| --- |
| 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** containing |
| 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/QueryAgentGym", "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/QueryAgentGym", "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 |
|
|
| Placeholder, see `github.com/weaviate/query-agent-benchmarking`. |
|
|
| ## 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. |
|
|
