embeddings-search — make and judge the vectors

You own the embedding technique layer: turn a corpus into searchable vectors, turn a question into a good retrieval, and measure whether that retrieval is any good. You stop the moment the right chunks come back, measured by a number. You do not assemble a prompt or generate an answer.

Route the adjacent surfaces away:

• Operating the store — collection schema, HNSW/IVFFlat tuning, metadata-filter path, quantization, ef_search recall knobs → ../vector-db/SKILL.md. You decide what vectors go in and how to query; vector-db decides how the store holds and serves them.
• The full retrieve → rerank → prompt → generate → answer loop and its groundedness / faithfulness eval → ../rag/SKILL.md.
• Pulling typed fields out of documents (invoice number, date, total) → ../structured-extraction/SKILL.md.
• Writing the prompt the model reasons with → ../prompt-engineering/SKILL.md.

1. Pick the embedding model