Empowering library professionals to build, integrate, and deploy AI solutions—without writing a single line of code.
The Coding track of the TACTIC initiative focuses on providing intuitive, graphical interfaces and state-of-the-art consumer AI tools designed specifically for academic librarians. By eliminating the programming barrier, you can dive straight into strategy, optimization, and innovation.
Master prompt engineering, contextual retrieval, and document analysis using Google's powerful LLMs. Transform lengthy library datasets and archives into conversable, structured knowledge bases.
Utilize AI-assisted OpenRefine and ChatGPT Data Analysis mode to automatically clean, normalize, and format metadata records without complex regular expressions or scripts.
Connect cloud APIs to library systems via Zapier and AWS Auto Programming. Create seamless autonomous workflows that connect patron inquiries directly to your digital archives and discovery layers.
While No-Code tools are incredibly powerful, mastering the basics of Python will unlock even more possibilities and lay a solid foundation for more complex AI projects in our Coding Pathway.
Start Interactive Python Tutorial →Experience a practical demonstration of Hugging Face machine learning models in action by classifying text data sentiment. Open the Google Colab Notebook and watch its corresponding walkthrough video to see the process in detail.
* Note: Project Set B uses the Rotten Tomatoes movie review dataset for sentiment analysis.
🚀 Now that you've explored the coding projects, let's complete a quick mini exercise!
Scenario: “Read a library policy in one minute.”
In this task, learners will use AI to condense a lengthy "Library Opening Hours & Borrowing
Policy" into a concise summary.
# 1. Installation & Imports
from transformers import pipeline
# 2. Initialize the summarization pipeline
# We use a lightweight model specifically trained for high-quality summaries
summarizer = pipeline("summarization", model="sshleifer/distil-bart-12-6")
# 3. Sample Document (Library Policy)
library_policy = """
The Central Library is committed to providing a quiet and safe environment for study.
Starting next month, opening hours will be extended from 8 AM to 10 PM on weekdays.
Borrowing limits for students will increase from 5 books to 10 books per person,
provided they have no overdue fines. All physical resources must be returned within 21 days.
"""
# 4. Run Summarization
summary = summarizer(library_policy, max_length=30, min_length=10, do_sample=False)
print(f"Summary: {summary[0]['summary_text']}")