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main.py
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main.py
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import time
from fastapi import FastAPI
from sentence_transformers import SentenceTransformer
from sentence_transformers.util import cos_sim
# Bread.ai model(s)
model = SentenceTransformer("mixedbread-ai/mxbai-embed-large-v1")
default_instruction = "Represent this sentence for searching relevant passages:"
app = FastAPI(title="BreadVectorizer",
description="Text embedding using mixedbread.ai model mxbai-embed-large-v1. Instruction is optional, it will represent the text for retrieval by default.",
version="1.0",
contact={
"name": "Pat Wendorf",
"email": "pat.wendorf@mongodb.com",
},
license_info={
"name": "MIT",
"url": "https://opensource.org/license/mit/",
})
@app.get("/")
async def root():
return {"message": "Vectorize some text! See /docs for more info."}
@app.get("/vectorize/")
async def vectorize(text: str, instruction: str = default_instruction):
embedding = model.encode([[instruction,text]]).tolist()[0]
return embedding
@app.get("/compare/")
async def compare(text1: str, text2: str, instruction: str = default_instruction):
embedding1 = model.encode([[instruction,text1]]).tolist()[0]
embedding2 = model.encode([[instruction,text2]]).tolist()[0]
similarity = cos_sim(embedding1, embedding2)
similarity = float(similarity[0][0])
return {"cosine": similarity}
@app.get("/benchmark/")
async def benchmark(text: str, instruction: str = default_instruction):
# Perform 10 calls to the model and calculate the average time
times = []
for _ in range(10):
start_time = time.time()
model.encode([[instruction, text]])
end_time = time.time()
times.append(end_time - start_time)
average_time = sum(times) / 10
return {"average_time": average_time}