https://github.com/mdh266/speech-chatbot
A Serverless Multimodal AI Chatbot For Free
https://github.com/mdh266/speech-chatbot
docker gen-ai google-cloud-platform groq langchain llama3 llms streamlit
Last synced: 7 months ago
JSON representation
A Serverless Multimodal AI Chatbot For Free
- Host: GitHub
- URL: https://github.com/mdh266/speech-chatbot
- Owner: mdh266
- Created: 2024-12-06T04:09:25.000Z (10 months ago)
- Default Branch: main
- Last Pushed: 2025-01-18T21:36:08.000Z (9 months ago)
- Last Synced: 2025-01-22T22:48:52.507Z (9 months ago)
- Topics: docker, gen-ai, google-cloud-platform, groq, langchain, llama3, llms, streamlit
- Language: Jupyter Notebook
- Homepage:
- Size: 2.87 MB
- Stars: 0
- Watchers: 1
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Building & Deploying A Serverless Multimodal ChatBot
------------------------------------------------------
In this blog post I will go over how to create a create multimodal chatbot using a [Large Language Model (LLM)](https://en.wikipedia.org/wiki/Large_language_model). Specifically, I'll build an app that you can speak to and get an audio reply. The app will also optionally transcribe conversation. I will go over how to do this all in a serverless framework and using cloud-based APIs so that (baring the app getting really popular) the costs will be next to nothing!
### Part 1
------------
In part 1 I'll go over building the app and running it locally by using [LangChain](https://www.langchain.com/) & [Groq API](https://groq.com/) to interact with the [Llama 3](https://ai.meta.com/blog/meta-llama-3/) Open Source LLM. I'll use the Google Cloud API for [Text-To-Speech](https://cloud.google.com/text-to-speech?hl=en) and [Speech-To-Text](https://cloud.google.com/speech-to-text/?hl=en) and for the front end and deployment I'll use [Streamlit](https://streamlit.io/).
### Part 2
------------
In Part 2, I'll cover how to deploy this app using [Docker](https://www.docker.com/) for containerization which will allow us to run the app both locally and on the cloud. Then well cover [GitHub Actions](https://github.com/features/actions) for automatically building the image and pushing it to [Docker Hub](https://hub.docker.com/) where it can be pulled and run on [Google Cloud Run](https://cloud.google.com/run) to create a serverless application.
### Running The App Locally
--------------------------------------
To run the app locally you'll have to get API keys for Groq and Google API.
You can then build and run the app with Docker,
docker image build -t .
docker run -ip 8080:8080 -e GROQ_API_KEY= -e GOOGLE_API_KEY=
Or using placing your api keys in as defined above in a file labeled `.env` and use [Docker Compose](https://docs.docker.com/compose/),
docker compose up
You can the visit the website in your browser, https://localhost:8080 and it will look like the below,
