query

query runs path-based expressions with analysis functions against a document. It lets you ask specific questions — what is the entropy at this path? Which values at this path are anomalous? What is the null rate for this field?

Where other commands analyze everything and present results, query lets you target a specific path and a specific measurement.

Usage

vajra query <input> '<expression>' [flags]

Arguments:

Argument	Description
`<input>`	Path to a JSON file, `-` for stdin, or an HTTP URL
`<expression>`	Query expression (path filter or analysis function)

Flags:

Flag	Description	Default
`--format <fmt>`	Output format: `text`, `json`, `markdown`, `compact-ai`	`text`
`--input-format <fmt>`	Override auto-detected input format	auto
`--redact`	Apply built-in redaction before output	off
`--quiet`	Suppress progress output	off

Expression Language

Vajra defines its own expression language inspired by JSONPath with analysis extensions. This is not JSONAta — it is a purpose-built query system for structural analysis.

Path Filtering

Select values at a specific path:

vajra query claim.json '$.claims[*].service_lines[*].charge_amount'

Path: $.claims[*].service_lines[*].charge_amount
Values (14):
  125.00, 285.00, 45.00, 890.00, 310.00, 425.00, 285.00,
  1250.00, 175.00, 520.00, 95.00, 680.00, 340.00, 410.00

Analysis Functions

Apply analysis functions to a path:

vajra query claim.json 'entropy($.claims[*].service_lines[*].status)'

entropy($.claims[*].service_lines[*].status)
  Shannon entropy: 1.22 bits
  Normalized entropy: 0.77
  Cardinality: 3
  Interpretation: enum-like, few distinct states

Available Functions

Function	Returns	Description
`entropy(path)`	Shannon entropy and normalized entropy	Information content at this path
`rarity(path, value)`	Self-information in bits	How rare a specific value is at this path
`instability(path)`	Type instability ratio	Fraction of values deviating from dominant type
`null_rate(path)`	Null and absent rates	Missingness profile at this path
`stats(path)`	Full statistical summary	Entropy, frequency, numeric distribution
`anomaly_score(path)`	Composite anomaly score	Maximum anomaly strength across dimensions
`motif(path)`	Dominant motif description	Repeated structural pattern at an array path

Conditional Expressions

Filter by analysis thresholds:

vajra query claim.json 'entropy($.claims[*].service_lines[*].status) > 0.5'

entropy($.claims[*].service_lines[*].status) = 1.22
  Condition: > 0.5
  Result: TRUE

vajra query claim.json 'anomaly_score($.claims[*].service_lines[*].charge_amount) > 3.5'

anomaly_score($.claims[*].service_lines[*].charge_amount)
  Max z_MAD across values: 6.3 (at value 47,250.00)
  Condition: > 3.5
  Result: TRUE
  Flagged values:
    47,250.00 (z_MAD = 6.3)

Example: Text Output

vajra query claim.json 'stats($.claims[*].service_lines[*].charge_amount)'

stats($.claims[*].service_lines[*].charge_amount)
  Count:       14
  Cardinality: 12
  Entropy:     3.41 bits (normalized: 0.88)
  Type:        number (100%)
  Min:         45.00
  Max:         1250.00
  Mean:        312.50
  Median:      285.00
  MAD:         195.00
  p25:         125.00
  p75:         425.00
  p95:         890.00
  p99:         1125.00

Example: JSON Output

vajra query claim.json 'entropy($.claims[*].status)' --format json

{
  "function": "entropy",
  "path": "$.claims[*].status",
  "result": {
    "shannon_entropy": 1.22,
    "normalized_entropy": 0.77,
    "cardinality": 3,
    "support": ["adjudicated", "pending", "denied"]
  }
}

Example: Rarity Check

vajra query claims_batch.ndjson 'rarity($.claims[*].status, "voided")'

rarity($.claims[*].status, "voided")
  Self-information: 10.3 bits
  Frequency: 1 of 1,247
  Interpretation: extremely rare (> 10 bits)

Example: Null Rate Investigation

vajra query claim.json 'null_rate($.claims[*].service_lines[*].allowed_amount)'

null_rate($.claims[*].service_lines[*].allowed_amount)
  Null rate:   0.000 (0 of 14 are JSON null)
  Absent rate: 0.214 (3 of 14 parent records lack this field)
  Empty rate:  0.000
  Total missingness: 0.214

When to Use It

Targeted investigation. You saw an anomaly in the essence. Now drill into the specific path.
Threshold checks in CI. vajra query data.json 'instability($.status) > 0.01' — fail the build if type instability exceeds tolerance.
Statistical spot-checks. What is the entropy of this field? What is the null rate? How rare is this value?
Script integration. The --format json output is machine-readable. Parse it in your pipeline.

Pairs Well With

stats — query targets a single path; stats gives you everything
anomalies — query lets you drill into a specific anomaly with anomaly_score(path)
essence — the AI profile’s drill section tells downstream models which paths to query
inspect — inspect reveals the paths; query interrogates them

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