import get_schema_repository +from ..services.schema_builder_service import SchemaBuilderService + +logger = logging.getLogger(__name__) + +router = APIRouter(prefix="/api/v1/schemas", tags=["schemas"]) + + +@router.post("/", response_model=CustomSchema, status_code=201) +async def create_schema(schema: CustomSchema): + """ + Crea un nuevo schema personalizado. + + Args: + schema: Definición del schema + + Returns: + Schema creado con ID y timestamps + + Raises: + HTTPException 400: Si el schema es inválido + HTTPException 409: Si ya existe un schema con ese ID + """ + try: + # Generar ID si no viene + if not schema.schema_id: + schema.schema_id = f"schema_{uuid.uuid4().hex[:12]}" + + # Verificar que no exista + repo = get_schema_repository() + if repo.exists(schema.schema_id): + raise HTTPException( + status_code=409, + detail=f"Ya existe un schema con ID: {schema.schema_id}" + ) + + # Validar que se puede construir el schema + builder = SchemaBuilderService() + validation = builder.validate_schema(schema) + + if not validation["valid"]: + raise HTTPException( + status_code=400, + detail={ + "message": "Schema inválido", + "errors": validation["errors"] + } + ) + + # Guardar + saved_schema = repo.save(schema) + + logger.info(f"Schema creado: {saved_schema.schema_id} - {saved_schema.schema_name}") + return saved_schema + + except HTTPException: + raise + except Exception as e: + logger.error(f"Error creando schema: {e}") + raise HTTPException(status_code=500, detail=f"Error interno: {str(e)}") + + +@router.get("/", response_model=List[CustomSchema]) +async def list_schemas( + tema: Optional[str] = Query(None, description="Filtrar por tema (incluye globales)") +): + """ + Lista todos los schemas o filtrados por tema. + + Args: + tema: Nombre del tema para filtrar (opcional) + + Returns: + Lista de schemas + """ + try: + repo = get_schema_repository() + + if tema: + schemas = repo.list_by_tema(tema) + else: + schemas = repo.list_all() + + return schemas + + except Exception as e: + logger.error(f"Error listando schemas: {e}") + raise HTTPException(status_code=500, detail=f"Error interno: {str(e)}") + + +@router.get("/{schema_id}", response_model=CustomSchema) +async def get_schema(schema_id: str): + """ + Obtiene un schema por su ID. + + Args: + schema_id: ID del schema + + Returns: + Schema solicitado + + Raises: + HTTPException 404: Si el schema no existe + """ + try: + repo = get_schema_repository() + schema = repo.get_by_id(schema_id) + + if not schema: + raise HTTPException( + status_code=404, + detail=f"Schema no encontrado: {schema_id}" + ) + + return schema + + except HTTPException: + raise + except Exception as e: + logger.error(f"Error obteniendo schema {schema_id}: {e}") + raise HTTPException(status_code=500, detail=f"Error interno: {str(e)}") + + +@router.put("/{schema_id}", response_model=CustomSchema) +async def update_schema(schema_id: str, schema: CustomSchema): + """ + Actualiza un schema existente. + + Args: + schema_id: ID del schema a actualizar + schema: Nueva definición del schema + + Returns: + Schema actualizado + + Raises: + HTTPException 404: Si el schema no existe + HTTPException 400: Si el nuevo schema es inválido + """ + try: + repo = get_schema_repository() + + # Verificar que existe + existing = repo.get_by_id(schema_id) + if not existing: + raise HTTPException( + status_code=404, + detail=f"Schema no encontrado: {schema_id}" + ) + + # Mantener el ID original + schema.schema_id = schema_id + schema.created_at = existing.created_at # Mantener fecha de creación + + # Validar nuevo schema + builder = SchemaBuilderService() + validation = builder.validate_schema(schema) + + if not validation["valid"]: + raise HTTPException( + status_code=400, + detail={ + "message": "Schema inválido", + "errors": validation["errors"] + } + ) + + # Guardar + updated_schema = repo.save(schema) + + logger.info(f"Schema actualizado: {schema_id}") + return updated_schema + + except HTTPException: + raise + except Exception as e: + logger.error(f"Error actualizando schema {schema_id}: {e}") + raise HTTPException(status_code=500, detail=f"Error interno: {str(e)}") + + +@router.delete("/{schema_id}") +async def delete_schema(schema_id: str): + """ + Elimina un schema. + + Args: + schema_id: ID del schema a eliminar + + Returns: + Mensaje de confirmación + + Raises: + HTTPException 404: Si el schema no existe + """ + try: + repo = get_schema_repository() + + if not repo.delete(schema_id): + raise HTTPException( + status_code=404, + detail=f"Schema no encontrado: {schema_id}" + ) + + logger.info(f"Schema eliminado: {schema_id}") + return { + "success": True, + "message": f"Schema {schema_id} eliminado exitosamente" + } + + except HTTPException: + raise + except Exception as e: + logger.error(f"Error eliminando schema {schema_id}: {e}") + raise HTTPException(status_code=500, detail=f"Error interno: {str(e)}") + + +@router.post("/validate", response_model=SchemaValidationResponse) +async def validate_schema(schema: CustomSchema): + """ + Valida un schema sin guardarlo. + Útil para preview en el frontend antes de guardar. + + Args: + schema: Schema a validar + + Returns: + Resultado de validación con detalles + """ + try: + builder = SchemaBuilderService() + validation = builder.validate_schema(schema) + + return SchemaValidationResponse(**validation) + + except Exception as e: + logger.error(f"Error validando schema: {e}") + return SchemaValidationResponse( + valid=False, + message="Error en validación", + errors=[str(e)] + ) + + +@router.get("/stats/count") +async def get_schemas_count(): + """ + Obtiene estadísticas de schemas. + + Returns: + Conteo de schemas total y por tema + """ + try: + repo = get_schema_repository() + all_schemas = repo.list_all() + + # Contar por tema + tema_counts = {} + global_count = 0 + + for schema in all_schemas: + if schema.is_global: + global_count += 1 + elif schema.tema: + tema_counts[schema.tema] = tema_counts.get(schema.tema, 0) + 1 + + return { + "total": len(all_schemas), + "global": global_count, + "by_tema": tema_counts + } + + except Exception as e: + logger.error(f"Error obteniendo estadísticas: {e}") + raise HTTPException(status_code=500, detail=f"Error interno: {str(e)}") diff --git a/backend/app/services/__pycache__/__init__.cpython-312.pyc b/backend/app/services/__pycache__/__init__.cpython-312.pyc index 89e2ae36b7d5608ec47e82ed3c5da9185b3bb23d..e6b5b79c1372079a06b2df31ecf1741de19e6e5b 100644 GIT binary patch delta 33 ncmbQuSjTgkmzRqH2-@R#GAHsFvC8Tv78FcOkYv0yu}TpDh7}0n delta 83 zcmb=M&B$|_mzRqH2x>R&$e74uWMieDk)NBYU!0nhSX`2sn5XZOAC#z{l3$dlpOl!K kotl@TpIA@;lrJjFOinGGLyYP0mnM5V&FPcum-8h2{j$8EKbAR4;9wqj-ji@z>-` Gl?VVf=OsV@ diff --git a/backend/app/services/landingai_service.py b/backend/app/services/landingai_service.py new file mode 100644 index 0000000..a570e54 --- /dev/null +++ b/backend/app/services/landingai_service.py @@ -0,0 +1,353 @@ +""" +LandingAI Service - Servicio independiente +Maneja toda la interacción con LandingAI ADE API. +Usa parse() para extracción de chunks y extract() para datos estructurados. +""" +import logging +import tempfile +from pathlib import Path +from typing import Dict, Any, List, Optional + +from langchain_core.documents import Document + +from ..models.schema_models import