# You can also run models locally or on you own server and use them instead if they are compatible with HuggingFace API
# For local models seletct HF_API as a type because they usse HuggingFace API

# Most probalby you don't need a key for your local model
# But if you have some kind of authentication compatible with HuggingFace API you can use it here
HF_API_KEY=None

# The main usecase for the local models in locally running LLMs
# You can serve any model using Text Generation Inference from HuggingFace
# https://github.com/huggingface/text-generation-inference
# This project uses Messages API that is compatible with Open AI API and allows you to just plug and play OS models
# Don't gorget to add '/v1' to the end of the URL
# Assuming you have Meta-Llama-3-8B-Instruct model running on your local server, your configuration will look like this
LLM_URL=http://192.168.1.1:8080/v1
LLM_TYPE=HF_API
LLM_NAME=Meta-Llama-3-8B-Instruct

# Running STT model locally is not straightforward
# But for example you can one of the whispers models on your laptop
# It requires some simple wrapper over the model to make it compatible with HuggingFace API. Maybe I will share some in the future
# But assuming you manages to run a local whisper-server, your configuration will look like this
STT_URL=http://127.0.0.1:5000/transcribe
STT_TYPE=HF_API
STT_NAME=whisper-base.en

# I don't see much value in running TTS models locally given the quality of online models
# But if you have some kind of TTS model running on your local server you can use it here
TTS_URL=http://127.0.0.1:5001/read
TTS_TYPE=HF_API
TTS_NAME=my-tts-model