mirror of
https://github.com/farcasclaudiu/Flowise.git
synced 2026-06-28 19:00:59 +03:00
change agent/chain with memory to use runnable
This commit is contained in:
Henry
@@ -1,14 +1,16 @@
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import { ICommonObject, INode, INodeData, INodeParams } from '../../../src/Interface'
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import { FlowiseMemory, ICommonObject, IMessage, INode, INodeData, INodeParams } from '../../../src/Interface'
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import { ConversationChain } from 'langchain/chains'
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import { getBaseClasses, mapChatHistory } from '../../../src/utils'
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import { getBaseClasses } from '../../../src/utils'
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import { ChatPromptTemplate, HumanMessagePromptTemplate, MessagesPlaceholder, SystemMessagePromptTemplate } from 'langchain/prompts'
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import { BufferMemory } from 'langchain/memory'
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import { BaseChatModel } from 'langchain/chat_models/base'
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import { ConsoleCallbackHandler, CustomChainHandler, additionalCallbacks } from '../../../src/handler'
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import { flatten } from 'lodash'
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import { Document } from 'langchain/document'
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import { RunnableSequence } from 'langchain/schema/runnable'
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import { StringOutputParser } from 'langchain/schema/output_parser'
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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.`
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const inputKey = 'input'
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class ConversationChain_Chains implements INode {
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label: string
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@@ -20,8 +22,9 @@ class ConversationChain_Chains implements INode {
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baseClasses: string[]
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description: string
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inputs: INodeParams[]
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sessionId?: string
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constructor() {
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constructor(fields?: { sessionId?: string }) {
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this.label = 'Conversation Chain'
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this.name = 'conversationChain'
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this.version = 1.0
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@@ -32,7 +35,7 @@ class ConversationChain_Chains implements INode {
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this.baseClasses = [this.type, ...getBaseClasses(ConversationChain)]
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this.inputs = [
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{
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label: 'Language Model',
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label: 'Chat Model',
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name: 'model',
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type: 'BaseChatModel'
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},
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@@ -60,76 +63,99 @@ class ConversationChain_Chains implements INode {
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placeholder: 'You are a helpful assistant that write codes'
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}
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]
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this.sessionId = fields?.sessionId
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}
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async init(nodeData: INodeData): Promise<any> {
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const model = nodeData.inputs?.model as BaseChatModel
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const memory = nodeData.inputs?.memory as BufferMemory
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const prompt = nodeData.inputs?.systemMessagePrompt as string
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const docs = nodeData.inputs?.document as Document[]
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const flattenDocs = docs && docs.length ? flatten(docs) : []
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const finalDocs = []
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for (let i = 0; i < flattenDocs.length; i += 1) {
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if (flattenDocs[i] && flattenDocs[i].pageContent) {
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finalDocs.push(new Document(flattenDocs[i]))
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}
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}
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let finalText = ''
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for (let i = 0; i < finalDocs.length; i += 1) {
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finalText += finalDocs[i].pageContent
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}
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const replaceChar: string[] = ['{', '}']
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for (const char of replaceChar) finalText = finalText.replaceAll(char, '')
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if (finalText) systemMessage = `${systemMessage}\nThe AI has the following context:\n${finalText}`
