Run ALL Your AI Locally in Minutes (LLMs, RAG, and more)

Cole Medin
15 Sept 202420:19

TLDRThis video introduces a comprehensive local AI package developed by the n8n team, suitable for running AI models like LLMs and RAG on your own machine. It includes components like an LLM, vector database, SQL database, and workflow automations. The tutorial walks through setting up the package using Docker and extends it for a full RAG AI agent in n8n, demonstrating the integration of various local AI services and showcasing the creation of a local RAG AI agent using n8n, PostgreSQL for chat memory, and Quadrant for the vector database.

Takeaways

  • 😀 The video introduces a comprehensive local AI package developed by the n8n team, suitable for running AI models like LLMs, RAG, and more on your own infrastructure.
  • 🎓 The package includes components like LLaMA for language models, Quadrant for vector databases, PostgreSQL for SQL databases, and n8n for workflow automations.
  • 🛠️ The setup process is streamlined, requiring only Git and Docker, with Docker Compose used to orchestrate the services.
  • 📝 The video provides a step-by-step guide on installing and customizing the environment variables and Docker Compose file for the local AI setup.
  • 🔗 It emphasizes the importance of exposing the necessary ports for services like PostgreSQL and customizing the Docker Compose file to include additional functionalities.
  • 🧩 The video demonstrates how to extend the package to create a fully functional RAG AI agent within n8n, utilizing local infrastructure for chat memory, vector databases, and embeddings.
  • 🔍 The workflow for ingesting files from Google Drive into a local Quadrant vector database is detailed, showcasing the integration of Google Drive with the local AI system.
  • 💾 The script highlights the need to manage document versions in the knowledge base to avoid duplicates, which is crucial for the RAG system's accuracy.
  • 🔧 Custom code snippets are provided to handle tasks like deleting old document vectors before reinserting new ones, ensuring the knowledge base remains accurate and up-to-date.
  • 🌟 The video concludes with a live test of the local AI agent, demonstrating its ability to retrieve information from the knowledge base and respond accurately.
  • 🚀 The presenter shares plans for future enhancements to the local AI setup, including potential additions like caching with Redis and exploring self-hosted alternatives for databases.

Q & A

  • What is the main topic of the video?

    -The main topic of the video is setting up a local AI infrastructure using a package developed by the n8n team, which includes LLMs, RAG, vector databases, and workflow automations.

  • Why is the presenter excited about the package they're introducing?

    -The presenter is excited because the package is a comprehensive solution for local AI that is easy to install and has everything needed for running AI models like LLMs and RAG locally.

  • What are the components included in the local AI package mentioned in the video?

    -The package includes LLaMA for the LLMs, Quadrant for the vector database, PostgreSQL for the SQL database, and n8n for workflow automations.

  • What is the significance of using open-source models like LLaMA in local AI setups?

    -Open-source models like LLaMA are significant because they are becoming powerful enough to compete with closed-source models, making local AI more accessible and reducing the need for proprietary solutions.

  • What are the prerequisites for setting up the local AI environment as described in the video?

    -The prerequisites include having Git and Docker installed, with Docker Desktop also recommended for its inclusion of Docker Compose.

  • How does the video guide viewers to download and set up the local AI package?

    -The video guides viewers to download the package using a Git clone command, then edit environment variables and Docker Compose files to customize the setup, and finally start the services using Docker Compose.

  • What customizations does the presenter make to the original Docker Compose file?

    -The presenter adds a line to expose the PostgreSQL port and another line to pull an embedding model for LLaMA, which are necessary for using PostgreSQL as a database and for RAG functionality.

  • How does the video demonstrate the use of the local AI setup?

    -The video demonstrates the use of the local AI setup by creating a fully local RAG AI agent within n8n, using PostgreSQL for chat memory, Quadrant for the vector database, and LLaMA for the LLM and embedding model.

  • What is the purpose of the custom code in the n8n workflow shown in the video?

