Summary
Highlights
The video starts by debunking the recent trend of using Claude Code with Obsidian for RAG. Many videos have promoted this setup, but the presenter argues it's an illusion for production use. He tested Claude with Obsidian using common RAG documents to observe its capabilities in extracting, ingesting, and organizing relations between notes.
The presenter demonstrates Obsidian's generated knowledge graph for fault diagnostics. He aimed for clusters or links around symptoms, but the graph showed a large 'index' cluster linked to everything, offering little practical value for information retrieval. He also noted issues with similar concepts appearing far apart in the graph, indicating poor entity handling.
A test query was made to Obsidian asking for fault diagnostics for a 'self-changing engine speed' symptom. Obsidian provided a long, somewhat accurate but incomplete and partially hallucinated response. Many relevant solutions from the documentation were not included. This highlights Obsidian's inability to precisely retrieve desired information from its generated links.
Obsidian is useful for personal note consolidation and linking, especially with tools like Web Clipper. However, it's not suitable for RAG due to volume issues. Obsidian's approach passes the entire document structure as context to the LLM, leading to context saturation, higher costs, slower responses, and 'context rot,' where the LLM forgets mid-context information.
Obsidian's local storage and context-heavy approach make it unscalable and costly for multi-user, production environments. An optimized RAG system, in contrast, targets specific data elements, is cheaper, more scalable, and supports multi-user interaction with persistent memory. Obsidian's auto-generated entities also lack control, leading to an explosion of false relations.
Auto-generated entities (e.g., 'Apple' for fruit, company, or typo) lack granularity and can lead to false relationships and polluted searches. The AI forms entities based on textual proximity, not business logic, causing irrelevant information to be retrieved and saturating the context. Lack of normalization further fragments knowledge, making comprehensive retrieval difficult.
Knowledge graphs allow manual navigation, whereas Graph RAG creates relations between small document chunks (based on ontology and entities) for more precise retrieval. An optimized Graph RAG can increase accuracy by 15-25% if entities are intentionally defined. Obsidian's approach is inverse, navigating from the global knowledge graph, making it less effective for specific queries.
Different RAG architectures suit different needs. Structured data benefits from SQL-based retrieval, while hybrid SQL and Vector search is often optimal. Graph RAG excels with multi-document reasoning for distantly related concepts. Regardless of the choice, it's crucial to define an intentional data model and ingestion pipelines to transform raw information into a retrievable base.
For precision in RAG, a data audit is essential to understand business logic and entity relationships, leading to a well-defined data model. This model guides ingestion pipelines to transform documents into a querable base, typically a vector database. This setup is crucial for effective RAG, especially for production, and is further explored in other videos.