RAG with BigQuery Vector Store

This project demonstrates a simple Retrieval-Augmented Generation (RAG) pipeline using Python. It retrieves information from a PDF document, embeds the text into vectors, stores them in a BigQuery Vector Store, and uses a Generative AI model to answer questions based on the document's content.

Description

The script performs the following steps:

1. Loads a PDF document from a URL.
2. Splits the document's text into smaller chunks.
3. Generates embeddings for each chunk using a Hugging Face sentence transformer model.
4. Stores the text chunks and their corresponding embeddings in a BigQuery Vector Store.
5. Takes a user query and retrieves the most relevant text chunks from BigQuery.
6. Uses a Gemini Large Language Model (LLM) to generate a response to the user's query based on the retrieved context.

Setup and Installation

1. Clone the repository:

   git clone <repository-url>
   cd <repository-directory>

2. Create a virtual environment:

   python3 -m venv .venv
   source .venv/bin/activate

3. Install the dependencies:

   pip install -r req.txt

4. Set up your environment variables: Create a .env file in the root directory of the project and add the following variables:

   GEMINI_API_KEYS="YOUR_GEMINI_API_KEY"
   PROJECT_ID="YOUR_GOOGLE_CLOUD_PROJECT_ID"
   DATASET="YOUR_BIGQUERY_DATASET_NAME"
   TABLE="YOUR_BIGQUERY_TABLE_NAME"
   REGION="YOUR_BIGQUERY_DATASET_REGION"
   

   Replace the placeholder values with your actual credentials and configuration.

Usage

To run the script, execute the main.py file:

python main.py

The script will then:

1. Fetch the PDF and process it. The default PDF is an Nvidia earnings call transcript, but you can change this in main.py: https://s201.q4cdn.com/141608511/files/doc_financials/2026/q3/NVDA-Q3-2026-Earnings-Call-19-November-2025-5_00-PM-ET.pdf
2. Store the data in BigQuery.
3. Run a sample query: "What is the main topic of the document?".
4. Print the generated answer to the console.

Snapshots for reference

Terminal BigQuery