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Book Overview & Buying
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Table Of Contents
Harnessing Ollama - Create Secure Local LLM Solutions with Python
By :
Harnessing Ollama - Create Secure Local LLM Solutions with Python
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Overview of this book
Discover how to deploy and harness the power of local LLMs with Ollama in this hands-on course. You'll begin by setting up and configuring Ollama on your system, gaining full control of large language models without relying on the cloud. Learn how to optimize resources, explore model parameters, and test various LLMs, including multimodal models like Llava, for text, vision, and code-generation tasks.
Delve deeper into customizing models with the Modelfile and command-line tools to meet specific needs. The course covers executing terminal commands for monitoring, troubleshooting, and deploying models. You'll also integrate Ollama models with Python, leveraging its library and OpenAI API compatibility to build interactive applications. Advanced modules guide you through creating Retrieval-Augmented Generation (RAG) systems using LangChain, embedding databases, and querying capabilities for enhanced performance.
As you progress, you'll set up ChatGPT-like interfaces for seamless model interaction and explore advanced workflows like function calling and voice-enabled RAG systems. By the end, you’ll master Ollama’s ecosystem, equipping you with the skills to build secure, private, and highly efficient LLM-based applications that can operate independently of cloud services.
Table of Contents (10 chapters)
Development Environment Setup
Ollama Deep Dive - Introduction to Ollama and Setup
Ollama CLI Commands and the REST API - Hands-on
Ollama - User Interfaces for Ollama Models
Ollama Python Library - Using Python to Interact with Ollama Models
Building LLM Applications with Ollama Models
Ollama Tool Function Calling - Hands-on
Final RAG System with Ollama and Voice Response
Wrap Up
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