259 lines
9.7 KiB
TypeScript
259 lines
9.7 KiB
TypeScript
import axios from 'axios'
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import { BaseLanguageModel } from '@langchain/core/language_models/base'
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import { AgentExecutor } from 'langchain/agents'
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import { LLMChain } from 'langchain/chains'
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import { ICommonObject, INode, INodeData, INodeParams, PromptTemplate } from '../../../src/Interface'
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import { getBaseClasses, getCredentialData, getCredentialParam } from '../../../src/utils'
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import { ConsoleCallbackHandler, CustomChainHandler, additionalCallbacks } from '../../../src/handler'
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import { LoadPyodide, finalSystemPrompt, systemPrompt } from './core'
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import { checkInputs, Moderation } from '../../moderation/Moderation'
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import { formatResponse } from '../../outputparsers/OutputParserHelpers'
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class Airtable_Agents implements INode {
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label: string
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name: string
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version: number
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description: string
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type: string
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icon: string
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category: string
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baseClasses: string[]
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credential: INodeParams
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inputs: INodeParams[]
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constructor() {
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this.label = 'Airtable Agent'
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this.name = 'airtableAgent'
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this.version = 2.0
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this.type = 'AgentExecutor'
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this.category = 'Agents'
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this.icon = 'airtable.svg'
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this.description = 'Agent used to to answer queries on Airtable table'
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this.baseClasses = [this.type, ...getBaseClasses(AgentExecutor)]
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this.credential = {
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label: 'Connect Credential',
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name: 'credential',
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type: 'credential',
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credentialNames: ['airtableApi']
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}
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this.inputs = [
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{
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label: 'Language Model',
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name: 'model',
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type: 'BaseLanguageModel'
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},
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{
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label: 'Base Id',
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name: 'baseId',
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type: 'string',
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placeholder: 'app11RobdGoX0YNsC',
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description:
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'If your table URL looks like: https://airtable.com/app11RobdGoX0YNsC/tblJdmvbrgizbYICO/viw9UrP77Id0CE4ee, app11RovdGoX0YNsC is the base id'
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},
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{
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label: 'Table Id',
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name: 'tableId',
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type: 'string',
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placeholder: 'tblJdmvbrgizbYICO',
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description:
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'If your table URL looks like: https://airtable.com/app11RobdGoX0YNsC/tblJdmvbrgizbYICO/viw9UrP77Id0CE4ee, tblJdmvbrgizbYICO is the table id'
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},
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{
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label: 'Return All',
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name: 'returnAll',
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type: 'boolean',
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default: true,
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additionalParams: true,
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description: 'If all results should be returned or only up to a given limit'
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},
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{
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label: 'Limit',
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name: 'limit',
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type: 'number',
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default: 100,
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additionalParams: true,
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description: 'Number of results to return'
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},
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{
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label: 'Input Moderation',
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description: 'Detect text that could generate harmful output and prevent it from being sent to the language model',
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name: 'inputModeration',
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type: 'Moderation',
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optional: true,
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list: true
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}
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]
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}
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async init(): Promise<any> {
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// Not used
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return undefined
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}
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async run(nodeData: INodeData, input: string, options: ICommonObject): Promise<string | object> {
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const model = nodeData.inputs?.model as BaseLanguageModel
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const baseId = nodeData.inputs?.baseId as string
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const tableId = nodeData.inputs?.tableId as string
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const returnAll = nodeData.inputs?.returnAll as boolean
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const limit = nodeData.inputs?.limit as string
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const moderations = nodeData.inputs?.inputModeration as Moderation[]
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if (moderations && moderations.length > 0) {
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try {
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// Use the output of the moderation chain as input for the Vectara chain
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input = await checkInputs(moderations, input)
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} catch (e) {
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await new Promise((resolve) => setTimeout(resolve, 500))
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//streamResponse(options.socketIO && options.socketIOClientId, e.message, options.socketIO, options.socketIOClientId)
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return formatResponse(e.message)
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}
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}
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const credentialData = await getCredentialData(nodeData.credential ?? '', options)
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const accessToken = getCredentialParam('accessToken', credentialData, nodeData)
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let airtableData: ICommonObject[] = []
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if (returnAll) {
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airtableData = await loadAll(baseId, tableId, accessToken)
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} else {
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airtableData = await loadLimit(limit ? parseInt(limit, 10) : 100, baseId, tableId, accessToken)
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}
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let base64String = Buffer.from(JSON.stringify(airtableData)).toString('base64')
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const loggerHandler = new ConsoleCallbackHandler(options.logger)
