LLM Chat Interface & Document Ingestion: Chapter 18

Weights & Biases · Intermediate ·🧠 Large Language Models ·2y ago

Key Takeaways

Develops a baseline Large Language Model application with an interactive chat interface and document ingestion

Original Description

🤖 Dive into LLM Chat App Development - Join in our guide to building a baseline Large Language Model application with an interactive chat interface. 🧑🏾‍🎓 Full course with certification and class materials available free at http://wandb.me/building-llm-powered-apps 🏆 Daily swag draw and grand prize Airpods draw from Dec 1 and 31, 2023. Details at http://wandb.me/llm-apps-contest 🗣️ Join the course conversation on our Discord channel at http://wandb.me/course-discord *Episode Description* In this chapter of our "Building LLM-Powered Apps" course, offered by Weights & Biases, we embark on the practical phase of building a baseline Large Language Model (LLM) application. Darek Kleczek, Machine Learning Engineer at Weights & Biases, guides you through the critical steps of ingesting documents into a vector store for an interactive chat interface. 🌟 Chapter Highlights Constructing a Chat Interface: Learn the initial steps of building a baseline application with a chat interface for user interaction. Document Ingestion Process: Explore the detailed process of loading, chunking, and embedding documents for efficient retrieval in LLM applications. Utilizing Langchain and Chroma: See how Langchain and Chroma DB vector store are used for processing and storing embeddings. Version Control with Weights & Biases: Understand the importance of version controlling your documents and prompts using Weights & Biases artifacts. Practical Implementation: Follow the hands-on approach to run the ingestion script, specifying parameters like document directory, chunk size, and overlap. 🎓 Enroll for Free: Join us on this educational journey to master the art of building LLM-powered applications. Enroll at http://wandb.me/building-llm-powered-apps. 👉 Next Chapter Sneak Peek: Don't miss our next chapter, where we delve deeper into the enhancement and optimization of LLM applications.
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1 0. What is machine learning?
0. What is machine learning?
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 2 1. Build Your First Machine Learning Model
1. Build Your First Machine Learning Model
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 3 Intro to ML: Course Overview
Intro to ML: Course Overview
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 4 2. Multi-Layer Perceptrons
2. Multi-Layer Perceptrons
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 5 3. Convolutional Neural Networks
3. Convolutional Neural Networks
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 6 Weights & Biases at OpenAI
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 7 Why Experiment Tracking is Crucial to OpenAI
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 8 4. Autoencoders
4. Autoencoders
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 9 5. Sentiment Analysis
5. Sentiment Analysis
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 10 6. Recurrent Neural Networks [RNNs]
6. Recurrent Neural Networks [RNNs]
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 11 7. Text Generation using LSTMs and GRUs
7. Text Generation using LSTMs and GRUs
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 12 8. Text Classification Using Convolutional Neural Networks
8. Text Classification Using Convolutional Neural Networks
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 13 9. Hybrid LSTMs [Long Short-Term Memory]
9. Hybrid LSTMs [Long Short-Term Memory]
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 14 Toyota Research Institute on Experiment Tracking with Weights & Biases
Toyota Research Institute on Experiment Tracking with Weights & Biases
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 15 Weights and Biases - Developer Tools for Deep Learning
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 16 Introducing Weights & Biases
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 17 10. Seq2Seq Models
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 18 11. Transfer Learning for Domain-Specific Image Classification with Small Datasets
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 19 12. One-shot learning for teaching neural networks to classify objects never seen before
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 20 13. Speech Recognition with Convolutional Neural Networks in Keras/TensorFlow
13. Speech Recognition with Convolutional Neural Networks in Keras/TensorFlow
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 21 14. Data Augmentation | Keras
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 22 15. Batch Size and Learning Rate in CNNs
15. Batch Size and Learning Rate in CNNs
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 23 Applied Deep Learning Fellowship Overview and Project Selection with Josh Tobin (2019)
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 24 Grading Rubric for AI Applications with Sergey Karayev (2019)
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 27 17. Build and Deploy an Emotion Classifier (2019)
17. Build and Deploy an Emotion Classifier (2019)
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 28 Applied Deep Learning - Data Management with Josh Tobin (2019)
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 29 Snorkel: Programming Training Data with Paroma Varma of Stanford University (2019)
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 30 Applied Deep Learning - Troubleshooting and Debugging with Josh Tobin (2019)
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 31 Troubleshooting and Iterating ML Models with Lee Redden (2019)
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 32 Designing a Machine Learning Project with Neal Khosla (2019)
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 33 Lukas Beiwald on ML Tools and Experiment Management (2019)
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 34 Building Machine Learning Teams with Josh Tobin (2019)
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 35 Pieter Abeel on Potential Deep Learning Research Directions (2019)
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 36 Testing and Deployment of Deep Learning Models with Josh Tobin (2019)
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 37 Five Lessons for Team-Oriented Research with Peter Welder (2019)
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 38 Applied Deep Learning - Rosanne Liu on AI Research (2019)
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 39 Making the Mid-career Leap from Urban Design to Deep Learning/Data Science
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 40 Organizing ML projects — W&B walkthrough (2020)
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 41 Brandon Rohrer — Machine Learning in Production for Robots
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 42 Nicolas Koumchatzky — Machine Learning in Production for Self-Driving Cars
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 43 My experiments with Reinforcement Learning with Jariullah Safi
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 44 Applications of Machine Learning to COVID-19 Research with Isaac Godfried
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 45 Testing Machine Learning Models with Eric Schles
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 46 How Linear Algebra is not like Algebra with Charles Frye
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 47 Predicting Protein Structures using Deep Learning with Jonathan King
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 48 Rachael Tatman — Conversational AI and Linguistics
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 49 Reformer by Han Lee
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 50 Sequence Models with Pujaa Rajan
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 51 GitHub Actions & Machine Learning Workflows with Hamel Husain
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 52 Look Mom, No Indices! Vector Calculus with the Fréchet Derivative by Charles Frye
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 53 Jack Clark — Building Trustworthy AI Systems
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 54 Surprising Utility of Surprise: Why ML Uses Negative Log Probabilities - Charles Frye
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 55 Track your machine learning experiments locally, with W&B Local - Chris Van Pelt
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 56 Antipatterns in open source research code with Jariullah Safi
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 57 Attention for time series forecasting & COVID predictions - Isaac Godfried
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