leon-ai/leon
1
1
Fork 0
mirror of https://github.com/leon-ai/leon.git synced 2024-11-27 08:06:03 +03:00
Code Issues Projects Releases Wiki Activity

feat(server): add utterance as expected_item type

Browse Source
This commit is contained in:
louistiti 2024-04-20 14:29:00 +08:00
parent da49a4dd82
commit 26af271edc
No known key found for this signature in database
GPG Key ID: 334C1BA3550BBDE5
16 changed files with 131 additions and 44 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

14
core/skills-endpoints.json
View File

@ -195,6 +195,11 @@
"route": "/api/action/social_communication/mbti/quiz",
"params": []
},
{
"method": "GET",
"route": "/api/action/unknown/widget-playground/run",
"params": []
},
{
"method": "GET",
"route": "/api/action/utilities/date_time/current_date_time",
@ -242,14 +247,13 @@
"params": []
},
{
"method": "POST",
"route": "/api/action/utilities/translator-poc/short_translate",
"params": [null],
"entitiesType": "trim"
"method": "GET",
"route": "/api/action/utilities/translator-poc/translate_loop_entry",
"params": []
},
{
"method": "GET",
"route": "/api/action/unknown/widget-playground/run",
"route": "/api/action/utilities/translator-poc/translate",
"params": []
}
]

19
server/src/constants.ts
View File

@ -161,12 +161,15 @@ export const LEON_FILE_PATH = path.join(process.cwd(), 'leon.json')
/**
* LLMs
*/
// export const LLM_VERSION = 'v0.2.Q4_K_M'
export const LLM_VERSION = '1.1-7b-it-Q4_K_M'
// export const LLM_NAME = 'Mistral 7B Instruct'
export const LLM_NAME = 'Gemma 1.1 7B (IT)'
// export const LLM_FILE_NAME = `mistral-7b-instruct-${LLM_VERSION}.gguf`
export const LLM_FILE_NAME = `gemma-${LLM_VERSION}.gguf`
export const LLM_VERSION = 'v0.2.Q4_K_S'
// export const LLM_VERSION = '1.1-7b-it-Q4_K_M'
// export const LLM_VERSION = '8B-Instruct-Q4_K_S'
export const LLM_NAME = 'Mistral 7B Instruct'
// export const LLM_NAME = 'Gemma 1.1 7B (IT)'
// export const LLM_NAME = 'Meta Llama 3 8B Instruct'
export const LLM_FILE_NAME = `mistral-7b-instruct-${LLM_VERSION}.gguf`
// export const LLM_FILE_NAME = `gemma-${LLM_VERSION}.gguf`
// export const LLM_FILE_NAME = `Meta-Llama-3-${LLM_VERSION}.gguf`
export const LLM_NAME_WITH_VERSION = `${LLM_NAME} (${LLM_VERSION})`
export const LLM_DIR_PATH = path.join(MODELS_PATH, 'llm')
export const LLM_PATH = path.join(LLM_DIR_PATH, LLM_FILE_NAME)
@ -176,10 +179,14 @@ export const LLM_HF_DOWNLOAD_URL =
'https://huggingface.co/bartowski/gemma-1.1-7b-it-GGUF/resolve/main/gemma-1.1-7b-it-Q4_K_M.gguf?download=true'
/*export const LLM_HF_DOWNLOAD_URL =
'https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.2-GGUF/resolve/main/mistral-7b-instruct-v0.2.Q4_K_M.gguf?download=true'*/
/*export const LLM_HF_DOWNLOAD_URL =
'https://huggingface.co/bartowski/Meta-Llama-3-8B-Instruct-GGUF/resolve/main/Meta-Llama-3-8B-Instruct-Q4_K_S.gguf?download=true'*/
export const LLM_MIRROR_DOWNLOAD_URL =
'https://hf-mirror.com/bartowski/gemma-1.1-7b-it-GGUF/resolve/main/gemma-1.1-7b-it-Q4_K_M.gguf?download=true'
/*export const LLM_MIRROR_DOWNLOAD_URL =
'https://hf-mirror.com/TheBloke/Mistral-7B-Instruct-v0.2-GGUF/resolve/main/mistral-7b-instruct-v0.2.Q4_K_M.gguf?download=true'*/
/*export const LLM_MIRROR_DOWNLOAD_URL =
'https://hf-mirror.com/bartowski/Meta-Llama-3-8B-Instruct-GGUF/resolve/main/Meta-Llama-3-8B-Instruct-Q4_K_S.gguf?download=true'*/
/**
* @see llama.cpp releases: https://github.com/ggerganov/llama.cpp/releases
*/