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const handler = new CustomChainHandler(options.socketIO, options.socketIOClientId)
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const callbacks = await additionalCallbacks(nodeData, options)
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const pyodide = await LoadPyodide()
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// First load the csv file and get the dataframe dictionary of column types
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// For example using titanic.csv: {'PassengerId': 'int64', 'Survived': 'int64', 'Pclass': 'int64', 'Name': 'object', 'Sex': 'object', 'Age': 'float64', 'SibSp': 'int64', 'Parch': 'int64', 'Ticket': 'object', 'Fare': 'float64', 'Cabin': 'object', 'Embarked': 'object'}
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let dataframeColDict = ''
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try {
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const code = `import pandas as pd
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import base64
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import json
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base64_string = "${base64String}"
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decoded_data = base64.b64decode(base64_string)
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json_data = json.loads(decoded_data)
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df = pd.DataFrame(json_data)
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my_dict = df.dtypes.astype(str).to_dict()
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print(my_dict)
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json.dumps(my_dict)`
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dataframeColDict = await pyodide.runPythonAsync(code)
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} catch (error) {
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throw new Error(error)
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}
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// Then tell GPT to come out with ONLY python code
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// For example: len(df), df[df['SibSp'] > 3]['PassengerId'].count()
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let pythonCode = ''
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if (dataframeColDict) {
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const chain = new LLMChain({
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llm: model,
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prompt: PromptTemplate.fromTemplate(systemPrompt),
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verbose: process.env.DEBUG === 'true' ? true : false
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})
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const inputs = {
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dict: dataframeColDict,
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question: input
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}
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const res = await chain.call(inputs, [loggerHandler, ...callbacks])
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pythonCode = res?.text
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// Regex to get rid of markdown code blocks syntax
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pythonCode = pythonCode.replace(/^```[a-z]+\n|\n```$/gm, '')
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}
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// Then run the code using Pyodide
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let finalResult = ''
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if (pythonCode) {
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try {
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const code = `import pandas as pd\n${pythonCode}`
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// TODO: get print console output
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finalResult = await pyodide.runPythonAsync(code)
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} catch (error) {
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throw new Error(`Sorry, I'm unable to find answer for question: "${input}" using follwoing code: "${pythonCode}"`)
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}
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}
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// Finally, return a complete answer
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if (finalResult) {
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const chain = new LLMChain({
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llm: model,
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prompt: PromptTemplate.fromTemplate(finalSystemPrompt),
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verbose: process.env.DEBUG === 'true' ? true : false
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})
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const inputs = {
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question: input,
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answer: finalResult
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}
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if (options.socketIO && options.socketIOClientId) {
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const result = await chain.call(inputs, [loggerHandler, handler, ...callbacks])
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return result?.text
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} else {
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const result = await chain.call(inputs, [loggerHandler, ...callbacks])
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return result?.text
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}
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}
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return pythonCode
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}
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}
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interface AirtableLoaderResponse {
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records: AirtableLoaderPage[]
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offset?: string
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}
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interface AirtableLoaderPage {
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id: string
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createdTime: string
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fields: ICommonObject
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}
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const fetchAirtableData = async (url: string, params: ICommonObject, accessToken: string): Promise<AirtableLoaderResponse> => {
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try {
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const headers = {
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Authorization: `Bearer ${accessToken}`,
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'Content-Type': 'application/json',
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Accept: 'application/json'
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}
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const response = await axios.get(url, { params, headers })
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return response.data
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} catch (error) {
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throw new Error(`Failed to fetch ${url} from Airtable: ${error}`)
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}
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}
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const loadAll = async (baseId: string, tableId: string, accessToken: string): Promise<ICommonObject[]> => {
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const params: ICommonObject = { pageSize: 100 }
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let data: AirtableLoaderResponse
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let returnPages: AirtableLoaderPage[] = []
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do {
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data = await fetchAirtableData(`https://api.airtable.com/v0/${baseId}/${tableId}`, params, accessToken)
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returnPages.push.apply(returnPages, data.records)
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params.offset = data.offset
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} while (data.offset !== undefined)
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return data.records.map((page) => page.fields)
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}
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const loadLimit = async (limit: number, baseId: string, tableId: string, accessToken: string): Promise<ICommonObject[]> => {
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const params = { maxRecords: limit }
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const data = await fetchAirtableData(`https://api.airtable.com/v0/${baseId}/${tableId}`, params, accessToken)
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if (data.records.length === 0) {
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return []
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}
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return data.records.map((page) => page.fields)
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}
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module.exports = { nodeClass: Airtable_Agents }
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