Merge branch 'main' into feature/ChatGPT-Plugin
This commit is contained in:
Henry
commit
2a04c8baea
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import { INode, INodeData, INodeParams } from '../../../src/Interface'
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import { SqlDatabaseChain } from 'langchain/chains'
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import { getBaseClasses } from '../../../src/utils'
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import { DataSource } from 'typeorm'
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import { SqlDatabase } from 'langchain/sql_db'
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import { BaseLLM } from 'langchain/llms/base'
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class SqlDatabaseChain_Chains implements INode {
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label: string
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name: 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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description: string
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inputs: INodeParams[]
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constructor() {
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this.label = 'Sql Database Chain'
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this.name = 'sqlDatabaseChain'
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this.type = 'SqlDatabaseChain'
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this.icon = 'sqlchain.svg'
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this.category = 'Chains'
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this.description = 'Answer questions over a SQL database'
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this.baseClasses = [this.type, ...getBaseClasses(SqlDatabaseChain)]
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this.inputs = [
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{
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label: 'LLM',
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name: 'llm',
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type: 'BaseLLM'
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},
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{
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label: 'Database',
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name: 'database',
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type: 'options',
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options: [
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{
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label: 'SQlite',
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name: 'sqlite'
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}
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],
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default: 'sqlite'
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},
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{
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label: 'Database File Path',
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name: 'dbFilePath',
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type: 'string',
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placeholder: 'C:/Users/chinook.db'
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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 databaseType = nodeData.inputs?.database
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const llm = nodeData.inputs?.llm as BaseLLM
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const dbFilePath = nodeData.inputs?.dbFilePath
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const datasource = new DataSource({
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type: databaseType,
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database: dbFilePath
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})
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const db = await SqlDatabase.fromDataSourceParams({
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appDataSource: datasource
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})
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const chain = new SqlDatabaseChain({
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llm,
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database: db
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})
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return chain
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}
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async run(nodeData: INodeData, input: string): Promise<string> {
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const chain = nodeData.instance as SqlDatabaseChain
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const res = await chain.run(input)
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return res
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}
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}
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module.exports = { nodeClass: SqlDatabaseChain_Chains }
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<svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-sql" width="24" height="24" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round">
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<path stroke="none" d="M0 0h24v24H0z" fill="none"></path>
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<path d="M12 8a2 2 0 0 1 2 2v4a2 2 0 1 1 -4 0v-4a2 2 0 0 1 2 -2z"></path>
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<path d="M17 8v8h4"></path>
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<path d="M13 15l1 1"></path>
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<path d="M3 15a1 1 0 0 0 1 1h2a1 1 0 0 0 1 -1v-2a1 1 0 0 0 -1 -1h-2a1 1 0 0 1 -1 -1v-2a1 1 0 0 1 1 -1h2a1 1 0 0 1 1 1"></path>
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</svg>
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After Width: | Height: | Size: 560 B |
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import { INode, INodeData, INodeParams } from '../../../src/Interface'
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import { TextSplitter } from 'langchain/text_splitter'
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import { CheerioWebBaseLoader } from 'langchain/document_loaders/web/cheerio'
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class Cheerio_DocumentLoaders implements INode {
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label: string
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name: string
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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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inputs: INodeParams[]
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constructor() {
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this.label = 'Cheerio Web Scraper'
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this.name = 'cheerioWebScraper'
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this.type = 'Document'
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this.icon = 'cheerio.svg'
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this.category = 'Document Loaders'
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this.description = `Load data from webpages`
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this.baseClasses = [this.type]
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this.inputs = [
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{
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label: 'URL',
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name: 'url',
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type: 'string'
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},
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{
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label: 'Text Splitter',
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name: 'textSplitter',
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type: 'TextSplitter',
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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 textSplitter = nodeData.inputs?.textSplitter as TextSplitter
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let url = nodeData.inputs?.url as string
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var urlPattern = new RegExp(
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'^(https?:\\/\\/)?' + // validate protocol
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'((([a-z\\d]([a-z\\d-]*[a-z\\d])*)\\.)+[a-z]{2,}|' + // validate domain name
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'((\\d{1,3}\\.){3}\\d{1,3}))' + // validate OR ip (v4) address
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'(\\:\\d+)?(\\/[-a-z\\d%_.~+]*)*' + // validate port and path
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'(\\?[;&a-z\\d%_.~+=-]*)?' + // validate query string
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'(\\#[-a-z\\d_]*)?$',
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'i'
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) // validate fragment locator
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const loader = new CheerioWebBaseLoader(urlPattern.test(url.trim()) ? url.trim() : '')
