mirror of
https://github.com/farcasclaudiu/Flowise.git
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Merge pull request #1611 from FlowiseAI/feature/LlamaIndex
Feature/llama index
This commit is contained in:
Henry Heng
@@ -0,0 +1,135 @@
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import { ICommonObject, INode, INodeData, INodeParams } from '../../../src/Interface'
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import { getBaseClasses, getCredentialData, getCredentialParam } from '../../../src/utils'
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import { OpenAI, ALL_AVAILABLE_OPENAI_MODELS } from 'llamaindex'
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interface AzureOpenAIConfig {
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apiKey?: string
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endpoint?: string
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apiVersion?: string
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deploymentName?: string
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}
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class AzureChatOpenAI_LlamaIndex_ChatModels 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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description: string
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baseClasses: string[]
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tags: string[]
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credential: INodeParams
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inputs: INodeParams[]
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constructor() {
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this.label = 'AzureChatOpenAI'
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this.name = 'azureChatOpenAI_LlamaIndex'
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this.version = 1.0
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this.type = 'AzureChatOpenAI'
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this.icon = 'Azure.svg'
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this.category = 'Chat Models'
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this.description = 'Wrapper around Azure OpenAI Chat LLM specific for LlamaIndex'
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this.baseClasses = [this.type, 'BaseChatModel_LlamaIndex', ...getBaseClasses(OpenAI)]
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this.tags = ['LlamaIndex']
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this.credential = {
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label: 'Connect Credential',
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name: 'credential',
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type: 'credential',
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credentialNames: ['azureOpenAIApi']
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}
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this.inputs = [
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{
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label: 'Model Name',
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name: 'modelName',
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type: 'options',
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options: [
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{
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label: 'gpt-4',
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name: 'gpt-4'
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},
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{
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label: 'gpt-4-32k',
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name: 'gpt-4-32k'
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},
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{
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label: 'gpt-3.5-turbo',
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name: 'gpt-3.5-turbo'
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},
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{
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label: 'gpt-3.5-turbo-16k',
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name: 'gpt-3.5-turbo-16k'
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}
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],
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default: 'gpt-3.5-turbo-16k',
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optional: true
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},
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{
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label: 'Temperature',
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name: 'temperature',
|
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type: 'number',
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step: 0.1,
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default: 0.9,
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optional: true
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},
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{
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label: 'Max Tokens',
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name: 'maxTokens',
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type: 'number',
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step: 1,
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optional: true,
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additionalParams: true
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},
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{
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label: 'Top Probability',
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name: 'topP',
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type: 'number',
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step: 0.1,
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optional: true,
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additionalParams: true
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},
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{
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label: 'Timeout',
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name: 'timeout',
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type: 'number',
|
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step: 1,
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optional: true,
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additionalParams: true
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}
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]
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}
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async init(nodeData: INodeData, _: string, options: ICommonObject): Promise<any> {
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const modelName = nodeData.inputs?.modelName as keyof typeof ALL_AVAILABLE_OPENAI_MODELS
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const temperature = nodeData.inputs?.temperature as string
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const maxTokens = nodeData.inputs?.maxTokens as string
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const topP = nodeData.inputs?.topP as string
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const timeout = nodeData.inputs?.timeout as string
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const credentialData = await getCredentialData(nodeData.credential ?? '', options)
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const azureOpenAIApiKey = getCredentialParam('azureOpenAIApiKey', credentialData, nodeData)
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const azureOpenAIApiInstanceName = getCredentialParam('azureOpenAIApiInstanceName', credentialData, nodeData)
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const azureOpenAIApiDeploymentName = getCredentialParam('azureOpenAIApiDeploymentName', credentialData, nodeData)
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const azureOpenAIApiVersion = getCredentialParam('azureOpenAIApiVersion', credentialData, nodeData)
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const obj: Partial<OpenAI> & { azure?: AzureOpenAIConfig } = {
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temperature: parseFloat(temperature),
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model: modelName,
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azure: {
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apiKey: azureOpenAIApiKey,
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endpoint: `https://${azureOpenAIApiInstanceName}.openai.azure.com`,
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apiVersion: azureOpenAIApiVersion,
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deploymentName: azureOpenAIApiDeploymentName
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}
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}
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if (maxTokens) obj.maxTokens = parseInt(maxTokens, 10)
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if (topP) obj.topP = parseFloat(topP)
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if (timeout) obj.timeout = parseInt(timeout, 10)
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const model = new OpenAI(obj)
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return model
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}
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}
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module.exports = { nodeClass: AzureChatOpenAI_LlamaIndex_ChatModels }
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@@ -0,0 +1,104 @@
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import { ICommonObject, INode, INodeData, INodeParams } from '../../../src/Interface'
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import { getBaseClasses, getCredentialData, getCredentialParam } from '../../../src/utils'
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import { Anthropic } from 'llamaindex'
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class ChatAnthropic_LlamaIndex_ChatModels 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
|
||||
description: string
|
||||
tags: string[]
|
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baseClasses: string[]
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||||
credential: INodeParams
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inputs: INodeParams[]
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constructor() {
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this.label = 'ChatAnthropic'
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this.name = 'chatAnthropic_LlamaIndex'
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this.version = 1.0
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this.type = 'ChatAnthropic'
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this.icon = 'Anthropic.svg'
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this.category = 'Chat Models'
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this.description = 'Wrapper around ChatAnthropic LLM specific for LlamaIndex'
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this.baseClasses = [this.type, 'BaseChatModel_LlamaIndex', ...getBaseClasses(Anthropic)]
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this.tags = ['LlamaIndex']
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this.credential = {
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label: 'Connect Credential',
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name: 'credential',
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type: 'credential',
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credentialNames: ['anthropicApi']
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}
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this.inputs = [
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{
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label: 'Model Name',
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name: 'modelName',
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type: 'options',
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options: [
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{
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label: 'claude-2',
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name: 'claude-2',
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description: 'Claude 2 latest major version, automatically get updates to the model as they are released'
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},
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{
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label: 'claude-instant-1',
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name: 'claude-instant-1',
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description: 'Claude Instant latest major version, automatically get updates to the model as they are released'
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}
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],
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default: 'claude-2',
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optional: true
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},
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{
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label: 'Temperature',
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name: 'temperature',
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type: 'number',
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step: 0.1,
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||||
default: 0.9,
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optional: true
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||||
},
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{
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label: 'Max Tokens',
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name: 'maxTokensToSample',
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type: 'number',
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step: 1,
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optional: true,
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additionalParams: true
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||||
},
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{
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label: 'Top P',
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name: 'topP',
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||||
type: 'number',
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step: 0.1,
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optional: true,
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additionalParams: true
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||||
}
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]
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}
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async init(nodeData: INodeData, _: string, options: ICommonObject): Promise<any> {
