Building LLM Powered Applications: Create intelligent apps and agents with large language models

Cover
Copyright
Contributors
Table of Contents
Preface
Chapter 1: Introduction to Large Language Models
	What are large foundation models and LLMs?
		AI paradigm shift – an introduction to foundation models
		Under the hood of an LLM
	Most popular LLM transformers-based architectures
		Early experiments
		Introducing the transformer architecture
	Training and evaluating LLMs
		Training an LLM
		Model evaluation
	Base models versus customized models
		How to customize your model
	Summary
	References
Chapter 2: LLMs for AI-Powered Applications
	How LLMs are changing software development
	The copilot system
	Introducing AI orchestrators to embed LLMs into applications
		The main components of AI orchestrators
		LangChain
		Haystack
		Semantic Kernel
		How to choose a framework
	Summary
	References
Chapter 3: Choosing an LLM for Your Application
	The most promising LLMs in the market
		Proprietary models
			GPT-4
			Gemini 1.5
			Claude 2
		Open-source models
			LLaMA-2
			Falcon LLM
			Mistral
	Beyond language models
	A decision framework to pick the right LLM
		Considerations
		Case study
	Summary
	References
Chapter 4: Prompt Engineering
	Technical requirements
	What is prompt engineering?
	Principles of prompt engineering
		Clear instructions
		Split complex tasks into subtasks
		Ask for justification
		Generate many outputs, then use the model to pick the best one
		Repeat instructions at the end
		Use delimiters
	Advanced techniques
		Few-shot approach
		Chain of thought
		ReAct
	Summary
	References
Chapter 5: Embedding LLMs within Your Applications
	Technical requirements
	A brief note about LangChain
	Getting started with LangChain
		Models and prompts
		Data connections
		Memory
		Chains
		Agents
	Working with LLMs via the Hugging Face Hub
		Create a Hugging Face user access token
		Storing your secrets in an .env file
		Start using open-source LLMs
	Summary
	References
Chapter 6: Building Conversational Applications
	Technical requirements
	Getting started with conversational applications
		Creating a plain vanilla bot
		Adding memory
		Adding non-parametric knowledge
		Adding external tools
	Developing the front-end with Streamlit
	Summary
	References
Chapter 7: Search and Recommendation Engines with LLMs
	Technical requirements
	Introduction to recommendation systems
	Existing recommendation systems
		K-nearest neighbors
		Matrix factorization
		Neural networks
	How LLMs are changing recommendation systems
	Implementing an LLM-powered recommendation system
		Data preprocessing
		Building a QA recommendation chatbot in a cold-start scenario
		Building a content-based system
	Developing the front-end with Streamlit
	Summary
	References
Chapter 8: Using LLMs with Structured Data
	Technical requirements
	What is structured data?
	Getting started with relational databases
		Introduction to relational databases
		Overview of the Chinook database
		How to work with relational databases in Python
	Implementing the DBCopilot with LangChain
		LangChain agents and SQL Agent
		Prompt engineering
		Adding further tools
	Developing the front-end with Streamlit
	Summary
	References
Chapter 9: Working with Code
	Technical requirements
	Choosing the right LLM for code
	Code understanding and generation
		Falcon LLM
		CodeLlama
		StarCoder
	Act as an algorithm
	Leveraging Code Interpreter
	Summary
	References
Chapter 10: Building Multimodal Applications with LLMs
	Technical requirements
	Why multimodality?
	Building a multimodal agent with LangChain
	Option 1: Using an out-of-the-box toolkit for Azure AI Services
		Getting Started with AzureCognitiveServicesToolkit
			Setting up the toolkit
			Leveraging a single tool
			Leveraging multiple tools
			Building an end-to-end application for invoice analysis
	Option 2: Combining single tools into one agent
		YouTube tools and Whisper
		DALL·E and text generation
		Putting it all together
	Option 3: Hard-coded approach with a sequential chain
	Comparing the three options
	Developing the front-end with Streamlit
	Summary
	References
Chapter 11: Fine-Tuning Large Language Models
	Technical requirements
	What is fine-tuning?
	When is fine-tuning necessary?
	Getting started with fine-tuning
		Obtaining the dataset
		Tokenizing the data
		Fine-tuning the model
		Using evaluation metrics
		Training and saving
	Summary
	References
Chapter 12: Responsible AI
	What is Responsible AI and why do we need it?
	Responsible AI architecture
		Model level
		Metaprompt level
		User interface level
	Regulations surrounding Responsible AI
	Summary
	References
Chapter 13: Emerging Trends and Innovations
	The latest trends in language models and generative AI
		GPT-4V(ision)
		DALL-E 3
		AutoGen
		Small language models
	Companies embracing generative AI
		Coca-Cola
		Notion
		Malbek
		Microsoft
	Summary
	References
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