How to tune chunking parameters, choose optimal distance metrics, and optimize vector databases for enterprise semantic search.
Fine-Tuning Ingestion and Chunking
For optimal retrieval, chunk size must align with your data structure. We use semantic chunking models rather than rigid token counts, and configure pgvector metadata tags to restrict query spaces during vector retrieval.
Frequently Asked Questions
Which distance metric is best for vector search?
Cosine similarity is the standard for most OpenAI and Cohere embeddings, while Inner Product is preferred for normalized vector spaces.