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Gerbil Scheme in Action

AI Applications, Network Programming, and Utilities

This book is 100% completeLast updated on 2026-05-11
Mark Watson

Lisp languages are a secret weapon! Use the modern Gerbil Scheme for AI and other applications.

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About

About

About the Book

Gerbil Scheme is a practical modern Lisp language that is used in this book for Large Language Model (LLMs) and other AI projects, network programming, and for writing utilities.

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Author

About the Author

Mark Watson

Mark Watson is a consultant specializing in LLMs, deep learning, machine learning, knowledge graphs, and general artificial intelligence software development. He uses Common Lisp, Clojure, Python, Java, Haskell, and Ruby for development.

He is the author of 20+ published books on Artificial Intelligence, Deep Learning, Java, Ruby, Machine Learning, Common LISP, Clojure, JavaScript, Semantic Web, NLP, C++, Linux, and Scheme. He has 55 US Patents.

Mark's consulting customer list includes: Google, Capital One, Olive AI, CompassLabs, Disney, Sitescout.com, Embed.ly, and Webmind Corporation.

Mark wrote ten traditional published books for McGraw Hill, Springer Verlag, J Wiley, and Morgan Kaufman publishers before adopting the LeanPub self-publishing platform.

Leanpub Podcast

Episode 253

An Interview with Mark Watson

Contents

Table of Contents

Preface

  1. A Comment on Licenses
  2. Running Gerbil Scheme on macOS and Linux
  3. History and Background to the Gerbil Scheme Project
  4. Core Gerbil Toolkits Used in this Book: :std/net/request and :std/text/json
  5. Suppressing or Fixing Compilation Warning Messages on macOS

Setting Up Gerbil Scheme Development Environment

  1. Emacs Configuration

Google Gemini API

  1. Example Code
  2. Example Output

Ollama

  1. Example Code
  2. Install Ollama and Pull a Model to Experiment With
  3. Example Output
  4. Tool/Function Calling with Ollama
  5. Defining Tools: example_tools.ss
  6. The Agent Loop: use_tools.ss
  7. Tool Calling Example Output
  8. Building Your Own Tools

OpenAI API

  1. Example Code
  2. Example Output

Inexpensive and Fast LLM Inference Using the Groq Service

  1. Structure of Project and Build Instructions
  2. groq_inference.ss Utility
  3. Example scripts: kimi2.ss and gpt-oss-120b.ss

Wikidata API Using SPARQL Queries

  1. Example Code
  2. Example Output

Code for Natural Language Processing (NLP)

  1. Structure of Project and Build Instructions
  2. Top Level Project Code
  3. Other Source Files
  4. Test Run:
  5. Building and Running the Command Line Tool

Gerbil Scheme FFI Example Using the C Language Raptor RDF Library

  1. Implementation of a FFI Bridge Library for Raptor
  2. Test Code
  3. Build System

Complete FFI Example: C Language Wrapper for Rasqal SPARQL Library and Sord RDF Datastore Library

  1. Library Selection
  2. Overview of the Project Structure and Build System
  3. Implementation of the C Language Wrapper
  4. A Gerbil Scheme Shim to Call The C Language Wrapper Code
  5. Gerbil Scheme Main Program for this Example
  6. Example Output

WebKit Applications - macOS Only

  1. Architecture Overview
  2. Prerequisites and Building
  3. Project Structure
  4. The C Shim
  5. Gambit FFI Bindings
  6. The Bridge: JS to Scheme Communication
  7. High-Level API
  8. Example 1: Hello World
  9. Example 2: Counter App with Bridge
  10. Example 3: Markdown File Viewer
  11. The Build System
  12. Comparing Common Lisp and Gerbil Versions
  13. API Reference Summary
  14. Key Takeaways

A Simple In-Memory RDF Store and Query Language in Gerbil Scheme

  1. Introduction to RDF and the Goal of This Chapter
  2. Data Model: Triples and the Store
  3. Parsing SPARQL Queries
  4. Query Evaluation: Pattern Matching and Joining
  5. Gerbil RDF Implementation
  6. Running the Demo and Using the Code
  7. Summary and Further Directions

Introduction To Writing Command Line Utilities

  1. Overview of the Command Line Utilities Project Structure and Build System
  2. Simple Structure for Command Line Utilities
  3. A More Flexible Structure for Command Line Utilities
  4. Wrap Up for Writing Command Line Utilities in Gerbil Scheme and Notes On User Built Libraries

Command Line Applications For NLP

  1. Re-using the NLP Library For a Command Line Utility to Identify Categories or Topics in Input Text
  2. Using OpenAI’s GPT-5 model To Summarize Input Text

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