infinity-emb 0.0.13

Infinity is a high-throughput, low-latency REST API for serving vector embeddings, supporting a wide range of sentence-transformer models and frameworks.

Infinity ♾️

Infinity is a high-throughput, low-latency REST API for serving vector embeddings, supporting a wide range of sentence-transformer models and frameworks. Infinity is developed under MIT Licence: https://github.com/michaelfeil/infinity

Why Infinity:

Infinity provides the following features:

• Deploy virtually any SentenceTransformer - deploy the model you know from SentenceTransformers
• Fast inference backends: The inference server is built on top of torch, fastembed(onnx-cpu) and CTranslate2, getting most out of your CUDA or CPU hardware.
• Dynamic batching: New embedding requests are queued while GPU is busy with the previous ones. New requests are squeezed intro your GPU/CPU as soon as ready.
• Correct and tested implementation: Unit and end-to-end tested. Embeddings via infinity are identical to SentenceTransformers (up to numerical precision). Lets API users create embeddings till infinity and beyond.
• Easy to use: The API is built on top of FastAPI, Swagger makes it fully documented. API are aligned to OpenAI's Embedding specs. See below on how to get started.

Infinity demo:

In this gif below, we use sentence-transformers/all-MiniLM-L6-v2, deployed at batch-size=2. After initialization, from a second terminal 3 requests (payload 1,1,and 5 sentences) are sent via cURL.

Getting started

Install via pip

pip install infinity-emb[all]

Install from source with Poetry

Advanced: To install via Poetry use Poetry 1.6.1, Python 3.10 on Ubuntu 22.04

git clone https://github.com/michaelfeil/infinity
cd infinity
cd libs/infinity_emb
poetry install --extras all

Launch via Python

from infinity_emb import create_server
fastapi_app = create_server()

or use the AsyncAPI directly.:

import asyncio
from infinity_emb import AsyncEmbeddingEngine
sentences = ["Embed this is sentence via Infinity.", "Paris is in France."]
engine = AsyncEmbeddingEngine(model_name_or_path = "BAAI/bge-small-en-v1.5", engine="torch")
async def main(): 
    async with engine: # engine starts with engine.astart()
        embeddings, usage = await engine.embed(sentences=sentences)
    # engine stops with engine.astop()
asyncio.run(main())

You can also use rerank (beta, slowish and API subject to change):

import asyncio
from infinity_emb import AsyncEmbeddingEngine
query = "What is the python package infinity_emb?"
docs = ["This is a document not related to the python package infinity_emb, hence...", 
    "Paris is in France!",
    "infinity_emb is a package for sentence embeddings and rerankings using transformer models in Python!"]
engine = AsyncEmbeddingEngine(model_name_or_path = "BAAI/bge-reranker-base", 
    engine="torch", model_warmup=False)
async def main(): 
    async with engine:
        ranking, usage = await engine.rerank(query=query, docs=docs)
        print(list(zip(ranking, docs)))
asyncio.run(main())

You can also use text-classification (beta, slowish and API subject to change):

import asyncio
from infinity_emb import AsyncEmbeddingEngine

sentences = ["This is awesome.", "I am bored."]
engine = AsyncEmbeddingEngine(model_name_or_path = "SamLowe/roberta-base-go_emotions", 
    engine="torch", model_warmup=True)
async def main(): 
    async with engine:
        predictions, usage = await engine.classify(sentences=sentences)
        return predictions, usage
asyncio.run(main())

or launch the create_server() command via CLI

infinity_emb --help

or launch the CLI using a pre-built docker container

model=BAAI/bge-small-en-v1.5
port=8080
docker run -it --gpus all -p $port:$port michaelf34/infinity:latest --model-name-or-path $model --port $port

The download path at runtime, can be controlled via the environment variable SENTENCE_TRANSFORMERS_HOME.

Launch FAQ:

What are embedding models?

Embedding models can map any text to a low-dimensional dense vector which can be used for tasks like retrieval, classification, clustering, or semantic search. And it also can be used in vector databases for LLMs.

The most know architecture are encoder-only transformers such as BERT, and most popular implementation include SentenceTransformers.

What models are supported?

All models of the sentence transformers org are supported https://huggingface.co/sentence-transformers / sbert.net. LLM's like LLAMA2-7B are not intended for deployment.

With the command --engine torch the model must be compatible with https://github.com/UKPLab/sentence-transformers/. - only models from Huggingface are supported.

With the command --engine ctranslate2 - only BERT models are supported. - only models from Huggingface are supported.

For the latest trends, you might want to check out one of the folloing models. https://huggingface.co/spaces/mteb/leaderboard

Launching multiple models in one dockerfile

Multiple models on one GPU is in experimental mode. You can use the following temporary solution:

FROM michaelf34/infinity:latest
# Dockerfile-ENTRYPOINT for multiple models via multiple ports
ENTRYPOINT ["/bin/sh", "-c", \
 "(. /app/.venv/bin/activate && infinity_emb --port 8080 --model-name-or-path sentence-transformers/all-MiniLM-L6-v2 &);\
 (. /app/.venv/bin/activate && infinity_emb --port 8081 --model-name-or-path intfloat/e5-large-v2 )"]

You can build and run it via:

docker build -t custominfinity . && docker run -it --gpus all -p 8080:8080 -p 8081:8081 custominfinity

Both models now run on two instances in one dockerfile servers.

Using Langchain with Infinity

Infinity has a official integration into pip install langchain>=0.342. You can find more documentation on that here: https://python.langchain.com/docs/integrations/text_embedding/infinity

from langchain.embeddings.infinity import InfinityEmbeddings
from langchain.docstore.document import Document

documents = [Document(page_content="Hello world!", metadata={"source": "unknown"})]

emb_model = InfinityEmbeddings(model="BAAI/bge-small", infinity_api_url="http://localhost:7997/v1")
print(emb_model.embed_documents([doc.page_content for doc in docs]))

Documentation

After startup, the Swagger Ui will be available under {url}:{port}/docs, in this case http://localhost:8080/docs.

Contribute and Develop

Install via Poetry 1.6.1 and Python3.10 on Ubuntu 22.04

cd libs/infinity_emb
poetry install --extras all --with test

To pass the CI:

cd libs/infinity_emb
make format
make lint
poetry run pytest ./tests