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https://github.com/samestrin/llm-services-api
A FastAPI-powered REST API offering a comprehensive suite of natural language processing services using machine learning models with PyTorch and Transformers, packaged in a Docker container to run efficiently.
https://github.com/samestrin/llm-services-api
api docker fastapi hugging-face hugging-face-transformers huggingface-transformers keybert llm openai-compatible-api python python3 pytorch rest rest-api spacy torch transformers uvicorn
Last synced: 1 day ago
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A FastAPI-powered REST API offering a comprehensive suite of natural language processing services using machine learning models with PyTorch and Transformers, packaged in a Docker container to run efficiently.
- Host: GitHub
- URL: https://github.com/samestrin/llm-services-api
- Owner: samestrin
- License: mit
- Created: 2024-08-06T21:47:07.000Z (about 2 months ago)
- Default Branch: main
- Last Pushed: 2024-08-13T02:47:42.000Z (about 1 month ago)
- Last Synced: 2024-09-21T09:32:58.111Z (6 days ago)
- Topics: api, docker, fastapi, hugging-face, hugging-face-transformers, huggingface-transformers, keybert, llm, openai-compatible-api, python, python3, pytorch, rest, rest-api, spacy, torch, transformers, uvicorn
- Language: Python
- Homepage:
- Size: 60.5 KB
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# LLM Services API
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 [](https://opensource.org/licenses/MIT)[](https://www.python.org/)
LLM Services API is a FastAPI-based application that provides a suite of natural language processing services using various machine learning models from Hugging Face's `transformers` library through a REST API interface. The application is designed to run in a Docker container, providing endpoints for text summarization, sentiment analysis, named entity recognition, paraphrasing, keyword extraction, and embedding generation. The entire API is secured using an API key with `Bearer ` format, ensuring that only authorized users can access the endpoints.
The service allows flexibility in model selection through command-line arguments and a configuration file, `models_config.json`, enabling users to specify different Hugging Face models for various NLP tasks. This flexibility allows users to select lightweight models for lower-resource environments or more powerful models for advanced tasks.
## Updates
**0.0.4**
- **Tokenization:** Convert input text into a list of token IDs, allowing you to process and manipulate text at the token level, default model `all-MiniLM-L6-v2`.
- **Detokenization:** Reconstruct original text from a list of token IDs, allowing you to reverse the tokenization process, default model `all-MiniLM-L6-v2`.
**0.0.3**
- **Adaptive Throttling:** Implemented an adaptive throttling mechanism that delays requests using the `Retry-After` header when errors are encountered due to high request frequency or processing failures. The delay is dynamically adjusted based on the client’s request rate and error occurrences.
**0.0.2**
- **OpenAI-Compatible Embeddings:** Provides an endpoint that mimics the OpenAI embedding API, allowing easy integration with existing systems expecting OpenAI-like responses.
- **Configurable Model Loading:** Customize which Hugging Face NLP models are loaded by providing command-line arguments or configuring the `models_config.json` file. This flexibility allows the application to adapt to different resource environments or use cases.
## Features
- **Text Summarization:** Generate concise summaries of long texts, default model `BART`.
- **Sentiment Analysis:** Determine the sentiment of text inputs, default model `DistilBERT`.
- **Named Entity Recognition (NER):** Identify entities within text and sort them by frequency, default model `BERT` (dbmdz/bert-large-cased-finetuned-conll03-english).
- **Paraphrasing:** Rephrase sentences to produce semantically similar outputs, default model `T5`.
- **Keyword Extraction:** Extract important keywords from text, with customizable output count, default model `KeyBERT`.
- **Embedding Generation:** Create vector representations of text, default model `SentenceTransformers` (all-MiniLM-L6-v2).
- **Caching with LRU:** Frequently used computations, such as generating embeddings and tokenizations, are cached using the Least Recently Used (LRU) strategy. This reduces response times for repeated requests and enhances overall performance.
## Dependencies
- Python 3.7+
- FastAPI
- Uvicorn
- spaCy
- transformers
- sentence-transformers
- keybert
- torch
- python-dotenv (for environment variable management)
## Installation
To get started with the LLM Services API, follow these steps:
1. **Clone the Repository:**
```bash
git clone https://github.com/samestrin/llm-services-api.git
cd llm-services-api
```
2. **Create a Virtual Environment:**
```bash
python -m venv venv
source venv/bin/activate # On Windows use `venv\Scripts\activate`
```
3. **Install the Dependencies:**
```bash
pip install -r requirements.txt
```
4. **Download SpaCy Model:**
```bash
python -m spacy download en_core_web_sm
```
5. **Create Your .env File:**
```bash
echo "API_KEY=your-key-here" > .env
```
6. **Run the Application Locally:**
You can run the application locally in two ways:
- **Using Uvicorn:**
This is the recommended method for running in a development or production-like environment.
```bash
uvicorn main:app --reload --port 5000
```
- **Using Python:**
This method allows you to pass command-line arguments for customizing models.
