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🚀 Supercharged Vector Database!

Requirements  •  Installation  •  Using the Program  •  Request a Feature or Report a Bug  •  Contact

Create and search a vector database to get a response from the large language model that's more accurate. This is commonly referred to as "retrieval augmented generation" (RAG)! You can watch an introductory Video or read a Medium article about the program.

Graphic of How This Program Works

image

Requirements

🐍 Python 3.11 or Python 3.12  •  📁 Git  •  📁 Git LFS  •  🌐 Pandoc  •  🛠️ Compiler

The above link downloads Visual Studio as an example. Make sure to install the required SDKs, however.

EXAMPLE error when no compiler installed:
EXAMPLE of installing the correct SDKs:

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Installation

Step 1

Go to the latest "release" and download the ZIP file. Extract its contents and navigate to the src folder.

  • NOTE: If you simply clone this repository you will get the development version, which may or may not be stable.

Step 2

Within the src folder, create a virtual environment:

python -m venv .

Step 3

Activate the virtual environment:

.\Scripts\activate

Step 4

Run the setup script:

Only Windows is supported for now.

python setup_windows.py

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🖥️Usage🖥️

🔥Important🔥

  • More detailed instructions are being consolidated into the Ask Jeeves functionality, which can be accessed from the "Ask Jeeves" menu option. Please create an issue if Jeeves is not working.

Start the Program

.\Scripts\activate
python gui.py

🏗️ Create a Vector Database

  • Download a vector/embedding model from the Models Tab.
  • Select one or more files to be put into the queue.
    • 📄 Non-audio files (including images) can be selected by clicking the Choose Files button within the Create Database Tab.
    • 🎵 Audio files must be transcribed first within the Tools Tab.
    • 🖼️ Images can be selected just like other general file types.
  • Click the Create Vector Database button within the Create Database Tab.

    Make sure and change the chunk size and chunk overlap settings within the Settings Tab first.

🔍 Query a Vector Database

  • Within the Query Database Tab, select the database you want to search.
  • Select the backend that will analyze the results from the vector database and provide a response: Local Models, Kobold, LM Studio or ChatGPT.
  • Click Submit Question.
    • The chunks only checkbox will display the results from the vector database.

❓ Which Backend Should I Use?

If you use either the Kobold or LM Studio backends you must be familiar with those programs. For example, LM Studio must be running in "server mode" and handles the prompt formatting. However,Kobold automatically starts in server mode but requires you to specify the prompt formatting. This program no longer provides detailed instructions on how to use either of these two backends. For more information:

🗑️ Deleting a Database

  • In the Manage Databases Tab, select a database and click Delete Database.

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Request a Feature or Report a Bug

Feel free to report bugs or request enhancements by creating an issue on github and I will respond promptly.

CONTACT

I welcome all suggestions - both positive and negative. You can e-mail me directly at "bbc@chintellalaw.com" or I can frequently be seen on the KoboldAI Discord server (moniker is vic49). I am always happy to answer any quesitons or discuss anything vector database related! (no formal affiliation with KoboldAI).