Version 0.01 of Hierarchical RAG Chatbot by LibraryofCelsus.com

Installation Guide
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Recent Changes

• 07/17 First Release

This repository demonstrates a Retrieval-Augmented Generation (RAG) technique using a Hierarchical Search within Knowledge Domains. This technique was originally developed for use with my Aetherius Ai Assistant Project. This standalone example will be used to improve and test the technique before reintegration with the main project.

This project serves as the chatbot component for: https://github.com/libraryofcelsus/LLM_File_Parser

The actual DB search functions can be found in ./Resources/DB_Search

Main Ai Assistant Github: https://github.com/libraryofcelsus/Aetherius_AI_Assistant

My Ai work is self-funded by my day job, consider supporting me if you appreciate my work.

Join the Discord for help or to get more in-depth information!

Discord Server: https://discord.gg/pb5zcNa7zE

Subscribe to my youtube for Video Tutorials: https://www.youtube.com/@LibraryofCelsus (Channel not Launched Yet)

Code Tutorials available at: https://www.libraryofcelsus.com/research/public/code-tutorials/

Made by: https://github.com/libraryofcelsus

0.01

• First Release

Download the project zip folder by pressing the <> Code drop down menu.

1. Install Python 3.10.6, Make sure you add it to PATH: https://www.python.org/downloads/release/python-3106/

2. Run "install_requirements.bat" to install the needed dependencies. The bat will install Git, and the needed python dependencies.

(If you get an error when installing requirements run: python -m pip cache purge)

3. Set up Qdrant or Marqo DB. To change what DB is used, edit the "Vector_DB" Key in ./Settings.json. Qdrant is the default.

Qdrant Docs: https://qdrant.tech/documentation/guides/installation/

Marqo Docs: https://docs.marqo.ai/2.9/

�To use a local Qdrant server, first install Docker: https://www.docker.com.
Next type: docker pull qdrant/qdrant:v1.9.1 in the command prompt.
After it is finished downloading, type docker run -p 6333:6333 qdrant/qdrant:v1.9.1

To use a local Marqo server, first install Docker: https://www.docker.com.
Next type: docker pull marqoai/marqo:latest in the command prompt.
After it is finished downloading, type docker run --name marqo --gpus all -p 8882:8882 marqoai/marqo:latest

(If it gives an error, check the docker containers tab for a new container and press the start button. Sometimes it fails to start.)

See: https://docs.docker.com/desktop/backup-and-restore/ for how to make backups.

Once the local Vector DB server is running, it should be auto detected by the scripts.

6. Install your desired API. (Not needed if using OpenAi) To change what Api is used, edit the "API" Key in ./Settings.json
https://github.com/oobabooga/text-generation-webui
https://github.com/LostRuins/koboldcpp

Models tested with:
�- Meta-Llama-3-8B-Instruct-Q6_K.gguf

8. Launch a script with one of the run_*.bat

9. Change the information inside of the "Settings" tab to your preferences.

10. Start chatting with your data! Data can be uploaded from your own files using: https://github.com/libraryofcelsus/LLM_File_Parser

In January 2023, I had my inaugural experience with ChatGPT and LLMs in general. Since that moment, I've been deeply obsessed with AI, dedicating countless hours each day to studying it and to hands-on experimentation.

Discord: libraryofcelsus -> Old Username Style: Celsus#0262

MEGA Chat: https://mega.nz/C!pmNmEIZQ