CustomSchema +from ..services.schema_builder_service import SchemaBuilderService + +logger = logging.getLogger(__name__) + + +class LandingAIService: + """ + Servicio para procesamiento de PDFs con LandingAI. + + Flujo: + 1. Parse PDF → obtener chunks estructurados + markdown + 2. Extract (opcional) → extraer datos según schema personalizado + 3. Process chunks → filtrar, enriquecer, controlar tokens + 4. Return Documents → listos para embeddings y Qdrant + """ + + def __init__(self, api_key: str, environment: str = "production"): + """ + Inicializa el servicio LandingAI. + + Args: + api_key: API key de LandingAI + environment: "production" o "eu" + + Raises: + ImportError: Si landingai-ade no está instalado + """ + try: + from landingai_ade import LandingAIADE + + self.client = LandingAIADE( + apikey=api_key, + environment=environment, + timeout=480.0, # 8 minutos para PDFs grandes + max_retries=2 + ) + + self.schema_builder = SchemaBuilderService() + + logger.info(f"LandingAIService inicializado (environment: {environment})") + + except ImportError: + logger.error("landingai-ade no está instalado") + raise ImportError( + "Se requiere landingai-ade. Instalar con: pip install landingai-ade" + ) + + def process_pdf( + self, + pdf_bytes: bytes, + file_name: str, + custom_schema: Optional[CustomSchema] = None, + include_chunk_types: Optional[List[str]] = None, + model: str = "dpt-2-latest" + ) -> Dict[str, Any]: + """ + Procesa un PDF con LandingAI (modo rápido o con extracción). + + Args: + pdf_bytes: Contenido del PDF en bytes + file_name: Nombre del archivo + custom_schema: Schema personalizado para extract (None = modo rápido) + include_chunk_types: Tipos de chunks a incluir ["text", "table", "figure"] + model: Modelo de LandingAI a usar + + Returns: + Dict con: + - chunks: List[Document] listos para embeddings + - parse_metadata: Metadata del parse (páginas, duración, etc.) + - extracted_data: Datos extraídos (si usó schema) + - file_name: Nombre del archivo + + Raises: + Exception: Si hay error en parse o extract + """ + logger.info(f"=== Procesando PDF con LandingAI: {file_name} ===") + logger.info(f" Modo: {'Extracción' if custom_schema else 'Rápido'}") + logger.info(f" Tipos incluidos: {include_chunk_types or 'todos'}") + + # 1. Parse PDF + parse_result = self._parse_pdf(pdf_bytes, file_name, model) + + # 2. Extract (si hay schema) + extracted_data = None + if custom_schema: + logger.info(f" Extrayendo datos con schema: {custom_schema.schema_name}") + extracted_data = self._extract_data( + parse_result["markdown"], + custom_schema + ) + + # 3. Procesar chunks + documents = self._process_chunks( + parse_result, + extracted_data, + file_name, + include_chunk_types + ) + + logger.info(f"=== Procesamiento completado: {len(documents)} chunks ===") + + return { + "chunks": documents, + "parse_metadata": parse_result["metadata"], + "extracted_data": extracted_data, + "file_name": file_name + } + + def _parse_pdf( + self, + pdf_bytes: bytes, + file_name: str, + model: str + ) -> Dict[str, Any]: + """ + Parse PDF con LandingAI. + + Args: + pdf_bytes: Contenido del PDF + file_name: Nombre del archivo + model: Modelo de LandingAI + + Returns: + Dict con chunks, markdown, grounding y metadata + """ + logger.info(f" Parseando PDF con modelo {model}...") + + # LandingAI requiere Path, crear archivo temporal + with tempfile.NamedTemporaryFile(suffix=".pdf", delete=False) as tmp: + tmp.write(pdf_bytes) + tmp_path = Path(tmp.name) + + try: + # Parse con LandingAI + response = self.client.parse(document=tmp_path, model=model) + + # Procesar respuesta + chunks_data = [] + for chunk in response.chunks: + # Obtener grounding info del chunk + grounding_info = {} + if hasattr(response, 'grounding') and hasattr(response.grounding, chunk.id): + ground = getattr(response.grounding, chunk.id) + grounding_info = { + "bbox": ground.bbox if hasattr(ground, 'bbox') else None, + "page": ground.page if hasattr(ground, 'page') else 1 + } + + page_num = grounding_info.get("page", 1) if grounding_info else 1 + + chunks_data.append({ + "id": chunk.id, + "content": chunk.markdown, + "type": chunk.type, + "grounding": grounding_info, + "page": page_num + }) + + # Obtener metadata + metadata_dict = {} + if hasattr(response, 'metadata'): + metadata_dict = { + "page_count": getattr(response.metadata, 'page_count', None), + "duration_ms": getattr(response.metadata, 'duration_ms', None), + "version": getattr(response.metadata, 'version', None) + } + + logger.info( + f" Parse completado: {len(chunks_data)} chunks, " + f"{metadata_dict.get('page_count', 'N/A')} páginas" + ) + + return { + "chunks": chunks_data, + "markdown": response.markdown, + "grounding": response.grounding, + "metadata": metadata_dict + } + + finally: + # Limpiar archivo temporal + tmp_path.unlink(missing_ok=True) + + def _extract_data( + self, + markdown: str, + custom_schema: CustomSchema + ) -> Optional[Dict[str, Any]]: + """ + Extrae datos estructurados del markdown usando schema personalizado. + + Args: + markdown: Markdown completo del documento + custom_schema: Schema personalizado + + Returns: + Dict con extraction, extraction_metadata y schema_used + None si hay error + """ + try: + # 1. Construir Pydantic schema + pydantic_schema = self.schema_builder.build_pydantic_schema(custom_schema) + + # 2. Convertir a JSON schema + json_schema = self.schema_builder.to_json_schema(pydantic_schema) + + # 3. Crear archivo temporal con markdown + with tempfile.NamedTemporaryFile( + mode='w', + suffix=".md", + delete=False, + encoding='utf-8' + ) as tmp: + tmp.write(markdown) + tmp_path = Path(tmp.name) + + try: + # 4. Extract con LandingAI + response = self.client.extract( + schema=json_schema, + markdown=tmp_path + ) + + logger.info(f" Extracción completada: {len(response.extraction)} campos") + + return { + "extraction": response.extraction, + "extraction_metadata": response.extraction_metadata, + "schema_used": custom_schema.schema_id + } + + finally: + tmp_path.unlink(missing_ok=True) + + except Exception as e: + logger.error(f"Error en extract: {e}") + return None + + def _process_chunks( + self, + parse_result: Dict[str, Any], + extracted_data: Optional[Dict[str, Any]], + file_name: str, + include_chunk_types: Optional[List[str]] + ) -> List[Document]: + """ + Convierte chunks de LandingAI a Documents de LangChain con metadata rica. + + Args: + parse_result: Resultado del parse + extracted_data: Datos extraídos (opcional) + file_name: Nombre del archivo + include_chunk_types: Tipos a incluir + + Returns: + Lista de Documents listos para embeddings + """ + documents = [] + filtered_count = 0 + + for chunk in parse_result["chunks"]: + # Filtrar por tipo si se especificó + if include_chunk_types and chunk["type"] not in include_chunk_types: + filtered_count += 1 + continue + + # Construir metadata rica + metadata = { + "file_name": file_name, + "page": chunk["page"], + "chunk_id": chunk["id"], + "chunk_type": chunk["type"], + "bbox": chunk["grounding"].get("bbox"), + + # Metadata del documento + "document_metadata": { + "page_count": parse_result["metadata"].get("page_count"), + "processing_duration_ms": parse_result["metadata"].get("duration_ms"), + "landingai_version": parse_result["metadata"].get("version"), + } + } + + # Agregar