Deckard collects and exposes local knowledge by leveraging modern LLMs. It uses a hybrid Retrieval-Augmented Generation (RAG) approach to process queries and generate accurate, contextually relevant responses.

Some stub documentation is currently available in the documentation folder. Notable topics:

• For information on how queries are processed, see the RAG Query Processing document.
• For details on how the RAG pipeline is built, see the RAG Data Building Process document.

• Python 3.8+
• Poetry
• Nvidia drivers
• CUDA

Start the API server:

poetry run api:start

Build the RAG pipeline's underlying data to ready it for use. This may have requirements such as network requests, database tunnels, or on-disk data files.

poetry run build:rag <pipeline>

poetry run build:rag libpages

Other commands are available for convenience:

Query the configured RAG pipeline:

poetry run query:rag <pipeline> <query>

poetry run query:rag libpages 'Where is the bathroom?'
poetry run query:rag libpages 'Who is the dean of UNB libraries?'

Query the LLM model directly without a rag pipeline:

poetry run query:llm <query> <optional context>

poetry run query:llm 'Who is Donald Sutherland?'
poetry run query:llm 'Who is Donald Sutherland?' 'Donald sutherland is a duck'

Start the slackbot configured locally. Slack auth tokens need to be in ENV:

poetry run slackbot:start

• In line with our 'open' ethos, UNB Libraries makes its applications and workflows freely available to everyone whenever possible.
• As a result, the contents of this repository [unb-libraries/deckard] are licensed under the MIT License. This license explicitly excludes:
  • Any content that remains the exclusive property of its author(s).
  • The UNB logo and any associated visual identity assets remain the exclusive property of the University of New Brunswick.