mirror of
https://github.com/leon-ai/leon.git
synced 2024-11-27 16:16:48 +03:00
fix(server): handle exception on NLU
This commit is contained in:
louistiti
parent
0ff26739e7
commit
c172f2d6af
@ -173,8 +173,8 @@ SPACY_MODELS.set('fr', {
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// There is no CUDA support on macOS
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const commandToExecute =
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osType === OSTypes.MacOS
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? 'pipenv run pip install --ignore-installed torch==2.3.0'
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: 'pipenv run pip install --ignore-installed torch==2.3.0 --index-url https://download.pytorch.org/whl/cu121'
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? 'pipenv run pip install --ignore-installed --force-reinstall torch==2.3.0'
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: 'pipenv run pip install --ignore-installed --force-reinstall torch==2.3.0 --index-url https://download.pytorch.org/whl/cu121'
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await command(commandToExecute, {
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shell: true,
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@ -157,246 +157,253 @@ export default class NLU {
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const processingTimeStart = Date.now()
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return new Promise(async (resolve, reject) => {
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LogHelper.title('NLU')
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LogHelper.info('Processing...')
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try {
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LogHelper.title('NLU')
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LogHelper.info('Processing...')
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if (!MODEL_LOADER.hasNlpModels()) {
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if (!BRAIN.isMuted) {
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await BRAIN.talk(`${BRAIN.wernicke('random_errors')}!`)
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if (!MODEL_LOADER.hasNlpModels()) {
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if (!BRAIN.isMuted) {
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await BRAIN.talk(`${BRAIN.wernicke('random_errors')}!`)
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}
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const msg =
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'An NLP model is missing, please rebuild the project or if you are in dev run: npm run train'
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LogHelper.error(msg)
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return reject(msg)
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}
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const msg =
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'An NLP model is missing, please rebuild the project or if you are in dev run: npm run train'
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LogHelper.error(msg)
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return reject(msg)
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}
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if (this.shouldBreakActionLoop(utterance)) {
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this.conversation.cleanActiveContext()
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if (this.shouldBreakActionLoop(utterance)) {
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this.conversation.cleanActiveContext()
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await BRAIN.talk(`${BRAIN.wernicke('action_loop_stopped')}.`, true)
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await BRAIN.talk(`${BRAIN.wernicke('action_loop_stopped')}.`, true)
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return resolve({})
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}
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// Add spaCy entities
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await NER.mergeSpacyEntities(utterance)
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// Pre NLU processing according to the active context if there is one
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if (this.conversation.hasActiveContext()) {
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// When the active context is in an action loop, then directly trigger the action
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if (this.conversation.activeContext.isInActionLoop) {
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return resolve(await ActionLoop.handle(utterance))
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return resolve({})
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}
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// When the active context has slots filled
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if (Object.keys(this.conversation.activeContext.slots).length > 0) {
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// Add spaCy entities
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await NER.mergeSpacyEntities(utterance)
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// Pre NLU processing according to the active context if there is one
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if (this.conversation.hasActiveContext()) {
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// When the active context is in an action loop, then directly trigger the action
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if (this.conversation.activeContext.isInActionLoop) {
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return resolve(await ActionLoop.handle(utterance))
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}
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// When the active context has slots filled
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if (Object.keys(this.conversation.activeContext.slots).length > 0) {
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try {
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return resolve(await SlotFilling.handle(utterance))
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} catch (e) {
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return reject({})
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}
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}
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}
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const result: NLPJSProcessResult =
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await MODEL_LOADER.mainNLPContainer.process(utterance)
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const { locale, answers, classifications } = result
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const sentiment = {
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vote: result.sentiment.vote,
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score: result.sentiment.score
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}
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let { score, intent, domain } = result
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/**
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* If a context is active, then use the appropriate classification based on score probability.
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* E.g. 1. Create my shopping list; 2. Actually delete it.
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* If there are several "delete it" across skills, Leon needs to make use of
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* the current context ({domain}.{skill}) to define the most accurate classification
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*/
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if (this.conversation.hasActiveContext()) {
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classifications.forEach(({ intent: newIntent, score: newScore }) => {
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if (newScore > 0.6) {
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const [skillName] = newIntent.split('.')
