Optimized embedding and text processing pipeline

• Day: 2025-02-17
• Time: 20:00 to 21:00
• Project: Dev
• Workspace: WP 2: Operational
• Status: Completed
• Priority: MEDIUM
• Assignee: Matías Nehuen Iglesias
• Tags: Embeddings, Optimization, Spacy, ML, AI

Description

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.pipe for 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.

Evidence

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• event_ids: []