    -The custom code in the n8n workflow is used to delete old document vectors from the Quadrant vector database before inserting new ones, ensuring there are no duplicates and maintaining the integrity of the knowledge base.

  • What future enhancements does the presenter plan for the local AI setup?

    -The presenter plans to add enhancements like caching with Redis, using a self-hosted Superbase instead of vanilla PostgreSQL, and possibly including a frontend or baking in best practices for LLMs and n8n workflows.

Outlines

00:00

🚀 Introduction to Local AI Package

The speaker expresses excitement about a comprehensive local AI package developed by the n8n team. This package includes Llama for the LLMs, Quadrant for the vector database, Postgress for SQL, and n8n for workflow automations. The video aims to guide viewers through the setup process and explore potential extensions to enhance its capabilities. The speaker emphasizes the growing accessibility and power of open-source AI models, positioning this package as an excellent starting point for running one's own AI infrastructure.

05:00

🛠️ Setting Up the Local AI Environment

The speaker provides a step-by-step guide to setting up the local AI environment. The process begins with cloning the GitHub repository for the AI starter kit, which contains essential files like the environment variable file for credentials and a Docker Compose file for integrating services. The speaker points out the need for dependencies like Git and Docker and recommends using GitHub Desktop and Docker Desktop. They also address the limitations of the provided README instructions and offer their own extended version of the Docker Compose file for better functionality, including exposing the Postgress port and adding an Ollama embedding model.

10:01

🔧 Customizing and Starting the Docker Containers

The speaker details the necessary code changes for customization, such as setting up environment variables for Postgress and n8n secrets. They also explain how to modify the Docker Compose file to expose the Postgress port and include an Ollama embedding model. After the code customization, the speaker demonstrates how to start the Docker containers using the appropriate Docker Compose command based on the user's system architecture. They show the process of pulling the necessary images for Ollama, Postgress, n8n, and Quadrant, and starting the containers, which is crucial for setting up the local AI infrastructure.

15:03

🤖 Building a Local RAG AI Agent with n8n

The speaker walks through the process of using the local infrastructure to create a fully local RAG AI agent within n8n. They discuss accessing the self-hosted n8n instance and setting up a workflow that uses Postgress for chat memory, Quadrant for RAG, and Ollama for the LLM and embedding model. The speaker also covers the setup for ingesting files from Google Drive into the knowledge base using Quadrant's vector database. They highlight the importance of avoiding duplicate vectors in the knowledge base and demonstrate how to delete old vectors before inserting new ones, ensuring the knowledge base remains accurate and up-to-date.

20:03

🔄 Future Expansions and Conclusion

In the concluding part, the speaker shares their plans for future expansions to the local AI stack. They consider adding features like Redis for caching, a self-hosted Superbase, and possibly a frontend. The speaker also contemplates including best practices for databases, LLMs, and n8n workflows to create a more robust and user-friendly local AI tech stack. They invite viewers to like and subscribe for more content and express their enthusiasm for the potential of the local AI setup they've demonstrated.

Mindmap

Keywords

💡Local AI

Local AI refers to the practice of running artificial intelligence models and applications directly on a user's local machine or personal server, rather than relying on cloud-based services. This approach is highlighted in the video as a way to empower users with control over their AI infrastructure. The video showcases how to set up a local AI environment using various tools and services, emphasizing the benefits of autonomy and privacy.

💡n8n

n8n is an open-source workflow automation tool that is used to create event-based automations. In the context of the video, n8n is used to orchestrate the local AI setup, tying together different components such as databases and AI models. The video demonstrates how n8n can be configured to manage workflow automations for a local AI agent, showcasing its role in connecting and automating various tasks within the AI infrastructure.

💡LLM (Large Language Models)

LLM stands for Large Language Models, which are AI models trained on vast amounts of text data to understand and generate human-like text. The video mentions 'llama', which is likely a reference to a specific LLM. These models are used for various AI applications, including natural language processing tasks. The video discusses the integration of LLMs into the local AI setup, highlighting their importance in creating AI agents capable of understanding and responding to user inputs.