6
server/src/core/brain/brain.ts
View File

@ -332,7 +332,7 @@ export default class Brain {
}
/**
* Execute Python skills
* Execute skills
*/
public execute(nluResult: NLUResult): Promise<Partial<BrainProcessResult>> {
const executionTimeStart = Date.now()
@ -373,6 +373,8 @@ export default class Brain {
? actions[action.next_action]
: null
console.log('nextAction', nextAction)
if (actionType === SkillActionTypes.Logic) {
/**
* "Logic" action skill execution
@ -582,6 +584,8 @@ export default class Brain {
SOCKET_SERVER.socket?.emit('suggest', nextAction.suggestions)
}
console.log('nluResult', nluResult, action, nextAction)
resolve({
utteranceID,
lang: this._lang,

2
server/src/core/llm-manager/llm-duties/custom-ner-llm-duty.ts
View File

@ -54,7 +54,7 @@ export class CustomNERLLMDuty<T> extends LLMDuty {
...this.data.schema
}
})
const prompt = `${this.systemPrompt} Utterance: ${this.input}`
const prompt = `${this.systemPrompt} Utterance to parse: ${this.input}`
const rawResult = await completion.generateCompletion(prompt, {
contextShiftSize: context.contextSize / 2,
grammar,

2
server/src/core/llm-manager/llm-duties/summarization-llm-duty.ts
View File

@ -48,7 +48,7 @@ export class SummarizationLLMDuty extends LLMDuty {
}
}
})
const prompt = `${this.systemPrompt} Text: ${this.input}`
const prompt = `${this.systemPrompt} Text to summarize: ${this.input}`
const rawResult = await completion.generateCompletion(prompt, {
grammar,
maxTokens: context.contextSize

2
server/src/core/llm-manager/llm-duties/translation-llm-duty.ts
View File

@ -65,7 +65,7 @@ export class TranslationLLMDuty extends LLMDuty {
}
}
})
const prompt = `${this.systemPrompt} Text: ${this.input}`
const prompt = `${this.systemPrompt} Text to translate: ${this.input}`
const rawResult = await completion.generateCompletion(prompt, {
contextShiftSize: context.contextSize / 2,
grammar,

13
server/src/core/nlp/nlu/action-loop.ts
View File

@ -20,6 +20,7 @@ export class ActionLoop {
public static async handle(
utterance: NLPUtterance
): Promise<Partial<BrainProcessResult> | null> {
console.log('here here here here here here here')
const { domain, intent } = NLU.conversation.activeContext
const [skillName, actionName] = intent.split('.') as [string, string]
const skillConfigPath = join(
@ -56,10 +57,14 @@ export class ActionLoop {
if (action?.loop) {
const { name: expectedItemName, type: expectedItemType } =
action.loop.expected_item
let hasMatchingUtterance = false
let hasMatchingEntity = false
let hasMatchingResolver = false
if (expectedItemType === 'entity') {
if (expectedItemType === 'utterance') {
console.log('action.loop.expected_item', action.loop.expected_item)
hasMatchingUtterance = true
} else if (expectedItemType === 'entity') {
hasMatchingEntity =
NLU.nluResult.entities.filter(
({ entity }) => expectedItemName === entity
@ -124,9 +129,10 @@ export class ActionLoop {
}
// Ensure expected items are in the utterance, otherwise clean context and reprocess
if (!hasMatchingEntity && !hasMatchingResolver) {
if (!hasMatchingEntity && !hasMatchingResolver && !hasMatchingUtterance) {
BRAIN.talk(`${BRAIN.wernicke('random_context_out_of_topic')}.`)
NLU.conversation.cleanActiveContext()
console.log('nlu process 2')
await NLU.process(utterance)
return null
}
@ -140,6 +146,7 @@ export class ActionLoop {
NLU.conversation.cleanActiveContext()
if (originalUtterance !== null) {
console.log('nlu process 3')
await NLU.process(originalUtterance)
}
@ -160,6 +167,8 @@ export class ActionLoop {
// Break the action loop and prepare for the next action if necessary
if (processedData.core?.isInActionLoop === false) {
console.log('actionloop switch 1')
NLU.conversation.activeContext.isInActionLoop =
!!processedData.action?.loop
NLU.conversation.activeContext.actionName = processedData.action

