Ep66: Neo4j Data Loading at Scale + Vector Search Filtering

What this episode covers

Hear about my hard-won lessons from loading a large-scale book dataset into Neo4j with Ollama embeddings, plus a preview of exciting new vector search features. Highlights: Data Loading Battle Stories Fixing Ollama OpenAI endpoint issues (drop the /v1 suffix!) Choosing embedding models with adequate context windows (nomic-embed-text: 8,192 tokens) Optimizing batch sizes and memory configuration Using EXPLAIN to identify and eliminate Cypher eager operations Error handling with ON ERROR CONTINUE for partial loads (achieved 83% coverage) Neo4j 2026.01 Preview: Vector Search with Filters Three new approaches that combine vector search with Cypher filtering in a single query: Vector Search + Keyword Filters Cypher After Vector (post-filtering GraphRAG) Cypher Before Vector (pre-filtering on subgraphs) No more two-step application logic for Graph RAG! Context Graph demo app: Level of detail and perspectives you can view of the context graph and interactions with agents Event I will be at Jfokus in Stockholm next week!

PodParley-generated summary based on available episode metadata and transcript content.