This is the code repository for Building LLM Powered Application, Published by Packt.
Create intelligent apps and agents with large language models
The book provides a solid theoretical foundation of what LLMs are, their architecture. With a hands-on approach we provide readers with a step-by-step guide to implementing LLM-powered apps for specific tasks and using powerful frameworks like LangChain.
- Explore the core components of LLM architecture, including encoder-decoder blocks and embeddings
- Understand the unique features of LLMs like GPT-3.5/4, Llama 2, and Falcon LLM
- Use AI orchestrators like LangChain, with Streamlit for the frontend
- Get familiar with LLM components such as memory, prompts, and tools
- Learn how to use non-parametric knowledge and vector databases
- Understand the implications of LFMs for AI research and industry applications
- Customize your LLMs with fine tuning
- Learn about the ethical implications of LLM-powered applications
- Introduction to Large Language Models
- LLMs for AI-Powered Applications
- Choosing an LLM for Your Application
- Prompt Engineering
- Embedding LLMs within Your Applications
- Building Conversational Applications
- Search and Recommendation Engines with LLMs
- Using LLMs with Structured Data
- Working with Code
- Building Multimodal Applications with LLMs
- Fine-Tuning Large Language Models
- Responsible AI
- Emerging Trends and Innovations
You can run the notebooks directly from the table below:
| Chapters | Colab | Kaggle |
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| Chapter 4: Prompt Engineering | ||
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| Chapter 5: Embedding LLMs within your Applications | ||
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| Chapter 6: Building Conversational Applications | ||
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| Chapter 7: Search and Recommendation Engines with LLMs | ||
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| Chapter 8: Using LLMs with Structured Data | ||
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| Chapter 9: Working with Code | ||
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| Chapter 10: Building Multimodal Applications with LLMs | ||
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| Chapter 11: Fine-Tuning Large Language Models | ||
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If you feel this book is for you, get your copy today!
With the following software and hardware list you can run all code files present in the book.
| Chapter | Software required | Link to the software | Hardware specifications | OS required |
|---|---|---|---|---|
| 4-11 | Python | Download | Suitable | Windows/Linux/MacOS |
- Page 8, Chapter 1 : P(“table”), P(“chain”), and P(“roof”) are the prior probabilities for each candidate word, based on the language model’s knowledge of the frequency of these words in the training data. Correction: P(“table”), P(“chair”), and P(“roof”) are the prior probabilities for each candidate word, based on the language model’s knowledge of the frequency of these words in the training data.
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We also provide a PDF file that has color images of the screenshots/diagrams used in this book at Color Images
After completing her bachelor's degree in finance, Valentina Alto pursued a master's degree in data science in 2021. She began her professional career at Microsoft as an Azure Solution Specialist, and since 2022, she has been primarily focused on working with Data & AI solutions in the Manufacturing and Pharmaceutical industries. Valentina collaborates closely with system integrators on customer projects, with a particular emphasis on deploying cloud architectures that incorporate modern data platforms, data mesh frameworks, and applications of Machine Learning and Artificial Intelligence.