mirror of
https://github.com/farcasclaudiu/Flowise.git
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add llamaindex
This commit is contained in:
Henry
@@ -0,0 +1,178 @@
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import { ICommonObject, IMessage, INode, INodeData, INodeOutputsValue, INodeParams } from '../../../src/Interface'
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import { ContextChatEngine, ChatMessage } from 'llamaindex'
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class ContextChatEngine_LlamaIndex 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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tags: string[]
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inputs: INodeParams[]
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outputs: INodeOutputsValue[]
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constructor() {
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this.label = 'Context Chat Engine'
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this.name = 'contextChatEngine'
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this.version = 1.0
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this.type = 'ContextChatEngine'
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this.icon = 'context-chat-engine.png'
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this.category = 'Engine'
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this.description = 'Answer question based on retrieved documents (context) with built-in memory to remember conversation'
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this.baseClasses = [this.type]
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this.tags = ['LlamaIndex']
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this.inputs = [
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{
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label: 'Chat Model',
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name: 'model',
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type: 'BaseChatModel_LlamaIndex'
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},
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{
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label: 'Vector Store Retriever',
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name: 'vectorStoreRetriever',
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type: 'VectorIndexRetriever'
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},
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{
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label: 'Memory',
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name: 'memory',
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type: 'BaseChatMemory'
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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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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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}
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async init(nodeData: INodeData): Promise<any> {
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const model = nodeData.inputs?.model
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const vectorStoreRetriever = nodeData.inputs?.vectorStoreRetriever
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const memory = nodeData.inputs?.memory
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const chatEngine = new ContextChatEngine({ chatModel: model, retriever: vectorStoreRetriever })
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;(chatEngine as any).memory = memory
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return chatEngine
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}
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async run(nodeData: INodeData, input: string, options: ICommonObject): Promise<string | object> {
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const chatEngine = nodeData.instance as ContextChatEngine
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const systemMessagePrompt = nodeData.inputs?.systemMessagePrompt as string
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const memory = nodeData.inputs?.memory
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const chatHistory = [] as ChatMessage[]
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let sessionId = ''
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if (memory) {
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if (memory.isSessionIdUsingChatMessageId) sessionId = options.chatId
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else sessionId = nodeData.inputs?.sessionId
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}
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if (systemMessagePrompt) {
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chatHistory.push({
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content: systemMessagePrompt,
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role: 'user'
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})
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}
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/* When incomingInput.history is provided, only force replace chatHistory if its ShortTermMemory
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* LongTermMemory will automatically retrieved chatHistory from sessionId
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*/
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if (options && options.chatHistory && memory.isShortTermMemory) {
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await memory.resumeMessages(options.chatHistory)
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}
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const msgs: IMessage[] = await memory.getChatMessages(sessionId)
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for (const message of msgs) {
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if (message.type === 'apiMessage') {
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chatHistory.push({
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content: message.message,
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role: 'assistant'
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})
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} else if (message.type === 'userMessage') {
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chatHistory.push({
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content: message.message,
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role: 'user'
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})
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}
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}
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if (options.socketIO && options.socketIOClientId) {
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let response = ''
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const stream = await chatEngine.chat(input, chatHistory, true)
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let isStart = true
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const onNextPromise = () => {
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return new Promise((resolve, reject) => {
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const onNext = async () => {
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try {
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const { value, done } = await stream.next()
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if (!done) {
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if (isStart) {
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options.socketIO.to(options.socketIOClientId).emit('start')
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isStart = false
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}
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options.socketIO.to(options.socketIOClientId).emit('token', value)
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response += value
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onNext()
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} else {
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resolve(response)
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}
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} catch (error) {
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reject(error)
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}
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}
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onNext()
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})
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}
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try {
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const result = await onNextPromise()
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if (memory) {
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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: result,
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type: 'apiMessage'
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}
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],
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sessionId
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)
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}
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return result as string
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} catch (error) {
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throw new Error(error)
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}
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} else {
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const response = await chatEngine.chat(input, chatHistory)
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if (memory) {
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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: response?.response,
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type: 'apiMessage'
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}
