Exploring Enhanced Knowledge Retrieval with Zettelgarden
In my latest update, I delve into the challenges and innovations in knowledge retrieval, specifically focusing on Zettelgarden’s entity extraction techniques. Here’s what I’ve discovered:
- Current Landscape: Many existing tools for RAG (chunking + embeddings + vector DB) fall short when processing larger data sets, often yielding subpar results.
- Experimentation Insights: For instance, Open WebUI’s Knowledge features struggle with data sets as small as 10 documents. I aim to tackle sets that are three magnitudes larger.
Innovative Approach
- Context-Aware Embeddings: Rather than embedding random text slices, I propose using structured, context-aware elements—theses, arguments, and facts—to significantly improve LLM-generated summaries.
- Enhanced Extraction Process: My altered prompt adapts with each chunk, ensuring LLMs consider prior context, which boosts the coherence and relevance of summaries.
This method not only simplifies summary generation but also enhances retrieval by producing actionable fact lists crucial for deeper insights.
Join the Conversation!
Curious about these findings? Let’s connect and discuss how these innovations can impact AI and tech! Share your thoughts below!