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const temperature = nodeData.inputs?.temperature as string
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const modelName = nodeData.inputs?.modelName as 'claude-2' | 'claude-instant-1' | undefined
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const maxTokensToSample = nodeData.inputs?.maxTokensToSample as string
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const topP = nodeData.inputs?.topP as string
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const credentialData = await getCredentialData(nodeData.credential ?? '', options)
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const anthropicApiKey = getCredentialParam('anthropicApiKey', credentialData, nodeData)
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const obj: Partial<Anthropic> = {
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temperature: parseFloat(temperature),
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model: modelName,
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apiKey: anthropicApiKey
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}
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if (maxTokensToSample) obj.maxTokens = parseInt(maxTokensToSample, 10)
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if (topP) obj.topP = parseFloat(topP)
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const model = new Anthropic(obj)
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return model
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}
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}
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module.exports = { nodeClass: ChatAnthropic_LlamaIndex_ChatModels }
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@@ -0,0 +1,156 @@
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import { ICommonObject, INode, INodeData, INodeParams } from '../../../src/Interface'
|
||||
import { getBaseClasses, getCredentialData, getCredentialParam } from '../../../src/utils'
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import { OpenAI, ALL_AVAILABLE_OPENAI_MODELS } from 'llamaindex'
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|
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class ChatOpenAI_LlamaIndex_LLMs implements INode {
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label: string
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||||
name: string
|
||||
version: number
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||||
type: string
|
||||
icon: string
|
||||
category: string
|
||||
description: string
|
||||
baseClasses: string[]
|
||||
tags: string[]
|
||||
credential: INodeParams
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||||
inputs: INodeParams[]
|
||||
|
||||
constructor() {
|
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this.label = 'ChatOpenAI'
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this.name = 'chatOpenAI_LlamaIndex'
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this.version = 1.0
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this.type = 'ChatOpenAI'
|
||||
this.icon = 'openai.svg'
|
||||
this.category = 'Chat Models'
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this.description = 'Wrapper around OpenAI Chat LLM specific for LlamaIndex'
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this.baseClasses = [this.type, 'BaseChatModel_LlamaIndex', ...getBaseClasses(OpenAI)]
|
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this.tags = ['LlamaIndex']
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this.credential = {
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label: 'Connect Credential',
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name: 'credential',
|
||||
type: 'credential',
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||||
credentialNames: ['openAIApi']
|
||||
}
|
||||
this.inputs = [
|
||||
{
|
||||
label: 'Model Name',
|
||||
name: 'modelName',
|
||||
type: 'options',
|
||||
options: [
|
||||
{
|
||||
label: 'gpt-4',
|
||||
name: 'gpt-4'
|
||||
},
|
||||
{
|
||||
label: 'gpt-4-turbo-preview',
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||||
name: 'gpt-4-turbo-preview'
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||||
},
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||||
{
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||||
label: 'gpt-4-0125-preview',
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||||
name: 'gpt-4-0125-preview'
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||||
},
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{
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label: 'gpt-4-1106-preview',
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name: 'gpt-4-1106-preview'
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||||
},
|
||||
{
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||||
label: 'gpt-4-vision-preview',
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||||
name: 'gpt-4-vision-preview'
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||||
},
|
||||
{
|
||||
label: 'gpt-4-0613',
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name: 'gpt-4-0613'
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||||
},
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||||
{
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label: 'gpt-4-32k',
|
||||
name: 'gpt-4-32k'
|
||||
},
|
||||
{
|
||||
label: 'gpt-4-32k-0613',
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||||
name: 'gpt-4-32k-0613'
|
||||
},
|
||||
{
|
||||
label: 'gpt-3.5-turbo',
|
||||
name: 'gpt-3.5-turbo'
|
||||
},
|
||||
{
|
||||
label: 'gpt-3.5-turbo-1106',
|
||||
name: 'gpt-3.5-turbo-1106'
|
||||
},
|
||||
{
|
||||
label: 'gpt-3.5-turbo-0613',
|
||||
name: 'gpt-3.5-turbo-0613'
|
||||
},
|
||||
{
|
||||
label: 'gpt-3.5-turbo-16k',
|
||||
name: 'gpt-3.5-turbo-16k'
|
||||
},
|
||||
{
|
||||
label: 'gpt-3.5-turbo-16k-0613',
|
||||
name: 'gpt-3.5-turbo-16k-0613'
|
||||
}
|
||||
],
|
||||
default: 'gpt-3.5-turbo',
|
||||
optional: true
|
||||
},
|
||||
{
|
||||
label: 'Temperature',
|
||||
name: 'temperature',
|
||||
type: 'number',
|
||||
step: 0.1,
|
||||
default: 0.9,
|
||||
optional: true
|
||||
},
|
||||
{
|
||||
label: 'Max Tokens',
|
||||
name: 'maxTokens',
|
||||
type: 'number',
|
||||
step: 1,
|
||||
optional: true,
|
||||
additionalParams: true
|
||||
},
|
||||
{
|
||||
label: 'Top Probability',
|
||||
name: 'topP',
|
||||
type: 'number',
|
||||
step: 0.1,
|
||||
optional: true,
|
||||
additionalParams: true
|
||||
},
|
||||
{
|
||||
label: 'Timeout',
|
||||
name: 'timeout',
|
||||
type: 'number',
|
||||
step: 1,
|
||||
optional: true,
|
||||
additionalParams: true
|
||||
}
|
||||
]
|
||||
}
|
||||
|
||||
async init(nodeData: INodeData, _: string, options: ICommonObject): Promise<any> {
|
||||
const temperature = nodeData.inputs?.temperature as string
|
||||
const modelName = nodeData.inputs?.modelName as keyof typeof ALL_AVAILABLE_OPENAI_MODELS
|
||||
const maxTokens = nodeData.inputs?.maxTokens as string
|
||||
const topP = nodeData.inputs?.topP as string
|
||||
const timeout = nodeData.inputs?.timeout as string
|
||||
|
||||
const credentialData = await getCredentialData(nodeData.credential ?? '', options)
|
||||
const openAIApiKey = getCredentialParam('openAIApiKey', credentialData, nodeData)
|
||||
|
||||
const obj: Partial<OpenAI> = {
|
||||
temperature: parseFloat(temperature),
|
||||
model: modelName,
|
||||
apiKey: openAIApiKey
|
||||
}
|
||||
|
||||
if (maxTokens) obj.maxTokens = parseInt(maxTokens, 10)
|
||||
if (topP) obj.topP = parseFloat(topP)
|
||||
if (timeout) obj.timeout = parseInt(timeout, 10)
|
||||
|
||||
const model = new OpenAI(obj)
|
||||
return model
|
||||
}
|
||||
}
|
||||
|
||||
module.exports = { nodeClass: ChatOpenAI_LlamaIndex_LLMs }
|
||||
+77
@@ -0,0 +1,77 @@
|
||||
import { ICommonObject, INode, INodeData, INodeParams } from '../../../src/Interface'
|
||||
import { getBaseClasses, getCredentialData, getCredentialParam } from '../../../src/utils'
|
||||
import { OpenAIEmbedding } from 'llamaindex'
|
||||
|
||||
interface AzureOpenAIConfig {
|
||||
apiKey?: string
|
||||
endpoint?: string
|
||||
apiVersion?: string
|
||||
deploymentName?: string
|
||||
}
|
||||
|
||||
class AzureOpenAIEmbedding_LlamaIndex_Embeddings implements INode {
|
||||
label: string
|
||||
name: string
|
||||
version: number
|
||||
type: string
|
||||
icon: string
|
||||
category: string
|
||||
description: string
|
||||
baseClasses: string[]
|
||||
credential: INodeParams
|
||||
tags: string[]
|
||||
inputs: INodeParams[]
|
||||
|
||||
constructor() {
|
||||
this.label = 'Azure OpenAI Embeddings'
|
||||
this.name = 'azureOpenAIEmbeddingsLlamaIndex'
|
||||
this.version = 1.0
|
||||
this.type = 'AzureOpenAIEmbeddings'
|
||||
this.icon = 'Azure.svg'
|
||||
this.category = 'Embeddings'
|
||||
this.description = 'Azure OpenAI API embeddings specific for LlamaIndex'
|
||||
this.baseClasses = [this.type, 'BaseEmbedding_LlamaIndex', ...getBaseClasses(OpenAIEmbedding)]
|
||||
this.tags = ['LlamaIndex']
|
||||
this.credential = {
|
||||
label: 'Connect Credential',
|
||||
name: 'credential',
|
||||
type: 'credential',
|
||||
credentialNames: ['azureOpenAIApi']
|
||||
}
|
||||
this.inputs = [
|
||||
{
|
||||
label: 'Timeout',
|
||||
name: 'timeout',
|
||||
type: 'number',
|
||||
optional: true,
|
||||
additionalParams: true
|
||||
}
|
||||
]
|
||||
}
|
||||
|
||||
async init(nodeData: INodeData, _: string, options: ICommonObject): Promise<any> {
|
||||
const timeout = nodeData.inputs?.timeout as string
|
||||
|
||||
const credentialData = await getCredentialData(nodeData.credential ?? '', options)
|
||||
const azureOpenAIApiKey = getCredentialParam('azureOpenAIApiKey', credentialData, nodeData)
|
||||
const azureOpenAIApiInstanceName = getCredentialParam('azureOpenAIApiInstanceName', credentialData, nodeData)
|
||||
const azureOpenAIApiDeploymentName = getCredentialParam('azureOpenAIApiDeploymentName', credentialData, nodeData)
|
||||
const azureOpenAIApiVersion = getCredentialParam('azureOpenAIApiVersion', credentialData, nodeData)
|
||||
|
||||
const obj: Partial<OpenAIEmbedding> & { azure?: AzureOpenAIConfig } = {
|
||||
azure: {
|
||||
apiKey: azureOpenAIApiKey,
|
||||
endpoint: `https://${azureOpenAIApiInstanceName}.openai.azure.com`,
|
||||
apiVersion: azureOpenAIApiVersion,
|
||||
deploymentName: azureOpenAIApiDeploymentName
|
||||
}
|
||||
}
|
||||
|
||||
if (timeout) obj.timeout = parseInt(timeout, 10)
|
||||
|
||||
const model = new OpenAIEmbedding(obj)
|
||||
return model
|
||||
}
|
||||
}
|
||||
|
||||
module.exports = { nodeClass: AzureOpenAIEmbedding_LlamaIndex_Embeddings }
|
||||
@@ -0,0 +1,91 @@
|
||||
import { ICommonObject, INode, INodeData, INodeParams } from '../../../src/Interface'
|
||||
import { getBaseClasses, getCredentialData, getCredentialParam } from '../../../src/utils'
|
||||
import { OpenAIEmbedding } from 'llamaindex'
|
||||
|
||||
class OpenAIEmbedding_LlamaIndex_Embeddings implements INode {
|
||||
label: string
|
||||
name: string
|
||||
version: number
|
||||
type: string
|
||||
icon: string
|
||||
category: string
|
||||
description: string
|
||||
baseClasses: string[]
|
||||
tags: string[]
|
||||
credential: INodeParams
|
||||
inputs: INodeParams[]
|
||||
|
||||
constructor() {
|
||||
this.label = 'OpenAI Embedding'
|
||||
this.name = 'openAIEmbedding_LlamaIndex'
|
||||
this.version = 1.0
|
||||
this.type = 'OpenAIEmbedding'
|
||||
this.icon = 'openai.svg'
|
||||
this.category = 'Embeddings'
|
||||
this.description = 'OpenAI Embedding specific for LlamaIndex'
|
||||
this.baseClasses = [this.type, 'BaseEmbedding_LlamaIndex', ...getBaseClasses(OpenAIEmbedding)]
|
||||
this.tags = ['LlamaIndex']
|
||||
this.credential = {
|
||||
label: 'Connect Credential',
|
||||
name: 'credential',
|
||||
type: 'credential',
|
||||
credentialNames: ['openAIApi']
|
||||
}
|
||||
this.inputs = [
|
||||
{
|
||||
label: 'Model Name',
|
||||
name: 'modelName',
|
||||
type: 'options',
|
||||
options: [
|
||||
{
|
||||
label: 'text-embedding-3-large',
|
||||
name: 'text-embedding-3-large'
|
||||
},
|
||||
{
|
||||
label: 'text-embedding-3-small',
|
||||
name: 'text-embedding-3-small'
|
||||
},
|
||||
{
|
||||
label: 'text-embedding-ada-002',
|
||||
name: 'text-embedding-ada-002'
|
||||
}
|
||||
],
|
||||
default: 'text-embedding-ada-002',
|
||||
optional: true
|
||||
},
|
||||
{
|
||||
label: 'Timeout',
|
||||
name: 'timeout',
|
||||
type: 'number',
|
||||
optional: true,
|
||||
additionalParams: true
|
||||
},
|
||||
{
|
||||
label: 'BasePath',
|
||||
name: 'basepath',
|
||||
type: 'string',
|
||||
optional: true,
|
||||
additionalParams: true
|
||||
}
|
||||
]
|
||||
}
|
||||
|
||||
async init(nodeData: INodeData, _: string, options: ICommonObject): Promise<any> {
|
||||
const timeout = nodeData.inputs?.timeout as string
|
||||
const modelName = nodeData.inputs?.modelName as string
|
||||
|
||||
const credentialData = await getCredentialData(nodeData.credential ?? '', options)
|
||||
const openAIApiKey = getCredentialParam('openAIApiKey', credentialData, nodeData)
|
||||
|
||||
const obj: Partial<OpenAIEmbedding> = {
|
||||
apiKey: openAIApiKey,
|
||||
model: modelName
|
||||
}
|
||||
if (timeout) obj.timeout = parseInt(timeout, 10)
|
||||
|
||||
const model = new OpenAIEmbedding(obj)
|
||||
return model
|
||||
}
|
||||
}
|
||||
|
||||
module.exports = { nodeClass: OpenAIEmbedding_LlamaIndex_Embeddings }
|
||||
@@ -0,0 +1,149 @@
|
||||
import { FlowiseMemory, ICommonObject, IMessage, INode, INodeData, INodeOutputsValue, INodeParams } from '../../../src/Interface'
|
||||
import { BaseNode, Metadata, BaseRetriever, LLM, ContextChatEngine, ChatMessage } from 'llamaindex'
|
||||
import { reformatSourceDocuments } from '../EngineUtils'
|
||||
|
||||
class ContextChatEngine_LlamaIndex implements INode {
|
||||
label: string
|
||||
name: string
|
||||
version: number
|
||||
description: string
|
||||
type: string
|
||||
icon: string
|
||||
category: string
|
||||
baseClasses: string[]
|
||||
tags: string[]
|
||||
inputs: INodeParams[]
|
||||
outputs: INodeOutputsValue[]
|
||||
sessionId?: string
|
||||
|
||||
constructor(fields?: { sessionId?: string }) {
|
||||
this.label = 'Context Chat Engine'
|
||||
this.name = 'contextChatEngine'
|
||||
this.version = 1.0
|
||||
this.type = 'ContextChatEngine'
|
||||
this.icon = 'context-chat-engine.png'
|
||||
this.category = 'Engine'
|
||||
this.description = 'Answer question based on retrieved documents (context) with built-in memory to remember conversation'
|
||||
this.baseClasses = [this.type]
|
||||
this.tags = ['LlamaIndex']
|
||||
this.inputs = [
|
||||
{
|
||||
label: 'Chat Model',
|
||||
name: 'model',
|
||||
type: 'BaseChatModel_LlamaIndex'
|
||||
},
|
||||
{
|
||||
label: 'Vector Store Retriever',
|
||||
name: 'vectorStoreRetriever',
|
||||
type: 'VectorIndexRetriever'
|
||||
},
|
||||
{
|
||||
label: 'Memory',
|
||||
name: 'memory',
|
||||
type: 'BaseChatMemory'
|
||||
},
|
||||
{
|
||||
label: 'Return Source Documents',
|
||||
name: 'returnSourceDocuments',
|
||||
type: 'boolean',
|
||||
optional: true
|
||||
},
|
||||
{
|
||||
label: 'System Message',
|
||||
name: 'systemMessagePrompt',
|
||||
type: 'string',
|
||||
rows: 4,
|
||||
optional: true,
|
||||
placeholder:
|
||||
'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.'
