Merge branch 'main' into feature/Milvus

# Conflicts:
#	packages/components/package.json

packages/components/nodes/agents/AirtableAgent/AirtableAgent.ts

@@ -0,0 +1,232 @@
+import { ICommonObject, INode, INodeData, INodeParams, PromptTemplate } from '../../../src/Interface'
+import { AgentExecutor } from 'langchain/agents'
+import { getBaseClasses, getCredentialData, getCredentialParam } from '../../../src/utils'
+import { LoadPyodide, finalSystemPrompt, systemPrompt } from './core'
+import { LLMChain } from 'langchain/chains'
+import { BaseLanguageModel } from 'langchain/base_language'
+import { ConsoleCallbackHandler, CustomChainHandler } from '../../../src/handler'
+import axios from 'axios'
+
+class Airtable_Agents implements INode {
+    label: string
+    name: string
+    version: number
+    description: string
+    type: string
+    icon: string
+    category: string
+    baseClasses: string[]
+    credential: INodeParams
+    inputs: INodeParams[]
+
+    constructor() {
+        this.label = 'Airtable Agent'
+        this.name = 'airtableAgent'
+        this.version = 1.0
+        this.type = 'AgentExecutor'
+        this.category = 'Agents'
+        this.icon = 'airtable.svg'
+        this.description = 'Agent used to to answer queries on Airtable table'
+        this.baseClasses = [this.type, ...getBaseClasses(AgentExecutor)]
+        this.credential = {
+            label: 'Connect Credential',
+            name: 'credential',
+            type: 'credential',
+            credentialNames: ['airtableApi']
+        }
+        this.inputs = [
+            {
+                label: 'Language Model',
+                name: 'model',
+                type: 'BaseLanguageModel'
+            },
+            {
+                label: 'Base Id',
+                name: 'baseId',
+                type: 'string',
+                placeholder: 'app11RobdGoX0YNsC',
+                description:
+                    'If your table URL looks like: https://airtable.com/app11RobdGoX0YNsC/tblJdmvbrgizbYICO/viw9UrP77Id0CE4ee, app11RovdGoX0YNsC is the base id'
+            },
+            {
+                label: 'Table Id',
+                name: 'tableId',
+                type: 'string',
+                placeholder: 'tblJdmvbrgizbYICO',
+                description:
+                    'If your table URL looks like: https://airtable.com/app11RobdGoX0YNsC/tblJdmvbrgizbYICO/viw9UrP77Id0CE4ee, tblJdmvbrgizbYICO is the table id'
+            },
+            {
+                label: 'Return All',
+                name: 'returnAll',
+                type: 'boolean',
+                default: true,
+                additionalParams: true,
+                description: 'If all results should be returned or only up to a given limit'
+            },
+            {
+                label: 'Limit',
+                name: 'limit',
+                type: 'number',
+                default: 100,
+                additionalParams: true,
+                description: 'Number of results to return'
+            }
+        ]
+    }
+
+    async init(): Promise<any> {
+        // Not used
+        return undefined
+    }
+
+    async run(nodeData: INodeData, input: string, options: ICommonObject): Promise<string> {
+        const model = nodeData.inputs?.model as BaseLanguageModel
+        const baseId = nodeData.inputs?.baseId as string
+        const tableId = nodeData.inputs?.tableId as string
+        const returnAll = nodeData.inputs?.returnAll as boolean
+        const limit = nodeData.inputs?.limit as string
+
+        const credentialData = await getCredentialData(nodeData.credential ?? '', options)
+        const accessToken = getCredentialParam('accessToken', credentialData, nodeData)
+
+        let airtableData: ICommonObject[] = []
+
+        if (returnAll) {
+            airtableData = await loadAll(baseId, tableId, accessToken)
+        } else {
+            airtableData = await loadLimit(limit ? parseInt(limit, 10) : 100, baseId, tableId, accessToken)
+        }
+
+        let base64String = Buffer.from(JSON.stringify(airtableData)).toString('base64')
+
+        const loggerHandler = new ConsoleCallbackHandler(options.logger)
+        const handler = new CustomChainHandler(options.socketIO, options.socketIOClientId)
+
+        const pyodide = await LoadPyodide()
+
+        // First load the csv file and get the dataframe dictionary of column types
+        // For example using titanic.csv: {'PassengerId': 'int64', 'Survived': 'int64', 'Pclass': 'int64', 'Name': 'object', 'Sex': 'object', 'Age': 'float64', 'SibSp': 'int64', 'Parch': 'int64', 'Ticket': 'object', 'Fare': 'float64', 'Cabin': 'object', 'Embarked': 'object'}
+        let dataframeColDict = ''
+        try {
+            const code = `import pandas as pd
+import base64
+import json
+
+base64_string = "${base64String}"
+
+decoded_data = base64.b64decode(base64_string)
+
+json_data = json.loads(decoded_data)
+
+df = pd.DataFrame(json_data)
+my_dict = df.dtypes.astype(str).to_dict()
+print(my_dict)
+json.dumps(my_dict)`
+            dataframeColDict = await pyodide.runPythonAsync(code)
+        } catch (error) {
+            throw new Error(error)
+        }
+
+        // Then tell GPT to come out with ONLY python code
+        // For example: len(df), df[df['SibSp'] > 3]['PassengerId'].count()
+        let pythonCode = ''
+        if (dataframeColDict) {
+            const chain = new LLMChain({
+                llm: model,
+                prompt: PromptTemplate.fromTemplate(systemPrompt),
+                verbose: process.env.DEBUG === 'true' ? true : false
+            })
+            const inputs = {
+                dict: dataframeColDict,
+                question: input
+            }
+            const res = await chain.call(inputs, [loggerHandler])
+            pythonCode = res?.text
+        }
+
+        // Then run the code using Pyodide
+        let finalResult = ''
+        if (pythonCode) {
+            try {
+                const code = `import pandas as pd\n${pythonCode}`
+                finalResult = await pyodide.runPythonAsync(code)
+            } catch (error) {
+                throw new Error(`Sorry, I'm unable to find answer for question: "${input}" using follwoing code: "${pythonCode}"`)
+            }
+        }
+
+        // Finally, return a complete answer
+        if (finalResult) {
+            const chain = new LLMChain({
+                llm: model,
+                prompt: PromptTemplate.fromTemplate(finalSystemPrompt),
+                verbose: process.env.DEBUG === 'true' ? true : false
+            })
+            const inputs = {
+                question: input,
+                answer: finalResult
+            }
+
+            if (options.socketIO && options.socketIOClientId) {
+                const result = await chain.call(inputs, [loggerHandler, handler])
+                return result?.text
+            } else {
+                const result = await chain.call(inputs, [loggerHandler])
+                return result?.text
+            }
+        }
+
+        return pythonCode
+    }
+}
+
+interface AirtableLoaderResponse {
+    records: AirtableLoaderPage[]
+    offset?: string
+}
+
+interface AirtableLoaderPage {
+    id: string
+    createdTime: string
+    fields: ICommonObject
+}
+
+const fetchAirtableData = async (url: string, params: ICommonObject, accessToken: string): Promise<AirtableLoaderResponse> => {
+    try {
+        const headers = {
+            Authorization: `Bearer ${accessToken}`,
+            'Content-Type': 'application/json',
+            Accept: 'application/json'
+        }
+        const response = await axios.get(url, { params, headers })
+        return response.data
+    } catch (error) {
+        throw new Error(`Failed to fetch ${url} from Airtable: ${error}`)
+    }
+}
+
+const loadAll = async (baseId: string, tableId: string, accessToken: string): Promise<ICommonObject[]> => {
+    const params: ICommonObject = { pageSize: 100 }
+    let data: AirtableLoaderResponse
+    let returnPages: AirtableLoaderPage[] = []
+
+    do {
+        data = await fetchAirtableData(`https://api.airtable.com/v0/${baseId}/${tableId}`, params, accessToken)
+        returnPages.push.apply(returnPages, data.records)
+        params.offset = data.offset
+    } while (data.offset !== undefined)
+
+    return data.records.map((page) => page.fields)
+}
+
+const loadLimit = async (limit: number, baseId: string, tableId: string, accessToken: string): Promise<ICommonObject[]> => {
+    const params = { maxRecords: limit }
+    const data = await fetchAirtableData(`https://api.airtable.com/v0/${baseId}/${tableId}`, params, accessToken)
+    if (data.records.length === 0) {
+        return []
+    }
+    return data.records.map((page) => page.fields)
+}
+
+module.exports = { nodeClass: Airtable_Agents }

