- Created a new notebook `07_Fine_Tuning_Dataset.ipynb` for fine-tuning tasks.
- Implemented code to load blog posts, update metadata, and split documents.
- Integrated LangChain's ChatOpenAI for generating questions based on document context.
- Added functions for extracting questions from the generated responses.
- Included asynchronous processing for creating questions from multiple documents.
- Handled potential kernel crashes during execution.
feat: Implement build vector store workflow and local script for artifact generation
- Introduced a new Jupyter notebook `update_blog_data.ipynb` for updating blog data and vector store with new posts.
- Created a Python script `pipeline.py` to handle the blog data update process, including command-line argument parsing and statistics saving.
- Added a shell script `build-vector-db.sh` to facilitate the execution of the pipeline script.
- Generated multiple JSON files in the `stats` directory to track blog statistics over time, including total documents, character counts, and length metrics.