|
||||
}
|
||||
]
|
||||
this.sessionId = fields?.sessionId
|
||||
}
|
||||
|
||||
async init(): Promise<any> {
|
||||
return null
|
||||
}
|
||||
|
||||
async run(nodeData: INodeData, input: string, options: ICommonObject): Promise<string | ICommonObject> {
|
||||
const model = nodeData.inputs?.model as LLM
|
||||
const vectorStoreRetriever = nodeData.inputs?.vectorStoreRetriever as BaseRetriever
|
||||
const systemMessagePrompt = nodeData.inputs?.systemMessagePrompt as string
|
||||
const memory = nodeData.inputs?.memory as FlowiseMemory
|
||||
const returnSourceDocuments = nodeData.inputs?.returnSourceDocuments as boolean
|
||||
|
||||
const chatHistory = [] as ChatMessage[]
|
||||
|
||||
if (systemMessagePrompt) {
|
||||
chatHistory.push({
|
||||
content: systemMessagePrompt,
|
||||
role: 'user'
|
||||
})
|
||||
}
|
||||
|
||||
const chatEngine = new ContextChatEngine({ chatModel: model, retriever: vectorStoreRetriever })
|
||||
|
||||
const msgs = (await memory.getChatMessages(this.sessionId, false, options.chatHistory)) as IMessage[]
|
||||
for (const message of msgs) {
|
||||
if (message.type === 'apiMessage') {
|
||||
chatHistory.push({
|
||||
content: message.message,
|
||||
role: 'assistant'
|
||||
})
|
||||
} else if (message.type === 'userMessage') {
|
||||
chatHistory.push({
|
||||
content: message.message,
|
||||
role: 'user'
|
||||
})
|
||||
}
|
||||
}
|
||||
|
||||
let text = ''
|
||||
let isStreamingStarted = false
|
||||
let sourceDocuments: ICommonObject[] = []
|
||||
let sourceNodes: BaseNode<Metadata>[] = []
|
||||
const isStreamingEnabled = options.socketIO && options.socketIOClientId
|
||||
|
||||
if (isStreamingEnabled) {
|
||||
const stream = await chatEngine.chat({ message: input, chatHistory, stream: true })
|
||||
for await (const chunk of stream) {
|
||||
text += chunk.response
|
||||
if (chunk.sourceNodes) sourceNodes = chunk.sourceNodes
|
||||
if (!isStreamingStarted) {
|
||||
isStreamingStarted = true
|
||||
options.socketIO.to(options.socketIOClientId).emit('start', chunk.response)
|
||||
}
|
||||
|
||||
options.socketIO.to(options.socketIOClientId).emit('token', chunk.response)
|
||||
}
|
||||
|
||||
if (returnSourceDocuments) {
|
||||
sourceDocuments = reformatSourceDocuments(sourceNodes)
|
||||
options.socketIO.to(options.socketIOClientId).emit('sourceDocuments', sourceDocuments)
|
||||
}
|
||||
} else {
|
||||
const response = await chatEngine.chat({ message: input, chatHistory })
|
||||
text = response?.response
|
||||
sourceDocuments = reformatSourceDocuments(response?.sourceNodes ?? [])
|
||||
}
|
||||
|
||||
await memory.addChatMessages(
|
||||
[
|
||||
{
|
||||
text: input,
|
||||
type: 'userMessage'
|
||||
},
|
||||
{
|
||||
text: text,
|
||||
type: 'apiMessage'
|
||||
}
|
||||
],
|
||||
this.sessionId
|
||||
)
|
||||
|
||||
if (returnSourceDocuments) return { text, sourceDocuments }
|
||||
else return { text }
|
||||
}
|
||||
}
|
||||
|
||||
module.exports = { nodeClass: ContextChatEngine_LlamaIndex }
|
||||
@@ -0,0 +1,124 @@
|
||||
import { FlowiseMemory, ICommonObject, IMessage, INode, INodeData, INodeOutputsValue, INodeParams } from '../../../src/Interface'
|
||||
import { LLM, ChatMessage, SimpleChatEngine } from 'llamaindex'
|
||||
|
||||
class SimpleChatEngine_LlamaIndex implements INode {
|
||||
label: string
|
||||
name: string
|
||||
version: number
|
||||
description: string
|
||||
type: string
|
||||
icon: string
|
||||
category: string
|
||||
baseClasses: string[]
|
||||
tags: string[]
|
||||
inputs: INodeParams[]
|
||||
outputs: INodeOutputsValue[]
|
||||
sessionId?: string
|
||||
|
||||
constructor(fields?: { sessionId?: string }) {
|
||||
this.label = 'Simple Chat Engine'
|
||||
this.name = 'simpleChatEngine'
|
||||
this.version = 1.0
|
||||
this.type = 'SimpleChatEngine'
|
||||
this.icon = 'chat-engine.png'
|
||||
this.category = 'Engine'
|
||||
this.description = 'Simple engine to handle back and forth conversations'
|
||||
this.baseClasses = [this.type]
|
||||
this.tags = ['LlamaIndex']
|
||||
this.inputs = [
|
||||
{
|
||||
label: 'Chat Model',
|
||||
name: 'model',
|
||||
type: 'BaseChatModel_LlamaIndex'
|
||||
},
|
||||
{
|
||||
label: 'Memory',
|
||||
name: 'memory',
|
||||
type: 'BaseChatMemory'
|
||||
},
|
||||
{
|
||||
label: 'System Message',
|
||||
name: 'systemMessagePrompt',
|
||||
type: 'string',
|
||||
rows: 4,
|
||||
optional: true,
|
||||
placeholder: 'You are a helpful assistant'
|
||||
}
|
||||
]
|
||||
this.sessionId = fields?.sessionId
|
||||
}
|
||||
|
||||
async init(): Promise<any> {
|
||||
return null
|
||||
}
|
||||
|
||||
async run(nodeData: INodeData, input: string, options: ICommonObject): Promise<string> {
|
||||
const model = nodeData.inputs?.model as LLM
|
||||
const systemMessagePrompt = nodeData.inputs?.systemMessagePrompt as string
|
||||
const memory = nodeData.inputs?.memory as FlowiseMemory
|
||||
|
||||
const chatHistory = [] as ChatMessage[]
|
||||
|
||||
if (systemMessagePrompt) {
|
||||
chatHistory.push({
|
||||
content: systemMessagePrompt,
|
||||
role: 'user'
|
||||
})
|
||||
}
|
||||
|
||||
const chatEngine = new SimpleChatEngine({ llm: model })
|
||||
|
||||
const msgs = (await memory.getChatMessages(this.sessionId, false, options.chatHistory)) as IMessage[]
|
||||
for (const message of msgs) {
|
||||
if (message.type === 'apiMessage') {
|
||||
chatHistory.push({
|
||||
content: message.message,
|
||||
role: 'assistant'
|
||||
})
|
||||
} else if (message.type === 'userMessage') {
|
||||
chatHistory.push({
|
||||
content: message.message,
|
||||
role: 'user'
|
||||
})
|
||||
}
|
||||
}
|
||||
|
||||
let text = ''
|
||||
let isStreamingStarted = false
|
||||
const isStreamingEnabled = options.socketIO && options.socketIOClientId
|
||||
|
||||
if (isStreamingEnabled) {
|
||||
const stream = await chatEngine.chat({ message: input, chatHistory, stream: true })
|
||||
for await (const chunk of stream) {
|
||||
text += chunk.response
|
||||
if (!isStreamingStarted) {
|
||||
isStreamingStarted = true
|
||||
options.socketIO.to(options.socketIOClientId).emit('start', chunk.response)
|
||||
}
|
||||
|
||||
options.socketIO.to(options.socketIOClientId).emit('token', chunk.response)
|
||||
}
|
||||
} else {
|
||||
const response = await chatEngine.chat({ message: input, chatHistory })
|
||||
text = response?.response
|
||||
}
|
||||
|
||||
await memory.addChatMessages(
|
||||
[
|
||||
{
|
||||
text: input,
|
||||
type: 'userMessage'
|
||||
},
|
||||
{
|
||||
text: text,
|
||||
type: 'apiMessage'
|
||||
}
|
||||
],
|
||||
this.sessionId
|
||||
)
|
||||
|
||||
return text
|
||||
}
|
||||
}
|
||||
|
||||
module.exports = { nodeClass: SimpleChatEngine_LlamaIndex }
|
||||
Binary file not shown.
|
After Width: | Height: | Size: 9.8 KiB |
Binary file not shown.