packages/components/nodes/agents/AirtableAgent/airtable.svg

@@ -0,0 +1,9 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<svg width="256px" height="215px" viewBox="0 0 256 215" version="1.1" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" preserveAspectRatio="xMidYMid">
+	<g>
+		<path d="M114.25873,2.70101695 L18.8604023,42.1756384 C13.5552723,44.3711638 13.6102328,51.9065311 18.9486282,54.0225085 L114.746142,92.0117514 C123.163769,95.3498757 132.537419,95.3498757 140.9536,92.0117514 L236.75256,54.0225085 C242.08951,51.9065311 242.145916,44.3711638 236.83934,42.1756384 L141.442459,2.70101695 C132.738459,-0.900338983 122.961284,-0.900338983 114.25873,2.70101695" fill="#FFBF00"></path>
+		<path d="M136.349071,112.756863 L136.349071,207.659101 C136.349071,212.173089 140.900664,215.263892 145.096461,213.600615 L251.844122,172.166219 C254.281184,171.200072 255.879376,168.845451 255.879376,166.224705 L255.879376,71.3224678 C255.879376,66.8084791 251.327783,63.7176768 247.131986,65.3809537 L140.384325,106.815349 C137.94871,107.781496 136.349071,110.136118 136.349071,112.756863" fill="#26B5F8"></path>
+		<path d="M111.422771,117.65355 L79.742409,132.949912 L76.5257763,134.504714 L9.65047684,166.548104 C5.4112904,168.593211 0.000578531073,165.503855 0.000578531073,160.794612 L0.000578531073,71.7210757 C0.000578531073,70.0173017 0.874160452,68.5463864 2.04568588,67.4384994 C2.53454463,66.9481944 3.08848814,66.5446689 3.66412655,66.2250305 C5.26231864,65.2661153 7.54173107,65.0101153 9.47981017,65.7766689 L110.890522,105.957098 C116.045234,108.002206 116.450206,115.225166 111.422771,117.65355" fill="#ED3049"></path>
+		<path d="M111.422771,117.65355 L79.742409,132.949912 L2.04568588,67.4384994 C2.53454463,66.9481944 3.08848814,66.5446689 3.66412655,66.2250305 C5.26231864,65.2661153 7.54173107,65.0101153 9.47981017,65.7766689 L110.890522,105.957098 C116.045234,108.002206 116.450206,115.225166 111.422771,117.65355" fill-opacity="0.25" fill="#000000"></path>
+	</g>
+</svg>

