/** * Model Extractor - Extract model information from AI Group data * * Parses and extracts model information from semantic steps and subagent processes. */import { type ModelInfo, parseModelString } from '@shared/utils/modelParser';import type { Process, SemanticStep } from '@renderer/types/data';/** * Extract the main model used in an AI Group. * * Strategy: * 1. Look through semantic steps to find tool_call steps with sourceModel * 2. Count occurrences of each model * 3. Return the most common model (in case of mixed usage) * * @param steps - Semantic steps from the AI Group * @returns The most common model info, or null if no models found */export function extractMainModel(steps: SemanticStep[]): ModelInfo | null { const modelCounts = new Map<string, { count: number; info: ModelInfo }>(); for (const step of steps) { // Tool call steps have sourceModel set from the assistant message if (step.type === 'tool_call' && step.content.sourceModel) { const model = step.content.sourceModel; if (model && model !== '<synthetic>') { const info = parseModelString(model); if (info) { const existing = modelCounts.get(info.name); if (existing) { existing.count++; } else { modelCounts.set(info.name, { count: 1, info }); } } } } } // Find most common model let maxCount = 0; let mainModel: ModelInfo | null = null; for (const { count, info } of modelCounts.values()) { if (count > maxCount) { maxCount = count; mainModel = info; } } return mainModel;}/** * Extract unique models used by subagents that differ from the main model. * * Strategy: * 1. Iterate through all processes (subagents) * 2. Find the first assistant message with a valid model in each process * 3. Parse and collect unique models that differ from mainModel * * @param processes - Subagent processes from the AI Group * @param mainModel - The main agent's model (to filter out) * @returns Array of unique model infos used by subagents */export function extractSubagentModels( processes: Process[], mainModel: ModelInfo | null): ModelInfo[] { const uniqueModels = new Map<string, ModelInfo>(); for (const process of processes) { // Find first assistant message with a valid model const assistantMsg = process.messages?.find( (m) => m.type === 'assistant' && m.model && m.model !== '<synthetic>' ); if (assistantMsg?.model) { const modelInfo = parseModelString(assistantMsg.model); if (modelInfo && modelInfo.name !== mainModel?.name) { uniqueModels.set(modelInfo.name, modelInfo); } } } return Array.from(uniqueModels.values());}
/**
* Model Extractor - Extract model information from AI Group data
*
* Parses and extracts model information from semantic steps and subagent processes.
*/
import { type ModelInfo, parseModelString } from '@shared/utils/modelParser';
import type { Process, SemanticStep } from '@renderer/types/data';
/**
* Extract the main model used in an AI Group.
*
* Strategy:
* 1. Look through semantic steps to find tool_call steps with sourceModel
* 2. Count occurrences of each model
* 3. Return the most common model (in case of mixed usage)
*
* @param steps - Semantic steps from the AI Group
* @returns The most common model info, or null if no models found
*/
export function extractMainModel(steps: SemanticStep[]): ModelInfo | null {
const modelCounts = new Map<string, { count: number; info: ModelInfo }>();
for (const step of steps) {
// Tool call steps have sourceModel set from the assistant message
if (step.type === 'tool_call' && step.content.sourceModel) {
const model = step.content.sourceModel;
if (model && model !== '<synthetic>') {
const info = parseModelString(model);
if (info) {
const existing = modelCounts.get(info.name);
if (existing) {
existing.count++;
} else {
modelCounts.set(info.name, { count: 1, info });
}
}
}
}
}
// Find most common model
let maxCount = 0;
let mainModel: ModelInfo | null = null;
for (const { count, info } of modelCounts.values()) {
if (count > maxCount) {
maxCount = count;
mainModel = info;
}
}
return mainModel;
}
/**
* Extract unique models used by subagents that differ from the main model.
*
* Strategy:
* 1. Iterate through all processes (subagents)
* 2. Find the first assistant message with a valid model in each process
* 3. Parse and collect unique models that differ from mainModel
*
* @param processes - Subagent processes from the AI Group
* @param mainModel - The main agent's model (to filter out)
* @returns Array of unique model infos used by subagents
*/
export function extractSubagentModels(
processes: Process[],
mainModel: ModelInfo | null
): ModelInfo[] {
const uniqueModels = new Map<string, ModelInfo>();
for (const process of processes) {
// Find first assistant message with a valid model
const assistantMsg = process.messages?.find(
(m) => m.type === 'assistant' && m.model && m.model !== '<synthetic>'
);
if (assistantMsg?.model) {
const modelInfo = parseModelString(assistantMsg.model);
if (modelInfo && modelInfo.name !== mainModel?.name) {
uniqueModels.set(modelInfo.name, modelInfo);
}
}
}
return Array.from(uniqueModels.values());
}