|
After Width: | Height: | Size: 9.5 KiB |
@@ -0,0 +1,12 @@
|
||||
import { BaseNode, Metadata } from 'llamaindex'
|
||||
|
||||
export const reformatSourceDocuments = (sourceNodes: BaseNode<Metadata>[]) => {
|
||||
const sourceDocuments = []
|
||||
for (const node of sourceNodes) {
|
||||
sourceDocuments.push({
|
||||
pageContent: (node as any).text,
|
||||
metadata: node.metadata
|
||||
})
|
||||
}
|
||||
return sourceDocuments
|
||||
}
|
||||
@@ -0,0 +1,143 @@
|
||||
import { ICommonObject, INode, INodeData, INodeOutputsValue, INodeParams } from '../../../src/Interface'
|
||||
import {
|
||||
RetrieverQueryEngine,
|
||||
ResponseSynthesizer,
|
||||
CompactAndRefine,
|
||||
TreeSummarize,
|
||||
Refine,
|
||||
SimpleResponseBuilder,
|
||||
BaseNode,
|
||||
Metadata
|
||||
} from 'llamaindex'
|
||||
import { reformatSourceDocuments } from '../EngineUtils'
|
||||
|
||||
class QueryEngine_LlamaIndex implements INode {
|
||||
label: string
|
||||
name: string
|
||||
version: number
|
||||
description: string
|
||||
type: string
|
||||
icon: string
|
||||
category: string
|
||||
baseClasses: string[]
|
||||
tags: string[]
|
||||
inputs: INodeParams[]
|
||||
outputs: INodeOutputsValue[]
|
||||
sessionId?: string
|
||||
|
||||
constructor(fields?: { sessionId?: string }) {
|
||||
this.label = 'Query Engine'
|
||||
this.name = 'queryEngine'
|
||||
this.version = 1.0
|
||||
this.type = 'QueryEngine'
|
||||
this.icon = 'query-engine.png'
|
||||
this.category = 'Engine'
|
||||
this.description = 'Simple query engine built to answer question over your data, without memory'
|
||||
this.baseClasses = [this.type]
|
||||
this.tags = ['LlamaIndex']
|
||||
this.inputs = [
|
||||
{
|
||||
label: 'Vector Store Retriever',
|
||||
name: 'vectorStoreRetriever',
|
||||
type: 'VectorIndexRetriever'
|
||||
},
|
||||
{
|
||||
label: 'Response Synthesizer',
|
||||
name: 'responseSynthesizer',
|
||||
type: 'ResponseSynthesizer',
|
||||
description:
|
||||
'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>',
|
||||
optional: true
|
||||
},
|
||||
{
|
||||
label: 'Return Source Documents',
|
||||
name: 'returnSourceDocuments',
|
||||
type: 'boolean',
|
||||
optional: true
|
||||
}
|
||||
]
|
||||
this.sessionId = fields?.sessionId
|
||||
}
|
||||
|
||||
async init(): Promise<any> {
|
||||
return null
|
||||
}
|
||||
|
||||
async run(nodeData: INodeData, input: string, options: ICommonObject): Promise<string | object> {
|
||||
const returnSourceDocuments = nodeData.inputs?.returnSourceDocuments as boolean
|
||||
const vectorStoreRetriever = nodeData.inputs?.vectorStoreRetriever
|
||||
const responseSynthesizerObj = nodeData.inputs?.responseSynthesizer
|
||||
|
||||
let queryEngine = new RetrieverQueryEngine(vectorStoreRetriever)
|
||||
|
||||
if (responseSynthesizerObj) {
|
||||
if (responseSynthesizerObj.type === 'TreeSummarize') {
|
||||
const responseSynthesizer = new ResponseSynthesizer({
|
||||
responseBuilder: new TreeSummarize(vectorStoreRetriever.serviceContext, responseSynthesizerObj.textQAPromptTemplate),
|
||||
serviceContext: vectorStoreRetriever.serviceContext
|
||||
})
|
||||
queryEngine = new RetrieverQueryEngine(vectorStoreRetriever, responseSynthesizer)
|
||||
} else if (responseSynthesizerObj.type === 'CompactAndRefine') {
|
||||
const responseSynthesizer = new ResponseSynthesizer({
|
||||
responseBuilder: new CompactAndRefine(
|
||||
vectorStoreRetriever.serviceContext,
|
||||
responseSynthesizerObj.textQAPromptTemplate,
|
||||
responseSynthesizerObj.refinePromptTemplate
|
||||
),
|
||||
serviceContext: vectorStoreRetriever.serviceContext
|
||||
})
|
||||
queryEngine = new RetrieverQueryEngine(vectorStoreRetriever, responseSynthesizer)
|
||||
} else if (responseSynthesizerObj.type === 'Refine') {
|
||||
const responseSynthesizer = new ResponseSynthesizer({
|
||||
responseBuilder: new Refine(
|
||||
vectorStoreRetriever.serviceContext,
|
||||
responseSynthesizerObj.textQAPromptTemplate,
|
||||
responseSynthesizerObj.refinePromptTemplate
|
||||
),
|
||||
serviceContext: vectorStoreRetriever.serviceContext
|
||||
})
|
||||
queryEngine = new RetrieverQueryEngine(vectorStoreRetriever, responseSynthesizer)
|
||||
} else if (responseSynthesizerObj.type === 'SimpleResponseBuilder') {
|
||||
const responseSynthesizer = new ResponseSynthesizer({
|
||||
responseBuilder: new SimpleResponseBuilder(vectorStoreRetriever.serviceContext),
|
||||
serviceContext: vectorStoreRetriever.serviceContext
|
||||
})
|
||||
queryEngine = new RetrieverQueryEngine(vectorStoreRetriever, responseSynthesizer)
|
||||
}
|
||||
}
|
||||
|
||||
let text = ''
|
||||
let sourceDocuments: ICommonObject[] = []
|
||||
let sourceNodes: BaseNode<Metadata>[] = []
|
||||
let isStreamingStarted = false
|
||||
const isStreamingEnabled = options.socketIO && options.socketIOClientId
|
||||
|
||||
if (isStreamingEnabled) {
|
||||
const stream = await queryEngine.query({ query: input, stream: true })
|
||||
for await (const chunk of stream) {
|
||||
text += chunk.response
|
||||
if (chunk.sourceNodes) sourceNodes = chunk.sourceNodes
|
||||
if (!isStreamingStarted) {
|
||||
isStreamingStarted = true
|
||||
options.socketIO.to(options.socketIOClientId).emit('start', chunk.response)
|
||||
}
|
||||
|
||||
options.socketIO.to(options.socketIOClientId).emit('token', chunk.response)
|
||||
}
|
||||
|
||||
if (returnSourceDocuments) {
|
||||
sourceDocuments = reformatSourceDocuments(sourceNodes)
|
||||
options.socketIO.to(options.socketIOClientId).emit('sourceDocuments', sourceDocuments)
|
||||
}
|
||||
} else {
|
||||
const response = await queryEngine.query({ query: input })
|
||||
text = response?.response
|
||||
sourceDocuments = reformatSourceDocuments(response?.sourceNodes ?? [])
|
||||
}
|
||||
|
||||
if (returnSourceDocuments) return { text, sourceDocuments }
|
||||
else return { text }
|
||||
}
|
||||
}
|
||||
|
||||
module.exports = { nodeClass: QueryEngine_LlamaIndex }
|
||||
Binary file not shown.
|
After Width: | Height: | Size: 12 KiB |
@@ -0,0 +1,193 @@
|
||||
import { flatten } from 'lodash'
|
||||
import { ICommonObject, INode, INodeData, INodeOutputsValue, INodeParams } from '../../../src/Interface'
|
||||
import {
|
||||
TreeSummarize,
|
||||
SimpleResponseBuilder,
|
||||
Refine,
|
||||
BaseEmbedding,
|
||||
ResponseSynthesizer,
|
||||
CompactAndRefine,
|
||||
QueryEngineTool,
|
||||
LLMQuestionGenerator,
|
||||
SubQuestionQueryEngine,
|
||||
BaseNode,
|
||||
Metadata,
|
||||
serviceContextFromDefaults
|
||||
} from 'llamaindex'
|
||||
import { reformatSourceDocuments } from '../EngineUtils'
|
||||
|
||||
class SubQuestionQueryEngine_LlamaIndex implements INode {
|
||||
label: string
|
||||
name: string
|
||||
version: number
|
||||
description: string
|
||||
type: string
|
||||
icon: string
|
||||
category: string
|
||||
baseClasses: string[]
|
||||
tags: string[]
|
||||
inputs: INodeParams[]
|
||||
outputs: INodeOutputsValue[]
|
||||
sessionId?: string
|
||||
|
||||
constructor(fields?: { sessionId?: string }) {
|
||||
this.label = 'Sub Question Query Engine'
|
||||
this.name = 'subQuestionQueryEngine'
|
||||
this.version = 1.0
|
||||
this.type = 'SubQuestionQueryEngine'
|
||||
this.icon = 'subQueryEngine.svg'
|
||||
this.category = 'Engine'
|
||||
this.description =
|
||||
'Breaks complex query into sub questions for each relevant data source, then gather all the intermediate reponses and synthesizes a final response'
|
||||
this.baseClasses = [this.type]
|
||||
this.tags = ['LlamaIndex']
|
||||
this.inputs = [
|
||||
{
|
||||
label: 'QueryEngine Tools',
|
||||
name: 'queryEngineTools',
|
||||
type: 'QueryEngineTool',
|
||||
list: true
|
||||
},
|
||||
{
|
||||
label: 'Chat Model',
|
||||
name: 'model',
|
||||
type: 'BaseChatModel_LlamaIndex'
|
||||
},
|
||||
{
|
||||
label: 'Embeddings',
|
||||
name: 'embeddings',
|
||||
type: 'BaseEmbedding_LlamaIndex'
|
||||
},
|
||||
{
|
||||
label: 'Response Synthesizer',
|
||||
name: 'responseSynthesizer',
|
||||
type: 'ResponseSynthesizer',
|
||||
description:
|
||||
'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>',
|
||||
optional: true
|
||||
},
|
||||
{
|
||||
label: 'Return Source Documents',
|
||||
name: 'returnSourceDocuments',
|
||||
type: 'boolean',
|
||||
optional: true
|
||||
}
|
||||
]
|
||||
this.sessionId = fields?.sessionId
|
||||
}
|
||||
|
||||
async init(): Promise<any> {
|
||||
return null
|
||||
}
|
||||
|
||||
async run(nodeData: INodeData, input: string, options: ICommonObject): Promise<string | object> {
|
||||
const returnSourceDocuments = nodeData.inputs?.returnSourceDocuments as boolean
|
||||
const embeddings = nodeData.inputs?.embeddings as BaseEmbedding
|
||||
const model = nodeData.inputs?.model
|
||||
|
||||
const serviceContext = serviceContextFromDefaults({
|
||||
llm: model,
|
||||
embedModel: embeddings
|
||||
})
|
||||
|
||||
let queryEngineTools = nodeData.inputs?.queryEngineTools as QueryEngineTool[]
|
||||
queryEngineTools = flatten(queryEngineTools)
|
||||
|
||||
let queryEngine = SubQuestionQueryEngine.fromDefaults({
|
||||
serviceContext,
|
||||
queryEngineTools,
|
||||
questionGen: new LLMQuestionGenerator({ llm: model })
|
||||
})
|
||||
|
||||