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const obj: any = {
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llm: model,
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memory,
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verbose: process.env.DEBUG === 'true' ? true : false
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}
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const chatPrompt = ChatPromptTemplate.fromMessages([
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SystemMessagePromptTemplate.fromTemplate(prompt ? `${prompt}\n${systemMessage}` : systemMessage),
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new MessagesPlaceholder(memory.memoryKey ?? 'chat_history'),
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HumanMessagePromptTemplate.fromTemplate('{input}')
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])
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obj.prompt = chatPrompt
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const chain = new ConversationChain(obj)
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async init(nodeData: INodeData, _: string, options: ICommonObject): Promise<any> {
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const chain = prepareChain(nodeData, this.sessionId, options.chatHistory)
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return chain
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}
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async run(nodeData: INodeData, input: string, options: ICommonObject): Promise<string> {
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const chain = nodeData.instance as ConversationChain
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const memory = nodeData.inputs?.memory as BufferMemory
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memory.returnMessages = true // Return true for BaseChatModel
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if (options && options.chatHistory) {
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const chatHistoryClassName = memory.chatHistory.constructor.name
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// Only replace when its In-Memory
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if (chatHistoryClassName && chatHistoryClassName === 'ChatMessageHistory') {
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memory.chatHistory = mapChatHistory(options)
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}
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}
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chain.memory = memory
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const memory = nodeData.inputs?.memory
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const chain = prepareChain(nodeData, this.sessionId, options.chatHistory)
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const loggerHandler = new ConsoleCallbackHandler(options.logger)
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const callbacks = await additionalCallbacks(nodeData, options)
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let res = ''
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if (options.socketIO && options.socketIOClientId) {
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const handler = new CustomChainHandler(options.socketIO, options.socketIOClientId)
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const res = await chain.call({ input }, [loggerHandler, handler, ...callbacks])
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return res?.response
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res = await chain.invoke({ input }, { callbacks: [loggerHandler, handler, ...callbacks] })
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} else {
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const res = await chain.call({ input }, [loggerHandler, ...callbacks])
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return res?.response
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res = await chain.invoke({ input }, { callbacks: [loggerHandler, ...callbacks] })
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}
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await memory.addChatMessages(
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[
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{
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text: input,
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type: 'userMessage'
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},
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{
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text: res,
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type: 'apiMessage'
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}
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],
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this.sessionId
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)
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return res
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}
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}
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const prepareChatPrompt = (nodeData: INodeData) => {
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const memory = nodeData.inputs?.memory as FlowiseMemory
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const prompt = nodeData.inputs?.systemMessagePrompt as string
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const docs = nodeData.inputs?.document as Document[]
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const flattenDocs = docs && docs.length ? flatten(docs) : []
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const finalDocs = []
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for (let i = 0; i < flattenDocs.length; i += 1) {