datos extraídos si existen + if extracted_data: + metadata["extracted_data"] = extracted_data["extraction"] + metadata["extraction_metadata"] = extracted_data["extraction_metadata"] + metadata["schema_used"] = extracted_data["schema_used"] + + # Crear Document + doc = Document( + page_content=chunk["content"], + metadata=metadata + ) + documents.append(doc) + + if filtered_count > 0: + logger.info(f" Filtrados {filtered_count} chunks por tipo") + + logger.info(f" Generados {len(documents)} documents") + return documents + + +# Singleton factory +_landingai_service: Optional[LandingAIService] = None + + +def get_landingai_service() -> LandingAIService: + """ + Factory para obtener instancia singleton del servicio. + + Returns: + Instancia única de LandingAIService + + Raises: + RuntimeError: Si la configuración no está disponible + """ + global _landingai_service + + if _landingai_service is None: + try: + from ..core.config import settings + + api_key = settings.LANDINGAI_API_KEY + if not api_key: + raise ValueError("LANDINGAI_API_KEY no está configurada") + + environment = getattr(settings, 'LANDINGAI_ENVIRONMENT', 'production') + + _landingai_service = LandingAIService( + api_key=api_key, + environment=environment + ) + + logger.info("LandingAIService singleton inicializado") + + except Exception as e: + logger.error(f"Error inicializando LandingAIService: {e}") + raise RuntimeError(f"No se pudo inicializar LandingAIService: {str(e)}") + + return _landingai_service diff --git a/backend/app/services/schema_builder_service.py b/backend/app/services/schema_builder_service.py new file mode 100644 index 0000000..6c701aa --- /dev/null +++ b/backend/app/services/schema_builder_service.py @@ -0,0 +1,215 @@ +""" +Schema Builder Service - Patrón Builder +Construye schemas Pydantic dinámicamente desde definiciones JSON del frontend. +""" +import logging +from typing import Dict, Any, Type, get_origin, get_args +from pydantic import BaseModel, Field, create_model +from pydantic.fields import FieldInfo + +from ..models.schema_models import CustomSchema, FieldType, SchemaField + +logger = logging.getLogger(__name__) + + +class SchemaBuilderService: + """ + Servicio para construir schemas Pydantic dinámicamente. + Implementa patrón Builder para construcción step-by-step. + """ + + @staticmethod + def build_pydantic_schema(custom_schema: CustomSchema) -> Type[BaseModel]: + """ + Convierte un CustomSchema a una clase Pydantic dinámica. + + Este método es el núcleo del patrón Builder, construyendo + una clase Pydantic válida que puede ser usada por LandingAI. + + Args: + custom_schema: Schema personalizado del usuario + + Returns: + Clase Pydantic generada dinámicamente + + Raises: + ValueError: Si el schema es inválido + """ + logger.info(f"Construyendo Pydantic schema: {custom_schema.schema_name}") + + field_definitions = {} + + for field in custom_schema.fields: + try: + # 1. Mapear tipo Python + python_type = SchemaBuilderService._map_field_type(field.type) + + # 2. Crear FieldInfo con validaciones + field_info = SchemaBuilderService._build_field_info(field) + + # 3. Agregar al diccionario de definiciones + field_definitions[field.name] = (python_type, field_info) + + logger.debug(f" Campo '{field.name}': {python_type} - {field.description[:50]}...") + + except Exception as e: + logger.error(f"Error construyendo campo '{field.name}': {e}") + raise ValueError(f"Campo inválido '{field.name}': {str(e)}") + + # 4. Crear clase dinámica + try: + # Nombre de clase válido (sin espacios ni caracteres especiales) + class_name = custom_schema.schema_name.replace(" ", "").replace("-", "") + if not class_name[0].isalpha(): + class_name = "Schema" + class_name + + DynamicSchema = create_model( + class_name, + **field_definitions + ) + + logger.info(f"Schema Pydantic creado exitosamente: {class_name} con {len(field_definitions)} campos") + return DynamicSchema + + except Exception as e: + logger.error(f"Error creando modelo Pydantic: {e}") + raise ValueError(f"No se pudo crear el schema: {str(e)}") + + @staticmethod + def _map_field_type(field_type: FieldType) -> Type: + """ + Mapea FieldType a tipo Python nativo. + + Args: + field_type: Tipo de campo del schema + + Returns: + Tipo Python correspondiente + """ + from typing import List + + type_mapping = { + FieldType.STRING: str, + FieldType.INTEGER: int, + FieldType.FLOAT: float, + FieldType.BOOLEAN: bool, + FieldType.ARRAY_STRING: List[str], + FieldType.ARRAY_INTEGER: List[int], + FieldType.ARRAY_FLOAT: List[float], + FieldType.DATE: str, # Dates como strings ISO 8601 + } + + if field_type not in type_mapping: + raise ValueError(f"Tipo de campo no soportado: {field_type}") + + return type_mapping[field_type] + + @staticmethod + def _build_field_info(field: SchemaField) -> FieldInfo: + """ + Construye FieldInfo con validaciones apropiadas. + + Args: + field: Definición del campo + + Returns: + FieldInfo configurado + """ + # Configuración base + field_kwargs = { + "description": field.description, + } + + # Default value según si es requerido + if field.required: + field_kwargs["default"] = ... # Ellipsis = required + else: + field_kwargs["default"] = None + + # Validaciones numéricas + if field.min_value is not None: + field_kwargs["ge"] = field.min_value # greater or equal + + if field.max_value is not None: + field_kwargs["le"] = field.max_value # less or equal + + # Validaciones de string + if field.pattern: + field_kwargs["pattern"] = field.pattern + + return Field(**field_kwargs) + + @staticmethod + def to_json_schema(pydantic_schema: Type[BaseModel]) -> Dict[str, Any]: + """ + Convierte un Pydantic schema a JSON Schema para LandingAI. + + Args: + pydantic_schema: Clase Pydantic + + Returns: + JSON Schema dict compatible con LandingAI + + Raises: + ImportError: Si landingai-ade no está instalado + """ + try: + from landingai_ade.lib import pydantic_to_json_schema + + json_schema = pydantic_to_json_schema(pydantic_schema) + logger.info("Schema convertido a JSON schema exitosamente") + return json_schema + + except ImportError: + logger.error("landingai-ade no está instalado") + raise ImportError( + "Se requiere landingai-ade para convertir a JSON schema. " + "Instalar con: pip install landingai-ade" + ) + + @staticmethod + def validate_schema(custom_schema: CustomSchema) -> Dict[str, Any]: + """ + Valida que un schema se pueda construir correctamente. + + Args: + custom_schema: Schema a validar + + Returns: + Dict con resultado de validación: + { + "valid": bool, + "message": str, + "json_schema": dict (si válido), + "errors": List[str] (si inválido) + } + """ + errors = [] + + try: + # Intentar construir el schema Pydantic + pydantic_schema = SchemaBuilderService.build_pydantic_schema(custom_schema) + + # Intentar convertir a JSON schema + json_schema = SchemaBuilderService.to_json_schema(pydantic_schema) + + return { + "valid": True, + "message": f"Schema '{custom_schema.schema_name}' es válido", + "json_schema": json_schema, + "errors": None + } + + except ValueError as e: + errors.append(f"Error de validación: {str(e)}") + except ImportError as e: + errors.append(f"Error de dependencias: {str(e)}") + except Exception as e: + errors.append(f"Error inesperado: {str(e)}") + + return { + "valid": False, + "message": f"Schema '{custom_schema.schema_name}' es inválido", + "json_schema": None, + "errors": errors + } diff --git a/backend/pyproject.toml b/backend/pyproject.toml index e5b2b78..f6bc0f3 100644 --- a/backend/pyproject.toml +++ b/backend/pyproject.toml @@ -25,6 +25,8 @@ dependencies = [ # WebSockets "websockets>=14.1", "langchain-text-splitters>=1.0.0", + # LandingAI Document AI + "landingai-ade>=0.2.1", ] [project.scripts] dev = "uvicorn app.main:app --host 0.0.0.0 --port 8000 --reload" diff --git a/backend/uv.lock b/backend/uv.lock index 44d80c6..6305699 100644 --- a/backend/uv.lock +++ b/backend/uv.lock @@ -67,6 +67,7 @@ dependencies = [ { name = "azure-storage-blob" }, { name = "fastapi" }, { name = "google-cloud-aiplatform" }, + { name = "landingai-ade" }, { name = "langchain" }, { name = "langchain-core" }, { name = "langchain-text-splitters" }, @@ -88,6 +89,7 @@ requires-dist = [ { name = "azure-storage-blob", specifier = ">=12.26.0" }, { name = "fastapi", specifier = ">=0.116.1" }, { name = "google-cloud-aiplatform", specifier = ">=1.77.0" }, + { name = "landingai-ade", specifier = ">=0.2.1" }, { name = "langchain", specifier = ">=0.3.12" }, { name = "langchain-core", specifier = ">=0.3.24" }, { name = "langchain-text-splitters", specifier = ">=1.0.0" }, @@ -810,6 +812,23 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/71/92/5e77f98553e9e75130c78900d000368476aed74276eb8ae8796f65f00918/jsonpointer-3.0.0-py2.py3-none-any.whl", hash = "sha256:13e088adc14fca8b6aa8177c044e12701e6ad4b28ff10e65f2267a90109c9942", size = 7595, upload-time = "2024-06-10T19:24:40.698Z" }, ] +[[package]] +name = "landingai-ade" +version = "0.20.3" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "anyio" }, + { name = "distro" }, + { name = "httpx" }, + { name = "pydantic" }, + { name = "sniffio" }, + { name = "typing-extensions" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/46/05/8e94b262625545683db8b0206929691715a0905c8227b776866885be7fb6/landingai_ade-0.20.3.tar.gz", hash = "sha256:8fd95fabdf3f72b5a1c0d0b3b485631e41e1c25c6e5636de10fbec83078772d2", size = 107576, upload-time = "2025-11-05T02:03:46.808Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/49/d0/6daeb909475d2b49847ebb27d1093aa586154a1d910e9b14addb1b356007/landingai_ade-0.20.3-py3-none-any.whl", hash = "sha256:599a7ec1b31ca3923160c20037cef5f3afbf84485ce98b8ead028ac540d50bbd", size = 86222, upload-time = "2025-11-05T02:03:45.825Z" }, +] + [[package]] name = "langchain" version = "1.0.3" diff --git a/frontend/package-lock.json b/frontend/package-lock.json index f16ae3d..45b6daa 100644 --- a/frontend/package-lock.json +++ b/frontend/package-lock.json @@ -64,6 +64,7 @@ "resolved": "https://registry.npmjs.org/@babel/code-frame/-/code-frame-7.27.1.tgz", "integrity": "sha512-cjQ7ZlQ0Mv3b47hABuTevyTuYN4i+loJKGeV9flcCgIK37cCXRh+L1bd3iBHlynerhQ7BhCkn2BPbQUL+rGqFg==", "dev": true, + "license": "MIT", "dependencies": { "@babel/helper-validator-identifier": "^7.27.1", "js-tokens": "^4.0.0", @@ -74,29 +75,31 @@ } }, "node_modules/@babel/compat-data": { - 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"dev": true + "dev": true, + "license": "ISC" }, "node_modules/yocto-queue": { "version": "0.1.0", "resolved": "https://registry.npmjs.org/yocto-queue/-/yocto-queue-0.1.0.tgz", "integrity": "sha512-rVksvsnNCdJ/ohGc6xgPwyN8eheCxsiLM8mxuE/t/mOVqJewPuO1miLpTHQiRgTKCLexL4MeAFVagts7HmNZ2Q==", "dev": true, + "license": "MIT", "engines": { "node": ">=10" }, diff --git a/frontend/src/components/ChunkingConfigModalLandingAI.tsx b/frontend/src/components/ChunkingConfigModalLandingAI.tsx new file mode 100644 index 0000000..6b8bd3a --- /dev/null +++ b/frontend/src/components/ChunkingConfigModalLandingAI.tsx @@ -0,0 +1,345 @@ +/** + * Chunking Config Modal con LandingAI + * Reemplaza el modal anterior, ahora usa LandingAI con modos flexible y schemas + */ +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 { Select, SelectContent, SelectItem, SelectTrigger, SelectValue } from './ui/select' +import { AlertCircle, Loader2, Settings, Zap, Target, FileText, Table2, Image } from 'lucide-react' +import { Tabs, TabsContent, TabsList, TabsTrigger } from './ui/tabs' +import type { CustomSchema } from '@/types/schema' + +interface ChunkingConfigModalProps { + isOpen: boolean + onClose: () => void + fileName: string + tema: string + collectionName: string + onProcess: (config: LandingAIConfig) => void +} + +export interface LandingAIConfig { + file_name: string + tema: string + collection_name: string + mode: 'quick' | 'extract' + schema_id?: string + include_chunk_types: string[] + max_tokens_per_chunk: number + merge_small_chunks: boolean +} + +export function ChunkingConfigModalLandingAI({ + isOpen, + onClose, + fileName, + tema, + collectionName, + onProcess, +}: ChunkingConfigModalProps) { + const [mode, setMode] = useState<'quick' | 'extract'>('quick') + const [schemas, setSchemas] = useState([]) + const [selectedSchema, setSelectedSchema] = useState() + const [chunkTypes, setChunkTypes] = useState(['text', 'table']) + const [maxTokens, setMaxTokens] = useState(1500) + const [mergeSmall, setMergeSmall] = useState(true) + const [loading, setLoading] = useState(false) + const [error, setError] = useState(null) + + useEffect(() => { + if (isOpen) { + loadSchemas() + } + }, [isOpen, tema]) + + const loadSchemas = async () => { + setLoading(true) + setError(null) + + try { + const data = await api.listSchemas(tema) + setSchemas(data) + } catch (err) { + console.error('Error loading schemas:', err) + setError(err instanceof Error ? err.message : 'Error cargando schemas') + } finally { + setLoading(false) + } + } + + const toggleChunkType = (type: string) => { + if (chunkTypes.includes(type)) { + setChunkTypes(chunkTypes.filter(t => t !== type)) + } else { + setChunkTypes([...chunkTypes, type]) + } + } + + const handleProcess = () => { + if (mode === 'extract' && !selectedSchema) { + setError('Debes seleccionar un schema en modo extracción') + return + } + + if (chunkTypes.length === 0) { + setError('Debes seleccionar al menos un tipo de contenido') + return + } + + const config: LandingAIConfig = { + file_name: fileName, + tema: tema, + collection_name: collectionName, + mode: mode, + schema_id: selectedSchema, + include_chunk_types: chunkTypes, + max_tokens_per_chunk: maxTokens, + merge_small_chunks: mergeSmall, + } + + onProcess(config) + } + + const handleClose = () => { + setError(null) + onClose() + } + + const selectedSchemaData = schemas.find(s => s.schema_id === selectedSchema) + + return ( + + + + + + Configurar Procesamiento con LandingAI + + + Configura cómo se procesará {fileName} usando LandingAI Document AI + + + + {error && ( +
+ + {error} +
+ )} + +
+ {/* Modo de Procesamiento */} +
+ +
+ } + title="Rápido" + description="Solo extracción de texto sin análisis estructurado" + time="~5-10 seg" + selected={mode === 'quick'} + onClick={() => setMode('quick')} + /> + } + title="Con Extracción" + description="Parse + extracción de datos estructurados con schema" + time="~15-30 seg" + selected={mode === 'extract'} + onClick={() => setMode('extract')} + /> +
+
+ + {/* Schema Selector (solo en modo extract) */} + {mode === 'extract' && ( +
+ + {loading ? ( +
+ + Cargando schemas... +
+ ) : schemas.length === 0 ? ( +
+

