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reflector/server/gpu/modal_deployments/README.md
2024-06-20 12:07:28 +05:30

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# Reflector GPU implementation - Transcription and LLM
This repository hold an API for the GPU implementation of the Reflector API service,
and use [Modal.com](https://modal.com)
- `reflector_llm.py` - LLM API
- `reflector_transcriber.py` - Transcription API
## Modal.com deployment
Create a modal secret, and name it `reflector-gpu`.
It should contain an `REFLECTOR_APIKEY` environment variable with a value.
The deployment is done using [Modal.com](https://modal.com) service.
```
$ modal deploy reflector_transcriber.py
...
└── 🔨 Created web => https://xxxx--reflector-transcriber-web.modal.run
$ modal deploy reflector_llm.py
...
└── 🔨 Created web => https://xxxx--reflector-llm-web.modal.run
```
Then in your reflector api configuration `.env`, you can set theses keys:
```
TRANSCRIPT_BACKEND=modal
TRANSCRIPT_URL=https://xxxx--reflector-transcriber-web.modal.run
TRANSCRIPT_MODAL_API_KEY=REFLECTOR_APIKEY
LLM_BACKEND=modal
LLM_URL=https://xxxx--reflector-llm-web.modal.run
LLM_MODAL_API_KEY=REFLECTOR_APIKEY
```
## API
Authentication must be passed with the `Authorization` header, using the `bearer` scheme.
```
Authorization: bearer <REFLECTOR_APIKEY>
```
### LLM
`POST /llm`
**request**
```
{
"prompt": "xxx"
}
```
**response**
```
{
"text": "xxx completed"
}
```
### Transcription
`POST /transcribe`
**request** (multipart/form-data)
- `file` - audio file
- `language` - language code (e.g. `en`)
**response**
```
{
"text": "xxx",
"words": [
{"text": "xxx", "start": 0.0, "end": 1.0}
]
}
```