13
server/src/core/nlp/nlu/nlu.ts
View File

@ -102,6 +102,8 @@ export default class NLU {
LogHelper.title('NLU')
LogHelper.info('Processing...')
console.log('HERE EHRHE RER UEIFJ EIF EIUF E')
if (!MODEL_LOADER.hasNlpModels()) {
if (!BRAIN.isMuted) {
BRAIN.talk(`${BRAIN.wernicke('random_errors')}!`)
@ -119,14 +121,19 @@ export default class NLU {
// Pre NLU processing according to the active context if there is one
if (this.conversation.hasActiveContext()) {
console.log('has active context', this.conversation.activeContext)
// When the active context is in an action loop, then directly trigger the action
if (this.conversation.activeContext.isInActionLoop) {
console.log('in action loop handle')
return resolve(await ActionLoop.handle(utterance))
}
// When the active context has slots filled
if (Object.keys(this.conversation.activeContext.slots).length > 0) {
try {
console.log('in slot filling handle')
// TODO: active action loop if next action has it?
return resolve(await SlotFilling.handle(utterance))
} catch (e) {
return reject({})
@ -244,6 +251,7 @@ export default class NLU {
const shouldSlotLoop = await SlotFilling.route(intent)
if (shouldSlotLoop) {
console.log('should slot loop')
return resolve({})
}
@ -253,6 +261,7 @@ export default class NLU {
Object.keys(this.conversation.activeContext.slots).length > 0
) {
try {
console.log('slot filling here')
return resolve(await SlotFilling.handle(utterance))
} catch (e) {
return reject({})
@ -282,11 +291,15 @@ export default class NLU {
this.nluResult.entities = this.conversation.activeContext.entities
try {
console.log('before brain execute')
const processedData = await BRAIN.execute(this.nluResult)
console.log('processedData.nextAction', processedData.nextAction)
// Prepare next action if there is one queuing
if (processedData.nextAction) {
this.conversation.cleanActiveContext()
console.log('actionloop switch 2')
await this.conversation.setActiveContext({
...DEFAULT_ACTIVE_CONTEXT,
lang: BRAIN.lang,

7
server/src/core/nlp/nlu/slot-filling.ts
View File

@ -16,11 +16,14 @@ export class SlotFilling {
): Promise<Partial<BrainProcessResult> | null> {
const processedData = await this.fillSlot(utterance)
console.log('processedData', processedData)
/**
* In case the slot filling has been interrupted. e.g. context change, etc.
* Then reprocess with the new utterance
*/
if (!processedData) {
console.log('nlu process 1')
await NLU.process(utterance)
return null
}
@ -108,6 +111,8 @@ export class SlotFilling {
if (!NLU.conversation.areSlotsAllFilled()) {
BRAIN.talk(`${BRAIN.wernicke('random_context_out_of_topic')}.`)
} else {
console.log('slot filling active context', NLU.conversation.activeContext)
NLU.nluResult = {
...DEFAULT_NLU_RESULT, // Reset entities, slots, etc.
// Assign slots only if there is a next action
@ -124,7 +129,7 @@ export class SlotFilling {
}
}
NLU.conversation.cleanActiveContext()
// NLU.conversation.cleanActiveContext()
return BRAIN.execute(NLU.nluResult)
}

1
server/src/core/socket-server.ts
View File

@ -116,6 +116,7 @@ export default class SocketServer {
LogHelper.time('Utterance processed in')
BRAIN.isMuted = false
console.log('nlu process 4')
const processedData = await NLU.process(utterance)
if (processedData) {

