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Welcome to the Alexandria Project

A modern ode to the ancient seat of knowledge. At its core, Alexandria is a virtual library, meticulously organized and managed by an AI librarian. This project is designed to leverage the power of NoSQL and VectorDB to create an efficient, intelligent storage and retrieval system for a vast array of information.

What is the Alexandria Project?

The Alexandria Project is capable of handling tasks ranging from simple searches for books or chapters to more complex activities like comparing research papers, coding, or synthesizing new books and research papers based on existing materials. This system is an innovative platform designed to harness the power of AI and Retrieval-Augmented Generation (RAG) for document management and interaction. It integrates Large Language Models (LLM) with a vector database, enabling advanced search, response, and document creation capabilities.

Central to its design is the flexibility to use various pre-trained LLMs or to train custom models, enhancing performance and adaptability to specific needs. The project is open-source, ensuring accessibility and customization. It is optimized to run efficiently on personal computers using Docker compose or as a collection of individual services, while also being scalable and production-ready for deployment in more extensive environments like Kubernetes, OpenShift, EKS, GCP, and Azure.

This approach combines the vast potential of AI in document handling with the scalability of modern cloud and containerization technologies, making it a versatile tool for both personal and professional use.

Key Features:

  • Your AI Librarian: An intelligent assistant powered by artificial intelligence, designed to categorize, manage, and retrieve information efficiently. Use any LLM such as Llama 2, Mistral, or GPT as the core of the project.
  • MongoDB Integration: Utilizes MongoDB for robust data storage, handling a wide variety of document types and structures with ease.
  • Milvus Implementation: Incorporates Milvus for its advanced vector search capabilities, enhancing the speed and accuracy of data retrieval.
  • Scalable Architecture: Designed to scale horizontally, accommodating growing amounts of data and users seamlessly.
  • User-Friendly Interface: Offers a clean and intuitive interface for users to interact with the virtual library, ensuring a pleasant and productive experience.

Getting Started ( TBD ):

  • Installation: Provide instructions on how to install and set up the Alexandria Project on various platforms using Docker Compose.
  • Documentation: Link to detailed documentation, including how to use the AI librarian, how to add data to the library, and how to retrieve it.
  • Contribute: Encourage open-source contributions by providing guidelines for contributing to the project.

Vision and Goals:

Our vision is to leverage existing and upcoming AI technologies like LLMs and Vector Databases to create the most advanced Retrieval-Augmented Generation (RAG) implementation.

Short Term Goals:

  • Develop a Proof of Concept (POC) demonstrating the basic implementation of RAG working seamlessly.
  • Enable users to upload and manage their documents within the virtual library effortlessly.

Long Term Goals:

  • Enhance the AI's understanding of the data and the user, allowing for personalized and context-aware interactions.
  • Remember conversations and user preferences to provide more accurate and relevant results over time.
  • Develop specific vector representations for data to enhance the precision and relevance of search results.

Community and Support:

We invite you to join us in building this beacon of knowledge. Together, we can create a repository that stands the test of time, much like the ancient library it's named after.

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