Emotions: Use the full context for main API To prevent prompt reprocessing per turn, use the full context when prompting with the main API. Doing so keeps the preserved prefix while adding an extra sentence rather than providing two different prompts for each turn. In addition, JSON schemas might need a prompt to coerce a proper schema return from the model. Signed-off-by: kingbri <8082010+kingbri1@users.noreply.github.com>

e43e771cc534092e04edf30289066f9fef138147

kingbri <8082010+kingbri1@users.noreply.github.com>

public/scripts/extensions/expressions/index.js+9 -6 Viewed

 import { Fuse } from '../../../lib.js';
 import { characters, eventSource, event_types, generateQuietPrompt, generateRaw, getRequestHeaders, main_api, online_status, saveSettingsDebounced, substituteParams, substituteParamsExtended, system_message_types, this_chid } from '../../../script.js';
 import { dragElement, isMobile } from '../../RossAscends-mods.js';
 import { getContext, getApiUrl, modules, extension_settings, ModuleWorkerWrapper, doExtrasFetch, renderExtensionTemplateAsync } from '../../extensions.js';
 import { loadMovingUIState, performFuzzySearch, power_user } from '../../power-user.js';

  * @returns {Promise<string>} Prompt for the LLM API.
  */
 async function getLlmPrompt(labels) {
 -    if (isJsonSchemaSupported()) {
 -        return '';
 -    }
+-
     const labelsString = labels.map(x => `"${x}"`).join(', ');
     const prompt = substituteParamsExtended(String(extension_settings.expressions.llmPrompt), { labels: labelsString });
     return prompt;

                 const expressionsList = await getExpressionsList({ filterAvailable: filterAvailable });
                 const prompt = substituteParamsExtended(customPrompt, { labels: expressionsList }) || await getLlmPrompt(expressionsList);
                 eventSource.once(event_types.TEXT_COMPLETION_SETTINGS_READY, onTextGenSettingsReady);
 -                const emotionResponse = await generateRaw(text, main_api, false, false, prompt);
++
 +                let emotionResponse;
 +                try {
 +                    inApiCall = true;
 +                    emotionResponse = await generateQuietPrompt(prompt, false, false);
 +                } finally {
 +                    inApiCall = false;
 +                }
                 return parseLlmResponse(emotionResponse, expressionsList);
+            }
             // Using WebLLM