Ts/update stratify beaker context (#3584)

packages/client/hmi-client/src/components/llm/tera-notebook-jupyter-input.vue

@@ -58,7 +58,7 @@ const props = defineProps<{
  contextLanguage: string;
 }>();
 
-const emit = defineEmits(['llm-output', 'llm-thought-output']);
+const emit = defineEmits(['question-asked', 'llm-output', 'llm-thought-output']);
 
 const questionString = ref('');
 const kernelStatus = ref<string>('');
@@ -73,6 +73,7 @@ const submitQuestion = () => {
  const message = props.kernelManager.sendMessage('llm_request', {
  request: questionString.value
  });
+ emit('question-asked');
  // May prefer to use a manual status rather than following this. TBD. Both options work for now
  message.register('status', (data) => {
  kernelStatus.value = data.content.execution_state;
@@ -84,6 +85,10 @@ const submitQuestion = () => {
  thoughts.value = data;
  emit('llm-thought-output', data);
  });
+ message.register('llm_response', (data) => {
+ thoughts.value = data;
+ emit('llm-thought-output', data);
+ });
 };
 </script>
 

packages/client/hmi-client/src/components/llm/tera-notebook-jupyter-thought-output.vue

@@ -15,14 +15,22 @@ import { ref, computed } from 'vue';
 import Button from 'primevue/button';
 
 const props = defineProps<{
- llmThought?: any;
+ llmThoughts: any[];
 }>();
 const showThoughts = ref(false);
-const thought = computed(() => props?.llmThought?.content?.thought ?? '');
+const thought = computed(() => {
+ let aString = '';
+ props.llmThoughts.forEach((ele) => {
+ const llmResponse = ele.content?.thought ?? ele.content?.text ?? '';
+ aString = aString.concat(llmResponse, '\n \n');
+ });
+ return aString;
+});
 </script>
 
 <style scoped>
 .thought-bubble {
+ white-space: pre-line;
  border: 1px solid var(--surface-border-light);
  border-radius: var(--border-radius);
  padding: var(--gap-small);

packages/client/hmi-client/src/components/workflow/ops/decapodes/tera-decapodes-drilldown.vue

@@ -31,10 +31,11 @@
  :defaultOptions="sampleAgentQuestions"
  :context-language="contextLanguage"
  @llm-output="(data: any) => appendCode(data, 'code')"
- @llm-thought-output="(data: any) => (llmThought = data)"
+ @llm-thought-output="(data: any) => llmThoughts.push(data)"
+ @question-asked="llmThoughts = []"
  />
  </Suspense>
- <tera-notebook-jupyter-thought-output :llm-thought="llmThought" />
+ <tera-notebook-jupyter-thought-output :llm-thoughts="llmThoughts" />
  <v-ace-editor
  v-model:value="codeText"
  @init="initializeEditor"
@@ -118,7 +119,7 @@ const initializeEditor = (editorInstance: any) => {
  editor = editorInstance;
 };
 const codeText = ref();
-const llmThought = ref();
+const llmThoughts = ref<any[]>([]);
 
 const buildJupyterContext = () => ({
  context: 'decapodes',

packages/client/hmi-client/src/components/workflow/ops/model-comparison/tera-model-comparison.vue

@@ -86,10 +86,11 @@
  :kernelManager="kernelManager"
  :defaultOptions="sampleAgentQuestions"
  @llm-output="appendCode"
- @llm-thought-output="(data: any) => (llmThought = data)"
+ @llm-thought-output="(data: any) => llmThoughts.push(data)"
+ @question-asked="llmThoughts = []"
  :context-language="contextLanguage"
  />
- <tera-notebook-jupyter-thought-output :llm-thought="llmThought" />
+ <tera-notebook-jupyter-thought-output :llm-thoughts="llmThoughts" />
  </div>
  <v-ace-editor
  v-model:value="code"
@@ -191,7 +192,7 @@ const isLoadingStructuralComparisons = ref(false);
 const structuralComparisons = ref<string[]>([]);
 const llmAnswer = ref('');
 const code = ref(props.node.state.notebookHistory?.[0]?.code ?? '');
-const llmThought = ref();
+const llmThoughts = ref<any[]>([]);
 const isKernelReady = ref(false);
 const modelsToCompare = ref<Model[]>([]);
 const contextLanguage = ref<string>('python3');

packages/client/hmi-client/src/components/workflow/ops/model-config/tera-model-config.vue

