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Hellixia User Guide

Hellixia is BayesiaLab’s subject-matter assistant powered by Generative AI. It helps users create, enrich, and review semantic, causal, and probabilistic model assets inside BayesiaLab. This guide focuses on how to use Hellixia functions in BayesiaLab.

Before You Begin

Confirm the following before running a Hellixia function:

  • API provider configuration: set up the provider, model, API key, and endpoint in Settings.
  • Knowledge Files: add source files when the function should use documents, reports, papers, transcripts, or other source material.
  • General Context: provide domain guidance when the prompt alone is not enough.
  • Selections: select the required nodes, arcs, comments, classes, or Knowledge Files before opening functions that operate on existing model elements.
  • Model selection: choose a model/provider appropriate for the data sensitivity, task complexity, and token budget.
  • Privacy and data handling: check provider terms and internal policy before sending confidential material to an external model.

Key Concepts

Knowledge File

a file added as detailed LLM context or source material.

General Context

user-provided contextual guidance for a Hellixia operation.

Semantic Network

a learned graph representing semantic proximity.

Semantic Flowchart

a graph that emphasizes sequential relationships among concepts.

Knowledge Graph

a graph of entities or concepts connected by semantic relationships.

Causal Semantic Diagram

a graph of concepts connected by causal relationships, before full Bayesian-network quantification.

Causal Bayesian Network

a probabilistic causal graph with conditional probability tables.

Risk-Centric Causal Network

a causal model focused on triggering events, controls, consequences, and mitigation actions.

Function Index

The index below mirrors the Hellixia menu structure and links every visible function to its guide page.

Setup

Provider, key, endpoint, and connection settings required before Hellixia can call Generative AI services.

FunctionBest forInputsOutputsStatus
SettingsInitial setup; Provider configuration; Endpoint troubleshootingAPI provider account; API key; optional custom endpointConfigured Hellixia connectionComplete

Prompt-to-network generation

Functions that start with a question or prompt and generate semantic, causal, or probabilistic model structure.

FunctionBest forInputsOutputsStatus
Automatic Causal Network GeneratorRapid causal model prototyping; Risk-centric causal modeling; Starting from a domain questionPrompt or question; optional General Context; optional modeling constraintsNodes; causal arcs; arc comments; causal effects; conditional probability tablesComplete
Automatic Semantic Network GeneratorDomain exploration; Theme discovery; Semantic proximity mappingQuestion; keywords; optional General ContextDimensions; embeddings; dataset; learned Semantic NetworkComplete

Document-to-model workflows

Functions that use Knowledge Files as source material for structured semantic and causal analysis.

FunctionBest forInputsOutputsStatus
Document AnalysisReports; articles; transcripts; multi-document source materialKnowledge File(s); optional selected nodes or text; optional General ContextStructured graph artifacts depending on the selected generatorComplete
Semantic Flowchart GeneratorNarratives; procedures; process descriptionsKnowledge File content; optional selected node name, long name, or commentSemantic FlowchartComplete
Causal Semantic Diagram GeneratorCausal narratives; mechanism extraction; pre-Bayesian causal mappingKnowledge File content; optional selected node name, long name, or commentCausal Semantic DiagramComplete
Knowledge Graph GeneratorEntity and relation extraction; source-material explorationKnowledge FileKnowledge GraphComplete
Causal Network GeneratorDocument-derived Causal Bayesian Networks; risk-centric modelsKnowledge File content; optional General ContextNodes; causal links; mechanism comments; causal effects; conditional probability tables; root priorsComplete
Semantic Network GeneratorSemantic proximity among document-derived concepts; theme discovery in source materialKnowledge File; selected keywords; optional General ContextExtracted dimensions; embeddings; learned Semantic NetworkComplete
Doc-to-Node GeneratorMulti-document corpora; document-level semantic mapsSelected Knowledge FilesDocument nodes; comments containing file content; optional embeddings; optional Semantic NetworkComplete

Semantic and relationship analysis

Functions that identify concepts, relationships, flow, and meaning before or beside probabilistic modeling.

FunctionBest forInputsOutputsStatus
Semantic Flowchart GeneratorProcess descriptions; workflow narratives; ordered explanationsSelected node names, long names, comments, or supplied text; optional General ContextSemantic FlowchartComplete
Causal Semantic Diagram GeneratorCausal mechanism sketches; pre-model causal diagrams; hypothesis generationSelected node names, long names, comments, or supplied text; optional General ContextCausal Semantic DiagramComplete
Knowledge Graph GeneratorEntity relationship mapping; domain inventories; conceptual explorationPrompt, selected text, selected nodes, or optional General ContextKnowledge GraphComplete
Entity Relationship FinderExplaining links; building relationship candidates; checking entity pairsSelected nodes or entities; optional General ContextProposed relationships; relationship labels; reviewable explanationsComplete
Verbalize RelationshipsStakeholder communication; documentation; relationship reviewSelected arcs or relationships; optional General ContextRelationship descriptions; comments or text suitable for reviewComplete

Causal knowledge mining and priors

Functions that elicit causal directions, mechanisms, effects, priors, and reviewable causal hypotheses.

