---
title: Ask GC Library Guides
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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.