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if (textSplitter) {
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const docs = await loader.loadAndSplit(textSplitter)
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return docs
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} else {
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const docs = await loader.load()
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return docs
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}
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}
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}
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module.exports = { nodeClass: Cheerio_DocumentLoaders }
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<svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-circle-letter-c" width="24" height="24" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round">
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<path stroke="none" d="M0 0h24v24H0z" fill="none"></path>
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<path d="M12 12m-9 0a9 9 0 1 0 18 0a9 9 0 1 0 -18 0"></path>
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<path d="M14 10a2 2 0 1 0 -4 0v4a2 2 0 1 0 4 0"></path>
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</svg>
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import { INode, INodeData, INodeParams } from '../../../src/Interface'
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import { getBaseClasses } from '../../../src/utils'
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import { CohereEmbeddings } from 'langchain/embeddings/cohere'
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class CohereEmbedding_Embeddings implements INode {
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label: string
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name: string
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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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inputs: INodeParams[]
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constructor() {
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this.label = 'Cohere Embeddings'
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this.name = 'cohereEmbeddings'
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this.type = 'CohereEmbeddings'
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this.icon = 'cohere.png'
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this.category = 'Embeddings'
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this.description = 'Cohere API to generate embeddings for a given text'
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this.baseClasses = [this.type, ...getBaseClasses(CohereEmbeddings)]
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this.inputs = [
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{
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label: 'Cohere API Key',
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name: 'cohereApiKey',
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type: 'password'
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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 apiKey = nodeData.inputs?.cohereApiKey as string
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const model = new CohereEmbeddings({ apiKey })
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return model
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}
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}
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module.exports = { nodeClass: CohereEmbedding_Embeddings }
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Binary file not shown.
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After Width: | Height: | Size: 7.7 KiB |
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@ -20,7 +20,9 @@
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"@huggingface/inference": "^1.6.3",
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"@pinecone-database/pinecone": "^0.0.12",
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"axios": "^0.27.2",
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"cheerio": "^1.0.0-rc.12",
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"chromadb": "^1.3.1",
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"cohere-ai": "^6.2.0",
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"d3-dsv": "2",
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"dotenv": "^16.0.0",
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"express": "^4.17.3",
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{
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"description": "Answer questions over a SQL database",
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"nodes": [
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{
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"width": 300,
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"height": 424,
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"id": "sqlDatabaseChain_0",
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"position": {
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"x": 1271.2742585099204,
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"y": 232.91561199714107
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},
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"type": "customNode",
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"data": {
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"id": "sqlDatabaseChain_0",
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"label": "Sql Database Chain",
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"name": "sqlDatabaseChain",
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"type": "SqlDatabaseChain",
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"baseClasses": ["SqlDatabaseChain", "BaseChain"],
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"category": "Chains",
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"description": "Answer questions over a SQL database",
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"inputParams": [
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{
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"label": "Database",
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"name": "database",
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"type": "options",
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"options": [
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{
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"label": "SQlite",
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"name": "sqlite"
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}
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],
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"default": "sqlite"
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},
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{
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"label": "Database File Path",
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"name": "dbFilePath",
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"type": "string",
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"placeholder": "C:/Users/chinook.db"
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}
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],
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"inputAnchors": [
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{
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"label": "LLM",
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"name": "llm",
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"type": "BaseLLM",
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"id": "sqlDatabaseChain_0-input-llm-BaseLLM"
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}
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],
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"inputs": {
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"llm": "{{openAI_0.data.instance}}",
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"database": "sqlite",
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"dbFilePath": ""
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},
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"outputAnchors": [
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{
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"id": "sqlDatabaseChain_0-output-sqlDatabaseChain-SqlDatabaseChain|BaseChain",
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"name": "sqlDatabaseChain",
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"label": "SqlDatabaseChain",
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"type": "SqlDatabaseChain | BaseChain"
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}
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],
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"selected": false
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},
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"selected": false,
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"positionAbsolute": {
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"x": 1271.2742585099204,
