How to serve Embeddings models via OpenAI API

This demo shows how to deploy embeddings models in the OpenVINO Model Server for text feature extractions. Text generation use case is exposed via OpenAI API embeddings endpoint.

Get the docker image

Pull the image from Dockerhub with CPU support:

docker pull openvino/model_server:2024.5

or if you want to include also the support for GPU execution:

docker pull openvino/model_server:2024.5-gpu

## Model preparation
> **Note** Python 3.9 or higher is needed for that step
> 
Here, the original Pytorch LLM model and the tokenizer will be converted to IR format and optionally quantized.
That ensures faster initialization time, better performance and lower memory consumption.

Install python dependencies for the conversion script:
```bash
pushd .
cd demos/common/export_models
pip3 install -U -r requirements.txt

Run optimum-cli to download and quantize the model:

mkdir -p models
python export_model.py embeddings --source_model Alibaba-NLP/gte-large-en-v1.5 --weight-format int8 --config_file_path models/config.json

Note Change the --weight-format to quantize the model to fp16, int8 or int4 precision to reduce memory consumption and improve performance. You should have a model folder like below:

tree models
models
├── Alibaba-NLP
│   └── gte-large-en-v1.5
│       ├── embeddings
│       │   └── 1
│       │       ├── model.bin
│       │       └── model.xml
│       ├── graph.pbtxt
│       ├── subconfig.json
│       └── tokenizer
│           └── 1
│               ├── model.bin
│               └── model.xml
└── config.json

Note The actual models support version management and can be automatically swapped to newer version when new model is uploaded in newer version folder. In case you trained the pytorch model it can be exported like below: python export_model.py embeddings --source_model <pytorch model> --model_name Alibaba-NLP/gte-large-en-v1.5 --precision int8 --config_file_path models/config.json --version 2

The default configuration of the EmbeddingsCalculator should work in most cases but the parameters can be tuned inside the node_options section in the graph.pbtxt file. Runtime configuration for both models can be tuned in subconfig.json file. They can be set automatically via export parameters in the export_model.py script.

For example: python export_model.py embeddings --source_model Alibaba-NLP/gte-large-en-v1.5 --precision int8 --num_streams 2 --skip_normalize --config_file_path models/config.json

Tested models

All models supported by optimum-intel should be compatible. In serving validation are included Hugging Face models:

    nomic-ai/nomic-embed-text-v1.5
    Alibaba-NLP/gte-large-en-v1.5
    BAAI/bge-large-en-v1.5
    BAAI/bge-large-zh-v1.5
    thenlper/gte-small

Start-up

CPU

docker run -d --rm -p 8000:8000 -v $(pwd)/models:/workspace:ro openvino/model_server:2024.5 --port 9000 --rest_port 8000 --config_path /workspace/config.json

GPU

In case you want to use GPU device to run the embeddings model, add extra docker parameters --device /dev/dri --group-add=$(stat -c "%g" /dev/dri/render* | head -n 1) to docker run command, use the image with GPU support and make sure set the target_device in subconfig.json to GPU. Also make sure the export model quantization level and cache size fit to the GPU memory. All of that can be applied with the commands:

python demos/common/export_models/export_model.py embeddings --source_model Alibaba-NLP/gte-large-en-v1.5 --weight-format int8 --target_device GPU --config_file_path models/config.json --model_repository_path models

docker run -d --rm -p 8000:8000 --device /dev/dri --group-add=$(stat -c "%g" /dev/dri/render* | head -n 1) -v $(pwd)/models:/workspace:ro openvino/model_server:2024.5-gpu --rest_port 8000 --config_path /workspace/config.json

Check readiness

Wait for the model to load. You can check the status with a simple command below. Note that the slash / in the model name needs to be escaped with %2F:

curl -i http://localhost:8000/v2/models/Alibaba-NLP%2Fgte-large-en-v1.5/ready
HTTP/1.1 200 OK
Content-Type: application/json
Date: Sat, 09 Nov 2024 23:19:27 GMT
Content-Length: 0

Client code

curl http://localhost:8000/v3/embeddings \
  -H "Content-Type: application/json" -d '{ "model": "Alibaba-NLP/gte-large-en-v1.5", "input": "hello world"}' | jq .

{
  "object": "list",
  "data": [
    {
      "object": "embedding",
      "embedding": [
        -0.03440694510936737,
        -0.02553200162947178,
        -0.010130723007023335,
        -0.013917984440922737,
...
        0.02722850814461708,
        -0.017527244985103607,
        -0.0053995149210095406
      ],
      "index": 0
    }
  ]
}

Alternatively there could be used openai python client like in the example below:

