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README.md

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----
-license: cc-by-4.0
-pretty_name: Query Agent Gym
-language:
-- en
-size_categories:
-- 10K<n<100K
-task_categories:
-- question-answering
-- table-question-answering
-tags:
-- weaviate
-- query-agent
-- function-calling
-- text-to-query
-- database
-- retrieval
-- agents
-- synthetic
-configs:
-- config_name: queries
-  default: true
-  data_files:
-  - split: test
-    path:
-    - public-transit/queries.parquet
-    - tattoo-studios/queries.parquet
-    - nonprofits/queries.parquet
-    - surf-schools/queries.parquet
-    - childcare/queries.parquet
-- config_name: environments
-  data_files:
-  - split: test
-    path: environments.parquet
-- config_name: public-transit_TransitRoutes
-  data_files:
-  - split: test
-    path: public-transit/objects/TransitRoutes.parquet
-- config_name: public-transit_RidershipReports
-  data_files:
-  - split: test
-    path: public-transit/objects/RidershipReports.parquet
-- config_name: tattoo-studios_TattooArtists
-  data_files:
-  - split: test
-    path: tattoo-studios/objects/TattooArtists.parquet
-- config_name: tattoo-studios_TattooDesigns
-  data_files:
-  - split: test
-    path: tattoo-studios/objects/TattooDesigns.parquet
-- config_name: nonprofits_Charities
-  data_files:
-  - split: test
-    path: nonprofits/objects/Charities.parquet
-- config_name: nonprofits_Campaigns
-  data_files:
-  - split: test
-    path: nonprofits/objects/Campaigns.parquet
-- config_name: nonprofits_GivingPrograms
-  data_files:
-  - split: test
-    path: nonprofits/objects/GivingPrograms.parquet
-- config_name: nonprofits_Donations
-  data_files:
-  - split: test
-    path: nonprofits/objects/Donations.parquet
-- config_name: surf-schools_SurfBeaches
-  data_files:
-  - split: test
-    path: surf-schools/objects/SurfBeaches.parquet
-- config_name: surf-schools_SurfInstructors
-  data_files:
-  - split: test
-    path: surf-schools/objects/SurfInstructors.parquet
-- config_name: surf-schools_LessonPrograms
-  data_files:
-  - split: test
-    path: surf-schools/objects/LessonPrograms.parquet
-- config_name: surf-schools_SurfEquipment
-  data_files:
-  - split: test
-    path: surf-schools/objects/SurfEquipment.parquet
-- config_name: childcare_DaycareCenters
-  data_files:
-  - split: test
-    path: childcare/objects/DaycareCenters.parquet
-- config_name: childcare_ChildcareStaff
-  data_files:
-  - split: test
-    path: childcare/objects/ChildcareStaff.parquet
-- config_name: childcare_ChildEnrollments
-  data_files:
-  - split: test
-    path: childcare/objects/ChildEnrollments.parquet
----
-
-# Query Agent Gym
-
-Query Agent Gym is a set of procedurally generated database environments and
-natural-language queries with exactly known ground-truth queries.
-
-This release holds five **held-out test environments**:
-15 collections with 1,000 objects each (15,000 objects, 18,000 searchable
-documents) and 250 queries, 50 per environment. Every entity, name, and
-document is synthetic.
-
-| Environment | Collections (kind) | Objects | Queries |
-|---|---|---:|---:|
-| Public Transit | TransitRoutes (knowledge_base), RidershipReports (transactional) | 2,000 | 50 |
-| Tattoo Studios | TattooArtists, TattooDesigns (knowledge_base) | 2,000 | 50 |
-| Nonprofits | Charities, Campaigns, GivingPrograms (knowledge_base), Donations (transactional) | 4,000 | 50 |
-| Surf Schools | SurfBeaches, SurfInstructors, LessonPrograms, SurfEquipment (knowledge_base) | 4,000 | 50 |
-| Childcare | DaycareCenters, ChildcareStaff (knowledge_base), ChildEnrollments (transactional) | 3,000 | 50 |
-
-## Quick start
-
-```python
-from datasets import load_dataset
-import json
-
-queries = load_dataset("weaviate/query-agent-gym", "queries", split="test")
-surf = queries.filter(lambda r: r["environment"] == "surf-schools")
-q = queries[0]
-print(q["command"])                     # the natural-language request
-print(q["ground_truth_sql"])            # readable ground truth, one entry per sub-query
-plan = json.loads(q["ground_truth"])    # exact ground truth (query_database arguments)
-
-routes = load_dataset("weaviate/query-agent-gym", "public-transit_TransitRoutes", split="test")
-```
-
-## Layout
-
-Each environment has its own folder.