const responseSynthesizerObj = nodeData.inputs?.responseSynthesizer
|
||||
if (responseSynthesizerObj) {
|
||||
if (responseSynthesizerObj.type === 'TreeSummarize') {
|
||||
const responseSynthesizer = new ResponseSynthesizer({
|
||||
responseBuilder: new TreeSummarize(serviceContext, responseSynthesizerObj.textQAPromptTemplate),
|
||||
serviceContext
|
||||
})
|
||||
queryEngine = SubQuestionQueryEngine.fromDefaults({
|
||||
responseSynthesizer,
|
||||
serviceContext,
|
||||
queryEngineTools,
|
||||
questionGen: new LLMQuestionGenerator({ llm: model })
|
||||
})
|
||||
} else if (responseSynthesizerObj.type === 'CompactAndRefine') {
|
||||
const responseSynthesizer = new ResponseSynthesizer({
|
||||
responseBuilder: new CompactAndRefine(
|
||||
serviceContext,
|
||||
responseSynthesizerObj.textQAPromptTemplate,
|
||||
responseSynthesizerObj.refinePromptTemplate
|
||||
),
|
||||
serviceContext
|
||||
})
|
||||
queryEngine = SubQuestionQueryEngine.fromDefaults({
|
||||
responseSynthesizer,
|
||||
serviceContext,
|
||||
queryEngineTools,
|
||||
questionGen: new LLMQuestionGenerator({ llm: model })
|
||||
})
|
||||
} else if (responseSynthesizerObj.type === 'Refine') {
|
||||
const responseSynthesizer = new ResponseSynthesizer({
|
||||
responseBuilder: new Refine(
|
||||
serviceContext,
|
||||
responseSynthesizerObj.textQAPromptTemplate,
|
||||
responseSynthesizerObj.refinePromptTemplate
|
||||
),
|
||||
serviceContext
|
||||
})
|
||||
queryEngine = SubQuestionQueryEngine.fromDefaults({
|
||||
responseSynthesizer,
|
||||
serviceContext,
|
||||
queryEngineTools,
|
||||
questionGen: new LLMQuestionGenerator({ llm: model })
|
||||
})
|
||||
} else if (responseSynthesizerObj.type === 'SimpleResponseBuilder') {
|
||||
const responseSynthesizer = new ResponseSynthesizer({
|
||||
responseBuilder: new SimpleResponseBuilder(serviceContext),
|
||||
serviceContext
|
||||
})
|
||||
queryEngine = SubQuestionQueryEngine.fromDefaults({
|
||||
responseSynthesizer,
|
||||
serviceContext,
|
||||
queryEngineTools,
|
||||
questionGen: new LLMQuestionGenerator({ llm: model })
|
||||
})
|
||||
}
|
||||
}
|
||||
|
||||
let text = ''
|
||||
let sourceDocuments: ICommonObject[] = []
|
||||
let sourceNodes: BaseNode<Metadata>[] = []
|
||||
let isStreamingStarted = false
|
||||
const isStreamingEnabled = options.socketIO && options.socketIOClientId
|
||||
|
||||
if (isStreamingEnabled) {
|
||||
const stream = await queryEngine.query({ query: input, stream: true })
|
||||
for await (const chunk of stream) {
|
||||
text += chunk.response
|
||||
if (chunk.sourceNodes) sourceNodes = chunk.sourceNodes
|
||||
if (!isStreamingStarted) {
|
||||
isStreamingStarted = true
|
||||
options.socketIO.to(options.socketIOClientId).emit('start', chunk.response)
|
||||
}
|
||||
|
||||
options.socketIO.to(options.socketIOClientId).emit('token', chunk.response)
|
||||
}
|
||||
|
||||
if (returnSourceDocuments) {
|
||||
sourceDocuments = reformatSourceDocuments(sourceNodes)
|
||||
options.socketIO.to(options.socketIOClientId).emit('sourceDocuments', sourceDocuments)
|
||||
}
|
||||
} else {
|
||||
const response = await queryEngine.query({ query: input })
|
||||
text = response?.response
|
||||
sourceDocuments = reformatSourceDocuments(response?.sourceNodes ?? [])
|
||||
}
|
||||
|
||||
if (returnSourceDocuments) return { text, sourceDocuments }
|
||||
else return { text }
|
||||
}
|
||||
}
|
||||
|
||||
module.exports = { nodeClass: SubQuestionQueryEngine_LlamaIndex }
|
||||
@@ -0,0 +1 @@
|
||||
<svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-filter-question" width="24" height="24" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"/><path d="M15 19l-6 2v-8.5l-4.48 -4.928a2 2 0 0 1 -.52 -1.345v-2.227h16v2.172a2 2 0 0 1 -.586 1.414l-4.414 4.414" /><path d="M19 22v.01" /><path d="M19 19a2.003 2.003 0 0 0 .914 -3.782a1.98 1.98 0 0 0 -2.414 .483" /></svg>
|
||||
|
After Width: | Height: | Size: 506 B |
@@ -0,0 +1,75 @@
|
||||
import { INode, INodeData, INodeOutputsValue, INodeParams } from '../../../src/Interface'
|
||||
import { ResponseSynthesizerClass } from '../base'
|
||||
|
||||
class CompactRefine_LlamaIndex implements INode {
|
||||
label: string
|
||||
name: string
|
||||
version: number
|
||||
description: string
|
||||
type: string
|
||||
icon: string
|
||||
category: string
|
||||
baseClasses: string[]
|
||||
tags: string[]
|
||||
inputs: INodeParams[]
|
||||
outputs: INodeOutputsValue[]
|
||||
|
||||
constructor() {
|
||||
this.label = 'Compact and Refine'
|
||||
this.name = 'compactrefineLlamaIndex'
|
||||
this.version = 1.0
|
||||
this.type = 'CompactRefine'
|
||||
this.icon = 'compactrefine.svg'
|
||||
this.category = 'Response Synthesizer'
|
||||
this.description =
|
||||
'CompactRefine is a slight variation of Refine that first compacts the text chunks into the smallest possible number of chunks.'
|
||||
this.baseClasses = [this.type, 'ResponseSynthesizer']
|
||||
this.tags = ['LlamaIndex']
|
||||
this.inputs = [
|
||||
{
|
||||
label: 'Refine Prompt',
|
||||
name: 'refinePrompt',
|
||||
type: 'string',
|
||||
rows: 4,
|
||||
default: `The original query is as follows: {query}
|
||||
We have provided an existing answer: {existingAnswer}
|
||||
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 query. If the context isn't useful, return the original answer.
|
||||
Refined Answer:`,
|
||||
warning: `Prompt can contains no variables, or up to 3 variables. Variables must be {existingAnswer}, {context} and {query}`,
|
||||
optional: true
|
||||
},
|
||||
{
|
||||
label: 'Text QA Prompt',
|
||||
name: 'textQAPrompt',
|
||||
type: 'string',
|
||||
rows: 4,
|
||||
default: `Context information is below.
|
||||
---------------------
|
||||
{context}
|
||||
---------------------
|
||||
Given the context information and not prior knowledge, answer the query.
|
||||
Query: {query}
|
||||
Answer:`,
|
||||
warning: `Prompt can contains no variables, or up to 2 variables. Variables must be {context} and {query}`,
|
||||
optional: true
|
||||
}
|
||||
]
|
||||
}
|
||||
|
||||
async init(nodeData: INodeData): Promise<any> {
|
||||
const refinePrompt = nodeData.inputs?.refinePrompt as string
|
||||
const textQAPrompt = nodeData.inputs?.textQAPrompt as string
|
||||
|
||||
const refinePromptTemplate = ({ context = '', existingAnswer = '', query = '' }) =>
|
||||
refinePrompt.replace('{existingAnswer}', existingAnswer).replace('{context}', context).replace('{query}', query)
|
||||
const textQAPromptTemplate = ({ context = '', query = '' }) => textQAPrompt.replace('{context}', context).replace('{query}', query)
|
||||
|
||||
return new ResponseSynthesizerClass({ textQAPromptTemplate, refinePromptTemplate, type: 'CompactAndRefine' })
|
||||
}
|
||||
}
|
||||
|
||||
module.exports = { nodeClass: CompactRefine_LlamaIndex }
|
||||
@@ -0,0 +1 @@
|
||||
<svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-layers-difference" width="24" height="24" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"/><path d="M16 16v2a2 2 0 0 1 -2 2h-8a2 2 0 0 1 -2 -2v-8a2 2 0 0 1 2 -2h2v-2a2 2 0 0 1 2 -2h8a2 2 0 0 1 2 2v8a2 2 0 0 1 -2 2h-2" /><path d="M10 8l-2 0l0 2" /><path d="M8 14l0 2l2 0" /><path d="M14 8l2 0l0 2" /><path d="M16 14l0 2l-2 0" /></svg>
|
||||
|
After Width: | Height: | Size: 529 B |
@@ -0,0 +1,75 @@
|
||||
import { INode, INodeData, INodeOutputsValue, INodeParams } from '../../../src/Interface'
|
||||
import { ResponseSynthesizerClass } from '../base'
|
||||
|
||||
class Refine_LlamaIndex implements INode {
|
||||
label: string
|
||||
name: string
|
||||
version: number
|
||||
description: string
|
||||
type: string
|
||||
icon: string
|
||||
category: string
|
||||
baseClasses: string[]
|
||||
tags: string[]
|
||||
inputs: INodeParams[]
|
||||
outputs: INodeOutputsValue[]
|
||||
|
||||
constructor() {
|
||||
this.label = 'Refine'
|
||||
this.name = 'refineLlamaIndex'
|
||||
this.version = 1.0
|
||||
this.type = 'Refine'
|
||||
this.icon = 'refine.svg'
|
||||
this.category = 'Response Synthesizer'
|
||||
this.description =
|
||||
'Create and refine an answer by sequentially going through each retrieved text chunk. This makes a separate LLM call per Node. Good for more detailed answers.'
|
||||
this.baseClasses = [this.type, 'ResponseSynthesizer']
|
||||
this.tags = ['LlamaIndex']
|
||||
this.inputs = [
|
||||
{
|
||||
label: 'Refine Prompt',
|
||||
name: 'refinePrompt',
|
||||
type: 'string',
|
||||
rows: 4,
|
||||
default: `The original query is as follows: {query}
|
||||
We have provided an existing answer: {existingAnswer}
|
||||
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 query. If the context isn't useful, return the original answer.
|
||||
Refined Answer:`,
|
||||
warning: `Prompt can contains no variables, or up to 3 variables. Variables must be {existingAnswer}, {context} and {query}`,
|
||||
optional: true
|
||||
},
|
||||
{
|
||||
label: 'Text QA Prompt',
|
||||
name: 'textQAPrompt',
|
||||
type: 'string',
|
||||
rows: 4,
|
||||
default: `Context information is below.
|
||||
---------------------
|
||||
{context}
|
||||
---------------------
|
||||
Given the context information and not prior knowledge, answer the query.
