Tokenized Vector Data: Provenance, Permissions, and Deletion
Govern source text and derived embeddings with provenance, trusted authorization, versioned representations, deletion tests, and recovery controls.
NOTES FROM THE VECTOR FRONTIER
VectorToken Lab
Big concepts. Practical explanations. Ten deep dives into the representations, pipelines, and retrieval decisions behind vector AI.
Start with tokens and embeddings, then follow the path through chunking, storage, ranking, and evidence-aware generation. Each article takes one engineering question far enough to expose the useful tradeoffs.
Read by category, follow a subject, or begin with a problem you are trying to debug. The guides connect to each other so the vocabulary, implementation choices, and evaluation questions stay in view.
THE COMPLETE COLLECTION
All ten field notes.
Read the full collection, most recent publication first.
Hybrid Search: Combine Lexical and Vector Rankings
Combine lexical and vector candidates with deliberate ranking. Explore reciprocal rank fusion, candidate windows, duplication, and segment-level tests.
Vector Database Costs: Budget Beyond the Embedding
Estimate vector payloads without confusing them with total costs. Include passage counts, indexing, metadata, copies, compression, and migration.
Cosine Similarity in Vector AI: Geometry, Not Confidence
Work through cosine similarity, normalization, dot products, distance, and edge cases. Learn why a similarity score is not a confidence probability.
Vector LLM Architecture: Build RAG Around Evidence
Design retrieval-augmented generation around traceable evidence, controlled context, access boundaries, and separate retrieval and answer evaluations.
HNSW vs IVFFlat: Evaluate Your Vector Database Index
Compare HNSW and IVFFlat with your own workload. Measure recall, filtering, latency, resource use, updates, and recovery against an exact baseline.
Token Vector Search: A Practical Retrieval Guide
Build an inspectable semantic retrieval baseline. Evaluate compatible embeddings, exact search, relevance labels, permissions, and candidate coverage.
Build a Vector Tokenization Pipeline That You Can Debug
Plan a traceable vector tokenization pipeline: extraction, chunking, token budgets, encoding, metadata, and controlled collection releases.
Token Vectors: From Lookup Tables to Contextual Embeddings
Follow token IDs into contextual representations. Understand pooling, representation contracts, and the checks that make embeddings reproducible.
Vector Token Explained: Tokens, IDs, and Embeddings
Separate tokens, vocabulary IDs, and embeddings. Build a precise vocabulary for vector AI, model inputs, storage, and semantic retrieval.
Follow a subject
A clearer next step starts here.
Explore the concepts. Inspect the tradeoffs. Build with a better mental model.