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const newDomain = MODEL_LOADER.mainNLPContainer.getIntentDomain(
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locale,
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newIntent
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)
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const contextName = `${newDomain}.${skillName}`
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if (this.conversation.activeContext.name === contextName) {
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score = newScore
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intent = newIntent
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domain = newDomain
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}
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}
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})
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}
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const [skillName, actionName] = intent.split('.')
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await this.setNLUResult({
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...DEFAULT_NLU_RESULT, // Reset entities, slots, etc.
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utterance,
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newUtterance: utterance,
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answers, // For dialog action type
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sentiment,
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classification: {
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domain,
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skill: skillName || '',
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action: actionName || '',
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confidence: score
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}
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})
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const isSupportedLanguage = LangHelper.getShortCodes().includes(locale)
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if (!isSupportedLanguage) {
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await BRAIN.talk(
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`${BRAIN.wernicke('random_language_not_supported')}.`,
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true
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)
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return resolve({})
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}
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// Trigger language switching
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if (BRAIN.lang !== locale) {
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await this.switchLanguage(utterance, locale)
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return resolve(null)
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}
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if (intent === 'None') {
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const fallback = this.fallback(
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langs[LangHelper.getLongCode(locale)].fallbacks
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)
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if (!fallback) {
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if (!BRAIN.isMuted) {
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await BRAIN.talk(
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`${BRAIN.wernicke('random_unknown_intents')}.`,
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true
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)
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}
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LogHelper.title('NLU')
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const msg = 'Intent not found'
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LogHelper.warning(msg)
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Telemetry.utterance({ utterance, lang: BRAIN.lang })
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return resolve(null)
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}
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await this.setNLUResult(fallback)
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}
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LogHelper.title('NLU')
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LogHelper.success(
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`Intent found: ${this._nluResult.classification.skill}.${
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this._nluResult.classification.action
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} (domain: ${
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this._nluResult.classification.domain
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}); Confidence: ${this._nluResult.classification.confidence.toFixed(
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2
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)}`
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)
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const skillConfigPath = join(
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process.cwd(),
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'skills',
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this._nluResult.classification.domain,
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this._nluResult.classification.skill,
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'config',
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BRAIN.lang + '.json'
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)
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this._nluResult.skillConfigPath = skillConfigPath
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try {
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this._nluResult.entities = await NER.extractEntities(
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BRAIN.lang,
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skillConfigPath,
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this._nluResult
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)
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} catch (e) {
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LogHelper.error(`Failed to extract entities: ${e}`)
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}
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const shouldSlotLoop = await SlotFilling.route(intent, utterance)
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if (shouldSlotLoop) {
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return resolve({})
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}
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// In case all slots have been filled in the first utterance
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if (
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this.conversation.hasActiveContext() &&
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Object.keys(this.conversation.activeContext.slots).length > 0
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) {
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try {
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return resolve(await SlotFilling.handle(utterance))
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} catch (e) {
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return reject({})
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}
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}
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}
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const result: NLPJSProcessResult =
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await MODEL_LOADER.mainNLPContainer.process(utterance)
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const { locale, answers, classifications } = result
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const sentiment = {
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vote: result.sentiment.vote,
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score: result.sentiment.score
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}
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let { score, intent, domain } = result
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/**
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* If a context is active, then use the appropriate classification based on score probability.
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* E.g. 1. Create my shopping list; 2. Actually delete it.
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* If there are several "delete it" across skills, Leon needs to make use of
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* the current context ({domain}.{skill}) to define the most accurate classification
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*/
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if (this.conversation.hasActiveContext()) {
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classifications.forEach(({ intent: newIntent, score: newScore }) => {
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if (newScore > 0.6) {
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const [skillName] = newIntent.split('.')
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const newDomain = MODEL_LOADER.mainNLPContainer.getIntentDomain(
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locale,
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newIntent
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)
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const contextName = `${newDomain}.${skillName}`
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if (this.conversation.activeContext.name === contextName) {
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score = newScore
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intent = newIntent
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domain = newDomain
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}
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}
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})
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}
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const [skillName, actionName] = intent.split('.')