packages/components/nodes/agents/AirtableAgent/core.ts

@@ -0,0 +1,29 @@
+import type { PyodideInterface } from 'pyodide'
+import * as path from 'path'
+import { getUserHome } from '../../../src/utils'
+
+let pyodideInstance: PyodideInterface | undefined
+
+export async function LoadPyodide(): Promise<PyodideInterface> {
+    if (pyodideInstance === undefined) {
+        const { loadPyodide } = await import('pyodide')
+        const obj: any = { packageCacheDir: path.join(getUserHome(), '.flowise', 'pyodideCacheDir') }
+        pyodideInstance = await loadPyodide(obj)
+        await pyodideInstance.loadPackage(['pandas', 'numpy'])
+    }
+
+    return pyodideInstance
+}
+
+export const systemPrompt = `You are working with a pandas dataframe in Python. The name of the dataframe is df.
+
+The columns and data types of a dataframe are given below as a Python dictionary with keys showing column names and values showing the data types.
+{dict}
+
+I will ask question, and you will output the Python code using pandas dataframe to answer my question. Do not provide any explanations. Do not respond with anything except the output of the code.
+
+Question: {question}
+Output Code:`
+
+export const finalSystemPrompt = `You are given the question: {question}. You have an answer to the question: {answer}. Rephrase the answer into a standalone answer.
+Standalone Answer:`

packages/components/nodes/agents/AutoGPT/AutoGPT.ts

@@ -3,10 +3,12 @@ import { BaseChatModel } from 'langchain/chat_models/base'
 import { AutoGPT } from 'langchain/experimental/autogpt'
 import { Tool } from 'langchain/tools'
 import { VectorStoreRetriever } from 'langchain/vectorstores/base'
+import { flatten } from 'lodash'
 
 class AutoGPT_Agents implements INode {
     label: string
     name: string
+    version: number
     description: string
     type: string
     icon: string
@@ -17,6 +19,7 @@ class AutoGPT_Agents implements INode {
     constructor() {
         this.label = 'AutoGPT'
         this.name = 'autoGPT'
+        this.version = 1.0
         this.type = 'AutoGPT'
         this.category = 'Agents'
         this.icon = 'autogpt.png'
@@ -67,7 +70,7 @@ class AutoGPT_Agents implements INode {
         const model = nodeData.inputs?.model as BaseChatModel
         const vectorStoreRetriever = nodeData.inputs?.vectorStoreRetriever as VectorStoreRetriever
         let tools = nodeData.inputs?.tools as Tool[]
-        tools = tools.flat()
+        tools = flatten(tools)
         const aiName = (nodeData.inputs?.aiName as string) || 'AutoGPT'
         const aiRole = (nodeData.inputs?.aiRole as string) || 'Assistant'
         const maxLoop = nodeData.inputs?.maxLoop as string
@@ -89,7 +92,6 @@ class AutoGPT_Agents implements INode {
             const res = await executor.run([input])
             return res || 'I have completed all my tasks.'
         } catch (e) {
-            console.error(e)
             throw new Error(e)
         }
     }

packages/components/nodes/agents/BabyAGI/BabyAGI.ts

@@ -6,6 +6,7 @@ import { VectorStore } from 'langchain/vectorstores'
 class BabyAGI_Agents implements INode {
     label: string
     name: string
+    version: number
     description: string
     type: string
     icon: string
@@ -16,6 +17,7 @@ class BabyAGI_Agents implements INode {
     constructor() {
         this.label = 'BabyAGI'
         this.name = 'babyAGI'
+        this.version = 1.0
         this.type = 'BabyAGI'
         this.category = 'Agents'
         this.icon = 'babyagi.jpg'
@@ -45,8 +47,9 @@ class BabyAGI_Agents implements INode {
         const model = nodeData.inputs?.model as BaseChatModel
         const vectorStore = nodeData.inputs?.vectorStore as VectorStore
         const taskLoop = nodeData.inputs?.taskLoop as string
+        const k = (vectorStore as any)?.k ?? 4
 
