mirror of
https://github.com/farcasclaudiu/Flowise.git
synced 2026-06-28 21:00:58 +03:00
GPT Vision - OpenAIVisionChain
This commit is contained in:
vinodkiran
@@ -0,0 +1,216 @@
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import { ICommonObject, INode, INodeData, INodeOutputsValue, INodeParams } from '../../../src/Interface'
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import { getBaseClasses, handleEscapeCharacters } from '../../../src/utils'
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import { VLLMChain } from './VLLMChain'
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import { BaseLanguageModel } from 'langchain/base_language'
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import { ConsoleCallbackHandler, CustomChainHandler, additionalCallbacks } from '../../../src/handler'
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import { formatResponse } from '../../outputparsers/OutputParserHelpers'
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import { ChatOpenAI } from 'langchain/chat_models/openai'
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class OpenAIVisionChain_Chains implements INode {
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label: string
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name: string
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version: number
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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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description: string
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inputs: INodeParams[]
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outputs: INodeOutputsValue[]
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constructor() {
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this.label = 'Open AI Vision Chain'
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this.name = 'openAIVisionChain'
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this.version = 3.0
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this.type = 'OpenAIVisionChain'
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this.icon = 'chain.svg'
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this.category = 'Chains'
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this.description = 'Chain to run queries against OpenAI (GPT-4) Vision .'
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this.baseClasses = [this.type, ...getBaseClasses(VLLMChain)]
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this.inputs = [
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{
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label: 'Language Model (Works only with Open AI [gpt-4-vision-preview])',
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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: 'Prompt',
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name: 'prompt',
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type: 'BasePromptTemplate',
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optional: true
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},
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{
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label: 'Image Resolution',
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description: 'This parameter controls the resolution in which the model views the image.',
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name: 'imageResolution',
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type: 'options',
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options: [
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{
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label: 'Low',
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name: 'low'
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},
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{
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label: 'High',
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name: 'high'
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}
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],
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default: 'low',
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optional: false
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},
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{
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label: 'Chain Name',
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name: 'chainName',
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type: 'string',
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placeholder: 'Name Your Chain',
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optional: true
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}
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]
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this.outputs = [
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{
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label: 'Open AI Vision Chain',
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name: 'openAIVisionChain',
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baseClasses: [this.type, ...getBaseClasses(VLLMChain)]
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},
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{
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label: 'Output Prediction',
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name: 'outputPrediction',
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baseClasses: ['string', 'json']
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}
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]
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}
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async init(nodeData: INodeData, input: string, options: ICommonObject): Promise<any> {
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const model = nodeData.inputs?.model as BaseLanguageModel
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const prompt = nodeData.inputs?.prompt
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const output = nodeData.outputs?.output as string
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const imageResolution = nodeData.inputs?.imageResolution
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const promptValues = prompt.promptValues as ICommonObject
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if (!(model as any).openAIApiKey || (model as any).modelName !== 'gpt-4-vision-preview') {
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throw new Error('Chain works with OpenAI Vision model only')
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}
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const openAIModel = model as ChatOpenAI
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const fields = {
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openAIApiKey: openAIModel.openAIApiKey,
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imageResolution: imageResolution,
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verbose: process.env.DEBUG === 'true',
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imageUrls: options.url,
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openAIModel: openAIModel
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}
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if (output === this.name) {
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const chain = new VLLMChain({
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...fields,
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prompt: prompt
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})
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return chain
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} else if (output === 'outputPrediction') {
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const chain = new VLLMChain({
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...fields
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})
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const inputVariables: string[] = prompt.inputVariables as string[] // ["product"]
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const res = await runPrediction(inputVariables, chain, input, promptValues, options, nodeData)
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// eslint-disable-next-line no-console
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console.log('\x1b[92m\x1b[1m\n*****OUTPUT PREDICTION*****\n\x1b[0m\x1b[0m')
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// eslint-disable-next-line no-console
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console.log(res)
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/**
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* Apply string transformation to convert special chars:
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* FROM: hello i am ben\n\n\thow are you?
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* TO: hello i am benFLOWISE_NEWLINEFLOWISE_NEWLINEFLOWISE_TABhow are you?