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await this.setNLUResult({
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...DEFAULT_NLU_RESULT, // Reset entities, slots, etc.
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utterance,
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newUtterance: utterance,
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answers, // For dialog action type
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sentiment,
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classification: {
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domain,
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skill: skillName || '',
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action: actionName || '',
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confidence: score
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}
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})
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const isSupportedLanguage = LangHelper.getShortCodes().includes(locale)
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if (!isSupportedLanguage) {
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await BRAIN.talk(
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`${BRAIN.wernicke('random_language_not_supported')}.`,
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true
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)
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return resolve({})
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}
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// Trigger language switching
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if (BRAIN.lang !== locale) {
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await this.switchLanguage(utterance, locale)
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return resolve(null)
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}
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if (intent === 'None') {
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const fallback = this.fallback(
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langs[LangHelper.getLongCode(locale)].fallbacks
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)
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if (!fallback) {
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if (!BRAIN.isMuted) {
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await BRAIN.talk(
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`${BRAIN.wernicke('random_unknown_intents')}.`,
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true
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)
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}
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LogHelper.title('NLU')
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const msg = 'Intent not found'
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LogHelper.warning(msg)
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Telemetry.utterance({ utterance, lang: BRAIN.lang })
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return resolve(null)
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}
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await this.setNLUResult(fallback)
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}
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LogHelper.title('NLU')
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LogHelper.success(
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`Intent found: ${this._nluResult.classification.skill}.${
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this._nluResult.classification.action
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} (domain: ${
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this._nluResult.classification.domain
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}); Confidence: ${this._nluResult.classification.confidence.toFixed(2)}`
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)
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const skillConfigPath = join(
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process.cwd(),
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'skills',
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this._nluResult.classification.domain,
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this._nluResult.classification.skill,
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'config',
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BRAIN.lang + '.json'
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)
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this._nluResult.skillConfigPath = skillConfigPath
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try {
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this._nluResult.entities = await NER.extractEntities(
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BRAIN.lang,
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skillConfigPath,
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this._nluResult
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)
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} catch (e) {
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LogHelper.error(`Failed to extract entities: ${e}`)
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}
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const shouldSlotLoop = await SlotFilling.route(intent, utterance)
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if (shouldSlotLoop) {
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return resolve({})
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}
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// In case all slots have been filled in the first utterance
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if (
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this.conversation.hasActiveContext() &&
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Object.keys(this.conversation.activeContext.slots).length > 0
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) {
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try {
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return resolve(await SlotFilling.handle(utterance))
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} catch (e) {
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return reject({})
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}
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}
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const newContextName = `${this._nluResult.classification.domain}.${skillName}`
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if (this.conversation.activeContext.name !== newContextName) {
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this.conversation.cleanActiveContext()
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}
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await this.conversation.setActiveContext({
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...DEFAULT_ACTIVE_CONTEXT,
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lang: BRAIN.lang,
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slots: {},
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isInActionLoop: false,
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originalUtterance: this._nluResult.utterance,
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newUtterance: utterance,
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skillConfigPath: this._nluResult.skillConfigPath,
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actionName: this._nluResult.classification.action,
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domain: this._nluResult.classification.domain,
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intent,
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entities: this._nluResult.entities
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})
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// Pass current utterance entities to the NLU result object
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this._nluResult.currentEntities =
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this.conversation.activeContext.currentEntities
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// Pass context entities to the NLU result object
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this._nluResult.entities = this.conversation.activeContext.entities
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try {
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const processedData = await BRAIN.execute(this._nluResult)
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// Prepare next action if there is one queuing
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if (processedData.nextAction) {
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const newContextName = `${this._nluResult.classification.domain}.${skillName}`
