Docent Query Language (DQL)

Docent Query Language is a read-only SQL subset that supports ad-hoc exploration in Docent.

Queries can only run over a single collection by design (if you need multi-collection support, please reach out to us!)

Available Tables and Columns

Table	Description
agent_runs	Information about each agent run in a collection.
transcripts	Individual transcripts tied to an agent run; stores serialized messages and per-transcript metadata.
transcript_groups	Hierarchical groupings of transcripts for runs.
judge_results	Scored rubric outputs keyed by agent run and rubric version.

agent_runs

Column	Description
id	Agent run identifier (UUID).
collection_id	Collection that owns the run
name	Optional user-provided display name.
description	Optional description supplied at ingest time.
metadata_json	User suplied metadata, stored as JSON.
created_at	When the run was recorded in Docent.

transcripts

Column	Description
id	Transcript identifier (UUID).
collection_id	Collection that owns the transcript.
agent_run_id	Parent run identifier; joins back to agent_runs.id.
name	Optional transcript title.
description	Optional description.
transcript_group_id	Optional grouping identifier.
messages	Binary-encoded JSON payload of message turns.
metadata_json	Binary-encoded metadata describing the transcript.
created_at	Timestamp recorded during ingest.

transcript_groups

Column	Description
id	Transcript group identifier.
collection_id	Collection that owns the transcript.
agent_run_id	Parent run identifier; joins back to agent_runs.id.
name	Optional name for the group.
description	Optional descriptive text.
parent_transcript_group_id	Identifier of the parent group (for hierarchical groupings).
metadata_json	JSONB metadata payload for the group.
created_at	Timestamp recorded during ingest.

judge_results

Column	Description
id	Judge result identifier.
agent_run_id	Run scored by the rubric.
rubric_id	Rubric identifier.
rubric_version	Version of the rubric used when scoring.
output	JSON representation of rubric outputs.
result_metadata	Optional JSON metadata attached to the result.
result_type	Enum describing the rubric output type.

JSON Metadata Paths

Docent stores user-supplied metadata as JSON, and can be access in Postgres style. Here are some examples:

Access Patterns

-- Filter agent runs by a metadata attribute
SELECT id, name
FROM agent_runs
WHERE metadata_json->>'environment' = 'staging';

-- Retrieve nested transcript metadata
SELECT
  id,
  metadata_json->'conversation'->>'speaker' AS speaker,
  metadata_json->'conversation'->>'topic' AS topic
FROM transcripts
WHERE metadata_json->>'status' = 'flagged';

-- Cast numeric metadata for aggregation
SELECT
  AVG(CAST(metadata_json->>'latency_ms' AS DOUBLE PRECISION)) AS avg_latency_ms
FROM agent_runs
WHERE metadata_json ? 'latency_ms';

When querying JSON fields, comparisons default to string semantics. Cast values when you need numeric ordering or aggregation.

Allowed Syntax

DQL supported keywords:

Feature
SELECT, DISTINCT, FROM, WHERE, subqueries
JOIN, LEFT JOIN, RIGHT JOIN, FULL JOIN, CROSS JOIN
WITH (CTEs)
UNION [ALL], INTERSECT, EXCEPT
GROUP BY, HAVING
Aggregations (COUNT, AVG, MIN, MAX, SUM, STDDEV_POP, STDDEV_SAMP, VAR_POP, VAR_SAMP, ARRAY_AGG, STRING_AGG, JSON_AGG, JSONB_AGG, JSON_OBJECT_AGG, PERCENTILE_CONT, PERCENTILE_DISC (WITHIN GROUP))
Window functions (ROW_NUMBER, RANK, DENSE_RANK, NTILE, LAG, LEAD, FIRST_VALUE, LAST_VALUE, NTH_VALUE, PERCENT_RANK, CUME_DIST)
ORDER BY, LIMIT, OFFSET
Conditional & null helpers (CASE, COALESCE, NULLIF)
Boolean logic (AND, OR, NOT)
Comparison operators (=, !=, <, <=, >, >=, IS, IS NOT, IS DISTINCT FROM, IN, BETWEEN, LIKE, ILIKE, EXISTS, SIMILAR TO, ~, ~*, !~, !~*)
Arithmetic & math (+, -, *, /, %, POWER, ABS, SIGN, SQRT, LN, LOG, EXP, GREATEST, LEAST, FLOOR, CEIL, ROUND, RANDOM)
String helpers (SUBSTRING, LEFT, RIGHT, LENGTH, UPPER, LOWER, INITCAP, TRIM, REPLACE, SPLIT_PART, POSITION, CONCAT, CONCAT_WS, STRING_AGG)
JSON operators & functions (->, ->>, #>, #>>, @>, ?, ?|, ?&, jsonb_build_object, jsonb_build_array, json_agg, jsonb_agg, json_object_agg, jsonb_set, jsonb_path_query, jsonb_path_exists)
Date/time basics (CURRENT_DATE, CURRENT_TIME, CURRENT_TIMESTAMP, NOW(), EXTRACT, DATE_TRUNC, AGE, AT TIME ZONE, timezone())
Interval arithmetic (timestamp +/- INTERVAL, INTERVAL literals, MAKE_INTERVAL, JUSTIFY_DAYS, JUSTIFY_HOURS, JUSTIFY_INTERVAL)
Construction & conversion (MAKE_DATE, MAKE_TIME, MAKE_TIMESTAMP, MAKE_TIMESTAMPTZ, TO_CHAR, TO_DATE, TO_TIMESTAMP, DATE_PART)
Array helpers (ARRAY[...], array_cat, array_length, cardinality, unnest, ARRAY(SELECT ...), = ANY, = ALL, array_position, array_remove)
Type helpers (CAST, ::)

Unsupported constructs include *, user-defined functions, and any DDL or DML commands.