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*/
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return handleEscapeCharacters(res, false)
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}
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}
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async run(nodeData: INodeData, input: string, options: ICommonObject): Promise<string | object> {
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const prompt = nodeData.inputs?.prompt
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const inputVariables: string[] = prompt.inputVariables as string[] // ["product"]
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const chain = nodeData.instance as VLLMChain
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let promptValues: ICommonObject | undefined = nodeData.inputs?.prompt.promptValues as ICommonObject
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const res = await runPrediction(inputVariables, chain, input, promptValues, options, nodeData)
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// eslint-disable-next-line no-console
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console.log('\x1b[93m\x1b[1m\n*****FINAL RESULT*****\n\x1b[0m\x1b[0m')
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// eslint-disable-next-line no-console
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console.log(res)
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return res
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}
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}
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const runPrediction = async (
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inputVariables: string[],
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chain: VLLMChain,
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input: string,
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promptValuesRaw: ICommonObject | undefined,
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options: ICommonObject,
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nodeData: INodeData
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) => {
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const loggerHandler = new ConsoleCallbackHandler(options.logger)
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const callbacks = await additionalCallbacks(nodeData, options)
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const isStreaming = options.socketIO && options.socketIOClientId
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const socketIO = isStreaming ? options.socketIO : undefined
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const socketIOClientId = isStreaming ? options.socketIOClientId : ''
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/**
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* Apply string transformation to reverse converted special chars:
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* FROM: { "value": "hello i am benFLOWISE_NEWLINEFLOWISE_NEWLINEFLOWISE_TABhow are you?" }
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* TO: { "value": "hello i am ben\n\n\thow are you?" }
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*/
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const promptValues = handleEscapeCharacters(promptValuesRaw, true)
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if (options?.url) {
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chain.imageUrls = options.url
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}
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if (promptValues && inputVariables.length > 0) {
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let seen: string[] = []
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for (const variable of inputVariables) {
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seen.push(variable)
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if (promptValues[variable]) {
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chain.inputKey = variable
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seen.pop()
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}
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}
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if (seen.length === 0) {
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// All inputVariables have fixed values specified
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const options = { ...promptValues }
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if (isStreaming) {
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const handler = new CustomChainHandler(socketIO, socketIOClientId)
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const res = await chain.call(options, [loggerHandler, handler, ...callbacks])
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return formatResponse(res?.text)
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} else {
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const res = await chain.call(options, [loggerHandler, ...callbacks])
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return formatResponse(res?.text)
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}
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} else if (seen.length === 1) {
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// If one inputVariable is not specify, use input (user's question) as value
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const lastValue = seen.pop()
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if (!lastValue) throw new Error('Please provide Prompt Values')
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chain.inputKey = lastValue as string
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const options = {
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...promptValues,
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[lastValue]: input
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}
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if (isStreaming) {
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const handler = new CustomChainHandler(socketIO, socketIOClientId)
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const res = await chain.call(options, [loggerHandler, handler, ...callbacks])
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return formatResponse(res?.text)
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} else {
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const res = await chain.call(options, [loggerHandler, ...callbacks])
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return formatResponse(res?.text)
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}
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} else {
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throw new Error(`Please provide Prompt Values for: ${seen.join(', ')}`)
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}
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} else {
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if (isStreaming) {
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const handler = new CustomChainHandler(socketIO, socketIOClientId)
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const res = await chain.run(input, [loggerHandler, handler, ...callbacks])
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return formatResponse(res)
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} else {
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const res = await chain.run(input, [loggerHandler, ...callbacks])
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return formatResponse(res)
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}
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}
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}
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module.exports = { nodeClass: OpenAIVisionChain_Chains }
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@@ -0,0 +1,146 @@
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import { OpenAI as OpenAIClient, ClientOptions } from 'openai'
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import { BaseChain, ChainInputs } from 'langchain/chains'
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import { ChainValues } from 'langchain/schema'
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import { BasePromptTemplate, ChatPromptTemplate, SystemMessagePromptTemplate } from 'langchain/prompts'
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import { ChatOpenAI } from 'langchain/chat_models/openai'
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/**
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* Interface for the input parameters of the OpenAIVisionChain class.
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*/
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export interface OpenAIVisionChainInput extends ChainInputs {
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openAIApiKey?: string
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openAIOrganization?: string
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throwError?: boolean
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prompt?: BasePromptTemplate
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configuration?: ClientOptions
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imageUrls?: []
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imageResolution?: string
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openAIModel: ChatOpenAI
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}
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/**
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* Class representing a chain for generating text from an image using the OpenAI
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* Vision API. It extends the BaseChain class and implements the
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* OpenAIVisionChainInput interface.
