Moving your tax return to the cloud with chatbots. #EfficientTaxation

Part 2 of the tutorial explores running processes on the cloud after discussing local operations in Part 1. Prerequisites for cloud operations include storing the knowledge base, running the LLM, and hosting the chatbot on the cloud. The tutorial focuses on moving the knowledge base to the cloud using vector databases like Vespa, Supabase, Elasticsearch, and Pinecone. Pinecone is chosen for the tutorial due to its free-tier plan. The process involves creating an index, setting up the environment, and running the code to load vectors into the index.

For hosting the LLM, Groq is suggested as a cloud provider with a simple API. The tutorial provides steps for signing up, generating an API key, and updating the chatbot code to use Groq for hosting the LLM. Additionally, instructions are given for deploying the app using Streamlit Community, managing secrets, setting up requirements, and connecting the code to GitHub for deployment. The final steps involve creating the app, setting API keys, and deploying the chatbot.

The tutorial offers detailed code snippets, configuration settings, and visual aids to guide users through the process of moving operations to the cloud and deploying the chatbot. The GitHub repository link is provided for reference.

Source link

Source link: https://medium.com/theladlab/a-chatbot-for-your-tax-return-part-3-going-to-the-cloud-46e3a61dee46?source=rss——ai-5