-        const babyAgi = BabyAGI.fromLLM(model, vectorStore, parseInt(taskLoop, 10))
+        const babyAgi = BabyAGI.fromLLM(model, vectorStore, parseInt(taskLoop, 10), k)
         return babyAgi
     }
 

packages/components/nodes/agents/BabyAGI/core.ts

@@ -154,18 +154,22 @@ export class BabyAGI {
 
     maxIterations = 3
 
+    topK = 4
+
     constructor(
         taskCreationChain: TaskCreationChain,
         taskPrioritizationChain: TaskPrioritizationChain,
         executionChain: ExecutionChain,
         vectorStore: VectorStore,
-        maxIterations: number
+        maxIterations: number,
+        topK: number
     ) {
         this.taskCreationChain = taskCreationChain
         this.taskPrioritizationChain = taskPrioritizationChain
         this.executionChain = executionChain
         this.vectorStore = vectorStore
         this.maxIterations = maxIterations
+        this.topK = topK
     }
 
     addTask(task: Task) {
@@ -219,7 +223,7 @@ export class BabyAGI {
                 this.printNextTask(task)
 
                 // Step 2: Execute the task
-                const result = await executeTask(this.vectorStore, this.executionChain, objective, task.task_name)
+                const result = await executeTask(this.vectorStore, this.executionChain, objective, task.task_name, this.topK)
                 const thisTaskId = task.task_id
                 finalResult = result
                 this.printTaskResult(result)
@@ -257,10 +261,10 @@ export class BabyAGI {
         return finalResult
     }
 
-    static fromLLM(llm: BaseChatModel, vectorstore: VectorStore, maxIterations = 3): BabyAGI {
+    static fromLLM(llm: BaseChatModel, vectorstore: VectorStore, maxIterations = 3, topK = 4): BabyAGI {
         const taskCreationChain = TaskCreationChain.from_llm(llm)
         const taskPrioritizationChain = TaskPrioritizationChain.from_llm(llm)
         const executionChain = ExecutionChain.from_llm(llm)
-        return new BabyAGI(taskCreationChain, taskPrioritizationChain, executionChain, vectorstore, maxIterations)
+        return new BabyAGI(taskCreationChain, taskPrioritizationChain, executionChain, vectorstore, maxIterations, topK)
     }
 }

packages/components/nodes/agents/CSVAgent/CSVAgent.ts

@@ -0,0 +1,164 @@
+import { ICommonObject, INode, INodeData, INodeParams, PromptTemplate } from '../../../src/Interface'
+import { AgentExecutor } from 'langchain/agents'
+import { getBaseClasses } from '../../../src/utils'
+import { LoadPyodide, finalSystemPrompt, systemPrompt } from './core'
+import { LLMChain } from 'langchain/chains'
+import { BaseLanguageModel } from 'langchain/base_language'
+import { ConsoleCallbackHandler, CustomChainHandler } from '../../../src/handler'
+
+class CSV_Agents implements INode {
+    label: string
+    name: string
+    version: number
+    description: string
+    type: string
+    icon: string
+    category: string
+    baseClasses: string[]
+    inputs: INodeParams[]
+
+    constructor() {
+        this.label = 'CSV Agent'
+        this.name = 'csvAgent'
+        this.version = 1.0
+        this.type = 'AgentExecutor'
+        this.category = 'Agents'
+        this.icon = 'csvagent.png'
+        this.description = 'Agent used to to answer queries on CSV data'
+        this.baseClasses = [this.type, ...getBaseClasses(AgentExecutor)]
+        this.inputs = [
+            {
+                label: 'Csv File',
+                name: 'csvFile',
+                type: 'file',
+                fileType: '.csv'
+            },
+            {
+                label: 'Language Model',
+                name: 'model',
+                type: 'BaseLanguageModel'
+            },
+            {
+                label: 'System Message',
+                name: 'systemMessagePrompt',
+                type: 'string',
+                rows: 4,
+                additionalParams: true,
+                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.'
+            }
+        ]
+    }
+
+    async init(): Promise<any> {
+        // Not used
+        return undefined
+    }
+
+    async run(nodeData: INodeData, input: string, options: ICommonObject): Promise<string> {
+        const csvFileBase64 = nodeData.inputs?.csvFile as string
+        const model = nodeData.inputs?.model as BaseLanguageModel
+        const systemMessagePrompt = nodeData.inputs?.systemMessagePrompt as string
+
+        const loggerHandler = new ConsoleCallbackHandler(options.logger)
+        const handler = new CustomChainHandler(options.socketIO, options.socketIOClientId)
+
+        let files: string[] = []
+
+        if (csvFileBase64.startsWith('[') && csvFileBase64.endsWith(']')) {
+            files = JSON.parse(csvFileBase64)
+        } else {
+            files = [csvFileBase64]
+        }
+
+        let base64String = ''
+
+        for (const file of files) {
+            const splitDataURI = file.split(',')
+            splitDataURI.pop()
+            base64String = splitDataURI.pop() ?? ''
+        }
+
+        const pyodide = await LoadPyodide()
+
+        // First load the csv file and get the dataframe dictionary of column types
+        // For example using titanic.csv: {'PassengerId': 'int64', 'Survived': 'int64', 'Pclass': 'int64', 'Name': 'object', 'Sex': 'object', 'Age': 'float64', 'SibSp': 'int64', 'Parch': 'int64', 'Ticket': 'object', 'Fare': 'float64', 'Cabin': 'object', 'Embarked': 'object'}
+        let dataframeColDict = ''
+        try {
+            const code = `import pandas as pd
+import base64
+from io import StringIO
+import json
+
+base64_string = "${base64String}"
+
+decoded_data = base64.b64decode(base64_string)
+
+csv_data = StringIO(decoded_data.decode('utf-8'))
+
+df = pd.read_csv(csv_data)
+my_dict = df.dtypes.astype(str).to_dict()
+print(my_dict)
+json.dumps(my_dict)`
+            dataframeColDict = await pyodide.runPythonAsync(code)
+        } catch (error) {
+            throw new Error(error)
+        }
+
+        // Then tell GPT to come out with ONLY python code
+        // For example: len(df), df[df['SibSp'] > 3]['PassengerId'].count()
+        let pythonCode = ''
+        if (dataframeColDict) {
+            const chain = new LLMChain({
+                llm: model,
+                prompt: PromptTemplate.fromTemplate(systemPrompt),
+                verbose: process.env.DEBUG === 'true' ? true : false
+            })
+            const inputs = {
+                dict: dataframeColDict,
+                question: input
+            }
+            const res = await chain.call(inputs, [loggerHandler])
+            pythonCode = res?.text
+        }
+
+        // Then run the code using Pyodide
+        let finalResult = ''
+        if (pythonCode) {
+            try {
+                const code = `import pandas as pd\n${pythonCode}`
+                finalResult = await pyodide.runPythonAsync(code)
+            } catch (error) {
+                throw new Error(`Sorry, I'm unable to find answer for question: "${input}" using follwoing code: "${pythonCode}"`)
+            }
+        }
+
+        // Finally, return a complete answer
+        if (finalResult) {
+            const chain = new LLMChain({
+                llm: model,
+                prompt: PromptTemplate.fromTemplate(
+                    systemMessagePrompt ? `${systemMessagePrompt}\n${finalSystemPrompt}` : finalSystemPrompt
+                ),
+                verbose: process.env.DEBUG === 'true' ? true : false
+            })
+            const inputs = {
+                question: input,
+                answer: finalResult
+            }
+
+            if (options.socketIO && options.socketIOClientId) {
+                const result = await chain.call(inputs, [loggerHandler, handler])
+                return result?.text
+            } else {
+                const result = await chain.call(inputs, [loggerHandler])
+                return result?.text
+            }
+        }
+
+        return pythonCode
+    }
+}
+
+module.exports = { nodeClass: CSV_Agents }

