Spaces:
Sleeping
Sleeping
File size: 4,323 Bytes
c3aef13 909d9bf c3aef13 0a6cb95 c3aef13 909d9bf c3aef13 909d9bf c3aef13 0a6cb95 909d9bf c3aef13 909d9bf c3aef13 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 |
from fastapi import FastAPI, HTTPException
from typing import List
import torch
import uvicorn
import gc
import os
from models.schemas import EmbeddingRequest, EmbeddingResponse, ModelInfo
from utils.helpers import load_models, get_embeddings, cleanup_memory
app = FastAPI(
title="Multilingual & Legal Embedding API",
description="Multi-model embedding API for Spanish, Catalan, English and Legal texts",
version="3.0.0"
)
# Global model cache
models_cache = {}
@app.on_event("startup")
async def startup_event():
"""Load models on startup"""
global models_cache
models_cache = load_models()
print("All models loaded successfully!")
@app.get("/")
async def root():
return {
"message": "Multilingual & Legal Embedding API",
"models": ["jina", "robertalex", "jina-v3", "legal-bert", "roberta-ca"],
"status": "running",
"docs": "/docs",
"total_models": 5
}
@app.post("/embed", response_model=EmbeddingResponse)
async def create_embeddings(request: EmbeddingRequest):
"""Generate embeddings for input texts"""
try:
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"""
return {
"status": "healthy",
"models_loaded": len(models_cache) == 5,
"available_models": list(models_cache.keys())
}
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) |