227 lines
8.9 KiB
TypeScript
227 lines
8.9 KiB
TypeScript
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 { ConsoleCallbackHandler, CustomChainHandler, additionalCallbacks } from '../../../src/handler'
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import { ICommonObject, INode, INodeData, INodeParams, IServerSideEventStreamer, PromptTemplate } from '../../../src/Interface'
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import { getBaseClasses } from '../../../src/utils'
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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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import { getFileFromStorage } from '../../../src'
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class CSV_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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inputs: INodeParams[]
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constructor() {
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this.label = 'CSV Agent'
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this.name = 'csvAgent'
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this.version = 3.0
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this.type = 'AgentExecutor'
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this.category = 'Agents'
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this.icon = 'CSVagent.svg'
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this.description = 'Agent used to answer queries on CSV data'
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this.baseClasses = [this.type, ...getBaseClasses(AgentExecutor)]
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this.inputs = [
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{
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label: 'Csv File',
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name: 'csvFile',
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type: 'file',
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fileType: '.csv'
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},
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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: 'System Message',
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name: 'systemMessagePrompt',
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type: 'string',
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rows: 4,
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additionalParams: true,
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optional: true,
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placeholder:
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'I want you to act as a document that I am having a conversation with. Your name is "AI Assistant". You will provide me with answers from the given info. If the answer is not included, say exactly "Hmm, I am not sure." and stop after that. Refuse to answer any question not about the info. Never break character.'
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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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label: 'Custom Pandas Read_CSV Code',
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description:
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'Custom Pandas <a target="_blank" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.read_csv.html">read_csv</a> function. Takes in an input: "csv_data"',
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name: 'customReadCSV',
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default: 'read_csv(csv_data)',
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type: 'code',
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optional: true,
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additionalParams: 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 csvFileBase64 = nodeData.inputs?.csvFile as string
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const model = nodeData.inputs?.model as BaseLanguageModel
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const systemMessagePrompt = nodeData.inputs?.systemMessagePrompt as string
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const moderations = nodeData.inputs?.inputModeration as Moderation[]
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const _customReadCSV = nodeData.inputs?.customReadCSV as string
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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 CSV agent
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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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// if (options.shouldStreamResponse) {
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// streamResponse(options.sseStreamer, options.chatId, e.message)
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// }
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return formatResponse(e.message)
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}
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}
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const loggerHandler = new ConsoleCallbackHandler(options.logger, options?.orgId)
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const shouldStreamResponse = options.shouldStreamResponse
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const sseStreamer: IServerSideEventStreamer = options.sseStreamer as IServerSideEventStreamer
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const chatId = options.chatId
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const callbacks = await additionalCallbacks(nodeData, options)
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let files: string[] = []
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let base64String = ''
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if (csvFileBase64.startsWith('FILE-STORAGE::')) {
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const fileName = csvFileBase64.replace('FILE-STORAGE::', '')
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if (fileName.startsWith('[') && fileName.endsWith(']')) {
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files = JSON.parse(fileName)
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} else {
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files = [fileName]
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}
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const orgId = options.orgId
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const chatflowid = options.chatflowid
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for (const file of files) {
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if (!file) continue
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const fileData = await getFileFromStorage(file, orgId, chatflowid)
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base64String += fileData.toString('base64')
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}
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} else {
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if (csvFileBase64.startsWith('[') && csvFileBase64.endsWith(']')) {
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files = JSON.parse(csvFileBase64)
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} else {
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files = [csvFileBase64]
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}
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for (const file of files) {
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if (!file) continue
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const splitDataURI = file.split(',')
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splitDataURI.pop()
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base64String += splitDataURI.pop() ?? ''
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}
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}
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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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let customReadCSVFunc = _customReadCSV ? _customReadCSV : 'read_csv(csv_data)'
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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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from io import StringIO
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import json
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base64_string = "${base64String}"
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decoded_data = base64.b64decode(base64_string)
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csv_data = StringIO(decoded_data.decode('utf-8'))
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df = pd.${customReadCSVFunc}
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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: "${input}" using following code: "${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(
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systemMessagePrompt ? `${systemMessagePrompt}\n${finalSystemPrompt}` : finalSystemPrompt
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),
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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.shouldStreamResponse) {
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const handler = new CustomChainHandler(shouldStreamResponse ? sseStreamer : undefined, chatId)
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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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module.exports = { nodeClass: CSV_Agents }
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