|
||||
Query: {query}
|
||||
Answer:`,
|
||||
warning: `Prompt can contains no variables, or up to 2 variables. Variables must be {context} and {query}`,
|
||||
optional: true
|
||||
}
|
||||
]
|
||||
}
|
||||
|
||||
async init(nodeData: INodeData): Promise<any> {
|
||||
const refinePrompt = nodeData.inputs?.refinePrompt as string
|
||||
const textQAPrompt = nodeData.inputs?.textQAPrompt as string
|
||||
|
||||
const refinePromptTemplate = ({ context = '', existingAnswer = '', query = '' }) =>
|
||||
refinePrompt.replace('{existingAnswer}', existingAnswer).replace('{context}', context).replace('{query}', query)
|
||||
const textQAPromptTemplate = ({ context = '', query = '' }) => textQAPrompt.replace('{context}', context).replace('{query}', query)
|
||||
|
||||
return new ResponseSynthesizerClass({ textQAPromptTemplate, refinePromptTemplate, type: 'Refine' })
|
||||
}
|
||||
}
|
||||
|
||||
module.exports = { nodeClass: Refine_LlamaIndex }
|
||||
@@ -0,0 +1 @@
|
||||
<svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-filter-search" width="24" height="24" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"/><path d="M11.36 20.213l-2.36 .787v-8.5l-4.48 -4.928a2 2 0 0 1 -.52 -1.345v-2.227h16v2.172a2 2 0 0 1 -.586 1.414l-4.414 4.414" /><path d="M18 18m-3 0a3 3 0 1 0 6 0a3 3 0 1 0 -6 0" /><path d="M20.2 20.2l1.8 1.8" /></svg>
|
||||
|
After Width: | Height: | Size: 501 B |
+35
@@ -0,0 +1,35 @@
|
||||
import { INode, INodeOutputsValue, INodeParams } from '../../../src/Interface'
|
||||
import { ResponseSynthesizerClass } from '../base'
|
||||
|
||||
class SimpleResponseBuilder_LlamaIndex implements INode {
|
||||
label: string
|
||||
name: string
|
||||
version: number
|
||||
description: string
|
||||
type: string
|
||||
icon: string
|
||||
category: string
|
||||
baseClasses: string[]
|
||||
tags: string[]
|
||||
inputs: INodeParams[]
|
||||
outputs: INodeOutputsValue[]
|
||||
|
||||
constructor() {
|
||||
this.label = 'Simple Response Builder'
|
||||
this.name = 'simpleResponseBuilderLlamaIndex'
|
||||
this.version = 1.0
|
||||
this.type = 'SimpleResponseBuilder'
|
||||
this.icon = 'simplerb.svg'
|
||||
this.category = 'Response Synthesizer'
|
||||
this.description = `Apply a query to a collection of text chunks, gathering the responses in an array, and return a combined string of all responses. Useful for individual queries on each text chunk.`
|
||||
this.baseClasses = [this.type, 'ResponseSynthesizer']
|
||||
this.tags = ['LlamaIndex']
|
||||
this.inputs = []
|
||||
}
|
||||
|
||||
async init(): Promise<any> {
|
||||
return new ResponseSynthesizerClass({ type: 'SimpleResponseBuilder' })
|
||||
}
|
||||
}
|
||||
|
||||
module.exports = { nodeClass: SimpleResponseBuilder_LlamaIndex }
|
||||
@@ -0,0 +1 @@
|
||||
<svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-quote" width="24" height="24" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"/><path d="M10 11h-4a1 1 0 0 1 -1 -1v-3a1 1 0 0 1 1 -1h3a1 1 0 0 1 1 1v6c0 2.667 -1.333 4.333 -4 5" /><path d="M19 11h-4a1 1 0 0 1 -1 -1v-3a1 1 0 0 1 1 -1h3a1 1 0 0 1 1 1v6c0 2.667 -1.333 4.333 -4 5" /></svg>
|
||||
|
After Width: | Height: | Size: 481 B |
@@ -0,0 +1,56 @@
|
||||
import { INode, INodeData, INodeOutputsValue, INodeParams } from '../../../src/Interface'
|
||||
import { ResponseSynthesizerClass } from '../base'
|
||||
|
||||
class TreeSummarize_LlamaIndex implements INode {
|
||||
label: string
|
||||
name: string
|
||||
version: number
|
||||
description: string
|
||||
type: string
|
||||
icon: string
|
||||
category: string
|
||||
baseClasses: string[]
|
||||
tags: string[]
|
||||
inputs: INodeParams[]
|
||||
outputs: INodeOutputsValue[]
|
||||
|
||||
constructor() {
|
||||
this.label = 'TreeSummarize'
|
||||
this.name = 'treeSummarizeLlamaIndex'
|
||||
this.version = 1.0
|
||||
this.type = 'TreeSummarize'
|
||||
this.icon = 'treesummarize.svg'
|
||||
this.category = 'Response Synthesizer'
|
||||
this.description =
|
||||
'Given a set of text chunks and the query, recursively construct a tree and return the root node as the response. Good for summarization purposes.'
|
||||
this.baseClasses = [this.type, 'ResponseSynthesizer']
|
||||
this.tags = ['LlamaIndex']
|
||||
this.inputs = [
|
||||
{
|
||||
label: 'Prompt',
|
||||
name: 'prompt',
|
||||
type: 'string',
|
||||
rows: 4,
|
||||
default: `Context information from multiple sources is below.
|
||||
---------------------
|
||||
{context}
|
||||
---------------------
|
||||
Given the information from multiple sources and not prior knowledge, answer the query.
|
||||
Query: {query}
|
||||
Answer:`,
|
||||
warning: `Prompt can contains no variables, or up to 2 variables. Variables must be {context} and {query}`,
|
||||
optional: true
|
||||
}
|
||||
]
|
||||
}
|
||||
|
||||
async init(nodeData: INodeData): Promise<any> {
|
||||
const prompt = nodeData.inputs?.prompt as string
|
||||
|
||||
const textQAPromptTemplate = ({ context = '', query = '' }) => prompt.replace('{context}', context).replace('{query}', query)
|
||||
|
||||
return new ResponseSynthesizerClass({ textQAPromptTemplate, type: 'TreeSummarize' })
|
||||
}
|
||||
}
|
||||
|
||||
module.exports = { nodeClass: TreeSummarize_LlamaIndex }
|
||||
@@ -0,0 +1 @@
|
||||
<svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-tree" width="24" height="24" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"/><path d="M12 13l-2 -2" /><path d="M12 12l2 -2" /><path d="M12 21v-13" /><path d="M9.824 16a3 3 0 0 1 -2.743 -3.69a3 3 0 0 1 .304 -4.833a3 3 0 0 1 4.615 -3.707a3 3 0 0 1 4.614 3.707a3 3 0 0 1 .305 4.833a3 3 0 0 1 -2.919 3.695h-4z" /></svg>
|
||||
|
After Width: | Height: | Size: 512 B |
@@ -0,0 +1,11 @@
|
||||
export class ResponseSynthesizerClass {
|
||||
type: string
|
||||
textQAPromptTemplate?: any
|
||||
refinePromptTemplate?: any
|
||||
|
||||
constructor(params: { type: string; textQAPromptTemplate?: any; refinePromptTemplate?: any }) {
|
||||
this.type = params.type
|
||||
this.textQAPromptTemplate = params.textQAPromptTemplate
|
||||
this.refinePromptTemplate = params.refinePromptTemplate
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,68 @@
|
||||
import { INode, INodeData, INodeParams } from '../../../src/Interface'
|
||||
import { VectorStoreIndex } from 'llamaindex'
|
||||
|
||||
class QueryEngine_Tools implements INode {
|
||||
label: string
|
||||
name: string
|
||||
version: number
|
||||
description: string
|
||||
type: string
|
||||
icon: string
|
||||
category: string
|
||||
tags: string[]
|
||||
baseClasses: string[]
|
||||
inputs?: INodeParams[]
|
||||
|
||||
constructor() {
|
||||
this.label = 'QueryEngine Tool'
|
||||
this.name = 'queryEngineToolLlamaIndex'
|
||||
this.version = 1.0
|
||||
this.type = 'QueryEngineTool'
|
||||
this.icon = 'queryEngineTool.svg'
|
||||
this.category = 'Tools'
|
||||
this.tags = ['LlamaIndex']
|
||||
this.description = 'Tool used to invoke query engine'
|
||||
this.baseClasses = [this.type]
|
||||
this.inputs = [
|
||||
{
|
||||
label: 'Vector Store Index',
|
||||
name: 'vectorStoreIndex',
|
||||
type: 'VectorStoreIndex'
|
||||
},
|
||||
{
|
||||
label: 'Tool Name',
|
||||
name: 'toolName',
|
||||
type: 'string',
|
||||
description: 'Tool name must be small capital letter with underscore. Ex: my_tool'
|
||||
},
|
||||
{
|
||||
label: 'Tool Description',
|
||||
name: 'toolDesc',
|
||||
type: 'string',
|
||||
rows: 4
|
||||
}
|
||||
]
|
||||
}
|
||||
|
||||
async init(nodeData: INodeData): Promise<any> {
|
||||
const vectorStoreIndex = nodeData.inputs?.vectorStoreIndex as VectorStoreIndex
|
||||
const toolName = nodeData.inputs?.toolName as string
|
||||
const toolDesc = nodeData.inputs?.toolDesc as string
|
||||
const queryEngineTool = {
|
||||
queryEngine: vectorStoreIndex.asQueryEngine({
|
||||
preFilters: {
|
||||
...(vectorStoreIndex as any).metadatafilter
|
||||
}
|
||||
}),
|
||||
metadata: {
|
||||
name: toolName,
|
||||
description: toolDesc
|
||||
},
|
||||
vectorStoreIndex
|
||||
}
|
||||
|
||||
return queryEngineTool
|
||||
}
|
||||
}
|
||||
|
||||
module.exports = { nodeClass: QueryEngine_Tools }
|
||||
@@ -0,0 +1 @@
|
||||
<svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-brand-google-big-query" width="24" height="24" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"/><path d="M17.73 19.875a2.225 2.225 0 0 1 -1.948 1.125h-7.283a2.222 2.222 0 0 1 -1.947 -1.158l-4.272 -6.75a2.269 2.269 0 0 1 0 -2.184l4.272 -6.75a2.225 2.225 0 0 1 1.946 -1.158h7.285c.809 0 1.554 .443 1.947 1.158l3.98 6.75a2.33 2.33 0 0 1 0 2.25l-3.98 6.75v-.033z" /><path d="M11.5 11.5m-3.5 0a3.5 3.5 0 1 0 7 0a3.5 3.5 0 1 0 -7 0" /><path d="M14 14l2 2" /></svg>
|
||||
|
After Width: | Height: | Size: 654 B |
@@ -0,0 +1,383 @@
|
||||
import {
|
||||
BaseNode,
|
||||
Document,
|
||||
Metadata,
|
||||
VectorStore,
|
||||
VectorStoreQuery,
|
||||
VectorStoreQueryResult,
|
||||
serviceContextFromDefaults,
|
||||
storageContextFromDefaults,
|
||||
VectorStoreIndex,
|
||||
BaseEmbedding
|
||||
} from 'llamaindex'
|
||||
import { FetchResponse, Index, Pinecone, ScoredPineconeRecord } from '@pinecone-database/pinecone'
|
||||
import { flatten } from 'lodash'
|
||||
import { Document as LCDocument } from 'langchain/document'
|
||||
import { ICommonObject, INode, INodeData, INodeOutputsValue, INodeParams } from '../../../src/Interface'
|
||||
import { flattenObject, getCredentialData, getCredentialParam } from '../../../src/utils'
|
||||
|
||||
class PineconeLlamaIndex_VectorStores implements INode {
|
||||
label: string
|
||||
