The following command installs packages in bulk according to the configuration file, requirements.txt. In some environments, use pip3 instead of pip.
$ pip install -r requirements.txt The configuration file can be named arbitrarily, though requirements.txt is commonly used. Place requirements.txt in the directory where you plan to run the command. If the file is in a different directory, specify its path, for example, path/to/requirements.txt.
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.
This is the code repository for Building LLM Powered Application, Published by Packt and updated to use it with python and news functions call (langchain, lanceDB,..)
Create intelligent apps and agents with large language models
- 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
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.