pip3 install openai

echo '
from openai import OpenAI
import numpy as np

client = OpenAI(
  base_url="http://localhost:8000/v3",
  api_key="unused"
)
model = "Alibaba-NLP/gte-large-en-v1.5"
embedding_responses = client.embeddings.create(
    input=[
        "That is a happy person",
        "That is a happy very person"
    ],
    model=model,
)
embedding_from_string1 = np.array(embedding_responses.data[0].embedding)
embedding_from_string2 = np.array(embedding_responses.data[1].embedding)
cos_sim = np.dot(embedding_from_string1, embedding_from_string2)/(np.linalg.norm(embedding_from_string1)*np.linalg.norm(embedding_from_string2))
print("Similarity score as cos_sim", cos_sim)' >> openai_client.py

python3 openai_client.py

It will report results like Similarity score as cos_sim 0.97654650115054.

Benchmarking feature extraction

An asynchronous benchmarking client can be use to access the model server performance with various load conditions. Below are execution examples captured on Intel(R) Xeon(R) CPU Max 9480.

popd
pushd .
cd demos/benchmark/embeddings/
pip install -r requirements.txt
python benchmark_embeddings.py --api_url http://localhost:8000/v3/embeddings --dataset synthetic --synthetic_length 5 --request_rate 10 --batch_size 1 --model Alibaba-NLP/gte-large-en-v1.5
Number of documents: 1000
100%|████████████████████████████████████████████████████████████████| 1000/1000 [01:45<00:00,  9.50it/s]
Tokens: 5000
Success rate: 100.0%. (1000/1000)
Throughput - Tokens per second: 48.588129701166125
Mean latency: 17 ms
Median latency: 16 ms
Average document length: 5.0 tokens


python benchmark_embeddings.py --api_url http://localhost:8000/v3/embeddings --request_rate inf --batch_size 32 --dataset synthetic --synthetic_length 510 --model Alibaba-NLP/gte-large-en-v1.5
Number of documents: 1000
100%|████████████████████████████████████████████████████████████████| 50/50 [00:21<00:00,  2.32it/s]
32it [00:18,  1.76it/s]
Tokens: 510000
Success rate: 100.0%. (32/32)
Throughput - Tokens per second: 27995.652060806977
Mean latency: 10113 ms
Median latency: 10166 ms
Average document length: 510.0 tokens


python benchmark_embeddings.py --api_url http://localhost:8000/v3/embeddings --request_rate inf --batch_size 1 --dataset Cohere/wikipedia-22-12-simple-embeddings
Number of documents: 1000
100%|████████████████████████████████████████████████████████████████| 1000/1000 [00:15<00:00, 64.02it/s]
Tokens: 83208
Success rate: 100.0%. (1000/1000)
Throughput - Tokens per second: 5433.913083411673
Mean latency: 1424 ms
Median latency: 1451 ms
Average document length: 83.208 tokens

RAG with Model Server

Embeddings endpoint can be applied in RAG chains to delegated text feature extraction both for documented vectorization and in context retrieval. Check this demo to see the langchain code example which is using OpenVINO Model Server both for text generation and embedding endpoint in RAG application demo

Testing the model accuracy over serving API

A simple method of testing the response accuracy is via comparing the response for a sample prompt from the model server and with local python execution based on HuggingFace python code.

The script compare_results.py can assist with such experiment.

popd
cd demos/embeddings
python compare_results.py --model Alibaba-NLP/gte-large-en-v1.5 --service_url http://localhost:8000/v3/embeddings --input "hello world" --input "goodbye world"

input ['hello world', 'goodbye world']
HF Duration: 50.626 ms NewModel
OVMS Duration: 20.219 ms
Batch number: 0
OVMS embeddings: shape: (1024,) emb[:20]:
 [-0.0349 -0.0256 -0.0102 -0.0139 -0.0175 -0.0015 -0.0297 -0.0002 -0.0424
 -0.0145 -0.0141  0.0101  0.0057  0.0001  0.0316 -0.03   -0.04   -0.0474
  0.0084 -0.0097]
HF AutoModel: shape: (1024,) emb[:20]:
 [-0.0345 -0.0252 -0.0106 -0.0124 -0.0167 -0.0018 -0.0301  0.0002 -0.0408
 -0.0139 -0.015   0.0104  0.0054 -0.0006  0.0326 -0.0296 -0.04   -0.0457
  0.0087 -0.0102]
Difference score with HF AutoModel: 0.02175156185021083
Batch number: 1
OVMS embeddings: shape: (1024,) emb[:20]:
 [-0.0141 -0.0332 -0.0041 -0.0205 -0.0008  0.0189 -0.0278 -0.0083 -0.0511
  0.0043  0.0262 -0.0079  0.016   0.0084  0.0123 -0.0414 -0.0314 -0.0332
  0.0101 -0.0052]
HF AutoModel: shape: (1024,) emb[:20]:
 [-0.0146 -0.0333 -0.005  -0.0194  0.0004  0.0197 -0.0281 -0.0069 -0.0511
  0.005   0.0253 -0.0067  0.0167  0.0079  0.0128 -0.0407 -0.0317 -0.0329
  0.0095 -0.0051]
Difference score with HF AutoModel: 0.024787274668209857

It is easy also to run model evaluation using MTEB framework using a custom class based on openai model:

pip install mteb
python ovms_mteb.py --model Alibaba-NLP/gte-large-en-v1.5 --service_url http://localhost:8000/v3/embeddings