> ## Documentation Index
> Fetch the complete documentation index at: https://docs.reilabs.org/llms.txt
> Use this file to discover all available pages before exploring further.
# Discovery examples
> Create, inspect, and query Transition, Structure, Sequential, and mixed Discovery Domains through the production API.
These examples cover numeric and categorical Transition Discovery, Structure Discovery, Sequential Discovery, and mixed causal-binding discovery.
Create a fresh Domain, submit the included observations, inspect before/after responses, and query retained state from one Python file.
Choose a declaration below, then use the shared create → ingest → inspect → query loop. These payloads extend the complete tutorial; [HTTP and contracts](/docs/neuroadapt/request-contracts#schema-coverage) describes schema coverage.
All examples use:
```text theme={null}
https://rei-neuroadapt-api.reilabs.org/api/v1
```
Export a bearer token before running them:
```bash theme={null}
export ADAPT1_API_KEY="YOUR_SECRET_TOKEN"
```
The hosted API derives tenant ownership and effective session identity from the bearer token. Request examples use `session_id: ignored` for compatibility.
## Choose a declaration
This Domain declares the numeric target and lets Adapt-1 discover the stable input projection.
```json theme={null}
{
"domain_id": "process-output-demo",
"session_id": "ignored",
"description": "Predict an observed process output from available measurements.",
"schema": {
"event_types": ["observation"]
},
"learning": {
"enabled": true,
"transition": {
"enabled": true,
"event_types": ["observation"],
"targets": [
{
"path": "values.process.output",
"type": "number"
}
],
"required_support": 3,
"numeric_model": "auto",
"autonomous_projection": {
"enabled": true,
"minimum_observations": 3,
"minimum_availability": 0.8,
"maximum_input_paths": 8
}
}
}
}
```
Send complete observations in order. The first events can return `projection_accumulating`. Continue while storage succeeds and inspect the projection until it becomes `ready`.
At query time, omit the target:
```json theme={null}
{
"session_id": "ignored",
"question": "Predict the process output.",
"context": {
"values": {
"process": {
"temperature": 30,
"pressure": 1.2
}
}
},
"return_fields": ["transition_prediction", "learning_state"],
"update_memory_state": false,
"allow_exploration": false
}
```
Verify:
```text theme={null}
learning_state.subsystems.structured_transition.autonomous_projection.status == "ready"
learning_state.subsystems.structured_transition.autonomous_projection.input_source == "discovered"
transition_prediction.status == "predicted"
transition_prediction.support_count >= required_support
```
Use a categorical target when labels have no numeric geometry.
```json theme={null}
{
"domain_id": "case-disposition-demo",
"session_id": "ignored",
"description": "Predict case disposition from stable observed fields.",
"schema": {
"event_types": ["resolved_case"]
},
"learning": {
"enabled": true,
"transition": {
"enabled": true,
"event_types": ["resolved_case"],
"targets": [
{
"path": "values.case.disposition",
"type": "categorical"
}
],
"required_support": 3,
"discrete_model": "auto",
"autonomous_projection": {
"enabled": true,
"minimum_observations": 4,
"minimum_availability": 0.9,
"maximum_input_paths": 12
}
}
}
}
```
Eligible event:
```json theme={null}
{
"session_id": "ignored",
"event_type": "resolved_case",
"values": {
"case": {
"severity": "high",
"repeat_count": 3,
"verified": true,
"disposition": "review"
}
},
"metadata": {
"episode_id": "cases-001"
}
}
```
Query context omits `disposition`.
This Domain lets Adapt-1 inspect eligible scalar fields, evaluate useful combinations and lags, and induce typed target rules.
```json theme={null}
{
"domain_id": "case-structure-demo",
"session_id": "ignored",
"description": "Discover observable structure associated with case outcomes.",
"schema": {
"event_types": ["resolved_case"]
},
"learning": {
"enabled": true,
"structure": {
"enabled": true,
"event_types": ["resolved_case"],
"feature_paths": [],
"targets": [
{
"path": "values.outcome",
"type": "categorical"
}
],
"representation": {
"enabled": true,
"maximum_order": 2,
"maximum_lag": 3,
"minimum_support": 12,
"episode_path": "metadata.episode_id"
}
}
}
}
```
Inspect:
```text theme={null}
learning_state.subsystems.autonomous_structure.status
learning_state.subsystems.autonomous_structure.representation.selected_paths
learning_state.subsystems.autonomous_structure.representation.active_representations
induced_structure.hypotheses[].conditions
induced_structure.hypotheses[].supporting_memory_ids
induced_structure.hypotheses[].counterevidence_memory_ids
```
A reviewed base surface can remain explicit while representation discovery continues over combinations and lags derived from it.
