Name: Building AI Agents with LLMs, RAG, and Knowledge Graphs
Author: Salvatore Raieli Gabriele Iuculano
ISBN: 9781835080382

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Building AI Agents with LLMs, RAG, and Knowledge GraphsSalvatore Raieli Gabriele Iuculano

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Building AI Agents with LLMs, RAG, and Knowledge Graphs

Salvatore Raieli Gabriele Iuculano

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文前
Contributors About the authors
About the reviewers
Preface
Who this book is for
What this book covers
To get the most out of this book
Download the example code files
Conventions used
Get in touch
Share Your Thoughts
Download a free PDF copy of this book
Part 1 The AI Agent Engine: From Text to Large Language Models
1 Analyzing Text Data with Deep Learning
Technical requirements
Representing text for AI
One-hot encoding
Bag-of-words
TF-IDF
Embedding, application, and representation
Word2vec
A notion of similarity for text
Properties of embeddings
RNNs, LSTMs, GRUs, and CNNs for text
RNNs
LSTMs
GRUs
CNNs for text
Performing sentiment analysis with embedding and deep learning
Summary
2 The Transformer: The Model Behind the Modern AI Revolution
Technical requirements
Exploring attention and self-attention
Introducing the transformer model
Training a transformer
Exploring masked language modeling
Visualizing internal mechanisms
Applying a transformer
Summary
3 Exploring LLMs as a Powerful AI Engine
Technical requirements
Discovering the evolution of LLMs
The scaling law
Emergent properties
Context length
Mixture of experts
Instruction tuning, fine-tuning, and alignment
Exploring smaller and more efficient LLMs
Exploring multimodal models
Understanding hallucinations and ethical and legal issues
Prompt engineering
Summary
Further reading
Part 2 AI Agents and Retrieval of Knowledge
4 Building a Web Scraping Agent with an LLM
Technical requirements
Understanding the brain, perception, and action paradigm
The brain
The perception
Action
Classifying AI agents
Understanding the abilities of single-agent and multiple-agent systems
Exploring the principal libraries
LangChain
Haystack
LlamaIndex
Semantic Kernel
AutoGen
Choosing an LLM agent framework
Creating an agent to search the web
Summary
Further reading
5 Extending Your Agent with RAG to Prevent Hallucinations
Technical requirements
Exploring naïve RAG
Retrieval, optimization, and augmentation
Chunking strategies
Embedding strategies
Embedding databases
Evaluating the output
Comparison between RAG and fine-tuning
Using RAG to build a movie recommendation agent
Summary
Further reading
6 Advanced RAG Techniques for Information Retrieval and Augmentation
Technical requirements
Discussing naïve RAG issues
Exploring the advanced RAG pipeline
Hierarchical indexing
Hypothetical questions and HyDE
Context enrichment
Query transformation
Keyword-based search and hybrid search
Query routing
Reranking
Response optimization
Modular RAG and its integration with other systems
Training and training-free approaches
Implementing an advanced RAG pipeline
Understanding the scalability and performance of RAG
Data scalability, storage, and preprocessing
Parallel processing
Security and privacy
Open questions and future perspectives
Summary
Further reading
7 Creating and Connecting a Knowledge Graph to an AI Agent
Technical requirements
Introduction to knowledge graphs
A formal definition of graphs and knowledge graphs
Taxonomies and ontologies
Creating a knowledge graph with your LLM
Knowledge creation
Creating a knowledge graph with an LLM
Knowledge assessment
Knowledge cleaning
Knowledge enrichment
Knowledge hosting and deployment
Retrieving information with a knowledge graph and an LLM
Graph-based indexing
Graph-guided retrieval
GraphRAG applications
Understanding graph reasoning
Knowledge graph embeddings
Graph neural networks
LLMs reasoning on knowledge graphs
Ongoing challenges in knowledge graphs and GraphRAG
Summary
Further reading
8 Reinforcement Learning and AI Agents
Technical requirements
Introduction to reinforcement learning
The multi-armed bandit problem
Markov decision processes
Deep reinforcement learning
Model-free versus model-based approaches
On-policy versus off-policy methods
Exploring deep RL in detail
Q-learning and Deep Q-Network (DQN)
The REINFORCE algorithm
Proximal Policy Optimization (PPO)
The actor-critic algorithm
AlphaZero
Challenges and future direction for deep RL
Learning how to play a video game with reinforcement learning
Description of the scripts
Setting up the environment
Defining the model
Training the model
Testing the system
Connecting all the components
LLM interactions with RL models
RL-enhanced LLMs
LLM-enhanced RL
Information processor
Reward designer
Decision-maker
Generator
Key takeaways
Summary
Further reading
Part 3 Creating Sophisticated AI to Solve Complex Scenarios
9 Creating Single- and Multi-Agent Systems
Technical requirements
Introduction to autonomous agents
Toolformer
HuggingGPT
Task planning
Model selection
Model execution
Response generation
HuggingGPT limitations
ChemCrow
SwiftDossier
ChemAgent
Multi-agent for law
Multi-agent for healthcare applications
Working with HuggingGPT
Using HuggingGPT locally
Using HuggingGPT on the web
Multi-agent system
SaaS, MaaS, DaaS, and RaaS
Software as a Service (SaaS)
Model as a Service (MaaS)
Data as a Service (DaaS)
Results as a Service (RaaS)
A comparison of the different paradigms
Summary
Further reading
10 Building an AI Agent Application
Technical requirements
Introduction to Streamlit
Starting with Streamlit
Caching the results
Developing our frontend with Streamlit
Adding the text elements
Inserting images in a Streamlit app
Creating a dynamic app
Creating an application with Streamlit and AI agents
Machine learning operations and LLM operations
Model development
Model training
Model testing
Inference optimization
Model inference optimization
Data, pipeline, and tensor parallelism
Handling errors in production
Security considerations for production
Managing secrets in production
Data exposure prevention
Securing your deployment environment
Asynchronous programming
asyncio
Asynchronous programming and ML
Docker
Kubernetes
Docker with ML
Summary
Further reading
11 The Future Ahead
AI agents in healthcare
Biomedical AI agents
AI agents in other sectors
Physical agents
LLM agents for gaming
Web agents
Challenges and open questions
Challenges in human-agent communication
No clear superiority of multi-agents
Limits of reasoning
Creativity in LLM
Mechanistic interpretability
The road to artificial general intelligence
Ethical questions
Summary
Further reading
Index
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Building AI Agents with LLMs, RAG, and Knowledge Graphs