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*/
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export class VLLMChain extends BaseChain implements OpenAIVisionChainInput {
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static lc_name() {
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return 'VLLMChain'
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}
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get lc_secrets(): { [key: string]: string } | undefined {
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return {
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openAIApiKey: 'OPENAI_API_KEY'
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}
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}
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prompt: BasePromptTemplate | undefined
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inputKey = 'input'
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outputKey = 'text'
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imageUrls?: []
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imageResolution: string = 'low'
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openAIApiKey?: string
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openAIOrganization?: string
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openAIModel: ChatOpenAI
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clientConfig: ClientOptions
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client: OpenAIClient
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throwError: boolean
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constructor(fields: OpenAIVisionChainInput) {
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super(fields)
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this.throwError = fields?.throwError ?? false
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this.imageResolution = fields?.imageResolution ?? 'low'
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this.openAIApiKey = fields?.openAIApiKey
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this.prompt = fields?.prompt
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this.imageUrls = fields?.imageUrls ?? []
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if (!this.openAIApiKey) {
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throw new Error('OpenAI API key not found')
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}
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this.openAIOrganization = fields?.openAIOrganization
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this.openAIModel = fields.openAIModel
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this.clientConfig = {
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...fields?.configuration,
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apiKey: this.openAIApiKey,
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organization: this.openAIOrganization
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}
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this.client = new OpenAIClient(this.clientConfig)
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}
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async _call(values: ChainValues): Promise<ChainValues> {
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const userInput = values[this.inputKey]
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const vRequest: any = {
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model: 'gpt-4-vision-preview',
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temperature: this.openAIModel.temperature,
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top_p: this.openAIModel.topP,
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messages: []
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}
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if (this.openAIModel.maxTokens) vRequest.max_tokens = this.openAIModel.maxTokens
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const userRole: any = { role: 'user' }
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userRole.content = []
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userRole.content.push({
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type: 'text',
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text: userInput
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})
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if (this.imageUrls && this.imageUrls.length > 0) {
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this.imageUrls.forEach((imageUrl: any) => {
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userRole.content.push({
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type: 'image_url',
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image_url: {
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url: imageUrl?.data,
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detail: this.imageResolution
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}
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})
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})
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}
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vRequest.messages.push(userRole)
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if (this.prompt && this.prompt instanceof ChatPromptTemplate) {
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let chatPrompt = this.prompt as ChatPromptTemplate
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chatPrompt.promptMessages.forEach((message: any) => {
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if (message instanceof SystemMessagePromptTemplate) {
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vRequest.messages.push({
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role: 'system',
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content: [
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{
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type: 'text',
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text: (message.prompt as any).template
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}
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]
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})
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}
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})
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}
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let response
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try {
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// @ts-ignore
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response = await this.client.chat.completions.create(vRequest)
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} catch (error) {
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if (error instanceof Error) {
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throw error
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} else {
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throw new Error(error as string)
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}
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}
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const output = response.choices[0]
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return {
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[this.outputKey]: output.message.content
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}
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}
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_chainType() {
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return 'vision_chain'
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}
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get inputKeys() {
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return this.prompt?.inputVariables ?? [this.inputKey]
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}
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get outputKeys(): string[] {
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return [this.outputKey]
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}
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}
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@@ -403,6 +403,19 @@ export class App {
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return res.json(obj)
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})
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// Check if chatflow valid for uploads
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this.app.get('/api/v1/chatflows-uploads/:id', async (req: Request, res: Response) => {
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const chatflow = await this.AppDataSource.getRepository(ChatFlow).findOneBy({
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id: req.params.id
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})
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if (!chatflow) return res.status(404).send(`Chatflow ${req.params.id} not found`)
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const obj = {
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allowUploads: this.shouldAllowUploads(chatflow)
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}
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return res.json(obj)
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})
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// ----------------------------------------
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// ChatMessage
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// ----------------------------------------
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@@ -1241,6 +1254,19 @@ export class App {
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})
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}
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private uploadAllowedNodes = ['OpenAIVisionChain']
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private shouldAllowUploads(result: ChatFlow): boolean {
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const flowObj = JSON.parse(result.flowData)
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let allowUploads = false
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flowObj.nodes.forEach((node: IReactFlowNode) => {
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if (this.uploadAllowedNodes.indexOf(node.data.type) > -1) {
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logger.debug(`[server]: Found Eligible Node ${node.data.type}, Allowing Uploads.`)
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allowUploads = true
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}
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})
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return allowUploads
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}
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/**
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* Validate API Key
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* @param {Request} req
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