```bash
python main.py --embedding-model all-MiniLM-L6-v2 --summarization-model facebook/bart-large-cnn
```
Replace `--embedding-model` and `--summarization-model` with the models you wish to use. This approach offers flexibility by allowing you to specify different models for various NLP tasks.
### Options
```bash
-h, --help Show this help message and exit
--embedding-model EMBEDDING_MODEL Specify embedding model
--summarization-model SUMMARIZATION_MODEL Specify summarization model
--sentiment-model SENTIMENT_MODEL Specify sentiment analysis model
--ner-model NER_MODEL Specify named entity recognition model
--paraphrase-model PARAPHRASE_MODEL Specify paraphrasing model
--keyword-model KEYWORD_MODEL Specify keyword extraction mode
```
## Running with Docker
To run the application in a Docker container, follow these steps:
1. **Build the Docker Image:**
```bash
docker build -t llm-services-api .
```
2. **Run the Docker Container:**
```bash
docker run -p 5000:5000 llm-services-api
```
The application will be accessible at `http://localhost:5000`.
## Usage
The API provides several endpoints for various NLP tasks. Below is a summary of the available endpoints:
### Endpoints
#### 1. Text Summarization
- **Endpoint:** `/summarize`
- **Method:** `POST`
- **Request Body:**
```json
{
"text": "Your text here"
}
```
- **Response:**
```json
{
"summary": "The generated summary of the provided text."
}
```
#### 2. Sentiment Analysis
- **Endpoint:** `/sentiment`
- **Method:** `POST`
- **Request Body:**
```json
{
"text": "Your text here"
}
```
- **Response:**
```json
{
"sentiment": [
{
"label": "POSITIVE", # or "NEGATIVE"
"score": 0.99
}
]
}
```
#### 3. Named Entity Recognition
- **Endpoint:** `/entities`
- **Method:** `POST`
- **Request Body:**
```json
{
"text": "Your text here"
}
```
- **Response:**
```json
{
"entities": [
{
"entity": "PERSON",
"word": "John Doe",
"frequency": 3
},
...
]
}
```
#### 4. Paraphrasing
- **Endpoint:** `/paraphrase`
- **Method:** `POST`
- **Request Body:**
```json
{
"text": "Your text here"
}
```
- **Response:**
```json
{
"paraphrased_text": "The paraphrased version of the input text."
}
```
#### 5. Keyword Extraction
- **Endpoint:** `/extract_keywords`
- **Method:** `POST`
- **Query Parameters:**
- `num_keywords`: Optional, defaults to 5. Specifies the number of keywords to extract.
- **Request Body:**
```json
{
"text": "Your text here"
}
```
- **Response:**
```json
{
"keywords": [
{
"keyword": "important keyword",
"score": 0.95
},
...
]
}
```
#### 6. Embedding Generation
- **Endpoint:** `/embed`
- **Method:** `POST`
- **Request Body:**
```json
{
"text": "Your text here"
}
```
- **Response:**
```json
{
"embedding": [0.1, 0.2, 0.3, ...] # Array of float numbers representing the text embedding
}
```
### 7. OpenAI-Compatible Embedding
- **Endpoint:** `/v1/embeddings`
- **Method:** `POST`
- **Request Body:**
```json
{
"input": "Your text here",
"model": "all-MiniLM-L6-v2" # or another supported model
}
```
- **Response:**
```json
{
"object": "list",
"data": [
{
"object": "embedding",
"index": 0,
"embedding": [-0.006929283495992422, -0.005336422007530928, ...], # Embedding array
}
],
"model": "all-MiniLM-L6-v2",
"usage": {
"prompt_tokens": 5, # Number of tokens in the input
"total_tokens": 5 # Total number of tokens processed
}
}
```
#### 8. Tokenization
- **Endpoint:** `/tokenize`
- **Method:** `POST`
- **Request Body:**
```json
{
"text": "Your text here",
"model": "all-MiniLM-L6-v2" # Optional, specify a model for tokenization
}
```
- **Response:**
```json
{
"tokens": [101, 7592, 999, ...] # Array of token IDs representing the text
}
```
This endpoint allows you to tokenize input text using a specified or default model. If the model field is not provided, the default embeddings model `all-MiniLM-L6-v2` will be used.
#### 8. Detokenization
- **Endpoint:** `/detokenize`
- **Method:** `POST`
- **Request Body:**
```json
{
"tokens": [101, 2023, 2003, 2019, 2742, 6251, 2000, 19204, 1012, 102], # List of token IDs
"model": "all-MiniLM-L6-v2" # Optional, specify a model for detokenization
}
```
- **Response:**
```json
{
"text": "This is an example sentence to tokenize." # The reconstructed text
}
```
## Contribute
Contributions to this project are welcome. Please fork the repository and submit a pull request with your changes or improvements.
## License
This project is licensed under the MIT License - see the [LICENSE](/LICENSE) file for details.
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