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if (flattenDocs[i] && flattenDocs[i].pageContent) {
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finalDocs.push(new Document(flattenDocs[i]))
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}
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}
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let finalText = ''
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for (let i = 0; i < finalDocs.length; i += 1) {
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finalText += finalDocs[i].pageContent
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}
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const replaceChar: string[] = ['{', '}']
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for (const char of replaceChar) finalText = finalText.replaceAll(char, '')
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if (finalText) systemMessage = `${systemMessage}\nThe AI has the following context:\n${finalText}`
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const chatPrompt = ChatPromptTemplate.fromMessages([
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SystemMessagePromptTemplate.fromTemplate(prompt ? `${prompt}\n${systemMessage}` : systemMessage),
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new MessagesPlaceholder(memory.memoryKey ?? 'chat_history'),
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HumanMessagePromptTemplate.fromTemplate(`{${inputKey}}`)
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])
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return chatPrompt
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}
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const prepareChain = (nodeData: INodeData, sessionId?: string, chatHistory: IMessage[] = []) => {
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const model = nodeData.inputs?.model as BaseChatModel
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const memory = nodeData.inputs?.memory as FlowiseMemory
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const memoryKey = memory.memoryKey ?? 'chat_history'
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const conversationChain = RunnableSequence.from([
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{
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[inputKey]: (input: { input: string }) => input.input,
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[memoryKey]: async () => {
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const history = await memory.getChatMessages(sessionId, true, chatHistory)
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return history
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}
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},
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prepareChatPrompt(nodeData),
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model,
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new StringOutputParser()
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])
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return conversationChain
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}
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module.exports = { nodeClass: ConversationChain_Chains }
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+266
-114
@@ -1,20 +1,25 @@
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import { BaseLanguageModel } from 'langchain/base_language'
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import { ICommonObject, INode, INodeData, INodeParams } from '../../../src/Interface'
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import { getBaseClasses, mapChatHistory } from '../../../src/utils'
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import { ConversationalRetrievalQAChain, QAChainParams } from 'langchain/chains'
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import { ConversationalRetrievalQAChain } from 'langchain/chains'
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import { BaseRetriever } from 'langchain/schema/retriever'
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import { BufferMemory, BufferMemoryInput } from 'langchain/memory'
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import { BufferMemoryInput } from 'langchain/memory'
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import { PromptTemplate } from 'langchain/prompts'
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import { ConsoleCallbackHandler, CustomChainHandler, additionalCallbacks } from '../../../src/handler'
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import {
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default_map_reduce_template,
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default_qa_template,
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qa_template,
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map_reduce_template,
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CUSTOM_QUESTION_GENERATOR_CHAIN_PROMPT,
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refine_question_template,
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refine_template
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} from './prompts'
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import { QA_TEMPLATE, REPHRASE_TEMPLATE, RESPONSE_TEMPLATE } from './prompts'
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import { Runnable, RunnableSequence, RunnableMap, RunnableBranch, RunnableLambda } from 'langchain/schema/runnable'
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import { BaseMessage, HumanMessage, AIMessage } from 'langchain/schema'
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import { StringOutputParser } from 'langchain/schema/output_parser'
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import type { Document } from 'langchain/document'
