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 Kdan’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.