The content discusses building a chatbot for tax-related questions using RAG architecture. Part 1 focused on preparing data for the RAG, and Part 2 covers creating a knowledge base, feeding it into an LLM using LlamaIndex, and creating a chat UI with Streamlit. The process involves installing necessary packages, setting up an OpenAI API key, creating an index, and running the chatbot script. The code snippets demonstrate how to create an index using different models like BAAI/bge-base-en-v1.5 and Ollama, and how to refactor the code for the chatbot using these models. The chatbot UI allows users to ask tax-related questions, with the system providing responses based on the context and the user’s query. Different chat modes like CONDENSE_QUESTION and CONDENSE_PLUS_CONTEXT are explained, and users are encouraged to experiment with parameters like chat_mode and temperature to improve response quality. Additionally, information on installing Ollama and using different vectorization models is provided for further customization.
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Source link: https://medium.com/theladlab/a-chatbot-for-your-tax-return-part-2-build-a-local-rag-44aafd5028f7?source=rss——chatgpt-5
Building a local RAG chatbot for tax returns #AI