No hay schemas disponibles para este tema.

+

Crea uno primero en la sección de Schemas.

+
+ ) : ( + <> + + + {/* Preview del schema seleccionado */} + {selectedSchemaData && ( +
+

Campos a extraer:

+
+ {selectedSchemaData.fields.map((field, idx) => ( + + {field.name} + {field.required && *} + + ))} +
+
+ )} + + )} +
+ )} + + {/* Tipos de Contenido */} +
+ +
+ } + label="Texto" + selected={chunkTypes.includes('text')} + onClick={() => toggleChunkType('text')} + /> + } + label="Tablas" + selected={chunkTypes.includes('table')} + onClick={() => toggleChunkType('table')} + /> + } + label="Figuras" + selected={chunkTypes.includes('figure')} + onClick={() => toggleChunkType('figure')} + /> +
+
+ + {/* Configuración Avanzada */} +
+ + Configuración Avanzada + +
+
+ + setMaxTokens(parseInt(e.target.value))} + className="w-full" + /> +

+ Tablas y figuras pueden exceder hasta 50% más +

+
+ +
+ setMergeSmall(e.target.checked)} + className="rounded" + /> + +
+
+
+
+ + +
+ Tiempo estimado: {mode === 'quick' ? '~5-10 seg' : '~15-30 seg'} +
+
+ + +
+ + + + ) +} + +function ModeCard({ icon, title, description, time, selected, onClick }: any) { + return ( +
+
+
+ {icon} +
+

{title}

+
+

{description}

+

{time}

+
+ ) +} + +function ChunkTypeOption({ icon, label, selected, onClick }: any) { + return ( +
+
+ {icon} +
+