```json theme={null}
{
"domain_id": "reviewed-structure-demo",
"schema": {
"event_types": ["resolved_case"]
},
"learning": {
"enabled": true,
"structure": {
"enabled": true,
"event_types": ["resolved_case"],
"feature_paths": [
"values.signal_a",
"values.signal_b"
],
"targets": [
{
"path": "values.outcome",
"type": "categorical"
}
],
"representation": {
"enabled": true,
"maximum_order": 2,
"maximum_lag": 2,
"minimum_support": 12,
"episode_path": "metadata.episode_id"
}
}
}
}
```
The explicit base paths stay authoritative. Active rules can still update from evidence.
This pattern keeps the pre-outcome transition inputs explicit while allowing Adapt-1 to discover compatible before/after causal bindings.
```json theme={null}
{
"domain_id": "intervention-effect-demo",
"session_id": "ignored",
"description": "Learn observable intervention effects from before and after measurements.",
"schema": {
"event_types": ["intervention_result"]
},
"learning": {
"enabled": true,
"transition": {
"enabled": true,
"event_types": ["intervention_result"],
"input_paths": [
"values.before.temperature",
"values.before.pressure",
"values.intervention"
],
"action_path": "values.intervention",
"targets": [
{
"path": "values.after.output",
"type": "number"
}
],
"required_support": 3,
"autonomous_projection": {
"enabled": true,
"minimum_observations": 4,
"discover_causal_bindings": true,
"minimum_causal_variables": 2
},
"causal_graph": {
"enabled": true,
"event_types": ["intervention_result"]
}
}
}
}
```
Each event contains repeated numeric variable names under `before` and `after`, plus an intervention string matching one of those variable names. Inspect `causal_binding_source`, `causal_intervention_path`, `causal_variables`, and `causal_graph` before using the result.
## Sequential Discovery
Use Sequential Discovery when actions change later states and later rewards should revise earlier choices.
An opaque-action loop can expose public observation fields and legal actions while leaving their useful action values and action-effect structure for Adapt-1 to form:
1. query current public state
2. execute the selected legal action
3. observe the public next state and native reward
4. return episode ID, step, next state, reward, and terminal status
5. query again from retained Discovery state
Configure `learning.sequential.enabled: true` with real episode, step, next-state, reward, and terminal paths. Preserve the selected policy and `decision_id` through execution. Inspect sequential sample counts and selection attribution before claiming that retained state influenced a later action.
See [Sequential Discovery](/docs/neuroadapt/discovery-sequential) for the starting declaration and [Sequential learning, advanced](/docs/neuroadapt/sequential-learning) for the full policy, reward, training, and evaluation contract.
## Executable runner pattern
The following is a zero-start online pattern shared by the Discovery paths. For separate acquisition, run the evidence-formation steps before the declared new-run boundary, then freeze the resulting state or continue adapting according to the protocol:
1. create a unique Domain
2. send eligible events or feedback
3. inspect path-specific admission on every write
4. wait for usable discovered state
5. query, select, or predict
6. return the observable consequence when applicable
7. verify that a later result uses the intended retained state
Use a fresh unique `domain_id` for an independent rerun. When one reported run starts empty and retains state across its own episodes, later episodes show within-run retention rather than separate acquisition. For held-out frozen evaluation, split entities or episodes before ingestion, form state only on the acquisition partition, record the final learner state and version, stop event and feedback writes, and verify state identity remains unchanged through scoring.
## Common setup failures
| Symptom | Cause | Correction |
| ----------------------------------------------------------- | ------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------ |
| Domain creation returns `422` | Unknown field, missing target, or transition enabled without inputs or projection | Validate the declaration against the current Domain schema |
| Events store but learner count stays at zero | Projection is still accumulating or the event is missing a target | Inspect `learner_eligibility.rejection_reason` and `missing_paths` |
| Projection stays empty | Too few complete events or candidate fields do not meet availability | Send representative complete events and review observation and availability settings |
| Query abstains after projection is ready | Query context is missing a discovered path or lacks compatible support | Read `input_paths`, then send the same pre-target structure in `context` |
| Structural learner has samples but no active rule | The current evidence is still insufficient for a supported rule | Inspect representation and induced-structure diagnostics; do not force selection |
| Sequential feedback stores but no sequential sample appears | Episode, step, next state, reward, terminal status, or policy binding is missing or invalid | Inspect feedback admission and preserve the full sequential tuple |
| Temporal candidates cross unrelated records | Episode identity is absent or reused across independent sequences | Set `metadata.episode_id` consistently and start a new episode at each reset |
| A suspicious field dominates | Identifier, post-outcome value, or target-derived field entered the learner view | Remove it, create a clean Domain, and rerun Discovery and holdout evaluation |
Understand the Discovery boundary and lifecycle.
Review projection fields, revisions, and causal bindings.
Review discovered fields, combinations, temporal relationships, and induced rules.
Review ordered feedback, delayed credit, readiness, and action attribution.