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MemoBase Long-Term Memory for AI Apps and LLMs - Install Locally with Ollama

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MemoBase Long-Term Memory for AI Apps and LLMs - Install Locally with Ollama

AI Summary

Summary of Memoase Installation and Use

Overview

  • The video demonstrates how to install and use Memoase to integrate long-term memory into Generative AI applications, such as virtual companions and educational tools.
  • Memoase helps AI applications remember, understand, and evolve with users.

Installation Requirements

  1. Memoase: A memory tool used to enhance AI with user memory.
  2. Olama: A tool for running large language models locally.
  3. Docker: Required for containerization.

Installation Steps

  1. Download Memoase: Click the download button.
  2. For Linux: Run the command to install.
  3. For Mac and Windows: Use the provided executable.
  4. Set Up Olama: Download and run a supported model, e.g., the 2.5 model.

Configuration

  1. Clone the Memoase Repository: Use the command git clone <repository_url>.
  2. Create a virtual environment using Conda to keep dependencies isolated.
  3. Install memoase package using pip install memoase.
  4. Configure the application: Edit the config file for integration with Olama, specifying the model and API key.

Running Memoase

  • After building the docker images with docker compose, run the application to access the API endpoint.
  • Use the provided code template to interact with the model.
  • Store user information in memory, which allows the AI to recall details in future interactions.

Conclusion

  • The tutorial highlights the importance of selecting the right backend data store for performance and scalability in AI memory applications.
  • Links to the repository and additional resources are provided in the video description.

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