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if (this.conversation.activeContext.name !== newContextName) {
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this.conversation.cleanActiveContext()
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await this.conversation.setActiveContext({
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...DEFAULT_ACTIVE_CONTEXT,
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lang: BRAIN.lang,
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slots: {},
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isInActionLoop: !!processedData.nextAction.loop,
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originalUtterance: processedData.utterance ?? '',
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newUtterance: utterance ?? '',
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skillConfigPath: processedData.skillConfigPath || '',
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actionName: processedData.action?.next_action || '',
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domain: processedData.classification?.domain || '',
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intent: `${processedData.classification?.skill}.${processedData.action?.next_action}`,
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entities: []
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})
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}
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const processingTimeEnd = Date.now()
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const processingTime = processingTimeEnd - processingTimeStart
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return resolve({
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processingTime, // In ms, total time
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...processedData,
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await this.conversation.setActiveContext({
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...DEFAULT_ACTIVE_CONTEXT,
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lang: BRAIN.lang,
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slots: {},
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isInActionLoop: false,
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originalUtterance: this._nluResult.utterance,
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newUtterance: utterance,
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nluProcessingTime:
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processingTime - (processedData?.executionTime || 0) // In ms, NLU processing time only
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skillConfigPath: this._nluResult.skillConfigPath,
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actionName: this._nluResult.classification.action,
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domain: this._nluResult.classification.domain,
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intent,
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entities: this._nluResult.entities
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})
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} catch (e) {
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const errorMessage = `Failed to execute action: ${e}`
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// Pass current utterance entities to the NLU result object
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this._nluResult.currentEntities =
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this.conversation.activeContext.currentEntities
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// Pass context entities to the NLU result object
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this._nluResult.entities = this.conversation.activeContext.entities
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LogHelper.error(errorMessage)
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try {
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const processedData = await BRAIN.execute(this._nluResult)
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if (!BRAIN.isMuted) {
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SOCKET_SERVER.socket?.emit('is-typing', false)
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// Prepare next action if there is one queuing
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if (processedData.nextAction) {
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this.conversation.cleanActiveContext()
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await this.conversation.setActiveContext({
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...DEFAULT_ACTIVE_CONTEXT,
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lang: BRAIN.lang,
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slots: {},
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isInActionLoop: !!processedData.nextAction.loop,
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originalUtterance: processedData.utterance ?? '',
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newUtterance: utterance ?? '',
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skillConfigPath: processedData.skillConfigPath || '',
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actionName: processedData.action?.next_action || '',
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domain: processedData.classification?.domain || '',
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intent: `${processedData.classification?.skill}.${processedData.action?.next_action}`,
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entities: []
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})
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}
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const processingTimeEnd = Date.now()
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const processingTime = processingTimeEnd - processingTimeStart
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return resolve({
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processingTime, // In ms, total time
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...processedData,
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newUtterance: utterance,
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nluProcessingTime:
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processingTime - (processedData?.executionTime || 0) // In ms, NLU processing time only
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})
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} catch (e) {
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const errorMessage = `Failed to execute action: ${e}`
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LogHelper.error(errorMessage)
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if (!BRAIN.isMuted) {
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SOCKET_SERVER.socket?.emit('is-typing', false)
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}
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return reject(new Error(errorMessage))
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}
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return reject(new Error(errorMessage))
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} catch (e) {
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LogHelper.title('NLU')
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LogHelper.error(`Failed to process the utterance: ${e}`)
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}
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})
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}
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@ -1,5 +1,4 @@
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from cx_Freeze import setup, Executable
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import sysconfig
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import sys
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import os
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@ -47,11 +46,11 @@ options = {
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}
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# Include private libraries from the tokenizers package for Linux
|
||||
if 'linux' in sysconfig.get_platform():
|
||||
options['build_exe']['include_files'] = [
|
||||
*options['build_exe']['include_files'],
|
||||
('tcp_server/src/.venv/lib/python3.9/site-packages/tokenizers.libs', 'lib/tokenizers.libs')
|
||||
]
|
||||
# if 'linux' in sysconfig.get_platform():
|
||||
# options['build_exe']['include_files'] = [
|
||||
# *options['build_exe']['include_files'],
|
||||
# ('tcp_server/src/.venv/lib/python3.9/site-packages/tokenizers.libs', 'lib/tokenizers.libs')
|
||||
# ]
|
||||
|
||||
executables = [
|
||||
Executable(
|
||||
|
||||
Loading…
Reference in New Issue
Block a user