📅 2025-02-17 — Session: Session on NLP Techniques and Dependency Management
🕒 18:00–18:40
🏷️ Labels: NLP, Python, Dependency Management, Ai Optimization, Graph Systems
📂 Project: Dev
⭐ Priority: MEDIUM
Session Goal
The primary objective of this session was to explore various techniques in natural language processing (NLP) and address dependency management in Python.
Key Activities
- Detailed a systematic approach to aggregating micro-knowledge using NLP and clustering techniques.
- Explained the use of the ‘all-MiniLM-L6-v2’ model from Sentence Transformers for text processing, including embedding generation and clustering.
- Discussed methods to resolve dependency conflicts in Python, including enforcing compatible package versions and creating separate virtual environments.
- Explored strategies to optimize performance in AI model calls, focusing on model loading, GPU utilization, and batching techniques.
- Described a modular architecture for text processing pipelines, highlighting the generation of embeddings, storage in graphs, and search and clustering techniques.
- Investigated the definition and structuring of nodes in graph-based knowledge systems, discussing the implementation of multi-layered graphs.
Achievements
- Developed a comprehensive understanding of NLP techniques for knowledge aggregation and text processing.
- Identified effective methods for resolving Python dependency conflicts.
- Gained insights into optimizing AI model performance.
- Conceptualized a modular text processing pipeline architecture.
Pending Tasks
- Further exploration of multi-layered graph implementations for enhanced information retrieval.
- Testing and validation of the discussed NLP and dependency management techniques in real-world scenarios.