Book on how to build a chat with LLMs locally and Jan IA or LM Studio and Connect to Python

Tired of relying on cloud services, variable monthly bills, and worried about the privacy of your data? It's time to take absolute control!

Learning how to connect a Local AI (local LLM) using Python was one of the most revolutionary milestones in my personal technical workflow. With this practical guide, you will learn how to transform your own computer into a powerful Artificial Intelligence laboratory, 100% private and operating without an internet connection.

"Imagine having your own ChatGPT-like assistant, trained with your confidential documents, working without sending a single byte of information outside your physical machine. This book will guide you step by step to achieve it, regardless of whether you work on Windows, macOS, or Linux."

Why run Artificial Intelligence Locally today?

The traditional cloud model has two major pain points: the recurring cost of subscriptions/tokens and the immense risk of leaking private corporate information. By running local models using Jan AI or LM Studio, you completely eliminate both problems. Your computer processes all the graphical inference using your own CPU and GPU cores. You don't require external API keys, you don't pay for each question asked, and you guarantee that confidential documents never leave your local hard drive.

The Ecosystem: What do you need to master first?

The Decision in Inference: Which local tool should you learn to use?

The "Pro Approach": Cloud API vs Local Compatible Integration

The usual mistake made by those starting to experiment with AI is to rely directly on consuming paid endpoints from external clouds, risking information leaks and raising costs. Senior programmers set up local servers and instantiate standardized clients compatible with the OpenAI API but redirected to localhost:

# WRONG: Coupled to the cloud, paying for tokens 
# and sending sensitive data outside the PC
import requests

response = requests.post(
    "https://api.openai.com/v1/chat/completions",
    headers={"Authorization": "Bearer key_secreta_cloud"},
    json={
        "model": "gpt-4",
        "messages": [{"role": "user", "content": "Analizar datos privados"}]
    }
)
print(response.json()["choices"][0]["message"]["content"])

# RIGHT: OpenAI-compatible client redirected
# to your private local server (Jan on port 1337)
from openai import OpenAI

client = OpenAI(
    base_url="http://localhost:1337/v1", 
    api_key="not-needed" # 100% free and unlimited
)

completion = client.chat.completions.create(
    model="mistral-7b-instruct",
    messages=[
        {"role": "system", "content": "Responder de forma local y privada"},
        {"role": "user", "content": "Analizar datos de mi empresa"}
    ]
)
print(completion.choices[0].message.content)

You will learn to build **robust architectures** that disconnect your web applications from any external cost, processing inference on your own hardware flawlessly.

Your Structured Roadmap towards Development with Local LLMs

This manual proposes a flawless technical progression, designed to build from your initial model laboratory to a commercial interactive application:

With this practical guide, you will master:

• Local Installation: Learn to install and configure open-source tools like Jan.AI and LM Studio to run language models (LLMs) directly on your PC.
• Choosing the Perfect Model: Discover the differences between models like Gemma, Llama, and Mistral, and understand what sizes (4B, 8B, 12B) mean to choose the ideal one based on your hardware power.
• Creating Custom Assistants: We teach you how to give precise instructions (prompts) to your AI so it specializes in specific tasks, such as generating questions and answers from your own texts.
• Development of Your Own Chat App: Let's program! Together we will build a web application with Python and Flask that connects to your local LLM, allowing you to chat with your AI through a friendly interface.
• Image and Context Analysis: Take your AI to the next level by learning how to send it images for analysis and how to configure the "system role" so it remembers the conversation context and always responds in your preferred language.