packages/components/nodes/agents/CSVAgent/core.ts

@@ -0,0 +1,29 @@
+import type { PyodideInterface } from 'pyodide'
+import * as path from 'path'
+import { getUserHome } from '../../../src/utils'
+
+let pyodideInstance: PyodideInterface | undefined
+
+export async function LoadPyodide(): Promise<PyodideInterface> {
+    if (pyodideInstance === undefined) {
+        const { loadPyodide } = await import('pyodide')
+        const obj: any = { packageCacheDir: path.join(getUserHome(), '.flowise', 'pyodideCacheDir') }
+        pyodideInstance = await loadPyodide(obj)
+        await pyodideInstance.loadPackage(['pandas', 'numpy'])
+    }
+
+    return pyodideInstance
+}
+
+export const systemPrompt = `You are working with a pandas dataframe in Python. The name of the dataframe is df.
+
+The columns and data types of a dataframe are given below as a Python dictionary with keys showing column names and values showing the data types.
+{dict}
+
+I will ask question, and you will output the Python code using pandas dataframe to answer my question. Do not provide any explanations. Do not respond with anything except the output of the code.
+
+Question: {question}
+Output Code:`
+
+export const finalSystemPrompt = `You are given the question: {question}. You have an answer to the question: {answer}. Rephrase the answer into a standalone answer.
+Standalone Answer:`

packages/components/nodes/agents/ConversationalAgent/ConversationalAgent.ts

@@ -1,14 +1,23 @@
-import { ICommonObject, IMessage, INode, INodeData, INodeParams } from '../../../src/Interface'
+import { ICommonObject, INode, INodeData, INodeParams } from '../../../src/Interface'
 import { initializeAgentExecutorWithOptions, AgentExecutor, InitializeAgentExecutorOptions } from 'langchain/agents'
 import { Tool } from 'langchain/tools'
-import { BaseChatMemory, ChatMessageHistory } from 'langchain/memory'
-import { getBaseClasses } from '../../../src/utils'
-import { AIChatMessage, HumanChatMessage } from 'langchain/schema'
+import { BaseChatMemory } from 'langchain/memory'
+import { getBaseClasses, mapChatHistory } from '../../../src/utils'
 import { BaseLanguageModel } from 'langchain/base_language'
+import { flatten } from 'lodash'
+
+const DEFAULT_PREFIX = `Assistant is a large language model trained by OpenAI.
+
+Assistant is designed to be able to assist with a wide range of tasks, from answering simple questions to providing in-depth explanations and discussions on a wide range of topics. As a language model, Assistant is able to generate human-like text based on the input it receives, allowing it to engage in natural-sounding conversations and provide responses that are coherent and relevant to the topic at hand.
+
+Assistant is constantly learning and improving, and its capabilities are constantly evolving. It is able to process and understand large amounts of text, and can use this knowledge to provide accurate and informative responses to a wide range of questions. Additionally, Assistant is able to generate its own text based on the input it receives, allowing it to engage in discussions and provide explanations and descriptions on a wide range of topics.
+
+Overall, Assistant is a powerful system that can help with a wide range of tasks and provide valuable insights and information on a wide range of topics. Whether you need help with a specific question or just want to have a conversation about a particular topic, Assistant is here to assist.`
 