name: string
|
||||
version: number
|
||||
description: string
|
||||
type: string
|
||||
icon: string
|
||||
category: string
|
||||
tags: string[]
|
||||
baseClasses: string[]
|
||||
inputs: INodeParams[]
|
||||
credential: INodeParams
|
||||
outputs: INodeOutputsValue[]
|
||||
|
||||
constructor() {
|
||||
this.label = 'Pinecone'
|
||||
this.name = 'pineconeLlamaIndex'
|
||||
this.version = 1.0
|
||||
this.type = 'Pinecone'
|
||||
this.icon = 'pinecone.svg'
|
||||
this.category = 'Vector Stores'
|
||||
this.description = `Upsert embedded data and perform similarity search upon query using Pinecone, a leading fully managed hosted vector database`
|
||||
this.baseClasses = [this.type, 'VectorIndexRetriever']
|
||||
this.tags = ['LlamaIndex']
|
||||
this.credential = {
|
||||
label: 'Connect Credential',
|
||||
name: 'credential',
|
||||
type: 'credential',
|
||||
credentialNames: ['pineconeApi']
|
||||
}
|
||||
this.inputs = [
|
||||
{
|
||||
label: 'Document',
|
||||
name: 'document',
|
||||
type: 'Document',
|
||||
list: true,
|
||||
optional: true
|
||||
},
|
||||
{
|
||||
label: 'Chat Model',
|
||||
name: 'model',
|
||||
type: 'BaseChatModel_LlamaIndex'
|
||||
},
|
||||
{
|
||||
label: 'Embeddings',
|
||||
name: 'embeddings',
|
||||
type: 'BaseEmbedding_LlamaIndex'
|
||||
},
|
||||
{
|
||||
label: 'Pinecone Index',
|
||||
name: 'pineconeIndex',
|
||||
type: 'string'
|
||||
},
|
||||
{
|
||||
label: 'Pinecone Namespace',
|
||||
name: 'pineconeNamespace',
|
||||
type: 'string',
|
||||
placeholder: 'my-first-namespace',
|
||||
additionalParams: true,
|
||||
optional: true
|
||||
},
|
||||
{
|
||||
label: 'Pinecone Metadata Filter',
|
||||
name: 'pineconeMetadataFilter',
|
||||
type: 'json',
|
||||
optional: true,
|
||||
additionalParams: true
|
||||
},
|
||||
{
|
||||
label: 'Top K',
|
||||
name: 'topK',
|
||||
description: 'Number of top results to fetch. Default to 4',
|
||||
placeholder: '4',
|
||||
type: 'number',
|
||||
additionalParams: true,
|
||||
optional: true
|
||||
}
|
||||
]
|
||||
this.outputs = [
|
||||
{
|
||||
label: 'Pinecone Retriever',
|
||||
name: 'retriever',
|
||||
baseClasses: this.baseClasses
|
||||
},
|
||||
{
|
||||
label: 'Pinecone Vector Store Index',
|
||||
name: 'vectorStore',
|
||||
baseClasses: [this.type, 'VectorStoreIndex']
|
||||
}
|
||||
]
|
||||
}
|
||||
|
||||
//@ts-ignore
|
||||
vectorStoreMethods = {
|
||||
async upsert(nodeData: INodeData, options: ICommonObject): Promise<void> {
|
||||
const indexName = nodeData.inputs?.pineconeIndex as string
|
||||
const pineconeNamespace = nodeData.inputs?.pineconeNamespace as string
|
||||
const docs = nodeData.inputs?.document as LCDocument[]
|
||||
const embeddings = nodeData.inputs?.embeddings as BaseEmbedding
|
||||
const model = nodeData.inputs?.model
|
||||
|
||||
const credentialData = await getCredentialData(nodeData.credential ?? '', options)
|
||||
const pineconeApiKey = getCredentialParam('pineconeApiKey', credentialData, nodeData)
|
||||
|
||||
const pcvs = new PineconeVectorStore({
|
||||
indexName,
|
||||
apiKey: pineconeApiKey,
|
||||
namespace: pineconeNamespace
|
||||
})
|
||||
|
||||
const flattenDocs = docs && docs.length ? flatten(docs) : []
|
||||
const finalDocs = []
|
||||
for (let i = 0; i < flattenDocs.length; i += 1) {
|
||||
if (flattenDocs[i] && flattenDocs[i].pageContent) {
|
||||
finalDocs.push(new LCDocument(flattenDocs[i]))
|
||||
}
|
||||
}
|
||||
|
||||
const llamadocs: Document[] = []
|
||||
for (const doc of finalDocs) {
|
||||
llamadocs.push(new Document({ text: doc.pageContent, metadata: doc.metadata }))
|
||||
}
|
||||
|
||||
const serviceContext = serviceContextFromDefaults({ llm: model, embedModel: embeddings })
|
||||
const storageContext = await storageContextFromDefaults({ vectorStore: pcvs })
|
||||
|
||||
try {
|
||||
await VectorStoreIndex.fromDocuments(llamadocs, { serviceContext, storageContext })
|
||||
} catch (e) {
|
||||
throw new Error(e)
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
async init(nodeData: INodeData, _: string, options: ICommonObject): Promise<any> {
|
||||
const indexName = nodeData.inputs?.pineconeIndex as string
|
||||
const pineconeNamespace = nodeData.inputs?.pineconeNamespace as string
|
||||
const pineconeMetadataFilter = nodeData.inputs?.pineconeMetadataFilter
|
||||
const embeddings = nodeData.inputs?.embeddings as BaseEmbedding
|
||||
const model = nodeData.inputs?.model
|
||||
const topK = nodeData.inputs?.topK as string
|
||||
const k = topK ? parseFloat(topK) : 4
|
||||
|
||||
const credentialData = await getCredentialData(nodeData.credential ?? '', options)
|
||||
const pineconeApiKey = getCredentialParam('pineconeApiKey', credentialData, nodeData)
|
||||
|
||||
const obj: PineconeParams = {
|
||||
indexName,
|
||||
apiKey: pineconeApiKey
|
||||
}
|
||||
|
||||
if (pineconeNamespace) obj.namespace = pineconeNamespace
|
||||
|
||||
let metadatafilter = {}
|
||||
if (pineconeMetadataFilter) {
|
||||
metadatafilter = typeof pineconeMetadataFilter === 'object' ? pineconeMetadataFilter : JSON.parse(pineconeMetadataFilter)
|
||||
obj.queryFilter = metadatafilter
|
||||
}
|
||||
|
||||
const pcvs = new PineconeVectorStore(obj)
|
||||
|
||||
const serviceContext = serviceContextFromDefaults({ llm: model, embedModel: embeddings })
|
||||
const storageContext = await storageContextFromDefaults({ vectorStore: pcvs })
|
||||
|
||||
const index = await VectorStoreIndex.init({
|
||||
nodes: [],
|
||||
storageContext,
|
||||
serviceContext
|
||||
})
|
||||
|
||||
const output = nodeData.outputs?.output as string
|
||||
|
||||
if (output === 'retriever') {
|
||||
const retriever = index.asRetriever()
|
||||
retriever.similarityTopK = k
|
||||
;(retriever as any).serviceContext = serviceContext
|
||||
return retriever
|
||||
} else if (output === 'vectorStore') {
|
||||
;(index as any).k = k
|
||||
if (metadatafilter) {
|
||||
;(index as any).metadatafilter = metadatafilter
|
||||
}
|
||||
return index
|
||||
}
|
||||
return index
|
||||
}
|
||||
}
|
||||
|
||||
type PineconeParams = {
|
||||
indexName: string
|
||||
apiKey: string
|
||||
namespace?: string
|
||||
chunkSize?: number
|
||||
queryFilter?: object
|
||||
}
|
||||
|
||||
class PineconeVectorStore implements VectorStore {
|
||||
storesText: boolean = true
|
||||
db?: Pinecone
|
||||
indexName: string
|
||||
apiKey: string
|
||||
chunkSize: number
|
||||
namespace?: string
|
||||
queryFilter?: object
|
||||
|
||||
constructor(params: PineconeParams) {
|
||||
this.indexName = params?.indexName
|
||||
this.apiKey = params?.apiKey
|
||||
this.namespace = params?.namespace ?? ''
|
||||
this.chunkSize = params?.chunkSize ?? Number.parseInt(process.env.PINECONE_CHUNK_SIZE ?? '100')
|
||||
this.queryFilter = params?.queryFilter ?? {}
|
||||
}
|
||||
|
||||
private async getDb(): Promise<Pinecone> {
|
||||
if (!this.db) {
|
||||
this.db = new Pinecone({
|
||||
apiKey: this.apiKey
|
||||
})
|
||||
}
|
||||
return Promise.resolve(this.db)
|
||||
}
|
||||
|
||||
client() {
|
||||
return this.getDb()
|
||||
}
|
||||
|
||||
async index() {
|
||||
const db: Pinecone = await this.getDb()
|
||||
return db.Index(this.indexName)
|
||||
}
|
||||
|
||||
async clearIndex() {
|
||||
const db: Pinecone = await this.getDb()
|
||||
return await db.index(this.indexName).deleteAll()
|
||||
}
|
||||
|
||||
async add(embeddingResults: BaseNode<Metadata>[]): Promise<string[]> {
|
||||
if (embeddingResults.length == 0) {
|
||||
return Promise.resolve([])
|
||||
}
|
||||
|
||||
const idx: Index = await this.index()
|
||||
const nodes = embeddingResults.map(this.nodeToRecord)
|
||||
|
||||
for (let i = 0; i < nodes.length; i += this.chunkSize) {
|
||||
const chunk = nodes.slice(i, i + this.chunkSize)
|
||||
const result = await this.saveChunk(idx, chunk)
|
||||
if (!result) {
|
||||
return Promise.reject()
|
||||
}
|
||||
}
|
||||
return Promise.resolve([])
|
||||
}
|
||||
|
||||
protected async saveChunk(idx: Index, chunk: any) {
|
||||
try {
|
||||
const namespace = idx.namespace(this.namespace ?? '')
|
||||
await namespace.upsert(chunk)
|
||||
return true
|
||||
} catch (err) {
|
||||
return false
|
||||
}
|
||||
}
|
||||
|
||||
async delete(refDocId: string): Promise<void> {
|
||||
const idx = await this.index()
|
||||
const namespace = idx.namespace(this.namespace ?? '')
|
||||
return namespace.deleteOne(refDocId)
|
||||
}
|
||||
|
||||
async query(query: VectorStoreQuery): Promise<VectorStoreQueryResult> {
|
||||
const queryOptions: any = {
|
||||
vector: query.queryEmbedding,
|
||||
topK: query.similarityTopK,
|
||||
filter: this.queryFilter
|
||||
}
|
||||
|
||||
const idx = await this.index()
|
||||
const namespace = idx.namespace(this.namespace ?? '')
|
||||
const results = await namespace.query(queryOptions)
|
||||
|
||||
const idList = results.matches.map((row) => row.id)
|
||||
const records: FetchResponse<any> = await namespace.fetch(idList)
|
||||
const rows = Object.values(records.records)
|
||||
|
||||
const nodes = rows.map((row) => {
|
||||
return new Document({
|
||||
id_: row.id,
|
||||
text: this.textFromResultRow(row),
|
||||
metadata: this.metaWithoutText(row.metadata),
|
||||
embedding: row.values
|
||||
})
|
||||
})
|
||||
|
||||
const result = {
|
||||
nodes: nodes,
|
||||
similarities: results.matches.map((row) => row.score || 999),
|
||||
ids: results.matches.map((row) => row.id)
|
||||
}
|
||||
|
||||
return Promise.resolve(result)
|
||||
}
|
||||
|
||||
/**
|
||||
* Required by VectorStore interface. Currently ignored.
