Upload README.md

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, built to
+evaluate data agents that answer questions by writing database queries, such
+as the [Weaviate Query Agent](https://docs.weaviate.io/agents/query). How the
+environments and queries are generated is described in the blog post
+[Query Agent Gym: Post-Training our Query Agent](https://weaviate.io/blog/query-agent-gym).
+
+This release holds the five **held-out test environments** of that study:
+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
+
+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.
+
+## Citation
+
+```bibtex
+@misc{query_agent_gym_2026,
+  title  = {Query Agent Gym: Post-Training our Query Agent},
+  author = {Shorten, Connor},
+  year   = {2026},
+  url    = {https://weaviate.io/blog/query-agent-gym}
+}
+
+@article{shorten2025querying,
+  title   = {Querying Databases with Function Calling},
+  author  = {Shorten, Connor and others},
+  journal = {arXiv preprint arXiv:2502.00032},
+  year    = {2025}
+}
+```