@@ -240,10 +240,11 @@
  :defaultOptions="sampleAgentQuestions"
  :context-language="contextLanguage"
  @llm-output="(data: any) => appendCode(data, 'code')"
- @llm-thought-output="(data: any) => (llmThought = data)"
+ @llm-thought-output="(data: any) => llmThoughts.push(data)"
+ @question-asked="llmThoughts = []"
  />
  </Suspense>
- <tera-notebook-jupyter-thought-output :llm-thought="llmThought" />
+ <tera-notebook-jupyter-thought-output :llm-thoughts="llmThoughts" />
  </div>
  <v-ace-editor
  v-model:value="codeText"
@@ -442,7 +443,7 @@ const buildJupyterContext = () => {
 const codeText = ref(
  '# This environment contains the variable "model_config" to be read and updated'
 );
-const llmThought = ref();
+const llmThoughts = ref<any[]>([]);
 const notebookResponse = ref();
 const executeResponse = ref({
  status: OperatorStatus.DEFAULT,

packages/client/hmi-client/src/components/workflow/ops/model-edit/tera-model-edit.vue

@@ -33,10 +33,11 @@
  :default-options="sampleAgentQuestions"
  :context-language="contextLanguage"
  @llm-output="(data: any) => appendCode(data, 'code')"
- @llm-thought-output="(data: any) => (llmThought = data)"
+ @llm-thought-output="(data: any) => llmThoughts.push(data)"
+ @question-asked="llmThoughts = []"
  />
  </Suspense>
- <tera-notebook-jupyter-thought-output :llm-thought="llmThought" />
+ <tera-notebook-jupyter-thought-output :llm-thoughts="llmThoughts" />
  </div>
  <v-ace-editor
  v-model:value="codeText"
@@ -165,15 +166,16 @@ const sampleAgentQuestions = [
  'Add a new transition from S (to nowhere) with a rate constant of v with unit Days. The Rate depends on R',
  'Add an observable titled sample with the expression A * B * p.',
  'Rename the state S to Susceptible in the infection transition.',
- 'Rename the transition infection to inf.'
+ 'Rename the transition infection to inf.',
+ 'Change rate law of inf to S * I * z.'
 ];
 
 const contextLanguage = ref<string>('python3');
 
 const defaultCodeText =
  '# This environment contains the variable "model" \n# which is displayed on the right';
 const codeText = ref(defaultCodeText);
-const llmThought = ref();
+const llmThoughts = ref<any[]>([]);
 
 const executeResponse = ref({
  status: OperatorStatus.DEFAULT,

packages/client/hmi-client/src/components/workflow/ops/stratify-mira/tera-stratify-mira.vue

@@ -45,12 +45,13 @@
  <div class="toolbar">
  <tera-notebook-jupyter-input
  :kernel-manager="kernelManager"
- :default-options="[]"
+ :default-options="sampleAgentQuestions"
  :context-language="'python3'"
  @llm-output="(data: any) => processLLMOutput(data)"
- @llm-thought-output="(data: any) => (llmThought = data)"
+ @llm-thought-output="(data: any) => llmThoughts.push(data)"
+ @question-asked="llmThoughts = []"
  />
- <tera-notebook-jupyter-thought-output :llm-thought="llmThought" />
+ <tera-notebook-jupyter-thought-output :llm-thoughts="llmThoughts" />
  </div>
  <v-ace-editor
  v-model:value="codeText"
@@ -200,7 +201,13 @@ const kernelManager = new KernelSessionManager();
 
 let editor: VAceEditorInstance['_editor'] | null;
 const codeText = ref('');
-const llmThought = ref();
+const llmThoughts = ref<any[]>([]);
+
+const sampleAgentQuestions = [
+ 'Stratify my model by the ages young and old',
+ 'Stratify my model by the locations Toronto and Montreal where Toronto and Montreal cannot interact',
+ 'What is cartesian_control in stratify?'
+];
 
