What you'll learn
Create personal chatbot with Llama3.2, Ollama, Langchain, Qdrant database
Load a LLM via docker
Load a vector database Qdrant via docker
Create a personal chatbot
Requirements
Basic Python programming knowledge
Desire to learn and excel more
Description
Managing multiple documents and finding the right information quickly can be a challenge. A personal document assistant simplifies this by allowing you to upload documents, ask questions, and get instant responses or summaries, making your work more efficient.Benefits of Free Resources with Cutting-Edge Technology:This course enables you to build a powerful system using free resources without compromising on advanced technology. By leveraging the BGE Embeddings model, Llama 3.2b, and the Qdrant vector database, you can run everything locally on your machine, ensuring both privacy and performanceIn this course, we will build YOUR VERY OWN PERSONAL DOCUMENT ASSISTANT from scratchTechnologies Used:Large Language Model: Llama 3.2bEmbeddings: BGE EmbeddingsVector Database: Qdrant (running locally within a Docker Container)Features:Personal: All technologies run locally on your own system.Upload Documents: Easily upload your PDF documents.Free Embeddings: Run embeddings on your documents with the FREE BGE Embedding model.Chat: Interact with your documents via our intelligent chatbot. Ask questions, summarize documents, and much more.Why Sign Up?Personalized Learning: Hands-on exercises, real-world applications, and guided support.Cutting-Edge Technology: Learn how to work with state-of-the-art models and databases, like Llama 3.2b and Qdrant.Completely Local: Everything runs on your own system, no need to rely on external servers.Interactive Chatbot: Build a functional chatbot that can interact with your documents and provide valuable insights in real-time.Free Tools: Take advantage of the free BGE Embeddings model to enhance your assistant without any cost.Add this to your portfolio AI PROJECTS!Enroll NOW and create your very own personal document assistant system!
Overview
Section 1: AI - Your Personal Document Assistant
Lecture 1 Introduction
Lecture 2 Code files
Lecture 3 The Game Plan
Lecture 4 Create virtual environment and install dependencies
Lecture 5 Create a Streamlit App - Sidebar
Lecture 6 Create home page screen
Lecture 7 Upload a PDF Document
Lecture 8 Preview PDF document on the fly
Lecture 9 Initialize session states and store PDF file locally
Lecture 10 Load Qdrant vector database via Docker
Lecture 11 Create embeddings via BGE model
Lecture 12 Process unstructured PDF, store embeddings in Qdrant
Lecture 13 Load llama3.2 model via Ollama
Lecture 14 Create a chatbot RAG
Lecture 15 Define PromptTemplate, Initiate Qdrant client/vector store
Lecture 16 Create a chatbot
Lecture 17 Setup chatbot with RAG
Section 2: Congratulations and Thank You!
Lecture 18 Your feedback is very valuable!
Anyone who want to explore the world of AI, LLM, ChatBot, Vector Database,Anyone who want to step into Vector Database world with practical learning,Data engineers, database administrators and data professionals curious about the emerging field of vector databases,Software developers interested in integrating vector databases into their applications.
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