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import { ChatPromptTemplate, MessagesPlaceholder } from 'langchain/prompts'
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import { applyPatch } from 'fast-json-patch'
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import { convertBaseMessagetoIMessage, getBaseClasses } from '../../../src/utils'
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import { ConsoleCallbackHandler, additionalCallbacks } from '../../../src/handler'
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import { FlowiseMemory, ICommonObject, IMessage, INode, INodeData, INodeParams, MemoryMethods } from '../../../src/Interface'
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type RetrievalChainInput = {
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chat_history: string
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question: string
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}
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const sourceRunnableName = 'FindDocs'
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class ConversationalRetrievalQAChain_Chains implements INode {
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label: string
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@@ -26,11 +31,12 @@ class ConversationalRetrievalQAChain_Chains implements INode {
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baseClasses: string[]
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description: string
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inputs: INodeParams[]
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sessionId?: string
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constructor() {
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constructor(fields?: { sessionId?: string }) {
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this.label = 'Conversational Retrieval QA Chain'
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this.name = 'conversationalRetrievalQAChain'
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this.version = 1.0
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this.version = 2.0
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this.type = 'ConversationalRetrievalQAChain'
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this.icon = 'qa.svg'
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this.category = 'Chains'
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@@ -38,9 +44,9 @@ class ConversationalRetrievalQAChain_Chains implements INode {
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this.baseClasses = [this.type, ...getBaseClasses(ConversationalRetrievalQAChain)]
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this.inputs = [
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{
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label: 'Language Model',
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label: 'Chat Model',
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name: 'model',
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type: 'BaseLanguageModel'
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type: 'BaseChatModel'
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},
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{
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label: 'Vector Store Retriever',
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@@ -60,6 +66,29 @@ class ConversationalRetrievalQAChain_Chains implements INode {
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type: 'boolean',
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optional: true
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},
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{
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label: 'Rephrase Prompt',
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name: 'rephrasePrompt',
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type: 'string',
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description: 'Using previous chat history, rephrase question into a standalone question',
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warning: 'Prompt must include input variables: {chat_history} and {question}',
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rows: 4,
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additionalParams: true,
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optional: true,
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default: REPHRASE_TEMPLATE
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},
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{
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label: 'Response Prompt',
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name: 'responsePrompt',
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type: 'string',
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description: 'Taking the rephrased question, search for answer from the provided context',
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warning: 'Prompt must include input variable: {context}',
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rows: 4,
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additionalParams: true,
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optional: true,
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default: RESPONSE_TEMPLATE
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}
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/** Deprecated
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{
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label: 'System Message',
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name: 'systemMessagePrompt',
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@@ -70,6 +99,7 @@ class ConversationalRetrievalQAChain_Chains implements INode {
