https://github.com/jmservera/aithena

A system to extract knowledge from books
https://github.com/jmservera/aithena

Last synced: 8 days ago
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A system to extract knowledge from books

• Host: GitHub
• URL: https://github.com/jmservera/aithena
• Owner: jmservera
• License: mit
• Created: 2023-07-16T21:43:59.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2024-06-08T22:41:44.000Z (7 months ago)
• Last Synced: 2024-06-09T00:06:40.503Z (7 months ago)
• Language: Python
• Size: 1.57 MB
• Stars: 0
• Watchers: 2
• Forks: 1
• Open Issues: 16
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Aithena

A system to extract knowledge from books in pdf format. It uses the LLaMA embeddings

to generate a vector database of the books in Qdrant. The system is built using

Docker Compose with the following services:

* [Qdrant](https://qdrant.tech/) - Vector database

* [LLaMA.cpp Server](https://llama-cpp-python.readthedocs.io/en/latest/) - Embeddings

* [LLaMA model](https://huggingface.co/openlm-research/open_llama_7b) - Embeddings model: 

you can download the model and then generate the bin file running the llama-base

container `docker exec -it aithena-llama-base-1 python3 convert.py models/7B`.

* A document lister and document indexer that will go through all the pdf files

stored in an Azure Storage account and index them in Qdrant.

## Usage

Configure the volumes folders in the `docker-compose.yml` file.

Run `docker-compose up` to start the system. The first time you run it, it will

fail because you won't have the LLaMA model downloaded. You can download it from

the [Hugging Face model hub](https://huggingface.co/openlm-research/open_llama_7b)

and then generate the bin file running the llama-base container `docker exec -it

aithena-llama-base-1 python3 convert.py models/7B`.

# Reference

* https://python.langchain.com/docs/ecosystem/integrations/llamacpp