📅 2025-02-17 — Session: Optimized embedding and text processing pipeline
🕒 20:00–21:00
🏷️ Labels: Embeddings, Optimization, Spacy, ML, AI
📂 Project: Dev
⭐ Priority: MEDIUM
Session Goal
The session aimed to explore innovative techniques in ML/AI systems, focusing on embedding models, GraphStore integration, and optimization strategies for processing embeddings and text data.
Key Activities
- Reflection on ML/AI Innovations: Discussed the role of embedding models, GraphStore, and optimization techniques in enhancing semantic understanding and scalability.
- Script Development: Created a script to process and store embeddings from data chunks, integrating metadata loading, text filtering, and embedding computation.
- Process Optimization: Implemented batch processing and parallelization strategies to enhance the efficiency of the embedding workflow.
- Text Processing with spaCy: Improved text processing by utilizing spaCy’s
nlp.pipefor batch processing, significantly reducing processing time.
Achievements
- Developed a comprehensive pipeline for embedding computation and storage.
- Enhanced text processing efficiency using spaCy, reducing processing time and improving performance.
Pending Tasks
- Further testing and validation of the optimized pipeline in a production environment.
- Explore additional optimization techniques for large-scale data processing.