This repository provides code examples and resources for the book "Building LLM Powered Applications," aimed at AI practitioners and developers. It guides users through creating intelligent applications and agents using Large Language Models (LLMs), covering foundational concepts, architectural frameworks, and practical implementation with tools like LangChain and Streamlit.

How It Works

The book and its associated code explore LLM architecture, including encoder-decoder blocks and embeddings, and demonstrate the use of proprietary (GPT 3.5/4) and open-source models (Falcon LLM, Llama 2). It emphasizes practical application development using the LangChain framework for orchestrating LLMs, memory, prompts, and tools, and integrates with vector databases for non-parametric knowledge retrieval. The content also touches upon Large Foundation Models (LFMs) and multimodal applications.

Quick Start & Requirements

• Installation: Primarily Python-based.
• Prerequisites: Python (versions not specified, but likely 3.7+). Chapters include .ipynb files, suggesting Jupyter Notebooks or similar environments.
• Resources: Chapters 4-11 require Python. Specific hardware is listed as "Suitable."
• Links:
  • Python Download: https://www.python.org/downloads/
  • Colab, Kaggle, Gradient, Studio Lab links are indicated in the chapter table but not directly provided in the README.

Highlighted Details

• Covers both proprietary (GPT 3.5/4) and open-source LLMs (Falcon LLM, Llama 2).
• Focuses on practical application development using the LangChain framework.
• Includes chapters on prompt engineering, embeddings, conversational apps, and multimodal applications.
• Discusses fine-tuning LLMs and responsible AI practices.

Maintenance & Community

The repository is associated with Packt Publishing and the author, Valentina Alto, an AI App Tech Architect at Microsoft. No specific community links (Discord, Slack) or roadmap are provided in the README.

Licensing & Compatibility

The repository itself is not explicitly licensed in the README. The code examples are intended to support a published book, implying a permissive license for the code itself, but users should verify any specific licensing terms. Compatibility for commercial use would depend on the licenses of the underlying LLMs and libraries used.

Limitations & Caveats

The README does not specify Python version requirements beyond general availability, nor does it detail specific hardware needs beyond "Suitable." Links to Colab/Kaggle/etc. are mentioned but not directly provided. The errata notes a correction in Chapter 1, indicating potential for minor inaccuracies in early versions.