FunctionBest forInputsOutputsStatus
Causal Network GeneratorCausal model bootstrapping; risk-centric model generationPrompt, selected nodes, or General ContextNodes; causal arcs; causal effects; conditional probability tables; root priorsComplete
Causal Relationship FinderCausal link screening; mechanism elicitation; existing-node causal mappingSelected nodes; optional General ContextProposed causal links; causal mechanisms; arc commentsComplete
Multi-Engine Causal Relationship FinderCross-checking causal hypotheses; reducing single-model dependence; expert review preparationSelected nodes; multiple configured LLM engines; optional General ContextEngine-specific causal proposals; comparison material for analyst reviewComplete
Causal Structural PriorsConstrained structure learning; causal orientation review; expert prior elicitationSelected arcs or nodes; optional General ContextStructural priors; causal direction explanations; review materialComplete
Pairwise Causal LinkTwo-variable causal checks; arc-level review; focused causal elicitationTwo selected variables; optional General ContextProposed causal direction; mechanism explanation; optional arc commentComplete
Root Priors ElicitorInitial quantification; root-node priors; expert review before inferenceSelected root nodes; state definitions; optional General ContextPrior probability distributions; elicitation commentsComplete
ICI Local Effects ElicitorNoisy-OR style quantification; causal effect elicitation; parameter reviewSelected ICI relationships; node states; optional General ContextIndependent local effect estimates; reviewable parameter notesComplete

Authoring, enrichment, translation, and visualization

Functions that enrich nodes, arcs, comments, classes, embeddings, images, and translated labels.

FunctionBest forInputsOutputsStatus
Dimension ElicitorKeyword-based exploration; survey-item ideation; semantic scaffoldingKeywords; optional selected nodes; optional General ContextDimension nodes; node comments; optional classesComplete
Embedding GeneratorSemantic proximity learning; clustering; semantic network constructionSelected nodes; node names, long names, or commentsEmbedding variables or data suitable for learning and clusteringComplete
CommentsModel documentation; stakeholder communication; comment cleanupSelected nodes or arcs; optional General ContextNode comments; arc comments; long namesComplete
Keyword-Based Node Comment GeneratorFast node documentation; keyword-grounded descriptions; semantic enrichmentSelected nodes; keywords; optional General ContextNode commentsComplete
Definition-Based Comment GeneratorConcept definitions; node documentation; domain vocabulary cleanupSelected nodes; optional definitions; optional General ContextNode commentsComplete
Node Comment CondenserDense comments; tooltip-ready summaries; readability cleanupSelected nodes with commentsCondensed node commentsComplete
Node Comment ElaboratorStakeholder-facing explanations; documentation enrichment; concept clarificationSelected nodes with comments; optional General ContextElaborated node commentsComplete
Long Name Generator from Node CommentReadable labels; model cleanup; semantic documentationSelected nodes with commentsNode long namesComplete
Arc Comment CondenserArc-comment cleanup; mechanism summaries; readability reviewSelected arcs with commentsCondensed arc commentsComplete
Arc Comment ElaboratorMechanism explanations; causal review; stakeholder communicationSelected arcs with comments; optional General ContextElaborated arc commentsComplete
Class Description GeneratorCluster interpretation; factor naming; class documentationSelected classes or grouped nodes; optional General ContextClass descriptions; suggested class namesComplete
Semantic Variable ClusteringLarge network organization; survey item grouping; semantic class creationSelected nodes; names, long names, comments, or embeddingsSemantic clusters; classes; reviewable groupingsComplete
Image GeneratorVisual model communication; node illustration; presentation supportSelected nodes; node names, long names, or comments; optional prompt guidanceGenerated images associated with nodesComplete
TranslatorMultilingual models; localized documentation; cross-language reviewSelected nodes; source and target languages; names, states, or commentsTranslated labels, state names, and commentsComplete

Workflow Guides

I Have a Question or Prompt

Start with Automatic Causal Network Generator or Automatic Semantic Network Generator, then review the proposed structure.

I Have Documents

Use Document Analysis to turn Knowledge Files into graph artifacts. Common next steps include Document Analysis > Causal Network Generator and Document Analysis > Doc-to-Node Generator.

I Already Have Nodes

Enrich selected nodes with Dimension Elicitor, Embedding Generator, Semantic Variable Clustering, or Comments.

I Need Causal Orientation or Priors

Use Causal Relationship Finder, Causal Structural Priors, Root Priors Elicitor, or ICI Local Effects Elicitor to create reviewable hypotheses.

I Need to Communicate or Enrich a Model

Use Verbalize Relationships, Class Description Generator, Image Generator, or Translator.

Troubleshooting

SymptomWhat to check
API key not configuredOpen Settings and verify the provider, key, endpoint, and model access.
No nodes or arcs selectedSelect the required graph elements before opening functions that operate on existing nodes or arcs.
No Knowledge File loadedAdd one or more Knowledge Files before using Document Analysis functions.
Context is too broadNarrow the prompt, add General Context, or split the task into smaller runs.
Output is too denseReduce requested scope, constrain the number of concepts, or condense generated comments after review.
Causal directions look weakTreat them as hypotheses, compare against source material, and use expert review before creating structural priors.
Provider or rate-limit errorsCheck provider status, API limits, billing, model availability, and endpoint configuration.

Examples and Tutorials