143 lines
4.5 KiB
TypeScript
143 lines
4.5 KiB
TypeScript
import { INode, INodeData, INodeParams, PromptTemplate } from '../../../src/Interface'
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import { AgentExecutor } from 'langchain/agents'
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import { getBaseClasses } from '../../../src/utils'
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import { LoadPyodide, finalSystemPrompt, systemPrompt } from './core'
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import { LLMChain } from 'langchain/chains'
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import { BaseLanguageModel } from 'langchain/base_language'
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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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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 = 'csvAgentLLM'
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this.type = 'AgentExecutor'
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this.category = 'Agents'
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this.icon = 'csvagent.png'
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this.description = 'Agent used to 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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}
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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): Promise<string> {
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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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let files: string[] = []
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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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let base64String = ''
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for (const file of files) {
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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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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 executionResult = ''
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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.read_csv(csv_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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executionResult = 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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console.log('executionResult= ', executionResult)
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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 (executionResult) {
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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: executionResult,
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question: input
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}
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const res = await chain.call(inputs)
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pythonCode = res?.text
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}
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console.log('pythonCode= ', pythonCode)
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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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finalResult = 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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}
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console.log('finalResult= ', finalResult)
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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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const res = await chain.call(inputs)
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return res?.text
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}
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return executionResult
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}
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}
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module.exports = { nodeClass: CSV_Agents }
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