📅 2025-02-17 — Session: Optimized Embedding Processing Pipeline
🕒 20:00–21:00
🏷️ Labels: Embeddings, Optimization, Spacy, Neo4J, Text Processing
📂 Project: Dev
⭐ Priority: MEDIUM
Session Goal
The session aimed to enhance the efficiency and scalability of embedding processing pipelines by integrating innovative techniques and optimizing existing scripts.
Key Activities
- Reflected on innovative components in ML/AI systems, focusing on embeddings, GraphStore integration, and optimization techniques for semantic understanding and scalability.
- Developed a script for processing and storing embeddings from text chunks, incorporating text cleaning and storage in CSV format for Neo4j integration.
- Proposed optimization strategies for embedding processing, including batch processing, file reading parallelization, and text cleaning improvements.
- Enhanced text processing with spaCy by processing text in batches using
nlp.pipe, significantly improving performance. - Created a script to compute embeddings from chunk metadata, utilizing spaCy for text cleaning and storing results in CSV.
Achievements
- Successfully developed and optimized scripts for embedding processing, achieving improved performance and scalability.
- Implemented spaCy optimizations that reduced processing time and enhanced text processing efficiency.
Pending Tasks
- Further testing and validation of the optimized pipeline in a production environment.
- Explore additional integration opportunities with graph databases like Neo4j.