{label}

+
+ ) +} diff --git a/frontend/src/components/Dashboard.tsx b/frontend/src/components/Dashboard.tsx index 08b4461..c1eca7f 100644 --- a/frontend/src/components/Dashboard.tsx +++ b/frontend/src/components/Dashboard.tsx @@ -17,8 +17,7 @@ import { DeleteConfirmDialog } from './DeleteConfirmDialog' import { PDFPreviewModal } from './PDFPreviewModal' import { CollectionVerifier } from './CollectionVerifier' import { ChunkViewerModal } from './ChunkViewerModal' -import { ChunkingConfigModal, type ChunkingConfig } from './ChunkingConfigModal' -import { ChunkPreviewPanel } from './ChunkPreviewPanel' +import { ChunkingConfigModalLandingAI, type LandingAIConfig } from './ChunkingConfigModalLandingAI' import { Upload, Download, @@ -30,7 +29,11 @@ import { Scissors } from 'lucide-react' -export function Dashboard() { +interface DashboardProps { + onProcessingChange?: (isProcessing: boolean) => void +} + +export function Dashboard({ onProcessingChange }: DashboardProps = {}) { const { selectedTema, files, @@ -67,9 +70,7 @@ export function Dashboard() { const [chunkingFileName, setChunkingFileName] = useState('') const [chunkingFileTema, setChunkingFileTema] = useState('') const [chunkingCollectionName, setChunkingCollectionName] = useState('') - - const [chunkPreviewOpen, setChunkPreviewOpen] = useState(false) - const [chunkingConfig, setChunkingConfig] = useState(null) + const [processing, setProcessing] = useState(false) useEffect(() => { loadFiles() @@ -215,29 +216,41 @@ export function Dashboard() { setChunkingConfigOpen(true) } - const handlePreviewChunking = (config: ChunkingConfig) => { - setChunkingConfig(config) + const handleProcessWithLandingAI = async (config: LandingAIConfig) => { + setProcessing(true) + onProcessingChange?.(true) setChunkingConfigOpen(false) - setChunkPreviewOpen(true) - } - const handleAcceptChunking = async (config: ChunkingConfig) => { try { - const result = await api.processChunkingFull(config) - alert(`Procesamiento completado: ${result.chunks_added} chunks agregados a ${result.collection_name}`) - // Recargar archivos para actualizar el estado - loadFiles() - } catch (error) { - console.error('Error processing PDF:', error) - throw error - } - } + const result = await api.processWithLandingAI(config) - const handleCancelChunking = () => { - setChunkPreviewOpen(false) - setChunkingConfig(null) - // Opcionalmente volver al modal de configuración - // setChunkingConfigOpen(true) + // Mensaje detallado + let message = `Completado\n\n` + message += `• Modo: ${result.mode === 'quick' ? 'Rápido' : 'Con Extracción'}\n` + message += `• Chunks procesados: ${result.total_chunks}\n` + message += `• Chunks agregados: ${result.chunks_added}\n` + message += `• Colección: ${result.collection_name}\n` + message += `• Tiempo: ${result.processing_time_seconds}s\n` + + if (result.schema_used) { + message += `• Schema usado: ${result.schema_used}\n` + } + + if (result.extracted_data) { + message += `\nDatos extraídos disponibles en metadata` + } + + alert(message) + + // Recargar archivos + loadFiles() + } catch (error: any) { + console.error('Error processing with LandingAI:', error) + alert(`❌ Error: ${error.message}`) + } finally { + setProcessing(false) + onProcessingChange?.(false) + } } const filteredFiles = files.filter(file => @@ -282,6 +295,18 @@ export function Dashboard() { return (
+ {/* Processing Banner */} + {processing && ( +
+
+
+

+ Procesando archivo con LandingAI... Por favor no navegues ni realices otras acciones. +

+
+
+ )} + {/* Header */}
@@ -295,7 +320,7 @@ export function Dashboard() {
- @@ -311,24 +336,26 @@ export function Dashboard() { value={searchTerm} onChange={(e) => setSearchTerm(e.target.value)} className="pl-10" + disabled={processing} />
{selectedFiles.size > 0 && (
- -
) diff --git a/frontend/src/components/Layout.tsx b/frontend/src/components/Layout.tsx index 7f67018..a88360c 100644 --- a/frontend/src/components/Layout.tsx +++ b/frontend/src/components/Layout.tsx @@ -4,32 +4,65 @@ import { Button } from '@/components/ui/button' import { Menu } from 'lucide-react' import { Sidebar } from './Sidebar' import { Dashboard } from './Dashboard' +import { SchemaManagement } from '@/pages/SchemaManagement' + +type View = 'dashboard' | 'schemas' export function Layout() { const [sidebarOpen, setSidebarOpen] = useState(false) + const [currentView, setCurrentView] = useState('dashboard') + const [isProcessing, setIsProcessing] = useState(false) + + const handleNavigateToSchemas = () => { + if (isProcessing) { + alert('No puedes navegar mientras se está procesando un archivo. Por favor espera a que termine.') + return + } + setCurrentView('schemas') + setSidebarOpen(false) + } + + const handleNavigateToDashboard = () => { + if (isProcessing) { + alert('No puedes navegar mientras se está procesando un archivo. Por favor espera a que termine.') + return + } + setCurrentView('dashboard') + } return (
{/* Desktop Sidebar */}
- +
{/* Mobile Sidebar */} - - + {/* Main Content */}
- + {currentView === 'dashboard' ? ( + + ) : ( +
+ +
+ +
+
+ )}
) diff --git a/frontend/src/components/Sidebar.tsx b/frontend/src/components/Sidebar.tsx index 94bce8c..dbbb2a2 100644 --- a/frontend/src/components/Sidebar.tsx +++ b/frontend/src/components/Sidebar.tsx @@ -1,19 +1,26 @@ -import { useEffect } from 'react' +import { useEffect, useState } from 'react' import { useFileStore } from '@/stores/fileStore' import { api } from '@/services/api' import { Button } from '@/components/ui/button' -import { FolderIcon, FileText } from 'lucide-react' +import { FolderIcon, FileText, Trash2, Database } from 'lucide-react' -export function Sidebar() { - const { - temas, - selectedTema, - setTemas, - setSelectedTema, +interface SidebarProps { + onNavigateToSchemas?: () => void + disabled?: boolean +} + +export function Sidebar({ onNavigateToSchemas, disabled = false }: SidebarProps = {}) { + const { + temas, + selectedTema, + setTemas, + setSelectedTema, loading, - setLoading + setLoading } = useFileStore() + const [deletingTema, setDeletingTema] = useState(null) + useEffect(() => { loadTemas() }, []) @@ -34,26 +41,74 @@ export function Sidebar() { setSelectedTema(tema) } + const handleDeleteTema = async (tema: string, e: React.MouseEvent) => { + e.stopPropagation() // Evitar que se seleccione el tema al hacer clic en el icono + + const confirmed = window.confirm( + `¿Estás seguro de que deseas eliminar el tema "${tema}"?\n\n` + + `Esto eliminará:\n` + + `• Todos los archivos del tema en Azure Blob Storage\n` + + `• La colección "${tema}" en Qdrant (si existe)\n\n` + + `Esta acción no se puede deshacer.` + ) + + if (!confirmed) return + + try { + setDeletingTema(tema) + + // 1. Eliminar todos los archivos del tema en Azure Blob Storage + await api.deleteTema(tema) + + // 2. Intentar eliminar la colección en Qdrant (si existe) + try { + const collectionExists = await api.checkCollectionExists(tema) + if (collectionExists.exists) { + await api.deleteCollection(tema) + console.log(`Colección "${tema}" eliminada de Qdrant`) + } + } catch (error) { + console.warn(`No se pudo eliminar la colección "${tema}" de Qdrant:`, error) + // Continuar aunque falle la eliminación de la colección + } + + // 3. Actualizar la lista de temas + await loadTemas() + + // 4. Si el tema eliminado estaba seleccionado, deseleccionar + if (selectedTema === tema) { + setSelectedTema(null) + } + + } catch (error) { + console.error(`Error eliminando tema "${tema}":`, error) + alert(`Error al eliminar el tema: ${error instanceof Error ? error.message : 'Error desconocido'}`) + } finally { + setDeletingTema(null) + } + } + return (
{/* Header */}