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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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// TODO: create standalone chains for these 3 modes as they are not compatible with memory
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{
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label: 'Chain Option',
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name: 'chainOption',
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@@ -95,124 +125,246 @@ class ConversationalRetrievalQAChain_Chains implements INode {
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additionalParams: true,
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optional: true
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}
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*/
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]
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this.sessionId = fields?.sessionId
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}
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async init(nodeData: INodeData): Promise<any> {
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const model = nodeData.inputs?.model as BaseLanguageModel
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const vectorStoreRetriever = nodeData.inputs?.vectorStoreRetriever as BaseRetriever
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const systemMessagePrompt = nodeData.inputs?.systemMessagePrompt as string
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const returnSourceDocuments = nodeData.inputs?.returnSourceDocuments as boolean
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const chainOption = nodeData.inputs?.chainOption as string
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const externalMemory = nodeData.inputs?.memory
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const rephrasePrompt = nodeData.inputs?.rephrasePrompt as string
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const responsePrompt = nodeData.inputs?.responsePrompt as string
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const obj: any = {
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verbose: process.env.DEBUG === 'true' ? true : false,
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questionGeneratorChainOptions: {
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template: CUSTOM_QUESTION_GENERATOR_CHAIN_PROMPT
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}
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let customResponsePrompt = responsePrompt
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// If the deprecated systemMessagePrompt is still exists
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if (systemMessagePrompt) {
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customResponsePrompt = `${systemMessagePrompt}\n${QA_TEMPLATE}`
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}
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if (returnSourceDocuments) obj.returnSourceDocuments = returnSourceDocuments
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if (chainOption === 'map_reduce') {
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obj.qaChainOptions = {
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type: 'map_reduce',
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combinePrompt: PromptTemplate.fromTemplate(
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systemMessagePrompt ? `${systemMessagePrompt}\n${map_reduce_template}` : default_map_reduce_template
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)
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} as QAChainParams
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} else if (chainOption === 'refine') {
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const qprompt = new PromptTemplate({
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inputVariables: ['context', 'question'],
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template: refine_question_template(systemMessagePrompt)
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})
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const rprompt = new PromptTemplate({
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inputVariables: ['context', 'question', 'existing_answer'],
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template: refine_template
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})
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obj.qaChainOptions = {
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type: 'refine',
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questionPrompt: qprompt,
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refinePrompt: rprompt
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} as QAChainParams
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} else {
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obj.qaChainOptions = {
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type: 'stuff',
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prompt: PromptTemplate.fromTemplate(systemMessagePrompt ? `${systemMessagePrompt}\n${qa_template}` : default_qa_template)
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} as QAChainParams
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}
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if (externalMemory) {
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externalMemory.memoryKey = 'chat_history'
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externalMemory.inputKey = 'question'
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externalMemory.outputKey = 'text'
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externalMemory.returnMessages = true
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if (chainOption === 'refine') externalMemory.outputKey = 'output_text'
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obj.memory = externalMemory
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} else {