💡Vector Database

A vector database is a type of database designed to store and retrieve data based on mathematical vectors, which can be used for efficient similarity searches. In the video, the use of a vector database like 'quadrant' is discussed as part of the local AI setup. It is used to store and retrieve information for applications like RAG (Retrieval-Augmented Generation), where quick and accurate retrieval of relevant data is crucial.

💡Postgress

Postgress, mentioned in the video, is likely a misspelling of 'PostgreSQL', an open-source relational database management system. It is used in the local AI setup to manage structured data through SQL queries. The video explains how to configure PostgreSQL within a Docker environment to ensure it can be utilized by n8n for tasks such as chat memory management.

💡Docker

Docker is a platform that allows developers to package applications and their dependencies into containers, which can be run consistently across different computing environments. In the video, Docker is used to create a local AI environment by containerizing various services like PostgreSQL, n8n, and LLMs. The script details how Docker Compose is utilized to manage and run these containers, simplifying the setup process.

💡Workflow Automation

Workflow automation refers to the process of automating a series of tasks or steps in a business or technical process. In the video, workflow automation is a central theme, with n8n being used to automate tasks within the local AI setup. The script provides examples of how n8n can automate processes such as ingesting files into a knowledge base and managing interactions with an AI agent.

💡RAG (Retrieval-Augmented Generation)

RAG is a machine learning approach that combines retrieval and generation models to improve the accuracy and relevance of AI-generated responses. The video discusses creating a RAG AI agent using local infrastructure, which involves using a vector database for retrieval and an LLM for response generation. The script illustrates how these components are integrated within the local AI setup to enable more effective AI interactions.

💡Self-hosted

Self-hosted refers to the practice of hosting software, applications, or services on one's own servers or personal computers rather than using third-party hosting services. The video emphasizes the benefits of self-hosting AI, such as increased control and privacy. It provides a step-by-step guide on setting up a self-hosted AI environment, including the use of local servers and personal machines to run AI models and applications.

💡Docker Compose

Docker Compose is a tool for defining and running multi-container Docker applications. In the video, Docker Compose is used to orchestrate the local AI environment by bringing together various services like PostgreSQL, n8n, and LLMs into a single package. The script explains how to use Docker Compose files to configure and start these services, streamlining the process of setting up a complex AI infrastructure.

Highlights

A comprehensive package for local AI development is introduced, including LLMs, RAG, and more.

The package is developed by the n8n team and includes everything needed for local AI setup.

Features of the package include LLaMA for LLMs, Quadrant for the vector database, Postgres for SQL, and n8n for workflow automation.

The video provides a step-by-step guide on setting up the local AI infrastructure in minutes.

The importance of running your own AI infrastructure and the accessibility of open-source models like LLaMA are discussed.

The GitHub repository for the self-hosted AI starter kit by n8n is introduced, with basic setup instructions.

Dependencies required for the setup include Git and Docker, with recommendations for GitHub Desktop and Docker Desktop.

The process of downloading the repository and setting up the environment variables for Postgres is detailed.

Instructions for editing the Docker Compose file to customize the setup are provided.

The necessity of exposing the Postgres port and adding an LLaMA embedding model to the Docker Compose file is explained.

A custom Docker Compose command is suggested based on the user's architecture for efficient setup.

The video demonstrates how to check the running containers in Docker Desktop and interact with them.

A walkthrough of creating a fully local RAG AI agent within n8n using Postgres, Quadrant, and LLaMA is provided.

The setup for the agent's chat interaction and workflow for ingesting files from Google Drive into the knowledge base is discussed.

The importance of avoiding duplicate vectors in the knowledge base when updating documents is highlighted.

A demonstration of testing the local AI agent with a query that requires access to the knowledge base is shown.

Future plans for expanding the local AI stack, including caching and a self-hosted database, are mentioned.

The video concludes with a call to action for likes and subscriptions for further content on local AI development.

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