import { ICommonObject, IMessage, INode, INodeData, INodeParams } from '../../../src/Interface' import { ConversationChain } from 'langchain/chains' import { getBaseClasses } from '../../../src/utils' import { ChatPromptTemplate, HumanMessagePromptTemplate, MessagesPlaceholder, SystemMessagePromptTemplate } from 'langchain/prompts' import { BufferMemory, ChatMessageHistory } from 'langchain/memory' import { BaseChatModel } from 'langchain/chat_models/base' import { AIMessage, HumanMessage } from 'langchain/schema' import { ConsoleCallbackHandler, CustomChainHandler } from '../../../src/handler' import { flatten } from 'lodash' import { Document } from 'langchain/document' let systemMessage = `The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.` class ConversationChain_Chains implements INode { label: string name: string type: string icon: string category: string baseClasses: string[] description: string inputs: INodeParams[] constructor() { this.label = 'Conversation Chain' this.name = 'conversationChain' this.type = 'ConversationChain' this.icon = 'chain.svg' this.category = 'Chains' this.description = 'Chat models specific conversational chain with memory' this.baseClasses = [this.type, ...getBaseClasses(ConversationChain)] this.inputs = [ { label: 'Language Model', name: 'model', type: 'BaseChatModel' }, { label: 'Memory', name: 'memory', type: 'BaseMemory' }, { label: 'Document', name: 'document', type: 'Document', description: 'Include whole document into the context window', optional: true, list: true }, { label: 'System Message', name: 'systemMessagePrompt', type: 'string', rows: 4, additionalParams: true, optional: true, placeholder: 'You are a helpful assistant that write codes' } ] } async init(nodeData: INodeData): Promise { const model = nodeData.inputs?.model as BaseChatModel const memory = nodeData.inputs?.memory as BufferMemory const prompt = nodeData.inputs?.systemMessagePrompt as string const docs = nodeData.inputs?.document as Document[] const flattenDocs = docs && docs.length ? flatten(docs) : [] const finalDocs = [] for (let i = 0; i < flattenDocs.length; i += 1) { finalDocs.push(new Document(flattenDocs[i])) } let finalText = '' for (let i = 0; i < finalDocs.length; i += 1) { finalText += finalDocs[i].pageContent } if (finalText) systemMessage = `${systemMessage}\nThe AI has the following context:\n${finalText}` const obj: any = { llm: model, memory, verbose: process.env.DEBUG === 'true' ? true : false } const chatPrompt = ChatPromptTemplate.fromPromptMessages([ SystemMessagePromptTemplate.fromTemplate(prompt ? `${prompt}\n${systemMessage}` : systemMessage), new MessagesPlaceholder(memory.memoryKey ?? 'chat_history'), HumanMessagePromptTemplate.fromTemplate('{input}') ]) obj.prompt = chatPrompt const chain = new ConversationChain(obj) return chain } async run(nodeData: INodeData, input: string, options: ICommonObject): Promise { const chain = nodeData.instance as ConversationChain const memory = nodeData.inputs?.memory as BufferMemory if (options && options.chatHistory) { const chatHistory = [] const histories: IMessage[] = options.chatHistory for (const message of histories) { if (message.type === 'apiMessage') { chatHistory.push(new AIMessage(message.message)) } else if (message.type === 'userMessage') { chatHistory.push(new HumanMessage(message.message)) } } memory.chatHistory = new ChatMessageHistory(chatHistory) chain.memory = memory } const loggerHandler = new ConsoleCallbackHandler(options.logger) if (options.socketIO && options.socketIOClientId) { const handler = new CustomChainHandler(options.socketIO, options.socketIOClientId) const res = await chain.call({ input }, [loggerHandler, handler]) return res?.response } else { const res = await chain.call({ input }, [loggerHandler]) return res?.response } } } module.exports = { nodeClass: ConversationChain_Chains }