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const fields: BufferMemoryInput = {
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memoryKey: 'chat_history',
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inputKey: 'question',
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outputKey: 'text',
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returnMessages: true
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}
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if (chainOption === 'refine') fields.outputKey = 'output_text'
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obj.memory = new BufferMemory(fields)
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}
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const chain = ConversationalRetrievalQAChain.fromLLM(model, vectorStoreRetriever, obj)
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return chain
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const answerChain = createChain(model, vectorStoreRetriever, rephrasePrompt, customResponsePrompt)
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return answerChain
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}
|
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|
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async run(nodeData: INodeData, input: string, options: ICommonObject): Promise<string | ICommonObject> {
|
||||
const chain = nodeData.instance as ConversationalRetrievalQAChain
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||||
const model = nodeData.inputs?.model as BaseLanguageModel
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const externalMemory = nodeData.inputs?.memory
|
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const vectorStoreRetriever = nodeData.inputs?.vectorStoreRetriever as BaseRetriever
|
||||
const systemMessagePrompt = nodeData.inputs?.systemMessagePrompt as string
|
||||
const rephrasePrompt = nodeData.inputs?.rephrasePrompt as string
|
||||
const responsePrompt = nodeData.inputs?.responsePrompt as string
|
||||
const returnSourceDocuments = nodeData.inputs?.returnSourceDocuments as boolean
|
||||
const chainOption = nodeData.inputs?.chainOption as string
|
||||
|
||||
let model = nodeData.inputs?.model
|
||||
|
||||
// Temporary fix: https://github.com/hwchase17/langchainjs/issues/754
|
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model.streaming = false
|
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chain.questionGeneratorChain.llm = model
|
||||
|
||||
const obj = { question: input }
|
||||
|
||||
if (options && options.chatHistory && chain.memory) {
|
||||
const chatHistoryClassName = (chain.memory as any).chatHistory.constructor.name
|
||||
// Only replace when its In-Memory
|
||||
if (chatHistoryClassName && chatHistoryClassName === 'ChatMessageHistory') {
|
||||
;(chain.memory as any).chatHistory = mapChatHistory(options)
|
||||
}
|
||||
let customResponsePrompt = responsePrompt
|
||||
// If the deprecated systemMessagePrompt is still exists
|
||||
if (systemMessagePrompt) {
|
||||
customResponsePrompt = `${systemMessagePrompt}\n${QA_TEMPLATE}`
|
||||
}
|
||||
|
||||
let memory: FlowiseMemory | undefined = externalMemory
|
||||
if (!memory) {
|
||||
memory = new BufferMemory({
|
||||
returnMessages: true,
|
||||
memoryKey: 'chat_history',
|
||||
inputKey: 'input'
|
||||
})
|
||||
}
|
||||
|
||||
const answerChain = createChain(model, vectorStoreRetriever, rephrasePrompt, customResponsePrompt)
|
||||
|
||||
const history = ((await memory.getChatMessages(this.sessionId, false, options.chatHistory)) as IMessage[]) ?? []
|
||||
|
||||
const loggerHandler = new ConsoleCallbackHandler(options.logger)
|
||||
const callbacks = await additionalCallbacks(nodeData, options)
|
||||
|
||||
if (options.socketIO && options.socketIOClientId) {
|
||||
const handler = new CustomChainHandler(
|
||||
options.socketIO,
|
||||
options.socketIOClientId,
|
||||
chainOption === 'refine' ? 4 : undefined,
|
||||
returnSourceDocuments
|
||||
)
|
||||
const res = await chain.call(obj, [loggerHandler, handler, ...callbacks])
|
||||
if (chainOption === 'refine') {
|
||||
if (res.output_text && res.sourceDocuments) {
|
||||
return {
|
||||
text: res.output_text,
|
||||
sourceDocuments: res.sourceDocuments
|
||||
}
|
||||
}
|
||||
return res?.output_text
|
||||
const stream = answerChain.streamLog(
|
||||
{ question: input, chat_history: history },
|
||||
{ callbacks: [loggerHandler, ...callbacks] },
|
||||
{
|
||||
includeNames: [sourceRunnableName]
|
||||
}
|
||||
)
|
||||
|
||||
let streamedResponse: Record<string, any> = {}
|
||||
let sourceDocuments: ICommonObject[] = []
|
||||
let text = ''
|
||||
let isStreamingStarted = false
|
||||
const isStreamingEnabled = options.socketIO && options.socketIOClientId
|
||||
|
||||
for await (const chunk of stream) {
|
||||
streamedResponse = applyPatch(streamedResponse, chunk.ops).newDocument
|
||||
|
||||
if (streamedResponse.final_output) {
|
||||
text = streamedResponse.final_output?.output
|
||||
if (isStreamingEnabled) options.socketIO.to(options.socketIOClientId).emit('end')
|
||||
if (Array.isArray(streamedResponse?.logs?.[sourceRunnableName]?.final_output?.output)) {
|
||||
sourceDocuments = streamedResponse?.logs?.[sourceRunnableName]?.final_output?.output
|
||||
if (isStreamingEnabled && returnSourceDocuments)
|
||||
options.socketIO.to(options.socketIOClientId).emit('sourceDocuments', sourceDocuments)
|
||||
}
|
||||
}
|
||||
|
||||
if (
|
||||
Array.isArray(streamedResponse?.streamed_output) &&
|
||||
streamedResponse?.streamed_output.length &&
|
||||
!streamedResponse.final_output
|
||||
) {
|
||||
const token = streamedResponse.streamed_output[streamedResponse.streamed_output.length - 1]
|
||||
|
||||
if (!isStreamingStarted) {
|
||||
isStreamingStarted = true
|
||||
if (isStreamingEnabled) options.socketIO.to(options.socketIOClientId).emit('start', token)
|
||||
}
|
||||
