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from fastapi import FastAPI, HTTPException
from fastapi.middleware.cors import CORSMiddleware
from typing import List
import torch
import uvicorn
from models.schemas import EmbeddingRequest, EmbeddingResponse, ModelInfo
from utils.helpers import load_models, get_embeddings, cleanup_memory
# Global model cache - completely on-demand loading
models_cache = {}
# All models load on demand to test deployment
ON_DEMAND_MODELS = ["jina", "robertalex", "jina-v3", "legal-bert", "roberta-ca"]
def ensure_model_loaded(model_name: str):
"""Load a specific model on demand if not already loaded"""
global models_cache
if model_name not in models_cache:
if model_name in ON_DEMAND_MODELS:
try:
print(f"Loading model on demand: {model_name}...")
new_models = load_models([model_name])
models_cache.update(new_models)
print(f"Model {model_name} loaded successfully!")
except Exception as e:
print(f"Failed to load model {model_name}: {str(e)}")
raise HTTPException(status_code=500, detail=f"Model {model_name} loading failed: {str(e)}")
else:
raise HTTPException(status_code=400, detail=f"Unknown model: {model_name}")
app = FastAPI(
title="Multilingual & Legal Embedding API",
description="Multi-model embedding API for Spanish, Catalan, English and Legal texts",
version="3.0.0"
)
# Add CORS middleware to allow cross-origin requests
app.add_middleware(
CORSMiddleware,
allow_origins=["*"], # In production, specify actual domains
allow_credentials=True,
allow_methods=["*"],
allow_headers=["*"],
)
@app.get("/")
async def root():
return {
"message": "Multilingual & Legal Embedding API - Minimal Version",
"models": ["jina", "robertalex", "jina-v3", "legal-bert", "roberta-ca"],
"status": "running",
"docs": "/docs",
"total_models": 5,
"note": "All models load on first request"
}
@app.post("/embed", response_model=EmbeddingResponse)
async def create_embeddings(request: EmbeddingRequest):
"""Generate embeddings for input texts"""
try:
# Load specific model on demand
ensure_model_loaded(request.model)
if not request.texts:
raise HTTPException(status_code=400, detail="No texts provided")
if len(request.texts) > 50: # Rate limiting
raise HTTPException(status_code=400, detail="Maximum 50 texts per request")
embeddings = get_embeddings(
request.texts,
request.model,
models_cache,
request.normalize,
request.max_length
)
# Cleanup memory after large batches
if len(request.texts) > 20:
cleanup_memory()
return EmbeddingResponse(
embeddings=embeddings,
model_used=request.model,
dimensions=len(embeddings[0]) if embeddings else 0,
num_texts=len(request.texts)
)
except ValueError as e:
raise HTTPException(status_code=400, detail=str(e))
except Exception as e:
raise HTTPException(status_code=500, detail=f"Internal error: {str(e)}")
@app.get("/models", response_model=List[ModelInfo])
async def list_models():
"""List available models and their specifications"""
return [
ModelInfo(
model_id="jina",
name="jinaai/jina-embeddings-v2-base-es",
dimensions=768,
max_sequence_length=8192,
languages=["Spanish", "English"],
model_type="bilingual",
description="Bilingual Spanish-English embeddings with long context support"
),
ModelInfo(
model_id="robertalex",
name="PlanTL-GOB-ES/RoBERTalex",
dimensions=768,
max_sequence_length=512,
languages=["Spanish"],
model_type="legal domain",
description="Spanish legal domain specialized embeddings"
),
ModelInfo(
model_id="jina-v3",
name="jinaai/jina-embeddings-v3",
dimensions=1024,
max_sequence_length=8192,
languages=["Multilingual"],
model_type="multilingual",
description="Latest Jina v3 with superior multilingual performance"
),
ModelInfo(
model_id="legal-bert",
name="nlpaueb/legal-bert-base-uncased",
dimensions=768,
max_sequence_length=512,
languages=["English"],
model_type="legal domain",
description="English legal domain BERT model"
),
ModelInfo(
model_id="roberta-ca",
name="projecte-aina/roberta-large-ca-v2",
dimensions=1024,
max_sequence_length=512,
languages=["Catalan"],
model_type="general",
description="Catalan RoBERTa-large model trained on large corpus"
)
]
@app.get("/health")
async def health_check():
"""Health check endpoint"""
all_models_loaded = len(models_cache) == 5
return {
"status": "healthy",
"all_models_loaded": all_models_loaded,
"available_models": list(models_cache.keys()),
"on_demand_models": ON_DEMAND_MODELS,
"models_count": len(models_cache),
"note": "All models load on first embedding request - minimal deployment version"
}
if __name__ == "__main__":
# Set multi-threading for CPU
torch.set_num_threads(8)
torch.set_num_interop_threads(1)
uvicorn.run(app, host="0.0.0.0", port=7860) |