 class ConversationalAgent_Agents implements INode {
     label: string
     name: string
+    version: number
     description: string
     type: string
     icon: string
@@ -19,6 +28,7 @@ class ConversationalAgent_Agents implements INode {
     constructor() {
         this.label = 'Conversational Agent'
         this.name = 'conversationalAgent'
+        this.version = 1.0
         this.type = 'AgentExecutor'
         this.category = 'Agents'
         this.icon = 'agent.svg'
@@ -46,14 +56,7 @@ class ConversationalAgent_Agents implements INode {
                 name: 'systemMessage',
                 type: 'string',
                 rows: 4,
-                optional: true,
-                additionalParams: true
-            },
-            {
-                label: 'Human Message',
-                name: 'humanMessage',
-                type: 'string',
-                rows: 4,
+                default: DEFAULT_PREFIX,
                 optional: true,
                 additionalParams: true
             }
@@ -63,9 +66,8 @@ class ConversationalAgent_Agents implements INode {
     async init(nodeData: INodeData): Promise<any> {
         const model = nodeData.inputs?.model as BaseLanguageModel
         let tools = nodeData.inputs?.tools as Tool[]
-        tools = tools.flat()
+        tools = flatten(tools)
         const memory = nodeData.inputs?.memory as BaseChatMemory
-        const humanMessage = nodeData.inputs?.humanMessage as string
         const systemMessage = nodeData.inputs?.systemMessage as string
 
         const obj: InitializeAgentExecutorOptions = {
@@ -74,9 +76,6 @@ class ConversationalAgent_Agents implements INode {
         }
 
         const agentArgs: any = {}
-        if (humanMessage) {
-            agentArgs.humanMessage = humanMessage
-        }
         if (systemMessage) {
             agentArgs.systemMessage = systemMessage
         }
@@ -93,19 +92,10 @@ class ConversationalAgent_Agents implements INode {
         const memory = nodeData.inputs?.memory as BaseChatMemory
 
         if (options && options.chatHistory) {
-            const chatHistory = []
-            const histories: IMessage[] = options.chatHistory
-
-            for (const message of histories) {
-                if (message.type === 'apiMessage') {
-                    chatHistory.push(new AIChatMessage(message.message))
-                } else if (message.type === 'userMessage') {
-                    chatHistory.push(new HumanChatMessage(message.message))
-                }
-            }
-            memory.chatHistory = new ChatMessageHistory(chatHistory)
+            memory.chatHistory = mapChatHistory(options)
             executor.memory = memory
         }
+
         const result = await executor.call({ input })
 
         return result?.output

packages/components/nodes/agents/ConversationalRetrievalAgent/ConversationalRetrievalAgent.ts

