Create .env

.env

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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=os.getenv('HF_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
+
+# If you want to use any other model serving provider the configuration will be similar
+# Below is the example for Groq
+# GROQ_API_KEY=gsk_YOUR_GROQ_API_KEY
+# LLM_URL=https://api.groq.com/openai/v1
+# LLM_TYPE=GROQ_API
+# LLM_NAME=llama3-70b-8192
+
+
+# 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
+# In 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