Skip to content

Latest commit

 

History

History
102 lines (76 loc) · 2.96 KB

README.md

File metadata and controls

102 lines (76 loc) · 2.96 KB

LLM Tutorials and Learning Projects

This repository contains tutorials and learning projects to experiment with concepts in Large Language Models (LLMs). All projects are implemented in Jupyter Notebooks.

Tutorials and Projects

1. Introduction to LLMs

Notebook: introduction_to_llms.ipynb

  • Objective: Learn how to use the ChatGPT API through practice tasks.
  • Tasks:
    • Simple Translation: A basic translation task.
    • Controlling Outputs from Few-Shot Learning: Experiment with guiding LLM outputs using few-shot examples.

2. Basic RAG Pipeline

Notebook: basic_rag_pipeline.ipynb

  • Objective: Build a simple Retrieval-Augmented Generation (RAG) pipeline.
  • Steps:
    • Downloaded an article and performed text chunking.
    • Converted text into embeddings using OpenAI.
    • Retrieved embeddings using cosine similarity stored in a DataFrame.
    • Performed inference using:
      • Google GenAI (gemini-1.5-flash)
      • OpenAI (gpt-3.5-turbo)

3. LangChain Application

Notebook: langchain_application.ipynb

  • Objective: Explore simple LangChain applications.
  • Tasks:
    • "Hello World" level inferencing using HumanMessage Prompt templating with OpenAI (gpt-3.5-turbo).
    • Summarization of a PDF document using PyPDFLoader.

4. News Article Summarizer

Notebook: news_article_summarizer_langchain.ipynb

  • Objective: Summarize news articles using LangChain.
  • Steps:
    • Scraped news articles.
    • Used HumanMessage prompt templating from LangChain.
    • Generated bullet-point summaries with proper prompts.

News-Summarizer

5. Llama Index Overview

Notebook: llama_index_overview.ipynb

  • Objective: Understand and utilize LlamaIndex.
  • Steps:
    • Used download_loader for WikipediaReader.
    • Loaded and analyzed data with NLP and AI tools.

6. Customer Support QA Chatbot

Notebook: customer_support_qa_chatbot.ipynb

  • Objective: Build a chatbot for customer support.
  • Steps:
    • Downloaded articles using SeleniumURLLoader.
    • Stored data in a vector storage database using FAISS.
    • Queried the vector datastore and formatted output with PromptTemplate.

Customer Support QA Chatbot

7. YouTube Video Summarizer

Notebook: youtube_video_summarizer.ipynb

  • Objective: Summarize YouTube videos using Whisper and LangChain.
  • Steps:
    • Downloaded and transcribed YouTube videos using Whisper.
    • Summarized content using SummarizationChain.
    • Split content into chunks, converted to embeddings, and stored in a vector database.
    • Prepared prompts and queried the database.
    • Generated summaries using RetrievalQA.

YouTube Video Summarizer

How to Use

  1. Clone this repository: 
    git clone <repository_url>
cd <repository_name>
  2. Open the notebooks with Jupyter: 
    jupyter notebook