if (isStreamingEnabled) options.socketIO.to(options.socketIOClientId).emit('token', token)
|
||||
}
|
||||
if (res.text && res.sourceDocuments) return res
|
||||
return res?.text
|
||||
} else {
|
||||
const res = await chain.call(obj, [loggerHandler, ...callbacks])
|
||||
if (res.text && res.sourceDocuments) return res
|
||||
return res?.text
|
||||
}
|
||||
|
||||
await memory.addChatMessages(
|
||||
[
|
||||
{
|
||||
text: input,
|
||||
type: 'userMessage'
|
||||
},
|
||||
{
|
||||
text: text,
|
||||
type: 'apiMessage'
|
||||
}
|
||||
],
|
||||
this.sessionId
|
||||
)
|
||||
|
||||
if (returnSourceDocuments) return { text, sourceDocuments }
|
||||
else return { text }
|
||||
}
|
||||
}
|
||||
|
||||
const createRetrieverChain = (llm: BaseLanguageModel, retriever: Runnable, rephrasePrompt: string) => {
|
||||
// Small speed/accuracy optimization: no need to rephrase the first question
|
||||
// since there shouldn't be any meta-references to prior chat history
|
||||
const CONDENSE_QUESTION_PROMPT = PromptTemplate.fromTemplate(rephrasePrompt)
|
||||
const condenseQuestionChain = RunnableSequence.from([CONDENSE_QUESTION_PROMPT, llm, new StringOutputParser()]).withConfig({
|
||||
runName: 'CondenseQuestion'
|
||||
})
|
||||
|
||||
const hasHistoryCheckFn = RunnableLambda.from((input: RetrievalChainInput) => input.chat_history.length > 0).withConfig({
|
||||
runName: 'HasChatHistoryCheck'
|
||||
})
|
||||
|
||||
const conversationChain = condenseQuestionChain.pipe(retriever).withConfig({
|
||||
runName: 'RetrievalChainWithHistory'
|
||||
})
|
||||
|
||||
const basicRetrievalChain = RunnableLambda.from((input: RetrievalChainInput) => input.question)
|
||||
.withConfig({
|
||||
runName: 'Itemgetter:question'
|
||||
})
|
||||
.pipe(retriever)
|
||||
.withConfig({ runName: 'RetrievalChainWithNoHistory' })
|
||||
|
||||
return RunnableBranch.from([[hasHistoryCheckFn, conversationChain], basicRetrievalChain]).withConfig({ runName: sourceRunnableName })
|
||||
}
|
||||
|
||||
const formatDocs = (docs: Document[]) => {
|
||||
return docs.map((doc, i) => `<doc id='${i}'>${doc.pageContent}</doc>`).join('\n')
|
||||
}
|
||||
|
||||
const formatChatHistoryAsString = (history: BaseMessage[]) => {
|
||||
return history.map((message) => `${message._getType()}: ${message.content}`).join('\n')
|
||||
}
|
||||
|
||||
const serializeHistory = (input: any) => {
|
||||
const chatHistory: IMessage[] = input.chat_history || []
|
||||
const convertedChatHistory = []
|
||||
for (const message of chatHistory) {
|
||||
if (message.type === 'userMessage') {
|
||||
convertedChatHistory.push(new HumanMessage({ content: message.message }))
|
||||
}
|
||||
if (message.type === 'apiMessage') {
|
||||
convertedChatHistory.push(new AIMessage({ content: message.message }))
|
||||
}
|
||||
}
|
||||
return convertedChatHistory
|
||||
}
|
||||
|
||||
const createChain = (
|
||||
llm: BaseLanguageModel,
|
||||
retriever: Runnable,
|
||||
rephrasePrompt = REPHRASE_TEMPLATE,
|
||||
responsePrompt = RESPONSE_TEMPLATE
|
||||
) => {
|
||||
const retrieverChain = createRetrieverChain(llm, retriever, rephrasePrompt)
|
||||
|
||||
const context = RunnableMap.from({
|
||||
context: RunnableSequence.from([
|
||||
({ question, chat_history }) => ({
|
||||
question,
|
||||
chat_history: formatChatHistoryAsString(chat_history)
|
||||
}),
|
||||
retrieverChain,
|
||||
RunnableLambda.from(formatDocs).withConfig({
|
||||
runName: 'FormatDocumentChunks'
|
||||
})
|
||||
]),
|
||||
question: RunnableLambda.from((input: RetrievalChainInput) => input.question).withConfig({
|
||||
runName: 'Itemgetter:question'
|
||||
}),
|
||||
chat_history: RunnableLambda.from((input: RetrievalChainInput) => input.chat_history).withConfig({
|
||||
runName: 'Itemgetter:chat_history'
|
||||
})
|
||||
}).withConfig({ tags: ['RetrieveDocs'] })
|
||||
|
||||
const prompt = ChatPromptTemplate.fromMessages([
|
||||
['system', responsePrompt],
|
||||
new MessagesPlaceholder('chat_history'),
|
||||
['human', `{question}`]
|
||||
])
|
||||
|
||||
const responseSynthesizerChain = RunnableSequence.from([prompt, llm, new StringOutputParser()]).withConfig({
|
||||
tags: ['GenerateResponse']
|
||||
})
|
||||
|
||||
const conversationalQAChain = RunnableSequence.from([
|
||||
{
|
||||
question: RunnableLambda.from((input: RetrievalChainInput) => input.question).withConfig({
|
||||
runName: 'Itemgetter:question'
|
||||
}),
|
||||
chat_history: RunnableLambda.from(serializeHistory).withConfig({
|
||||
runName: 'SerializeHistory'
|
||||
})
|
||||
},
|
||||
context,
|
||||
responseSynthesizerChain
|
||||
])
|
||||
|
||||
return conversationalQAChain
|
||||
}
|
||||
|
||||
class BufferMemory extends FlowiseMemory implements MemoryMethods {
|
||||
constructor(fields: BufferMemoryInput) {
|
||||
super(fields)
|
||||
}
|
||||
|
||||
async getChatMessages(_?: string, returnBaseMessages = false, prevHistory: IMessage[] = []): Promise<IMessage[] | BaseMessage[]> {
|
||||
await this.chatHistory.clear()
|
||||
|
||||
for (const msg of prevHistory) {
|
||||
if (msg.type === 'userMessage') await this.chatHistory.addUserMessage(msg.message)
|
||||
else if (msg.type === 'apiMessage') await this.chatHistory.addAIChatMessage(msg.message)
|
||||
}
|
||||
|
||||
const memoryResult = await this.loadMemoryVariables({})
|
||||
const baseMessages = memoryResult[this.memoryKey ?? 'chat_history']
|
||||
return returnBaseMessages ? baseMessages : convertBaseMessagetoIMessage(baseMessages)
|
||||
}
|
||||
|
||||
async addChatMessages(): Promise<void> {
|
||||
// adding chat messages will be done on the fly in getChatMessages()
|
||||
return
|
||||
}
|
||||
|
||||
async clearChatMessages(): Promise<void> {
|
||||
await this.clear()
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
@@ -1,64 +1,27 @@
|
||||
export const default_qa_template = `Use the following pieces of context to answer the question at the end. If you don't know the answer, just say that you don't know, don't try to make up an answer.
