Building a Q&A engine with LangChain and open-source LLMs

The main object in this repository is the qa_engine.ipynb notebook.

It contains a presentation showing how to run a large language model (LLM) on a laptop using LangChain and then index Web documents, to query their content with natural language.

How to open?

There are different ways to view the presentation:

1. 🐍 create environment with poetry install, then load the notebook with jupyter notebook qa_engine.ipynb and press Alt+R to enter the presentation mode,
2. ▶️ open the notebook qa_engine.ipynb in GitLab/GitHub,
3. 📥 download one of the exported_*.html files (if you choose the "slides" file, you also to download the images/ directory).

Jupyter

Jupyter Notebook v6 is used, because the RISE extension does not work well with Jupyter Lab or v7. This may change when jupyterlab-contrib/rise becomes more mature.

Other small extensions for Jupyter

• execute_time
• export_embedded

jupyter contrib nbextension install --user

jupyter nbextension enable execute_time/ExecuteTime
jupyter nbextension enable export_embedded/main

Converting to HTML

jupyter nbconvert qa_engine.ipynb --to html_embed --output ./exported_qa_engine.one_page.html

# export_embedded does not add the option to export slides with embedded images
jupyter nbconvert qa_engine.ipynb --to slides --output ./exported_qa_engine