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],
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sessionId
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)
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}
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return response?.response
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}
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}
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}
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module.exports = { nodeClass: ContextChatEngine_LlamaIndex }
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@@ -0,0 +1,171 @@
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import { ICommonObject, IMessage, INode, INodeData, INodeOutputsValue, INodeParams } from '../../../src/Interface'
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import { ChatMessage, SimpleChatEngine } from 'llamaindex'
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class SimpleChatEngine_LlamaIndex 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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tags: string[]
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inputs: INodeParams[]
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outputs: INodeOutputsValue[]
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constructor() {
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this.label = 'Simple Chat Engine'
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this.name = 'simpleChatEngine'
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this.version = 1.0
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this.type = 'SimpleChatEngine'
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this.icon = 'chat-engine.png'
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this.category = 'Engine'
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this.description = 'Simple engine to handle back and forth conversations'
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this.baseClasses = [this.type]
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this.tags = ['LlamaIndex']
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this.inputs = [
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{
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label: 'Chat Model',
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name: 'model',
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type: 'BaseChatModel_LlamaIndex'
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},
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{
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label: 'Memory',
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name: 'memory',
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type: 'BaseChatMemory'
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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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optional: true,
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placeholder: 'You are a helpful assistant'
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}
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]
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}
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async init(nodeData: INodeData): Promise<any> {
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const model = nodeData.inputs?.model
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const memory = nodeData.inputs?.memory
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const chatEngine = new SimpleChatEngine({ llm: model })
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;(chatEngine as any).memory = memory
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return chatEngine
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}
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async run(nodeData: INodeData, input: string, options: ICommonObject): Promise<string> {
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const chatEngine = nodeData.instance as SimpleChatEngine
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const systemMessagePrompt = nodeData.inputs?.systemMessagePrompt as string
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const memory = nodeData.inputs?.memory
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const chatHistory = [] as ChatMessage[]
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let sessionId = ''
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if (memory) {
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if (memory.isSessionIdUsingChatMessageId) sessionId = options.chatId
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else sessionId = nodeData.inputs?.sessionId
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}
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if (systemMessagePrompt) {
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chatHistory.push({
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content: systemMessagePrompt,
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role: 'user'
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})
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}
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/* When incomingInput.history is provided, only force replace chatHistory if its ShortTermMemory
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* LongTermMemory will automatically retrieved chatHistory from sessionId
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*/
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if (options && options.chatHistory && memory.isShortTermMemory) {
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await memory.resumeMessages(options.chatHistory)
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}
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const msgs: IMessage[] = await memory.getChatMessages(sessionId)
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for (const message of msgs) {
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if (message.type === 'apiMessage') {
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chatHistory.push({
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content: message.message,
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role: 'assistant'
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})
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} else if (message.type === 'userMessage') {
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chatHistory.push({
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content: message.message,
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role: 'user'
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})
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}
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}
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if (options.socketIO && options.socketIOClientId) {
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let response = ''
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const stream = await chatEngine.chat(input, chatHistory, true)
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let isStart = true
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const onNextPromise = () => {
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return new Promise((resolve, reject) => {
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const onNext = async () => {
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try {
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const { value, done } = await stream.next()
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if (!done) {
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if (isStart) {
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options.socketIO.to(options.socketIOClientId).emit('start')
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isStart = false
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}
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options.socketIO.to(options.socketIOClientId).emit('token', value)
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response += value
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onNext()
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} else {
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resolve(response)
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}
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} catch (error) {
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reject(error)
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}
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}
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onNext()
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})
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}
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try {
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const result = await onNextPromise()
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if (memory) {
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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: result,
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type: 'apiMessage'
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}
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],
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sessionId
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)
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}
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return result as string
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} catch (error) {
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throw new Error(error)
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}
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} else {
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const response = await chatEngine.chat(input, chatHistory)
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if (memory) {
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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: response?.response,
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type: 'apiMessage'
