Tech Talk: Personalized Chatbots using Large Language Models and RAG
Retrieval Augement Generation (RAG) uses the retrieval and generation-based approach to generate responses; the RAG technique utilizes the concept of embeddings to create a semantic sense of the text in the corpus and answers more accurately in a personalized manner.
Ollama allows you to run Large Language Models (LLMs) such as Llama2, Mistral, and CodeLlama on your local machine and build chatbots using Langchain integrations and interfaces like Streamlit and Gradio.
In this guide, you will build a chatbot with RAG capabilities using Langchain to split the text, ChromaDB to store embeddings, and Streamlit as a chat interface to generate responses using the Mistral model.
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