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Kdan Live Episode 8: AI Insights and YouTube Summarization

Jun 27, 2024

Kdan Live Episode 8

Introduction

  • Welcome to episode 8 of Kdan Live.
  • Focus: insights on AI and generative AI (like LLMs).
  • Reminder to subscribe to Kdanโ€™s channels on YouTube, LinkedIn, Facebook, and TikTok.

Sections

  1. Ollama: Managing Open-Source Models
  2. Building a YouTube Video Summarization Application

Section 1: Ollama

What is Ollama?

  • Open-source application to manage LLMs (e.g., Llama 3, Gemma, 53).
  • Allows you to download and run LLMs on your machine and host an API.
  • Enables customizing chat sessions and managing LLM configuration files.

Supported Models (as of May 2024)

  • Llama 3, Gemma 53, Mistral, Code Llama
  • Models can be managed through the command line.
  • New in May 2024: enhanced versions of models like Llama 3 Lava (multimodal), Code Gemma, etc.

Key Features

  • Start a chat conversation with the LLM.
  • Q&A on a given text: Supports retrieval augmented generation (RAG).
  • Image description: Lola models support multimodal inputs (text + image).
  • Serve LLM as a local API: Can be hosted locally as an API or Docker container.

Integration and Usage

  • Three main categories for interaction:
    1. Command Line
    2. Python Library
    3. Community Integrations
  • Example commands:
    • ollama list: List downloaded models.
    • ollama pull <model>: Download/update models.
    • ollama remove <model>: Remove models.
    • ollama serve: Serve API locally.

Model File Configuration

  • Model File: Save configurations and parameters for sharing.
  • Example: Setting base model, system prompts, and parameters (e.g., temperature).
  • Allows easy sharing and reloading of configurations.

Section 2: Building a YouTube Video Summarization Application

Overview

  • Using tools like Ollama, Llama 3, Lang chain, and Gradio.
  • Creates a summarizer for YouTube videos using LLMs.

Tools and Libraries

  • pytube: Gets information from a YouTube URL.
  • Lang chain: Framework for building LLM applications.
  • Gradio: Builds user interface.
  • tiktoken: Library to estimate token counts.

Basic Idea

  1. Source Loading: Load text from various sources (e.g., YouTube transcription).
  2. Chunking: Split text into manageable chunks for LLM processing.
  3. Summarization: Use LLM to generate summaries for each chunk.
  4. Aggregation: Combine chunk summaries into a final coherent summary.

Steps for Summarizing Long Text

  1. Break text into chunks: Handle context length limitations.
  2. Create summarization prompts: Crafting chunk-specific prompts.
  3. Map-reduce process: Summarize chunks, then combine the summaries.

Implementation in Lang chain

  • Function: load_summarize_chain()
  • Handles chunking and summarization using models like GPT-3.5, Llama 3, etc.
  • Chain types: stuff, map_reduce, refine.
  • Example implementation replacing the older run with invoke function.

Building the Application

  • Functions to get YouTube descriptions, video info, transcripts, and token counts.
  • Defining LLM and summarization chains using Lang chain and Ollama running locally.
  • Example code of a Gradio interface for practical use.

Potential Improvements

  • Adding input methods for other sources (e.g., PDF, Excel, file uploads).
  • Ability to select models, tweak parameters (e.g., chunk size, overlap), and add new functionalities.
  • Improving summarization depth and quality, particularly for detailed content like lectures.

Conclusion

  • Summarized the capabilities of Ollama and Lang chain for building practical AI applications.
  • Discussed usage scenarios and customization options.
  • Encouraged subscription to Kdan's channels for updates and more content.