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"y": 232.91561199714107
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},
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"dragging": false
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},
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{
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"width": 300,
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"height": 472,
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"id": "openAI_0",
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"position": {
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"x": 867.8574087065126,
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"y": 209.58625096303308
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},
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"type": "customNode",
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"data": {
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"id": "openAI_0",
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"label": "OpenAI",
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"name": "openAI",
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"type": "OpenAI",
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"baseClasses": ["OpenAI", "BaseLLM", "BaseLanguageModel"],
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"category": "LLMs",
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"description": "Wrapper around OpenAI large language models",
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"inputParams": [
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{
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"label": "OpenAI Api Key",
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"name": "openAIApiKey",
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"type": "password"
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},
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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": "text-davinci-003",
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"name": "text-davinci-003"
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},
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{
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"label": "text-davinci-002",
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"name": "text-davinci-002"
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},
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{
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"label": "text-curie-001",
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"name": "text-curie-001"
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},
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{
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"label": "text-babbage-001",
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"name": "text-babbage-001"
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}
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],
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"default": "text-davinci-003",
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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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"default": 0.7,
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"optional": true
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}
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],
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"inputAnchors": [],
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"inputs": {
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"modelName": "text-davinci-003",
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"temperature": "0"
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},
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"outputAnchors": [
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{
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"id": "openAI_0-output-openAI-OpenAI|BaseLLM|BaseLanguageModel",
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"name": "openAI",
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"label": "OpenAI",
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"type": "OpenAI | BaseLLM | BaseLanguageModel"
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}
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],
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"selected": false
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},
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"selected": false,
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"positionAbsolute": {
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"x": 867.8574087065126,
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"y": 209.58625096303308
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},
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"dragging": false
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}
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],
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"edges": [
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{
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"source": "openAI_0",
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"sourceHandle": "openAI_0-output-openAI-OpenAI|BaseLLM|BaseLanguageModel",
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"target": "sqlDatabaseChain_0",
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"targetHandle": "sqlDatabaseChain_0-input-llm-BaseLLM",
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"type": "buttonedge",
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"id": "openAI_0-openAI_0-output-openAI-OpenAI|BaseLLM|BaseLanguageModel-sqlDatabaseChain_0-sqlDatabaseChain_0-input-llm-BaseLLM",
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"data": {
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"label": ""
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}
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}
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]
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}
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@ -39,6 +39,7 @@ export const ChatMessage = ({ chatflowid }) => {
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const customization = useSelector((state) => state.customization)
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const { confirm } = useConfirm()
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const dispatch = useDispatch()
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const ps = useRef()
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useNotifier()
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const enqueueSnackbar = (...args) => dispatch(enqueueSnackbarAction(...args))
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@ -54,7 +55,6 @@ export const ChatMessage = ({ chatflowid }) => {
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}
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])
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const messagesEndRef = useRef(null)
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const inputRef = useRef(null)
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const anchorRef = useRef(null)
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const prevOpen = useRef(open)
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@ -115,7 +115,9 @@ export const ChatMessage = ({ chatflowid }) => {
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}
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const scrollToBottom = () => {
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messagesEndRef.current?.scrollIntoView({ behavior: 'smooth' })
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if (ps.current) {
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ps.current.scrollTo({ top: Number.MAX_SAFE_INTEGER, behavior: 'smooth' })
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}
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}
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const addChatMessage = async (message, type) => {
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|
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@ -286,7 +288,7 @@ export const ChatMessage = ({ chatflowid }) => {
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|||
<ClickAwayListener onClickAway={handleClose}>
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<MainCard border={false} elevation={16} content={false} boxShadow shadow={theme.shadows[16]}>
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<div className='cloud'>
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<div className='messagelist'>
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<div ref={ps} className='messagelist'>
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{messages.map((message, index) => {
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return (
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// The latest message sent by the user will be animated while waiting for a response
|
||||
|
|
@ -331,7 +333,6 @@ export const ChatMessage = ({ chatflowid }) => {
|
|||
</Box>
|
||||
)
|
||||
})}
|
||||
<div ref={messagesEndRef} />
|
||||
</div>
|
||||
</div>
|
||||
<Divider />
|
||||
|
|
|
|||
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