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}
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],
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sessionId
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)
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}
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return response?.response
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}
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}
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}
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module.exports = { nodeClass: SimpleChatEngine_LlamaIndex }
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@@ -0,0 +1,126 @@
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import { INode, INodeData, INodeOutputsValue, INodeParams } from '../../../src/Interface'
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import {
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RetrieverQueryEngine,
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BaseNode,
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Metadata,
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ResponseSynthesizer,
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CompactAndRefine,
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TreeSummarize,
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Refine,
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SimpleResponseBuilder
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} from 'llamaindex'
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class QueryEngine_LlamaIndex 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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tags: string[]
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inputs: INodeParams[]
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outputs: INodeOutputsValue[]
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constructor() {
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this.label = 'Query Engine'
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this.name = 'queryEngine'
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this.version = 1.0
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this.type = 'QueryEngine'
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this.icon = 'query-engine.png'
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this.category = 'Engine'
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this.description = 'Simple query engine built to answer question over your data, without memory'
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this.baseClasses = [this.type]
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this.tags = ['LlamaIndex']
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this.inputs = [
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{
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label: 'Vector Store Retriever',
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name: 'vectorStoreRetriever',
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type: 'VectorIndexRetriever'
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},
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{
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label: 'Response Synthesizer',
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name: 'responseSynthesizer',
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type: 'ResponseSynthesizer',
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description:
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'ResponseSynthesizer is responsible for sending the query, nodes, and prompt templates to the LLM to generate a response. See <a target="_blank" href="https://ts.llamaindex.ai/modules/low_level/response_synthesizer">more</a>',
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optional: true
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},
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{
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label: 'Return Source Documents',
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name: 'returnSourceDocuments',
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type: 'boolean',
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optional: true
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}
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]
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}
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async init(nodeData: INodeData): Promise<any> {
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const vectorStoreRetriever = nodeData.inputs?.vectorStoreRetriever
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const responseSynthesizerObj = nodeData.inputs?.responseSynthesizer
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if (responseSynthesizerObj) {
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if (responseSynthesizerObj.type === 'TreeSummarize') {
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const responseSynthesizer = new ResponseSynthesizer({
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responseBuilder: new TreeSummarize(vectorStoreRetriever.serviceContext, responseSynthesizerObj.textQAPromptTemplate),
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serviceContext: vectorStoreRetriever.serviceContext
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})
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return new RetrieverQueryEngine(vectorStoreRetriever, responseSynthesizer)
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} else if (responseSynthesizerObj.type === 'CompactAndRefine') {
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const responseSynthesizer = new ResponseSynthesizer({
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responseBuilder: new CompactAndRefine(
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vectorStoreRetriever.serviceContext,
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responseSynthesizerObj.textQAPromptTemplate,
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responseSynthesizerObj.refinePromptTemplate
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),
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serviceContext: vectorStoreRetriever.serviceContext
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})
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return new RetrieverQueryEngine(vectorStoreRetriever, responseSynthesizer)
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} else if (responseSynthesizerObj.type === 'Refine') {
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const responseSynthesizer = new ResponseSynthesizer({
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responseBuilder: new Refine(
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vectorStoreRetriever.serviceContext,
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responseSynthesizerObj.textQAPromptTemplate,
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responseSynthesizerObj.refinePromptTemplate
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),
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serviceContext: vectorStoreRetriever.serviceContext
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})
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return new RetrieverQueryEngine(vectorStoreRetriever, responseSynthesizer)
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} else if (responseSynthesizerObj.type === 'SimpleResponseBuilder') {
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const responseSynthesizer = new ResponseSynthesizer({
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responseBuilder: new SimpleResponseBuilder(vectorStoreRetriever.serviceContext),
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serviceContext: vectorStoreRetriever.serviceContext
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})
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return new RetrieverQueryEngine(vectorStoreRetriever, responseSynthesizer)
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}
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}
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const queryEngine = new RetrieverQueryEngine(vectorStoreRetriever)
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return queryEngine
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}
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async run(nodeData: INodeData, input: string): Promise<string | object> {
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const queryEngine = nodeData.instance as RetrieverQueryEngine
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const returnSourceDocuments = nodeData.inputs?.returnSourceDocuments as boolean
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const response = await queryEngine.query(input)
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if (returnSourceDocuments && response.sourceNodes?.length)
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return { text: response?.response, sourceDocuments: reformatSourceDocuments(response.sourceNodes) }
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return response?.response
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}
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}
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const reformatSourceDocuments = (sourceNodes: BaseNode<Metadata>[]) => {
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const sourceDocuments = []
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for (const node of sourceNodes) {
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sourceDocuments.push({
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pageContent: (node as any).text,
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metadata: node.metadata
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})
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}
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return sourceDocuments
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}
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module.exports = { nodeClass: QueryEngine_LlamaIndex }
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