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Searching

Text search with rg and grep, meaning search with similar, and reordering a bounded set with rerank.

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By text

rg search

Keep whole records in order when their compact JSON or named field matches the pattern.

  • field optional top-level field with a leading dot
  • pattern a regular expression in double quotes
  • flags optional quoted -i, -s, -S, -w, -x, -F, -v and -U switches and their long forms; --no-ignore-case resets case sensitivity; -U dot matches newlines 
patients | rg(.ward; "4b"; "-i -F")
patients | grep(.ward; "4B"; "-v")
collection("notes") | rg("fall risk"; "-i")
grep search

Alias of rg: keep whole records in order when their compact JSON or named field matches the pattern.

  • field optional top-level field with a leading dot
  • pattern a regular expression in double quotes
  • flags optional quoted -i, -s, -S, -w, -x, -F, -v and -U switches and their long forms; --no-ignore-case resets case sensitivity; -U dot matches newlines

The third argument of rg and grep is a quoted string of switches.

-i --ignore-case
Match regardless of case. --no-ignore-case resets it.
-s --case-sensitive
Match case exactly.
-S --smart-case
Case-insensitive unless the pattern has an upper-case letter.
-w --word-regexp
Match whole words only.
-x --line-regexp
Match the whole value only.
-F --fixed-strings
Treat the pattern as literal text, not a regular expression.
-v --invert-match
Keep records that do not match.
-U --multiline
Let a dot match a newline.

By meaning

similar and rerank read a declared vector field. The question is either quoted text, which the service embeds, or record("id"), which uses that record’s stored vector.

The record form needs a vector stored on that field. The text form needs the complete embedding configuration, a declared embedded field and its matching model ready in the worker. A supplied-vector field alone cannot embed a text question. Missing settings are embed_not_configured; a missing source record or field has its own named refusal. The notes examples assume a notes collection and those embedding prerequisites. They are not steps that the first-records example has already performed.

similar search

Search by vector similarity first after its source or later over the records that reached it.

  • field a top-level vector field with a leading dot
  • question double-quoted text or record("id")
  • measure cosine, l2 or dot 
collection("patients") | similar(.embedding; record("p0"); cosine) | limit(10)
collection("notes") | similar(.embedding; "unsteady on feet overnight"; cosine) | limit(5)
record target

Use the stored vector of the named record as the similarity question.

  • id a record id in double quotes
rerank order

Drain the bounded upstream, reorder by vector score, replace scores and count unscored records last; no continuation.

  • field a top-level vector field with a leading dot
  • question double-quoted text or record("id")
  • measure cosine, l2 or dot 
collection("patients") | similar(.embedding; record("p0"); dot) | limit(20) | rerank(.embedding; record("p0"); dot) | limit(2)
cosine
Compare vectors by their cosine similarity.
l2
Compare vectors by their Euclidean distance.
dot
Compare vectors by their dot product.