Crea tu Propio Sistema de IA GRATIS y en LOCAL

How to Use AI Locally and for Free

Introduction to Local AI Solutions

• The video discusses how to utilize artificial intelligence (AI) without incurring costs, focusing on local implementations that allow for project development, agent creation, and automation.

• Four essential components are required: a local language model (LLM), software for automation, a method for document handling with agents, and storage solutions.

Language Model Selection

• The chosen LLM is Olama, which enables the execution of open-source models locally. Notable models include Meta's Llama 3.2 and 3.1.

• Users can access various models through Olama; it also provides embeddings necessary for working with documents in AI applications.

Understanding Embeddings

• Embeddings convert documents into vectors (numerical representations), allowing AI systems to process text effectively.

• Each word in a document is associated with a vector; this transformation is crucial as AI cannot directly interpret raw text.

• To upload documents into an AI system, embeddings are essential since they translate the content into a format the model understands.

Software for Automation

• N8n is recommended as the software tool because it facilitates local agent creation and automation without costs.

• Document integration with AI enhances functionality; thus, using vector databases like Krant allows efficient storage of embeddings created from documents.

Vector Database Utilization

• Krant is highlighted as a high-performance vector search solution suitable for managing embeddings generated by open-source models.

Storage Solutions Using Docker

• While data could be stored directly on personal computers, using Docker offers significant advantages such as environment isolation and ease of transfer between devices.

Installation and Setup of AI Tools

Downloading and Initial Setup

• Users are instructed to download the necessary software for MAC, Windows, or Linux. After creating an account, they will encounter a blank interface since the program has not been installed yet.

Installing Docker and Required Tools

• The installation process involves downloading Docker, Olama, n8n, and Kudr. The n8n team provides a GitHub repository with a self-hosted starter kit that simplifies this process.

Cloning the Repository

• To begin setup, users must clone the repository using git clone. If Git is not installed, users can easily download it from the official site.

Running Docker Commands

• After cloning the repository, users navigate to the self-hosted AI starter kit folder. They will execute specific Docker commands based on their operating system (NVIDIA Mac/Windows).

Installation Duration and Initial Configuration

• The first installation may take considerable time (up to 50 minutes), as it requires downloading large AI models. Once completed, users can access n8n through localhost.

Creating Workflows in n8n

Account Creation in n8n

• Upon accessing n8n for the first time, users need to create an account. It's crucial to remember this account name for future logins.

Starting a New Workflow

• Users are guided to start a new workflow by adding a chat message step and connecting it to an AI agent via Advanced AI options.

Installing Models in Olama

Accessing Installed Models

• Within Olama's interface, users can view installed models like Llama 3.1. Instructions are provided on how to install additional models if needed.

Executing Model Installations

• To install new models such as Llama 3.2, users copy provided commands into their terminal within Docker for execution.

Embedding Installation Requirements

Importance of Embeddings

Installation and Usage of Nomic Embeddings

Overview of Nomic Embeddings

• The speaker discusses various types of embeddings, recommending the "mxb" as a commonly used option within the Olama framework.

• To install the recommended embedding, users need to execute a specific command in their container setup, noting that embeddings are not pre-installed by default.

Model Selection and Memory Management

• Emphasizes that when using a chat model, it is crucial to select appropriate models instead of embeddings to avoid errors.

• The speaker suggests using a simple Windows buffer memory for document storage rather than more complex solutions like PostgreSQL.

Document Uploading and Model Configuration

• Instructions are provided on selecting documents for upload while ensuring the correct Olama model is chosen for processing.

• The importance of configuring the conversational agent correctly is highlighted; incorrect settings may lead to unexpected responses from the chat model.

Performance Considerations

• Local models may perform slower compared to those hosted by large companies due to hardware limitations but offer advantages such as no token or API usage costs.

• Local installations provide a secure environment for sensitive data since they operate entirely on personal computers without external access.

Community Engagement and Future Plans

• The speaker expresses gratitude towards their growing community, which has nearly reached 1000 members, emphasizing its free nature.