- DoRa Banorte + DoRa Luma

{/* Temas List */}
-

TEMAS

+

Collections

{/* Todos los archivos */} +
+ + +
)) )}
{/* Footer */} -
+
+ {onNavigateToSchemas && ( + + )} +
+ + {schema.fields.length === 0 ? ( +
+

No hay campos definidos

+ +
+ ) : ( +
+ {schema.fields.map((field, index) => ( + updateField(index, updates)} + onRemove={() => removeField(index)} + /> + ))} +
+ )} +
+ + {/* Actions */} +
+ {onCancel && ( + + )} + +
+
+ ) +} + +function SchemaFieldRow({ + field, + index, + onUpdate, + onRemove +}: { + field: SchemaField + index: number + onUpdate: (updates: Partial) => void + onRemove: () => void +}) { + const selectedTypeOption = FIELD_TYPE_OPTIONS.find(opt => opt.value === field.type) + + return ( +
+
+ {/* Field Number */} +
+
+ {index + 1} +
+
+ + {/* Field Name */} +
+ + onUpdate({ name: e.target.value.toLowerCase().replace(/\s+/g, '_') })} + placeholder="nombre_campo" + className="mt-1" + /> +
+ + {/* Field Type */} +
+ + +
+ + {/* Description */} +
+ + onUpdate({ description: e.target.value })} + placeholder="¿Qué debe extraer la IA?" + className="mt-1" + /> +
+ + {/* Required Checkbox */} +
+ +
+ + {/* Delete Button */} +
+ +
+
+ + {/* Min/Max Values (for numeric types) */} + {selectedTypeOption?.supportsMinMax && ( +
+
+ + onUpdate({ min_value: e.target.value ? parseFloat(e.target.value) : undefined })} + placeholder="Sin límite" + className="mt-1" + /> +
+
+ + onUpdate({ max_value: e.target.value ? parseFloat(e.target.value) : undefined })} + placeholder="Sin límite" + className="mt-1" + /> +
+
+ )} +
+ ) +} diff --git a/frontend/src/components/schemas/SchemaList.tsx b/frontend/src/components/schemas/SchemaList.tsx new file mode 100644 index 0000000..b67d93c --- /dev/null +++ b/frontend/src/components/schemas/SchemaList.tsx @@ -0,0 +1,151 @@ +/** + * Schema List Component + * Lista y gestiona schemas existentes + */ +import { useState } from 'react' +import { Button } from '@/components/ui/button' +import { Trash2, Edit, Globe, FolderClosed } from 'lucide-react' +import type { CustomSchema } from '@/types/schema' +import { api } from '@/services/api' + +interface SchemaListProps { + schemas: CustomSchema[] + onEdit: (schema: CustomSchema) => void + onDelete: (schemaId: string) => void + onRefresh: () => void +} + +export function SchemaList({ schemas, onEdit, onDelete, onRefresh }: SchemaListProps) { + const [deletingId, setDeletingId] = useState(null) + + const handleDelete = async (schema: CustomSchema) => { + if (!schema.schema_id) return + + const confirmed = window.confirm( + `¿Estás seguro de eliminar el schema "${schema.schema_name}"?\n\nEsta acción no se puede deshacer.` + ) + + if (!confirmed) return + + setDeletingId(schema.schema_id) + try { + await api.deleteSchema(schema.schema_id) + onDelete(schema.schema_id) + } catch (error: any) { + alert(`Error eliminando schema: ${error.message}`) + } finally { + setDeletingId(null) + } + } + + if (schemas.length === 0) { + return ( +
+

No hay schemas creados

+

Crea tu primer schema para empezar

+
+ ) + } + + return ( +
+ {schemas.map((schema) => ( + onEdit(schema)} + onDelete={() => handleDelete(schema)} + isDeleting={deletingId === schema.schema_id} + /> + ))} +
+ ) +} + +function SchemaCard({ + schema, + onEdit, + onDelete, + isDeleting +}: { + schema: CustomSchema + onEdit: () => void + onDelete: () => void + isDeleting: boolean +}) { + return ( +
+
+
+ {/* Header */} +
+

{schema.schema_name}

+ {schema.is_global ? ( + + + Global + + ) : ( + + + {schema.tema} + + )} +
+ + {/* Description */} +

{schema.description}

+ + {/* Fields Summary */} +
+ {schema.fields.slice(0, 5).map((field, idx) => ( + + {field.name} + ({field.type}) + {field.required && ( + * + )} + + ))} + {schema.fields.length > 5 && ( + + +{schema.fields.length - 5} más + + )} +
+ + {/* Metadata */} + {schema.created_at && ( +