|
||||
*/
|
||||
persist(): Promise<void> {
|
||||
return Promise.resolve()
|
||||
}
|
||||
|
||||
textFromResultRow(row: ScoredPineconeRecord<Metadata>): string {
|
||||
return row.metadata?.text ?? ''
|
||||
}
|
||||
|
||||
metaWithoutText(meta: Metadata): any {
|
||||
return Object.keys(meta)
|
||||
.filter((key) => key != 'text')
|
||||
.reduce((acc: any, key: string) => {
|
||||
acc[key] = meta[key]
|
||||
return acc
|
||||
}, {})
|
||||
}
|
||||
|
||||
nodeToRecord(node: BaseNode<Metadata>) {
|
||||
let id: any = node.id_.length ? node.id_ : null
|
||||
return {
|
||||
id: id,
|
||||
values: node.getEmbedding(),
|
||||
metadata: {
|
||||
...cleanupMetadata(node.metadata),
|
||||
text: (node as any).text
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
const cleanupMetadata = (nodeMetadata: ICommonObject) => {
|
||||
// Pinecone doesn't support nested objects, so we flatten them
|
||||
const documentMetadata: any = { ...nodeMetadata }
|
||||
// preserve string arrays which are allowed
|
||||
const stringArrays: Record<string, string[]> = {}
|
||||
for (const key of Object.keys(documentMetadata)) {
|
||||
if (Array.isArray(documentMetadata[key]) && documentMetadata[key].every((el: any) => typeof el === 'string')) {
|
||||
stringArrays[key] = documentMetadata[key]
|
||||
delete documentMetadata[key]
|
||||
}
|
||||
}
|
||||
const metadata: {
|
||||
[key: string]: string | number | boolean | string[] | null
|
||||
} = {
|
||||
...flattenObject(documentMetadata),
|
||||
...stringArrays
|
||||
}
|
||||
// Pinecone doesn't support null values, so we remove them
|
||||
for (const key of Object.keys(metadata)) {
|
||||
if (metadata[key] == null) {
|
||||
delete metadata[key]
|
||||
} else if (typeof metadata[key] === 'object' && Object.keys(metadata[key] as unknown as object).length === 0) {
|
||||
delete metadata[key]
|
||||
}
|
||||
}
|
||||
return metadata
|
||||
}
|
||||
|
||||
module.exports = { nodeClass: PineconeLlamaIndex_VectorStores }
|
||||
@@ -0,0 +1,145 @@
|
||||
import path from 'path'
|
||||
import { flatten } from 'lodash'
|
||||
import { storageContextFromDefaults, serviceContextFromDefaults, VectorStoreIndex, Document } from 'llamaindex'
|
||||
import { Document as LCDocument } from 'langchain/document'
|
||||
import { INode, INodeData, INodeOutputsValue, INodeParams } from '../../../src/Interface'
|
||||
import { getUserHome } from '../../../src'
|
||||
|
||||
class SimpleStoreUpsert_LlamaIndex_VectorStores implements INode {
|
||||
label: string
|
||||
name: string
|
||||
version: number
|
||||
description: string
|
||||
type: string
|
||||
icon: string
|
||||
category: string
|
||||
baseClasses: string[]
|
||||
tags: string[]
|
||||
inputs: INodeParams[]
|
||||
outputs: INodeOutputsValue[]
|
||||
|
||||
constructor() {
|
||||
this.label = 'SimpleStore'
|
||||
this.name = 'simpleStoreLlamaIndex'
|
||||
this.version = 1.0
|
||||
this.type = 'SimpleVectorStore'
|
||||
this.icon = 'simplevs.svg'
|
||||
this.category = 'Vector Stores'
|
||||
this.description = 'Upsert embedded data to local path and perform similarity search'
|
||||
this.baseClasses = [this.type, 'VectorIndexRetriever']
|
||||
this.tags = ['LlamaIndex']
|
||||
this.inputs = [
|
||||
{
|
||||
label: 'Document',
|
||||
name: 'document',
|
||||
type: 'Document',
|
||||
list: true,
|
||||
optional: true
|
||||
},
|
||||
{
|
||||
label: 'Chat Model',
|
||||
name: 'model',
|
||||
type: 'BaseChatModel_LlamaIndex'
|
||||
},
|
||||
{
|
||||
label: 'Embeddings',
|
||||
name: 'embeddings',
|
||||
type: 'BaseEmbedding_LlamaIndex'
|
||||
},
|
||||
{
|
||||
label: 'Base Path to store',
|
||||
name: 'basePath',
|
||||
description:
|
||||
'Path to store persist embeddings indexes with persistence. If not specified, default to same path where database is stored',
|
||||
type: 'string',
|
||||
optional: true
|
||||
},
|
||||
{
|
||||
label: 'Top K',
|
||||
name: 'topK',
|
||||
description: 'Number of top results to fetch. Default to 4',
|
||||
placeholder: '4',
|
||||
type: 'number',
|
||||
optional: true
|
||||
}
|
||||
]
|
||||
this.outputs = [
|
||||
{
|
||||
label: 'SimpleStore Retriever',
|
||||
name: 'retriever',
|
||||
baseClasses: this.baseClasses
|
||||
},
|
||||
{
|
||||
label: 'SimpleStore Vector Store Index',
|
||||
name: 'vectorStore',
|
||||
baseClasses: [this.type, 'VectorStoreIndex']
|
||||
}
|
||||
]
|
||||
}
|
||||
|
||||
//@ts-ignore
|
||||
vectorStoreMethods = {
|
||||
async upsert(nodeData: INodeData): Promise<void> {
|
||||
const basePath = nodeData.inputs?.basePath as string
|
||||
const docs = nodeData.inputs?.document as LCDocument[]
|
||||
const embeddings = nodeData.inputs?.embeddings
|
||||
const model = nodeData.inputs?.model
|
||||
|
||||
let filePath = ''
|
||||
if (!basePath) filePath = path.join(getUserHome(), '.flowise', 'llamaindex')
|
||||
else filePath = basePath
|
||||
|
||||
const flattenDocs = docs && docs.length ? flatten(docs) : []
|
||||
const finalDocs = []
|
||||
for (let i = 0; i < flattenDocs.length; i += 1) {
|
||||
finalDocs.push(new LCDocument(flattenDocs[i]))
|
||||
}
|
||||
|
||||
const llamadocs: Document[] = []
|
||||
for (const doc of finalDocs) {
|
||||
llamadocs.push(new Document({ text: doc.pageContent, metadata: doc.metadata }))
|
||||
}
|
||||
|
||||
const serviceContext = serviceContextFromDefaults({ llm: model, embedModel: embeddings })
|
||||
const storageContext = await storageContextFromDefaults({ persistDir: filePath })
|
||||
|
||||
try {
|
||||
await VectorStoreIndex.fromDocuments(llamadocs, { serviceContext, storageContext })
|
||||
} catch (e) {
|
||||
throw new Error(e)
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
async init(nodeData: INodeData): Promise<any> {
|
||||
const basePath = nodeData.inputs?.basePath as string
|
||||
const embeddings = nodeData.inputs?.embeddings
|
||||
const model = nodeData.inputs?.model
|
||||
const topK = nodeData.inputs?.topK as string
|
||||
const k = topK ? parseFloat(topK) : 4
|
||||
|
||||
let filePath = ''
|
||||
if (!basePath) filePath = path.join(getUserHome(), '.flowise', 'llamaindex')
|
||||
else filePath = basePath
|
||||
|
||||
const serviceContext = serviceContextFromDefaults({ llm: model, embedModel: embeddings })
|
||||
const storageContext = await storageContextFromDefaults({ persistDir: filePath })
|
||||
|
||||
const index = await VectorStoreIndex.init({ storageContext, serviceContext })
|
||||
|
||||
const output = nodeData.outputs?.output as string
|
||||
|
||||
if (output === 'retriever') {
|
||||
const retriever = index.asRetriever()
|
||||
retriever.similarityTopK = k
|
||||
;(retriever as any).serviceContext = serviceContext
|
||||
return retriever
|
||||
} else if (output === 'vectorStore') {
|
||||
;(index as any).k = k
|
||||
return index
|
||||
}
|
||||
return index
|
||||
}
|
||||
}
|
||||
|
||||
module.exports = { nodeClass: SimpleStoreUpsert_LlamaIndex_VectorStores }
|
||||
@@ -0,0 +1,6 @@
|
||||
<svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-database" width="24" height="24" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round">
|
||||
<path stroke="none" d="M0 0h24v24H0z" fill="none"></path>
|
||||
<path d="M12 6m-8 0a8 3 0 1 0 16 0a8 3 0 1 0 -16 0"></path>
|
||||
<path d="M4 6v6a8 3 0 0 0 16 0v-6"></path>
|
||||
<path d="M4 12v6a8 3 0 0 0 16 0v-6"></path>
|
||||
</svg>
|
||||
|
After Width: | Height: | Size: 451 B |
Reference in New Issue
Block a user