8
server/src/schemas/skill-schemas.ts
View File

@ -21,7 +21,8 @@ const skillActionTypes = [
const skillDataTypes = [
Type.Literal('skill_resolver'),
Type.Literal('global_resolver'),
Type.Literal('entity')
Type.Literal('entity'),
Type.Literal('utterance')
]
const answerTypes = Type.Union([
Type.String(),
@ -186,7 +187,10 @@ export const skillConfigSchemaObject = Type.Strict(
type: Type.Union(skillDataTypes),
name: Type.String()
},
{ description: 'An item can be a entity or a resolver.' }
{
description:
'An item can be a entity, a resolver or an utterance.'
}
)
},
{

41
skills/utilities/translator-poc/config/en.json
View File

@ -1,25 +1,32 @@
{
"$schema": "../../../../schemas/skill-schemas/skill-config.json",
"actions": {
"short_translate": {
"type": "logic",
"utterance_samples": [
"[Translate|Convert] @text_to_parse [to|in|into] @language",
"What is @text_to_parse in @language?",
"How [do|can] [I|we] say @text_to_parse in @language?",
"Can you tell [me|us] how to say @text_to_parse in @language?",
"[I|We] need to know how to say @text_to_parse in @language"
],
"entities": [
"translate_loop_entry": {
"type": "dialog",
"utterance_samples": ["Start a [translation|translate] loop"],
"slots": [
{
"type": "llm",
"schema": {
"text_to_parse": {
"type": "string"
}
}
"name": "target_language",
"item": {
"type": "entity",
"name": "language"
},
"questions": [
"What language would you like to translate to?",
"Sure, what language are you translating to?"
]
}
]
],
"next_action": "translate"
},
"translate": {
"type": "logic",
"loop": {
"expected_item": {
"type": "utterance",
"name": "text_to_translate"
}
}
}
},
"entities": {

2
skills/utilities/translator-poc/skill.json
View File

@ -1,7 +1,7 @@
{
"$schema": "../../../schemas/skill-schemas/skill.json",
"name": "Translator (PoC)",
"bridge": "nodejs",
"bridge": "python",
"version": "1.0.0",
"description": "Translate text from one language to another",
"author": {

2
skills/utilities/translator-poc/src/actions/short_translate.ts
View File

@ -8,7 +8,7 @@ export const run: ActionFunction = async function (params) {
let targetLanguage = null
for (const currentEntity of params.current_entities) {
if (currentEntity.entity === 'text_to_parse') {
if (currentEntity.entity === 'text_to_translate') {
textToTranslate = currentEntity.resolution.value
}
if (currentEntity.entity === 'language') {

21
skills/utilities/translator-poc/src/actions/translate.py Normal file
View File

@ -0,0 +1,21 @@
import os
from bridges.python.src.sdk.leon import leon
from bridges.python.src.sdk.network import Network
from bridges.python.src.sdk.types import ActionParams
def run(params: ActionParams) -> None:
"""Define the winner"""
print('params', params)
target_language = 'French'
text_to_translate = params['utterance']
network = Network({'base_url': f'{os.environ["LEON_HOST"]}:{os.environ["LEON_PORT"]}/api/v1'})
leon.answer({
'key': 'translate',
'data': {
'output': f'Translating "{text_to_translate}" to {target_language}'
}
})

22
skills/utilities/translator-poc/src/actions/translate.ts
View File

@ -4,31 +4,43 @@ import { Network } from '@sdk/network'
export const run: ActionFunction = async function (params) {
console.log('params', params)
const targetLanguage = params.slots.target_language.resolution.value
const textToTranslate = params.utterance
const network = new Network({
baseURL: `${process.env['LEON_HOST']}:${process.env['LEON_PORT']}/api/v1`
})
console.log('targetLanguage', targetLanguage)
console.log('textToTranslate', textToTranslate)
/**
* TODO: create SDK methods to handle request and response for every LLM duty
*/
const response = await network.request({
/*const response = await network.request({
url: '/llm-inference',
method: 'POST',
data: {
dutyType: 'translation',
// TODO: get text entity to translate
input: 'Bonjour tout le monde !',
input: textToTranslate,
data: {
target: 'English',
target: targetLanguage,
autoDetectLanguage: true
}
}
})
console.log('response', response)*/
await leon.answer({
key: 'translate',
core: {
isInActionLoop: true,
restart: true
},
data: {
output: response.data.output.o
output: `just a test ${targetLanguage} ${textToTranslate}`
// output: response.data.output.translation
}
})
}