 const updateStratifyGroupForm = (config: StratifyGroup) => {
  const state = _.cloneDeep(props.node.state);
@@ -253,16 +260,13 @@ const stratifyRequest = () => {
  });
 
  const messageContent = {
- stratify_args: {
- key: strataOption.name,
- strata: strataOption.groupLabels.split(',').map((d) => d.trim()),
- concepts_to_stratify: conceptsToStratify,
- params_to_stratify: parametersToStratify,
- cartesian_control: strataOption.cartesianProduct,
- structure: strataOption.useStructure === true ? null : []
- }
+ key: strataOption.name,
+ strata: strataOption.groupLabels.split(',').map((d) => d.trim()),
+ concepts_to_stratify: conceptsToStratify,
+ params_to_stratify: parametersToStratify,
+ cartesian_control: strataOption.cartesianProduct,
+ structure: strataOption.useStructure === true ? null : []
  };
-
  kernelManager.sendMessage('reset_request', {}).register('reset_response', () => {
  kernelManager
  .sendMessage('stratify_request', messageContent)
@@ -311,7 +315,7 @@ const buildJupyterContext = () => {
  }
 
  return {
- context: 'mira_model',
+ context: 'mira_model_edit',
  language: 'python3',
  context_info: {
  id: amr.value.id

testing/Stratify-LLM-Tests.md

@@ -0,0 +1,101 @@
+## [Name of the Test Scenario]
+Please go through __every__ step of the test scenario.\
+When blocked, an error, or a UI/UX anomaly occurs, please report which scenario and step to [\#askem-testing](https://unchartedsoftware.slack.com/archives/C06FGLXB2CE).
+
+### 1. Begin test
+1. Login to https://app.staging.terarium.ai using the test account
+ ```
+ email: [email protected]
+ password: askem-quality-assurance
+ ```
+2. Create, or open, project named `Q&A [Your Name] [YYMMDD]`
+
+### 2. Test Stratify LLM Responses
+1. Make sure you have a model in your project.
+2. Create a workflow
+ Drop in your model in the workflow
+ Create a Stratify node
+ Connect the model to the stratify node
+3. Drill down into the Stratify node and go to the notebook section.
+
+Ask the follow questions one at a time, wait for the response, check the response matches
+reset the code block move on to the next.
+
+Q) "Stratify my model by the ages young and old",
+A) 
+model = stratify(
+ template_model=model,
+ key= "Age",
+ strata=['young', 'old'],
+ structure= [],
+ directed=False,
+ cartesian_control=False,
+ modify_names=True
+)
+
+Q) Stratify my model by the ages young and old where young can transition to old
+A) 
+model = stratify(
+ template_model=model,
+ key= "Age",
+ strata=['young', 'old'],
+ structure= [['young', 'old']],
+ directed=True,
+ cartesian_control=False,
+ modify_names=True
+)
+
+Q) Stratify my model by the ages young and old where young and old can become old, but old cannot become young
+A) 
+model = stratify(
+ template_model=model,
+ key= "Age",
+ strata=['young', 'old'],
+ structure= [['young', 'old']],
+ directed=False,
+ cartesian_control=False,
+ modify_names=True
+)
+
+Q) Stratify my model by the locations Toronto and Montreal where Toronto and Montreal cannot interact
+A) 
+model = stratify(
+ template_model=model,
+ key= "Location",
+ strata=['Toronto', 'Montreal'],
+ structure= [],
+ directed=False,
+ cartesian_control=False,
+ modify_names=True
+)
+
+Q) Stratify my model by the locations Toronto and Montreal where Toronto and Montreal can interact
+A) 
+model = stratify(
+ template_model=model,
+ key= "Location",
+ strata=['Toronto', 'Montreal'],
+ structure= [['Toronto', 'Montreal'], ['Montreal', 'Toronto']],
+ directed=False,
+ cartesian_control=True,
+ modify_names=True
+)
+
+OR
+model = stratify(
+ template_model=model,
+ key= "Location",
+ strata=['Toronto', 'Montreal'],
+ structure= [['Toronto', 'Montreal']],
+ directed=True,
+ cartesian_control=True,
+ modify_names=True
+)
+
+Q) What is cartesian_control in stratify?
+A)
+No code response, instead just a message in the thought section.
+
+
+### 4. End test
+1. logout of the application