+ Creado: {new Date(schema.created_at).toLocaleDateString('es-ES')} +

+ )} +
+ + {/* Actions */} +
+ + +
+
+
+ ) +} diff --git a/frontend/src/pages/SchemaManagement.tsx b/frontend/src/pages/SchemaManagement.tsx new file mode 100644 index 0000000..af97efb --- /dev/null +++ b/frontend/src/pages/SchemaManagement.tsx @@ -0,0 +1,127 @@ +/** + * Schema Management Page + * Página principal para gestionar schemas personalizados + */ +import { useState, useEffect } from 'react' +import { Button } from '@/components/ui/button' +import { Plus, ArrowLeft } from 'lucide-react' +import { SchemaBuilder } from '@/components/schemas/SchemaBuilder' +import { SchemaList } from '@/components/schemas/SchemaList' +import { useFileStore } from '@/stores/fileStore' +import { api } from '@/services/api' +import type { CustomSchema } from '@/types/schema' + +type View = 'list' | 'create' | 'edit' + +export function SchemaManagement() { + const { selectedTema } = useFileStore() + const [view, setView] = useState('list') + const [schemas, setSchemas] = useState([]) + const [selectedSchema, setSelectedSchema] = useState() + const [loading, setLoading] = useState(true) + + useEffect(() => { + loadSchemas() + }, [selectedTema]) + + const loadSchemas = async () => { + setLoading(true) + try { + const data = await api.listSchemas(selectedTema || undefined) + setSchemas(data) + } catch (error: any) { + console.error('Error loading schemas:', error) + alert('Error cargando schemas: ' + error.message) + } finally { + setLoading(false) + } + } + + const handleSave = async (schema: CustomSchema) => { + try { + if (selectedSchema?.schema_id) { + // Update existing + await api.updateSchema(selectedSchema.schema_id, schema) + } else { + // Create new + await api.createSchema(schema) + } + + await loadSchemas() + setView('list') + setSelectedSchema(undefined) + } catch (error: any) { + throw new Error(error.message) + } + } + + const handleEdit = (schema: CustomSchema) => { + setSelectedSchema(schema) + setView('edit') + } + + const handleDelete = async (schemaId: string) => { + await loadSchemas() + } + + const handleCancel = () => { + setView('list') + setSelectedSchema(undefined) + } + + return ( +
+
+ {/* Header */} + {view === 'list' ? ( +
+
+
+

Schemas Personalizados

+

+ {selectedTema + ? `Schemas para el tema: ${selectedTema}` + : 'Todos los schemas disponibles'} +

+
+ +
+
+ ) : ( +
+ +
+ )} + + {/* Content */} +
+ {loading && view === 'list' ? ( +
+

Cargando schemas...

+
+ ) : view === 'list' ? ( + + ) : ( + + )} +
+
+
+ ) +} diff --git a/frontend/src/services/api.ts b/frontend/src/services/api.ts index bbdfebb..afc4373 100644 --- a/frontend/src/services/api.ts +++ b/frontend/src/services/api.ts @@ -420,4 +420,96 @@ export const api = { return response.json() }, + // ============================================================================ + // Schemas API + // ============================================================================ + + // Crear schema + createSchema: async (schema: any): Promise => { + const response = await fetch(`${API_BASE_URL}/schemas/`, { + method: 'POST', + headers: { 'Content-Type': 'application/json' }, + body: JSON.stringify(schema) + }) + if (!response.ok) { + const error = await response.json() + throw new Error(error.detail?.message || 'Error creando schema') + } + return response.json() + }, + + // Listar schemas + listSchemas: async (tema?: string): Promise => { + const url = tema + ? `${API_BASE_URL}/schemas/?tema=${encodeURIComponent(tema)}` + : `${API_BASE_URL}/schemas/` + const response = await fetch(url) + if (!response.ok) throw new Error('Error listando schemas') + return response.json() + }, + + // Obtener schema por ID + getSchema: async (schema_id: string): Promise => { + const response = await fetch(`${API_BASE_URL}/schemas/${schema_id}`) + if (!response.ok) throw new Error('Error obteniendo schema') + return response.json() + }, + + // Actualizar schema + updateSchema: async (schema_id: string, schema: any): Promise => { + const response = await fetch(`${API_BASE_URL}/schemas/${schema_id}`, { + method: 'PUT', + headers: { 'Content-Type': 'application/json' }, + body: JSON.stringify(schema) + }) + if (!response.ok) throw new Error('Error actualizando schema') + return response.json() + }, + + // Eliminar schema + deleteSchema: async (schema_id: string): Promise => { + const response = await fetch(`${API_BASE_URL}/schemas/${schema_id}`, { + method: 'DELETE' + }) + if (!response.ok) throw new Error('Error eliminando schema') + }, + + // Validar schema + validateSchema: async (schema: any): Promise => { + const response = await fetch(`${API_BASE_URL}/schemas/validate`, { + method: 'POST', + headers: { 'Content-Type': 'application/json' }, + body: JSON.stringify(schema) + }) + if (!response.ok) throw new Error('Error validando schema') + return response.json() + }, + + // ============================================================================ + // LandingAI Processing + // ============================================================================ + + // Procesar con LandingAI + processWithLandingAI: async (config: { + file_name: string + tema: string + collection_name: string + mode: 'quick' | 'extract' + schema_id?: string + include_chunk_types?: string[] + max_tokens_per_chunk?: number + merge_small_chunks?: boolean + }): Promise => { + const response = await fetch(`${API_BASE_URL}/chunking-landingai/process`, { + method: 'POST', + headers: { 'Content-Type': 'application/json' }, + body: JSON.stringify(config) + }) + if (!response.ok) { + const error = await response.json() + throw new Error(error.detail || 'Error procesando con LandingAI') + } + return response.json() + }, + } \ No newline at end of file diff --git a/frontend/src/types/schema.ts b/frontend/src/types/schema.ts new file mode 100644 index 0000000..6b3547b --- /dev/null +++ b/frontend/src/types/schema.ts @@ -0,0 +1,60 @@ +/** + * Tipos TypeScript para schemas personalizables + */ + +export type FieldType = + | 'string' + | 'integer' + | 'float' + | 'boolean' + | 'array_string' + | 'array_integer' + | 'array_float' + | 'date' + +export interface SchemaField { + name: string + type: FieldType + description: string + required: boolean + min_value?: number + max_value?: number + pattern?: string +} + +export interface CustomSchema { + schema_id?: string + schema_name: string + description: string + fields: SchemaField[] + created_at?: string + updated_at?: string + tema?: string + is_global: boolean +} + +export interface SchemaValidationResponse { + valid: boolean + message: string + json_schema?: any + errors?: string[] +} + +// Opciones para el selector de tipo +export interface FieldTypeOption { + value: FieldType + label: string + description: string + supportsMinMax: boolean +} + +export const FIELD_TYPE_OPTIONS: FieldTypeOption[] = [ + { value: 'string', label: 'Texto', description: 'Texto simple', supportsMinMax: false }, + { value: 'integer', label: 'Número Entero', description: 'Número sin decimales', supportsMinMax: true }, + { value: 'float', label: 'Número Decimal', description: 'Número con decimales', supportsMinMax: true }, + { value: 'boolean', label: 'Verdadero/Falso', description: 'Sí o No', supportsMinMax: false }, + { value: 'array_string', label: 'Lista de Textos', description: 'Múltiples textos', supportsMinMax: false }, + { value: 'array_integer', label: 'Lista de Números', description: 'Múltiples números enteros', supportsMinMax: true }, + { value: 'array_float', label: 'Lista de Decimales', description: 'Múltiples números decimales', supportsMinMax: true }, + { value: 'date', label: 'Fecha', description: 'Fecha en formato ISO', supportsMinMax: false }, +]