|
||||
|
||||
{context}
|
||||
|
||||
Question: {question}
|
||||
Helpful Answer:`
|
||||
|
||||
export const qa_template = `Use the following pieces of context to answer the question at the end.
|
||||
|
||||
{context}
|
||||
|
||||
Question: {question}
|
||||
Helpful Answer:`
|
||||
|
||||
export const default_map_reduce_template = `Given the following extracted parts of a long document and a question, create a final answer.
|
||||
If you don't know the answer, just say that you don't know. Don't try to make up an answer.
|
||||
|
||||
{summaries}
|
||||
|
||||
Question: {question}
|
||||
Helpful Answer:`
|
||||
|
||||
export const map_reduce_template = `Given the following extracted parts of a long document and a question, create a final answer.
|
||||
|
||||
{summaries}
|
||||
|
||||
Question: {question}
|
||||
Helpful Answer:`
|
||||
|
||||
export const refine_question_template = (sysPrompt?: string) => {
|
||||
let returnPrompt = ''
|
||||
if (sysPrompt)
|
||||
returnPrompt = `Context information is below.
|
||||
---------------------
|
||||
{context}
|
||||
---------------------
|
||||
Given the context information and not prior knowledge, ${sysPrompt}
|
||||
Answer the question: {question}.
|
||||
Answer:`
|
||||
if (!sysPrompt)
|
||||
returnPrompt = `Context information is below.
|
||||
---------------------
|
||||
{context}
|
||||
---------------------
|
||||
Given the context information and not prior knowledge, answer the question: {question}.
|
||||
Answer:`
|
||||
return returnPrompt
|
||||
}
|
||||
|
||||
export const refine_template = `The original question is as follows: {question}
|
||||
We have provided an existing answer: {existing_answer}
|
||||
We have the opportunity to refine the existing answer (only if needed) with some more context below.
|
||||
------------
|
||||
{context}
|
||||
------------
|
||||
Given the new context, refine the original answer to better answer the question.
|
||||
If you can't find answer from the context, return the original answer.`
|
||||
|
||||
export const CUSTOM_QUESTION_GENERATOR_CHAIN_PROMPT = `Given the following conversation and a follow up question, rephrase the follow up question to be a standalone question, answer in the same language as the follow up question. include it in the standalone question.
|
||||
|
||||
Chat History:
|
||||
{chat_history}
|
||||
Follow Up Input: {question}
|
||||
Standalone question:`
|
||||
|
||||
export const RESPONSE_TEMPLATE = `I want you to act as a document that I am having a conversation with. Your name is "AI Assistant". Using the provided context, answer the user's question to the best of your ability using the resources provided.
|
||||
If there is nothing in the context relevant to the question at hand, just say "Hmm, I'm not sure" and stop after that. Refuse to answer any question not about the info. Never break character.
|
||||
------------
|
||||
{context}
|
||||
------------
|
||||
REMEMBER: If there is no relevant information within the context, just say "Hmm, I'm not sure". Don't try to make up an answer. Never break character.`
|
||||
|
||||
export const QA_TEMPLATE = `Use the following pieces of context to answer the question at the end.
|
||||
|
||||
{context}
|
||||
|
||||
Question: {question}
|
||||
Helpful Answer:`
|
||||
|
||||
export const REPHRASE_TEMPLATE = `Given the following conversation and a follow up question, rephrase the follow up question to be a standalone question.
|
||||
|
||||
Chat History:
|
||||
{chat_history}
|
||||
Follow Up Input: {question}
|
||||
Standalone Question:`
|
||||
|
||||
Reference in New Issue
Block a user