328 lines
9.9 KiB
Python
328 lines
9.9 KiB
Python
"""API REST del microservicio de extraccion."""
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from __future__ import annotations
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from contextlib import asynccontextmanager
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from typing import Any
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from fastapi import FastAPI, HTTPException
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from pydantic import BaseModel, Field
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from sqlalchemy import select
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from domain.batch_processor import BatchProcessor
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from domain.config import extractor_llm_config, normalizer_llm_config, settings
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from domain.config import get_allowed_document_types
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from domain.estimator import Estimator
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from domain.service import InvoiceProcessingService
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from domain.strategies import OpenAICompatibleExtractor
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from infrastructure.database import (
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ExtractionBatch,
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ExtractionRow,
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init_db,
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session_scope,
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utcnow,
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)
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from infrastructure.serialization import flatten_extraction_result
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def build_processing_service() -> InvoiceProcessingService:
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extractor_config = extractor_llm_config()
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extractor = OpenAICompatibleExtractor(
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api_key=str(extractor_config["api_key"]),
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base_url=str(extractor_config["base_url"]),
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model=str(extractor_config["model"]),
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)
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return InvoiceProcessingService(
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extractor,
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batch_processor_factory=lambda service: BatchProcessor(service),
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estimator_factory=lambda service: Estimator(service),
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)
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processing_service = build_processing_service()
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@asynccontextmanager
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async def lifespan(app: FastAPI):
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init_db()
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yield
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app = FastAPI(
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title="IPSExtractor Extraction Service",
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version="1.0.0",
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lifespan=lifespan,
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)
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class DocumentType(BaseModel):
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name: str
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keyword: str = ""
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class SchemaRequest(BaseModel):
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document_path: str | None = None
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root_path: str | None = None
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doc_types: list[DocumentType] | None = None
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instruction: str | None = None
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tiff_page_range: tuple[int, int] | None = None
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structure_mode: str = "radicados"
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class ExtractionRequest(BaseModel):
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root_path: str
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keywords: list[str] | None = None
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doc_types: list[DocumentType] | None = None
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fields: list[str] = Field(default_factory=list)
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alias_map: dict[str, str] = Field(default_factory=dict)
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instruction: str | None = None
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tiff_page_range: tuple[int, int] | None = None
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structure_mode: str = "radicados"
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retry_on_empty: bool = False
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radicado_filter: list[str] | None = None
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class EstimateRequest(BaseModel):
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root_path: str
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keywords: list[str] | None = None
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doc_types: list[DocumentType] | None = None
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schema_map: dict[str, str] | None = Field(default=None, alias="schema")
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selected_fields: list[str] | None = None
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schema_by_type: dict[str, dict[str, str]] | None = None
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selected_paths: list[str] | None = None
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structure_mode: str = "radicados"
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class ExtractionBatchRead(BaseModel):
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id: str
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status: str
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root_path: str | None
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request: dict[str, Any]
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error: str | None
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created_at: str
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updated_at: str
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def serialize_batch(batch: ExtractionBatch) -> dict[str, Any]:
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return {
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"id": batch.id,
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"status": batch.status,
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"root_path": batch.root_path,
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"request": batch.request,
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"error": batch.error,
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"created_at": batch.created_at.isoformat(),
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"updated_at": batch.updated_at.isoformat(),
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}
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@app.get("/health")
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def health() -> dict[str, str]:
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return {"status": "ok"}
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@app.get("/config")
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def get_config() -> dict[str, Any]:
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extractor_config = extractor_llm_config()
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normalizer_config = normalizer_llm_config()
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return {
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"extractor": {
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"provider": extractor_config["provider"],
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"base_url": extractor_config["base_url"],
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"model": extractor_config["model"],
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"timeout_s": extractor_config["timeout_s"],
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"api_key_configured": bool(str(extractor_config["api_key"]).strip()),
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},
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"normalizer": {
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"provider": normalizer_config["provider"],
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"base_url": normalizer_config["base_url"],
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"model": normalizer_config["model"],
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"timeout_s": normalizer_config["timeout_s"],
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"api_key_configured": bool(str(normalizer_config["api_key"]).strip()),
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},
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"processing": {
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"concurrency_limit": settings.processing.concurrency_limit,
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"allowed_document_types": list(get_allowed_document_types()),
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},
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"imaging": {
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"pdf_dpi": settings.imaging.pdf_dpi,
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"pdf_thread_count": settings.imaging.pdf_thread_count,
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"tiff_pages": settings.imaging.tiff_pages,
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"poppler_path": settings.imaging.poppler_path,
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},
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}
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@app.get("/models")
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def list_models() -> dict[str, Any]:
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extractor_config = extractor_llm_config()
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normalizer_config = normalizer_llm_config()
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try:
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models = processing_service.extractor.llm.list_models()
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return {
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"available": models,
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"configured_extractor_model": extractor_config["model"],
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"configured_normalizer_model": normalizer_config["model"],
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"source": extractor_config["base_url"],
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}
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except Exception as exc:
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return {
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"available": [],
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"configured_extractor_model": extractor_config["model"],
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"configured_normalizer_model": normalizer_config["model"],
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"source": extractor_config["base_url"],
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"warning": f"No se pudieron consultar modelos del backend: {exc}",
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}
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@app.post("/schemas")
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def build_schema(request: SchemaRequest) -> dict[str, Any]:
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"""Descubre campos disponibles para que el cliente API elija `fields`."""