@@ -0,0 +1,101 @@
+import { ICommonObject, INode, INodeData, INodeParams } from '../../../src/Interface'
+import { initializeAgentExecutorWithOptions, AgentExecutor } from 'langchain/agents'
+import { getBaseClasses, mapChatHistory } from '../../../src/utils'
+import { flatten } from 'lodash'
+import { BaseChatMemory } from 'langchain/memory'
+import { ConsoleCallbackHandler, CustomChainHandler } from '../../../src/handler'
+
+const defaultMessage = `Do your best to answer the questions. Feel free to use any tools available to look up relevant information, only if necessary.`
+
+class ConversationalRetrievalAgent_Agents implements INode {
+    label: string
+    name: string
+    version: number
+    description: string
+    type: string
+    icon: string
+    category: string
+    baseClasses: string[]
+    inputs: INodeParams[]
+
+    constructor() {
+        this.label = 'Conversational Retrieval Agent'
+        this.name = 'conversationalRetrievalAgent'
+        this.version = 1.0
+        this.type = 'AgentExecutor'
+        this.category = 'Agents'
+        this.icon = 'agent.svg'
+        this.description = `An agent optimized for retrieval during conversation, answering questions based on past dialogue, all using OpenAI's Function Calling`
+        this.baseClasses = [this.type, ...getBaseClasses(AgentExecutor)]
+        this.inputs = [
+            {
+                label: 'Allowed Tools',
+                name: 'tools',
+                type: 'Tool',
+                list: true
+            },
+            {
+                label: 'Memory',
+                name: 'memory',
+                type: 'BaseChatMemory'
+            },
+            {
+                label: 'OpenAI Chat Model',
+                name: 'model',
+                type: 'ChatOpenAI'
+            },
+            {
+                label: 'System Message',
+                name: 'systemMessage',
+                type: 'string',
+                default: defaultMessage,
+                rows: 4,
+                optional: true,
+                additionalParams: true
+            }
+        ]
+    }
+
+    async init(nodeData: INodeData): Promise<any> {
+        const model = nodeData.inputs?.model
+        const memory = nodeData.inputs?.memory as BaseChatMemory
+        const systemMessage = nodeData.inputs?.systemMessage as string
+
+        let tools = nodeData.inputs?.tools
+        tools = flatten(tools)
+
+        const executor = await initializeAgentExecutorWithOptions(tools, model, {
+            agentType: 'openai-functions',
+            verbose: process.env.DEBUG === 'true' ? true : false,
+            agentArgs: {
+                prefix: systemMessage ?? defaultMessage
+            },
+            returnIntermediateSteps: true
+        })
+        executor.memory = memory
+        return executor
+    }
+
+    async run(nodeData: INodeData, input: string, options: ICommonObject): Promise<string> {
+        const executor = nodeData.instance as AgentExecutor
+
+        if (executor.memory) {
+            ;(executor.memory as any).memoryKey = 'chat_history'
+            ;(executor.memory as any).outputKey = 'output'
+            ;(executor.memory as any).chatHistory = mapChatHistory(options)
+        }
+
+        const loggerHandler = new ConsoleCallbackHandler(options.logger)
+
+        if (options.socketIO && options.socketIOClientId) {
+            const handler = new CustomChainHandler(options.socketIO, options.socketIOClientId)
+            const result = await executor.call({ input }, [loggerHandler, handler])
+            return result?.output
+        } else {
+            const result = await executor.call({ input }, [loggerHandler])
+            return result?.output
+        }
+    }
+}
+
+module.exports = { nodeClass: ConversationalRetrievalAgent_Agents }

packages/components/nodes/agents/ConversationalRetrievalAgent/agent.svg

@@ -0,0 +1,9 @@
+<svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-robot" 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="M7 7h10a2 2 0 0 1 2 2v1l1 1v3l-1 1v3a2 2 0 0 1 -2 2h-10a2 2 0 0 1 -2 -2v-3l-1 -1v-3l1 -1v-1a2 2 0 0 1 2 -2z"></path>
+   <path d="M10 16h4"></path>
+   <circle cx="8.5" cy="11.5" r=".5" fill="currentColor"></circle>
+   <circle cx="15.5" cy="11.5" r=".5" fill="currentColor"></circle>
+   <path d="M9 7l-1 -4"></path>
+   <path d="M15 7l1 -4"></path>
+</svg>

packages/components/nodes/agents/MRKLAgentChat/MRKLAgentChat.ts

@@ -3,10 +3,12 @@ import { initializeAgentExecutorWithOptions, AgentExecutor } from 'langchain/age
 import { getBaseClasses } from '../../../src/utils'
 import { Tool } from 'langchain/tools'
 import { BaseLanguageModel } from 'langchain/base_language'
+import { flatten } from 'lodash'
 
 class MRKLAgentChat_Agents implements INode {
     label: string
     name: string
+    version: number
     description: string
     type: string
     icon: string
@@ -17,6 +19,7 @@ class MRKLAgentChat_Agents implements INode {
     constructor() {
         this.label = 'MRKL Agent for Chat Models'
         this.name = 'mrklAgentChat'
+        this.version = 1.0
         this.type = 'AgentExecutor'
         this.category = 'Agents'
         this.icon = 'agent.svg'
@@ -40,7 +43,7 @@ class MRKLAgentChat_Agents implements INode {
     async init(nodeData: INodeData): Promise<any> {
         const model = nodeData.inputs?.model as BaseLanguageModel
         let tools = nodeData.inputs?.tools as Tool[]
-        tools = tools.flat()
+        tools = flatten(tools)
         const executor = await initializeAgentExecutorWithOptions(tools, model, {
             agentType: 'chat-zero-shot-react-description',
             verbose: process.env.DEBUG === 'true' ? true : false

packages/components/nodes/agents/MRKLAgentLLM/MRKLAgentLLM.ts

@@ -3,10 +3,12 @@ import { initializeAgentExecutorWithOptions, AgentExecutor } from 'langchain/age
 import { Tool } from 'langchain/tools'
 import { getBaseClasses } from '../../../src/utils'
 import { BaseLanguageModel } from 'langchain/base_language'
+import { flatten } from 'lodash'
 
 class MRKLAgentLLM_Agents implements INode {
     label: string
     name: string
+    version: number
     description: string
     type: string
     icon: string
@@ -17,6 +19,7 @@ class MRKLAgentLLM_Agents implements INode {
     constructor() {
         this.label = 'MRKL Agent for LLMs'
         this.name = 'mrklAgentLLM'
+        this.version = 1.0
         this.type = 'AgentExecutor'
         this.category = 'Agents'
         this.icon = 'agent.svg'
@@ -40,7 +43,7 @@ class MRKLAgentLLM_Agents implements INode {
     async init(nodeData: INodeData): Promise<any> {
         const model = nodeData.inputs?.model as BaseLanguageModel
         let tools = nodeData.inputs?.tools as Tool[]
-        tools = tools.flat()
+        tools = flatten(tools)
 
