Added env examples

.env.huggingface.example

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+# You can use any model that available to you and deployed on Hugging Face with compatible API
+# X_NAME variables are optional for HuggingFace API you can use them for your convenience
+
+# Make sure your key has permission to use all models
+# Set up you key here: https://huggingface.co/docs/api-inference/en/quicktour#get-your-api-token
+HF_API_KEY=hf_YOUR_HUGGINGFACE_API_KEY
+
+# For example you can try public Inference API endpoint for Meta-Llama-3-70B-Instruct model
+# This model quiality is comparable with GPT-4
+# But public API has strict limit for output tokens, so it is very hard to use it for this usecase
+# You can use your private API endpoint for this model
+# Or use any other Hugging Face model that supports Messages API
+# Don't forget to add '/v1' to the end of the URL
+LLM_URL=https://api-inference.huggingface.co/models/meta-llama/Meta-Llama-3-70B-Instruct/v1
+LLM_TYPE=HF_API
+LLM_NAME=Meta-Llama-3-70B-Instruct
+
+# The Open AI whisper family with more models is available on HuggingFace:
+# https://huggingface.co/collections/openai/whisper-release-6501bba2cf999715fd953013
+# You can also use any other compatible STT model from HuggingFace
+STT_URL=https://api-inference.huggingface.co/models/openai/whisper-tiny.en
+STT_TYPE=HF_API
+STT_NAME=whisper-tiny.en
+
+# You can use compatible TTS model from HuggingFace
+# For example you can try public Inference API endpoint for Facebook MMS-TTS model
+# Im my experience OS TTS models from HF sound much more robotic than OpenAI TTS models
+TTS_URL=https://api-inference.huggingface.co/models/facebook/mms-tts-eng
+TTS_TYPE=HF_API
+TTS_NAME=Facebook-mms-tts-eng

.env.local.example

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+# 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

.env.openai.example

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+# Easy way to set up all your models using only OpenAI API
+
+# Make sure your key has permission to use all models
+# Set up you key here: https://platform.openai.com/api-keys
+OPENAI_API_KEY=sk-YOUR_OPENAI_API_KEY
+
+# "gpt-3.5-turbo" - ~3 seconds delay with good quality, recommended model
+# "gpt-4-turbo","gpt-4", etc. 10+ seconds delay but higher quality of responses
+LLM_URL=https://api.openai.com/v1
+LLM_TYPE=OPENAI_API
+LLM_NAME=gpt-3.5-turbo
+
+# "whisper-1" is the only OpenAI STT model available with OpenAI API
+STT_URL=https://api.openai.com/v1
+STT_TYPE=OPENAI_API
+STT_NAME=whisper-1
+
+# "tts-1" - very good quality and close to real-time response
+# "tts-1-hd" - slightly better quality with slightly longer response time
+TTS_URL=https://api.openai.com/v1
+TTS_TYPE=OPENAI_API
+TTS_NAME=tts-1