-
-```
-environments.parquet                   one row per environment
-<environment>/schema.json              the schema exactly as generated
-<environment>/queries.parquet          the environment's 50 queries
-<environment>/objects/<Collection>.parquet   1,000 objects per collection
-```
-
-| Config | Rows | Contents |
-|---|---:|---|
-| `queries` (default) | 250 | All five environments' queries, with an `environment` column |
-| `environments` | 5 | One row per environment |
-| `<environment>_<Collection>` | 1,000 each | The objects of one collection, one typed column per property |
-
-To work with one environment, filter the `queries` config on `environment`,
-or read `<environment>/queries.parquet` directly.
-
-### `queries`
-
-| Field | Type | Description |
-|---|---|---|
-| `query_id` | string | e.g. `public-transit-013` |
-| `environment` | string | Environment id, e.g. `public-transit` |
-| `environment_name` | string | e.g. `Public Transit` |
-| `command` | string | The natural-language request given to the agent |
-| `phrasing` | string | `explicit` (names properties and values plainly) or `conversational` (a scenario, with values still verbatim) |
-| `plan` | string | The operators of the plan, sub-queries separated by `;` |
-| `num_sub_queries` | int | 1 to 3. Sub-queries are independent, never multi-hop |
-| `operator_classes` | list of strings | Operator classes the plan features (search, each filter kind, each aggregation kind, group-by, multi-query) |
-| `ground_truth` | string (JSON) | List of `query_database` argument objects, one per sub-query (format below) |
-| `ground_truth_sql` | list of strings | A SQL-like rendering of each sub-query, for reading only |
-| `search_grounds` | list of structs | For each sub-query with a search, its relevance-labeled result set (below) |
-| `reference_results` | string (JSON) | Each sub-query's ground truth executed on the objects (below) |
-
-### Objects
-
-Each `<environment>_<Collection>` config has a `uuid` column followed by one
-column per property, in schema order. The `uuid` is the object's id and is
-what `search_grounds` and `reference_results` refer to. Property types map to
-columns and to Weaviate data types as follows.
-
-| Property type | Column type | Weaviate data type |
-|---|---|---|
-| `searchable_text` | string | `text` (the prose a search should match) |
-| `filterable_text` | string | `text` (identifiers, references, categories) |
-| `int` / `number` | int64 / float64 | `int` / `number` |
-| `boolean` | bool | `boolean` |
-| `date` | string, `YYYY-MM-DD` | `date` (send as RFC 3339, e.g. `2025-11-16T00:00:00Z`) |
-| `text_array`, `int_array`, `number_array`, `boolean_array`, `date_array` | list | `text[]`, `int[]`, `number[]`, `boolean[]`, `date[]` |
-
-Each collection has exactly one identifier property (`is_identifier` in
-`schema.json`): an invented, unique name in a `knowledge_base` collection, a
-code in a `transactional` one. A `transactional` collection's reference
-properties (`references` in `schema.json`) hold the identifier of a
-`knowledge_base` record,
-stored directly in the row, so no query needs a join. No value is null.
-
-## Ground truth format
-
-A ground truth is a list of sub-queries, each a `query_database` function-call
-argument object.
-
-```json
-[{"collection_name": "TransitRoutes",
-  "search_query": "observatory and weather access",
-  "numeric_array_property_filters": [
-    {"property_name": "stopSequenceNumbers", "operator": "CONTAINS_ALL", "values": [1, 8]}]}]
-```
-
-| Argument | Items | Operators or metrics |
-|---|---|---|
-| `collection_name` | | The target collection |
-| `search_query` | string | A relevance search over the collection's searchable text |
-| `text_property_filters` | `property_name`, `operator`, `value` | `=`, `LIKE` (pattern uses `%`, e.g. `Nerith%`) |
-| `numeric_property_filters` | `property_name`, `operator`, `value` | `=`, `<`, `>`, `<=`, `>=` |
-| `date_property_filters` | `property_name`, `operator`, `value` (ISO date) | `=`, `<`, `>`, `<=`, `>=` |
-| `boolean_property_filters` | `property_name`, `operator`, `value` | `=`, `!=` |
-| `array_property_filters` (text arrays), `numeric_array_property_filters`, `boolean_array_property_filters`, `date_array_property_filters` | `property_name`, `operator`, `values` | `CONTAINS_ANY`, `CONTAINS_ALL` |
-| `numeric_property_aggregations` | `property_name`, `metric` | `COUNT`, `MIN`, `MAX`, `MEAN`, `MEDIAN`, `MODE`, `SUM` |
-| `text_property_aggregations` | `property_name`, `metric`, `top_occurrences_limit` | `COUNT`, `COUNT_DISTINCT`, `TOP_OCCURRENCES` |
-| `boolean_property_aggregations` | `property_name`, `metric` | `COUNT`, `TOTAL_TRUE`, `TOTAL_FALSE`, `PERCENTAGE_TRUE`, `PERCENTAGE_FALSE` |
-| `date_property_aggregations` | `property_name`, `metric` | `COUNT`, `EARLIEST`, `LATEST` |
-| `groupby_property` | string | Groups every aggregation of the sub-query |
-
-All filters in a sub-query are combined with AND. A sub-query without
-aggregations retrieves records, and one with aggregations returns only the
-statistics.
