/bibliothecarius

Quickly build services to integrate your local data and AI models.

Primary LanguageJavaMIT LicenseMIT

Bibliothecarius

中文文档

About

What is Bibliothecarius

Bibliothecarius is a local data analysis tool that can engage in conversation based on custom prompts. The tool supports multiple models, allowing for horizontal comparison, and supports data isolation and multiple data types.

What can Bibliothecarius do

  • Personal or enterprise knowledge base question-and-answer assistant
  • Analyze books or materials
  • AI assistant exclusively for a certain group
  • Can even organize AI to play murder mystery games with you
  • .....

Plans

Quick Start

Environment Setup

Use our prepareddocker-compose.yaml

After downloading the code, initialize the environment with docker-compose:

docker-compose up -d

Build the environment on your own

  • MySQL
    • Use an existing database: initialization SQL is available in ./mysql/init/init.sql, simply execute it.
  • Qdrant

Run the Service

  1. Modify the configuration file ./bibliothecarius/config/application.yaml, including:

    • MySQL configuration
    • opanai key
    • Qdrant service address
    • Storage configuration

  2. Start the backend service with docker-compose

    cd bibliothecarius
docker-compose up -d

  3. Verify the backend service by visiting http://127.0.0.1:8080/

  4. If you want to embed in your service, you can leave the front-end service unenabled

    Front-end project./web

Interface document

The Bibliothecarius interface has two main parts:

  1. dataset: Your local data-related interface, including the operation of the dataset and the interface to add and delete data to the dataset.
  2. scene: Your model requests parameters and prompt and data set association interfaces.

Example

You can use the following examples to familiarize yourself with Bibliothecarius.