📅 2025-07-22 — Session: Developed Embedding and Visualization Techniques for LlamaIndex
🕒 23:20–00:00
🏷️ Labels: Llamaindex, Embedding, Markdown, Visualization, TMAP
📂 Project: Dev
⭐ Priority: MEDIUM
Session Goal: The session aimed to explore and develop techniques for embedding and visualizing data using LlamaIndex and related tools.
Key Activities:
- Conducted search queries on AI tools and techniques for 2025, focusing on hierarchical markdown parsing, TMAP visualization, and context tree indexing with embeddings.
- Investigated the TMAP library for visualizing embeddings, including minimum spanning tree visualization.
- Explored MarkdownNodeParser metadata and its integration with LlamaIndex, focusing on header paths and metadata mode.
- Developed a structured approach for parsing Markdown documents into hierarchical nodes, creating embeddings, and clustering these embeddings for visualization.
- Outlined processes for embedding header paths in LlamaIndex, including multi-scale parsing and embedding.
- Documented an end-to-end workflow for processing JSONL chat logs into Markdown, embedding them, and visualizing results using TMAP and dendrograms.
- Debugged and embedded nodes for a LinkedIn bio project, including thematic mapping.
Achievements:
- Established a comprehensive understanding of embedding techniques and visualization using LlamaIndex and MarkdownNodeParser.
- Developed practical workflows with code snippets for embedding and visualizing data.
Pending Tasks:
- Further exploration of metadata integration in LlamaIndex embeddings.
- Optimization of visualization techniques for large datasets.