-
-**Searches.** The words of `search_query` are one valid phrasing and are not
-the target. A search sub-query's correct result is its labeled set:
-`search_grounds[i].candidate_ids` are the records the sub-query's filters
-select (R), and `relevant_ids` are the records a language model labeled
-relevant to the topic after reading every document in R (S). The `topic` and
-`description` fields say what the search is about.
-
-**Reference results.** `reference_results[i]` is sub-query `i` executed on the
-objects, with a search resolving to its `relevant_ids`. A retrieval is a sorted
-list of uuids, an aggregation is `{"METRIC(property)": value}`, and a group-by
-maps each group value (as a string) to its aggregations. Percentages are on a
-0 to 100 scale, and `TOP_OCCURRENCES` is a list of `[value, count]` pairs.
-Ties in `MODE` and `TOP_OCCURRENCES` are broken deterministically, which a
-database may break differently.
-
-## Evaluation
-
-The blog post evaluates agents with **Exact Match** over the plan. The
-predicted and ground-truth plans are canonicalized and matched sub-query to
-sub-query. The collection, every filter with its operator and value, every
-aggregation with its metric and property, and the group-by must agree, and
-extra sub-queries or statistics count as errors. A search must be present on
-the right collection, but its words are not compared. A few re-expressions
-that return the same answer on the data are accepted, such as a per-group
-count of any populated single-valued property, or a date bound that excludes
-no record. Answer-level comparison against `reference_results` is a looser
-alternative.
-
-To reproduce the setup, create one Weaviate collection per objects config with
-the data types above and a text vectorizer (the study used
-`text2vec_weaviate` with hybrid search), use each row's `uuid` as the object
-id, and point the agent at an environment's collections. Uploading each run
-under a fresh collection name avoids agents reusing statistics cached from an
-earlier run.
-
-### Reference results
-
-The Weaviate Query Agent, run once on the 250 queries with the same code and
-filter statistics, before and after a prompt revision made from failures on
-separate training environments (not released).
-
-| Environment | Before revision | After revision |
-|---|---:|---:|
-| Public Transit | 40/50 | 49/50 |
-| Tattoo Studios | 34/50 | 47/50 |
-| Nonprofits | 34/50 | 48/50 |
-| Surf Schools | 40/50 | 50/50 |
-| Childcare | 36/50 | 45/50 |
-| **Total** | **184/250 (73.6%)** | **239/250 (95.6%)** |
-
-## How the data was made
-
-Each environment is a position in a seeded order over 200 domain briefs (seed
-380, positions 5 to 9). Its structure is sampled procedurally: the number and
-kind of collections, property types, references, and each query's operator
-plan. Language models write the surface: the schema design (GPT-6 Sol),
-record names and documents (GPT-6 Luna), search topics and their relevance
-labels, and the natural-language commands (GPT-5.6 Luna). Ground truths are
-grounded in the saved objects and every command passes mechanical checks and
-a faithfulness verifier, so the plan can be recovered from the words. Each set
-of 50 queries is selected from a pool of 200 sampled plans so that every
-operator class appears at least 3 times, which makes the sets balanced across
-operators rather than distributed like natural traffic. A seeded coin chose
-each command's phrasing, giving 113 explicit and 137 conversational commands.
-
-## Limitations
-
-- All data is synthetic and English only. Entities are invented, so an agent
-  cannot answer from world knowledge, and documents are written as a real
-  system of record would write them.
-- Values are drawn independently, so a record can combine values that would
-  rarely co-occur in reality (a fin set tagged for zipper care). Documents are
-  written to stay consistent with each record's values.
-- Public Transit has no group-by queries. Its only groupable properties are
-  references with over 400 distinct values, which yield degenerate groups.
-- Relevance labels for searches come from a language model reading each
-  candidate document, not from human annotation.
-- Search topics can repeat a theme within an environment, and two Tattoo
-  Studios design searches share the phrase "melting snow".
-- The query sets exclude what the Weaviate Query Agent cannot express, such as
-  aggregating over the results of a semantic search.