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Party Parrots: Codes and slides for Generate Your L(AI)brary Hackathon (3rd prize winning team)

posted on 2023-09-11, 09:53 authored by Yun Wen CHAN, Eric Yong Wei NG, Heng Yim Nicole OO, Ryan Miguel Moralde SIA

Our application can be found at This is our submission for SMU Libraries' Generate Your L(AI)brary 2023 Hackathon. It is compatible with both desktop and mobile devices.


Our tagline is "The library in your Pocket". Our application is accessible on both desktop and mobile devices. We want to build an eager AI assistant who cares about your purpose, not just what you're asking for. It recommends reliable research resources to users based on their personal research goals and characteristics (e.g. Student/Librarian? Are they new to the material?). It can also remember recent interactions with the user.

Key Features:

  • Understands user intent and profile
  • Remembers recent interactions
  • Backed by reliable data sources (Primo Database, chatlogs)

Tech Stack:

  • Language(s): Python
  • Frontend: StreamLit
  • Backend: LangChain, Pandas for data cleaning, Pinecone Vector Database
  • LLM Model: OpenAI's GPT 3.5-Turbo

We used the LangChain framework to use OpenAI's GPT 3.5-turbo models to interact with external data sources and chat with the user. To allow the model to access SMU Libraries' resources, we used LangChain to instruct the GPT 3.5 model on how to use the Primo API to access the database. We tokenised chat logs after cleaning the plaintext logs with the Pandas package and stored them as embeddings in a Pinecone database, a vector database, for the application to access.

To better align with user needs, we allowed the model to access historical chat records provided by SMU Libraries.

To ensure that all recommended resources were real and not fabricated by the LLM model, we only allowed the application to recommend resources that were found in SMU Libraries' resource database.


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