Practical Artificial Intelligence Development With Racket
Practical Artificial Intelligence Development With Racket
Using Racket Scheme for implementing many short AI examples including LLMs, vector datastore, NLP, semantic web and non-AI utilities.
About the Book
Using Racket Scheme for implementing many short AI examples including LLMs, vector datastore, NLP, semantic web and non-AI utilities.
This is a live eBook and is frequently updated. There will never be a "second edition" of this live eBook, rather, the free to read web version and PDFs and eBooks for purchasers get updated as new material is added and corrections are made.
License
About the Author
Table of Contents
-
Preface
- License for Book Manuscript: Creative Commons
- Book Example Programs
- Racket, Scheme, and Common Lisp
- Personal Artificial Intelligence Journey: or, Life as a Lisp Developer
- Acknowledgements
-
A Quick Racket Tutorial
- Installing Packages
- Installing Local Packages In Place
- Mapping Over Lists
- Hash Tables
- Racket Structure Types
- Simple HTTP GET and POST Operations
- Using Racket ~/.racketrc Initialization File
- Tutorial Wrap Up
-
Datastores
- Accessing Public RDF Knowledge Graphs - a DBPedia Example
- Sqlite
-
Implementing a Simple RDF Datastore With Partial SPARQL Support in Racket
- 1. Core RDF Data Structures and Basic Operations
- 2. Query Parsing and Execution
- 3. Helper Functions and Utilities
- Conclusion
-
Web Scraping
- Getting Started Web Scraping
- Implementation of a Racket Web Scraping Library
-
Using the OpenAI, Anthropic, Mistral, and Local Hugging Face Large Language Model APIs in Racket
- Introduction to Large Language Models
- Using the OpenAI APIs in Racket
- Using the Anthropic APIs in Racket
- Using a Local Hugging Face Llama2-13b-orca Model with Llama.cpp Server
- Using a Local Mistral-7B Model with Ollama.ai
- Examples Using William J. Bowman’s Racket Language LLM
-
Retrieval Augmented Generation of Text Using Embeddings
- Example Implementation
- Retrieval Augmented Generation Wrap Up
-
Natural Language Processing
- NLP Wrap Up
-
Knowledge Graph Navigator
- Entity Types Handled by KGN
- KGN Implementation
- Knowledge Graph Navigator Wrap Up
- Conclusions
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