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if request.document_path:
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schema, sample = processing_service.build_schema_from_first(
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request.document_path,
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instruction=request.instruction,
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tiff_page_range=request.tiff_page_range,
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)
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return {"schema": schema, "sample": sample}
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if request.root_path and request.doc_types:
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schema_by_type, sample_by_type, first_radicado = (
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processing_service.build_schema_from_first_for_types(
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request.root_path,
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[dt.model_dump() for dt in request.doc_types],
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instruction=request.instruction,
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tiff_page_range=request.tiff_page_range,
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structure_mode=request.structure_mode,
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)
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)
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return {
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"schema_by_type": schema_by_type,
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"sample_by_type": sample_by_type,
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"first_radicado": first_radicado,
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}
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raise HTTPException(
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status_code=400,
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detail="Envia document_path o root_path con doc_types para descubrir campos.",
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)
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@app.post("/estimate")
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def estimate(request: EstimateRequest) -> dict[str, Any]:
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return processing_service.estimate_tokens_and_cost(
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request.root_path,
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request.keywords,
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request.schema_map,
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request.selected_fields,
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doc_types=[dt.model_dump() for dt in request.doc_types or []] or None,
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schema_by_type=request.schema_by_type,
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selected_paths=request.selected_paths,
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structure_mode=request.structure_mode,
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)
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@app.post("/batches", response_model=ExtractionBatchRead)
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def run_extraction(request: ExtractionRequest) -> dict[str, Any]:
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if not request.fields:
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raise HTTPException(
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status_code=400,
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detail="El API no tiene selector visual: envia fields. Usa POST /schemas para descubrirlos.",
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)
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with session_scope() as session:
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batch = ExtractionBatch(
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status="running",
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root_path=request.root_path,
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request=request.model_dump(),
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updated_at=utcnow(),
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)
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session.add(batch)
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session.flush()
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batch_id = batch.id
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try:
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results = processing_service.process_root_with_field_selection(
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request.root_path,
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request.keywords,
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doc_types=[dt.model_dump() for dt in request.doc_types or []] or None,
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fields=request.fields,
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csv_output_path=None,
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instruction_override=request.instruction,
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alias_map=request.alias_map,
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tiff_page_range=request.tiff_page_range,
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structure_mode=request.structure_mode,
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retry_on_empty=request.retry_on_empty,
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radicado_filter=request.radicado_filter,
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)
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with session_scope() as session:
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row_index = 0
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for item in results:
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raw = item if isinstance(item, dict) else {"data": item}
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rows = flatten_extraction_result(raw, request.fields, request.alias_map)
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for row in rows:
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row_index += 1
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session.add(
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ExtractionRow(
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batch_id=batch_id,
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radicado=str(raw.get("radicado") or row.get("radicado") or ""),
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source_pdf=raw.get("_source_pdf"),
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row_index=row_index,
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row_json=row,
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raw_json=raw,
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)
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)
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batch = session.get(ExtractionBatch, batch_id)
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if batch is None:
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raise RuntimeError("No se encontro el lote recien creado.")
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batch.status = "completed"
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batch.updated_at = utcnow()
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session.flush()
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return serialize_batch(batch)
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except Exception as exc:
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with session_scope() as session:
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batch = session.get(ExtractionBatch, batch_id)
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if batch is not None:
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batch.status = "failed"
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batch.error = str(exc)
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batch.updated_at = utcnow()
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raise HTTPException(status_code=500, detail=str(exc)) from exc
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@app.get("/batches")
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def list_batches() -> list[dict[str, Any]]:
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with session_scope() as session:
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batches = session.scalars(
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select(ExtractionBatch).order_by(ExtractionBatch.created_at.desc())
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).all()
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return [serialize_batch(batch) for batch in batches]
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@app.get("/batches/{batch_id}")
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def get_batch(batch_id: str) -> dict[str, Any]:
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with session_scope() as session:
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batch = session.get(ExtractionBatch, batch_id)
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if batch is None:
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raise HTTPException(status_code=404, detail="Lote no encontrado")
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return serialize_batch(batch)
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@app.get("/batches/{batch_id}/rows")
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def get_rows(batch_id: str, limit: int = 500, offset: int = 0) -> dict[str, Any]:
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with session_scope() as session:
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batch = session.get(ExtractionBatch, batch_id)
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if batch is None:
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raise HTTPException(status_code=404, detail="Lote no encontrado")
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rows = session.scalars(
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select(ExtractionRow)
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.where(ExtractionRow.batch_id == batch_id)
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.order_by(ExtractionRow.row_index, ExtractionRow.id)
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.offset(offset)
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.limit(limit)
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).all()
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return {
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"batch_id": batch_id,
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"rows": [row.row_json for row in rows],
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"limit": limit,
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"offset": offset,
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}
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