中文文档

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.

• 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
• .....

• Multi-round dialogue
• Better UI
• Support for more file types
  • txt
  • pdf
  • md
  • docx
• Support for more models
  • gpt3.5
  • Localized model
    • ChatGLM
• Localized vector computation
  • text2vec

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

docker-compose up -d

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

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 https://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.

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

• Analyze books ormaterials
• Personal or enterprise knowledge base question-and-answer assistant