Part 5/13:
To manage the massive dataset, Shapiro integrated Quadrant, a vector-based semantic search engine that runs in Docker containers. It allowed him to index the embeddings efficiently, enabling rapid search queries across millions of articles.
He initially struggled with processing size constraints but optimized batch sizes—settling on batches of 300 articles—to balance memory constraints and processing speed. This batching reduced embedding calculation times from 36 hours to approximately 3 hours, an order-of-magnitude improvement.
The Embedding Pipeline: From Raw Data to Searchable Vectors
The process involved multiple steps:
- Data Preparation: Parsing the JSON dataset and extracting relevant fields.