README.md

metadata

title: Ask GC Library Guides
emoji: 📚
colorFrom: blue
colorTo: green
sdk: streamlit
sdk_version: 1.44.0
app_file: app.py
pinned: false
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Ask GC Library Guides (RAG Demo)

This Space demonstrates a Retrieval-Augmented Generation (RAG) application built with Streamlit. It allows users to ask questions about the CUNY Graduate Center library guides.

How it works:

1. Data Source: Pre-computed embeddings (BAAI/bge-m3), documents, and metadata loaded from the Hugging Face Dataset Zwounds/Libguides_Embeddings (originally sourced from extracted_content.jsonl).
2. Database Initialization: On startup, the application downloads the dataset and loads the data into an in-memory ChromaDB collection stored in a temporary directory. This avoids slow re-embedding on every startup.
3. Query Processing:
   • User queries are optionally expanded using the generation model (google/gemma-3-27b-it via HF API).
   • Queries are embedded using the local BAAI/bge-m3 model (loaded into the Space).
   • ChromaDB performs a similarity search using the query embedding against the pre-computed document embeddings.
4. Generation: The relevant chunks and the original query are passed to the google/gemma-3-27b-it model via the Hugging Face Inference API to generate a final answer.

Configuration:

• Embedding: Pre-computed BAAI/bge-m3 embeddings loaded from HF Dataset Zwounds/Libguides_Embeddings. Query embedding uses local BAAI/bge-m3.
• Generation Model: google/gemma-3-27b-it (via HF Inference API).
• Requires Secret: A Hugging Face User Access Token must be added as a Space Secret named HF_TOKEN.

Note: Startup involves downloading the dataset and loading it into the ChromaDB collection, which is much faster than re-embedding all documents.