Examples

Recent Runs

SELECT
  id,
  name,
  metadata_json->'model'->>'name' AS model_name,
  created_at
FROM agent_runs
WHERE metadata_json->>'status' = 'completed'
ORDER BY created_at DESC
LIMIT 10;

Transcript Counts per Group

SELECT
  tg.id AS group_id,
  tg.name AS group_name,
  COUNT(t.id) AS transcript_count
FROM transcript_groups tg
JOIN transcripts t ON t.transcript_group_id = tg.id
GROUP BY tg.id, tg.name
HAVING COUNT(t.id) > 1
ORDER BY transcript_count DESC;

Flagged Judge Results

SELECT
  jr.agent_run_id,
  jr.rubric_id,
  jr.result_metadata->>'label' AS label,
  jr.output->>'score' AS score
FROM judge_results jr
WHERE jr.result_metadata->>'severity' = 'high'
  AND EXISTS (
    SELECT 1
    FROM agent_runs ar
    WHERE ar.id = jr.agent_run_id
      AND ar.metadata_json->>'environment' = 'prod'
  )
ORDER BY score DESC
LIMIT 25;

Completion Rate by Environment

Aggregates per-environment success rates by normalizing metadata into a CTE.

WITH normalized_runs AS (
  SELECT
    metadata_json->>'environment' AS environment,
    metadata_json->>'status' AS status
  FROM agent_runs
  WHERE metadata_json ? 'environment'
)
SELECT
  environment,
  COUNT(*) AS total_runs,
  SUM(CASE WHEN status = 'completed' THEN 1 ELSE 0 END) AS completed_runs,
  CAST(SUM(CASE WHEN status = 'completed' THEN 1 ELSE 0 END) AS DOUBLE PRECISION)
    / NULLIF(COUNT(*), 0) AS completion_rate
FROM normalized_runs
GROUP BY environment
ORDER BY total_runs DESC;

Latest Rubric Scores by Model

Pulls the most recent rubric result per run, then joins runs to surface the model responsible for the score.

WITH latest_scores AS (
  SELECT
    agent_run_id,
    MAX(rubric_version) AS rubric_version
  FROM judge_results
  WHERE rubric_id = 'helpful_response_v1'
  GROUP BY agent_run_id
)
SELECT
  ar.id,
  ar.metadata_json->'model'->>'name' AS model_name,
  jr.output->>'score' AS score,
  jr.result_metadata->>'label' AS label
FROM latest_scores ls
JOIN judge_results jr
  ON jr.agent_run_id = ls.agent_run_id
  AND jr.rubric_version = ls.rubric_version
  AND jr.rubric_id = 'helpful_response_v1'
JOIN agent_runs ar ON ar.id = jr.agent_run_id
WHERE ar.metadata_json->>'environment' = 'prod'
ORDER BY CAST(jr.output->>'score' AS DOUBLE PRECISION) DESC
LIMIT 15;

Transcript Coverage Audit

Finds transcript groups that are marked as must_have but have no associated transcripts.

SELECT
  tg.id AS group_id,
  tg.name AS group_name,
  COUNT(t.id) AS transcript_count
FROM transcript_groups tg
LEFT JOIN transcripts t
  ON t.transcript_group_id = tg.id
  AND t.collection_id = tg.collection_id
WHERE tg.metadata_json->>'priority' = 'must_have'
GROUP BY tg.id, tg.name
HAVING COUNT(t.id) = 0
ORDER BY group_name;

Restrictions and Best Practices

• Read-only: Only SELECT-style queries are permitted. Use bulk exports or ingestion utilities to modify data outside of DQL.
• Single statement: Batches or multiple statements are rejected to avoid mixed workloads.
• Explicit projection: Wildcard projections (*) are disallowed. List the columns you need so downstream tooling (schema builders, type generation) stays predictable.
• Collection scoping: A single query can only access data within a single collection.
• Limit enforcement: Every query is capped at 10,000 rows by the server. If you omit LIMIT or request more, Docent automatically applies the cap—use pagination (OFFSET/LIMIT) or offline exports for larger result sets.
• JSON performance: Metadata fields are stored as JSON; heavy traversal across large collections can be slower than filtering on indexed scalar columns. Prefer top-level fields when available.
• Type awareness: The registry tracks datatypes for JSON metadata paths. When a path supports multiple types Docent may fall back to string comparisons, so cast where precision matters (e.g., CAST(metadata_json->>'duration_ms' AS BIGINT)).