FastAPI Embedding API

Overview

The FastAPI Embedding API provides a simple interface for generating embedding vectors from input sentences. Embedding vectors are numerical representations of text that capture semantic information, allowing for various NLP tasks.

Getting Started

Installation

1. Make sure you have Python 3.x installed on your system.
2. Install the required dependencies using pip:

pip install -r requirements.txt

Running the API

To start the FastAPI server, run the following command:

uvicorn main:app --reload

The API will be accessible at http://127.0.0.1:8000.

Usage

Send a POST request to http://127.0.0.1:8000/embed with a JSON object containing a list of sentences.

Example Request

curl -X 'POST' \
  'http://127.0.0.1:8000/embed' \
  -H 'accept: application/json' \
  -H 'Content-Type: application/json' \
  -d '{
  "sentences": [
    "This is a sample sentence.",
    "Another example for embedding."
  ]
}'

Example Response

[
    {
        "sentence": "This is a sample sentence.",
        "embedding": [
            -0.30739638209342957,
            -0.3990561366081238,
            -0.6577262878417969,
            0.2739684283733368,
            -0.09149201959371567,
            0.2765969932079315,
            -0.010574031621217728,
            -0.9837483167648315,
            -0.9544899463653564,
            1.1655793190002441,
            0.4724787175655365,
            -0.8983410596847534,
            -0.4408877193927765,
            0.5333910584449768,
            0.30021241307258606,
            -0.7057181596755981,
            0.20055779814720154,
            -0.7414146661758423,
            -0.16691547632217407,
            -0.7831200361251831,
            0.2316111922264099,
            0.3517184257507324,
            -0.49855148792266846,
            0.3873681426048279,
            0.8929033279418945,
            -0.3907497525215149,
            -0.20189079642295837,
            -0.27217328548431396,
            -0.20150843262672424,
            -0.2696434557437897,
            -0.04416860267519951,
            -0.1778184473514557
        ]
    },
    {
        "sentence": "Another example for embedding.",
        "embedding": [
            -0.11383337527513504,
            -0.21959248185157776,
            -0.2680359482765198,
            0.2703028917312622,
            0.13453321158885956,
            0.3442433178424835,
            -0.641788125038147,
            -0.4218224287033081,
            -0.9396926760673523,
            -0.1322430521249771,
            0.825551450252533,
            0.362534761428833,
            -0.15542413294315338,
            -0.12511911988258362,
            -0.1556849330663681,
            0.13272254168987274,
            0.2515423595905304,
            -0.6910218596458435,
            0.16325613856315613,
            -0.41506609320640564,
            0.31772831082344055,
            0.14017413556575775,
            -0.10088400542736053,
            0.2694193124771118,
            0.1680845022201538,
            -0.4719347655773163,
            0.015851259231567383,
            -0.3199581801891327,
            -0.07567527890205383,
            -0.11492110788822174,
            -0.007399208843708038,
            0.29920971393585205
        ]
    }
]

Acknowledgments

• FastAPI Documentation
• Uvicorn Documentation

References

https://github.com/UKPLab/sentence-transformers

Reimers, Nils and Gurevych, Iryna
Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation.https://arxiv.org/abs/2004.09813
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing.

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Please remember to replace placeholders like http://127.0.0.1:8000 with your actual API endpoint if needed. Additionally, include any additional sections or information that is specific to your project.