main.py

from fastapi import FastAPI, HTTPException
from fastapi.responses import JSONResponse
from fastapi.middleware.cors import CORSMiddleware

from transformers import AutoModel, AutoTokenizer
import torch

device = torch.device("cpu")

# Load the model and tokenizer
model = AutoModel.from_pretrained(
    "nomic-ai/nomic-embed-text-v1.5", trust_remote_code=True
)
tokenizer = AutoTokenizer.from_pretrained(
    "nomic-ai/nomic-embed-text-v1.5", trust_remote_code=True
)


app = FastAPI()
app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)

def chunk_text(text, chunk_size=512):
    return [text[i:i + chunk_size] for i in range(0, len(text), chunk_size)]

@app.post("/get_embeding")
async def get_embeding(text):
    chunks = chunk_text(text)
    for chunk in chunks:
    
        # Tokenize the input text
        inputs = tokenizer(chunk, return_tensors="pt")
    
        # Generate embeddings
        with torch.no_grad():
            outputs = model(**inputs)
    
        # The embeddings can be found in the 'last_hidden_state'
        embeddings = outputs.last_hidden_state
    
        # Optionally, you can average the token embeddings to get a single vector for the sentence
        sentence_embedding = torch.mean(embeddings, dim=1)
    
        #print(sentence_embedding)
    
        return sentence_embedding