         const executor = await initializeAgentExecutorWithOptions(tools, model, {
             agentType: 'zero-shot-react-description',

packages/components/nodes/agents/OpenAIFunctionAgent/OpenAIFunctionAgent.ts

@@ -0,0 +1,101 @@
+import { ICommonObject, INode, INodeData, INodeParams } from '../../../src/Interface'
+import { initializeAgentExecutorWithOptions, AgentExecutor } from 'langchain/agents'
+import { getBaseClasses, mapChatHistory } from '../../../src/utils'
+import { BaseLanguageModel } from 'langchain/base_language'
+import { flatten } from 'lodash'
+import { BaseChatMemory } from 'langchain/memory'
+import { ConsoleCallbackHandler, CustomChainHandler } from '../../../src/handler'
+
+class OpenAIFunctionAgent_Agents implements INode {
+    label: string
+    name: string
+    version: number
+    description: string
+    type: string
+    icon: string
+    category: string
+    baseClasses: string[]
+    inputs: INodeParams[]
+
+    constructor() {
+        this.label = 'OpenAI Function Agent'
+        this.name = 'openAIFunctionAgent'
+        this.version = 1.0
+        this.type = 'AgentExecutor'
+        this.category = 'Agents'
+        this.icon = 'openai.png'
+        this.description = `An agent that uses OpenAI's Function Calling functionality to pick the tool and args to call`
+        this.baseClasses = [this.type, ...getBaseClasses(AgentExecutor)]
+        this.inputs = [
+            {
+                label: 'Allowed Tools',
+                name: 'tools',
+                type: 'Tool',
+                list: true
+            },
+            {
+                label: 'Memory',
+                name: 'memory',
+                type: 'BaseChatMemory'
+            },
+            {
+                label: 'OpenAI Chat Model',
+                name: 'model',
+                description:
+                    'Only works with gpt-3.5-turbo-0613 and gpt-4-0613. Refer <a target="_blank" href="https://platform.openai.com/docs/guides/gpt/function-calling">docs</a> for more info',
+                type: 'BaseChatModel'
+            },
+            {
+                label: 'System Message',
+                name: 'systemMessage',
+                type: 'string',
+                rows: 4,
+                optional: true,
+                additionalParams: true
+            }
+        ]
+    }
+
+    async init(nodeData: INodeData): Promise<any> {
+        const model = nodeData.inputs?.model as BaseLanguageModel
+        const memory = nodeData.inputs?.memory as BaseChatMemory
+        const systemMessage = nodeData.inputs?.systemMessage as string
+
+        let tools = nodeData.inputs?.tools
+        tools = flatten(tools)
+
+        const executor = await initializeAgentExecutorWithOptions(tools, model, {
+            agentType: 'openai-functions',
+            verbose: process.env.DEBUG === 'true' ? true : false,
+            agentArgs: {
+                prefix: systemMessage ?? `You are a helpful AI assistant.`
+            }
+        })
+        if (memory) executor.memory = memory
+
+        return executor
+    }
+
+    async run(nodeData: INodeData, input: string, options: ICommonObject): Promise<string> {
+        const executor = nodeData.instance as AgentExecutor
+        const memory = nodeData.inputs?.memory as BaseChatMemory
+
+        if (options && options.chatHistory) {
+            memory.chatHistory = mapChatHistory(options)
+            executor.memory = memory
+        }
+
+        const loggerHandler = new ConsoleCallbackHandler(options.logger)
+
+        if (options.socketIO && options.socketIOClientId) {
+            const handler = new CustomChainHandler(options.socketIO, options.socketIOClientId)
+            const result = await executor.run(input, [loggerHandler, handler])
+            return result
+        } else {
+            const result = await executor.run(input, [loggerHandler])
+            return result
+        }
+    }
+}
+
+module.exports = { nodeClass: OpenAIFunctionAgent_Agents }

packages/components/nodes/chains/ApiChain/GETApiChain.ts

@@ -0,0 +1,134 @@
+import { ICommonObject, INode, INodeData, INodeParams } from '../../../src/Interface'
+import { APIChain } from 'langchain/chains'
+import { getBaseClasses } from '../../../src/utils'
+import { BaseLanguageModel } from 'langchain/base_language'
+import { PromptTemplate } from 'langchain/prompts'
+import { ConsoleCallbackHandler, CustomChainHandler } from '../../../src/handler'
+
+export const API_URL_RAW_PROMPT_TEMPLATE = `You are given the below API Documentation:
+{api_docs}
+Using this documentation, generate the full API url to call for answering the user question.
+You should build the API url in order to get a response that is as short as possible, while still getting the necessary information to answer the question. Pay attention to deliberately exclude any unnecessary pieces of data in the API call.
+
+Question:{question}
+API url:`
+
+export const API_RESPONSE_RAW_PROMPT_TEMPLATE =
+    'Given this {api_response} response for {api_url}. use the given response to answer this {question}'
+
+class GETApiChain_Chains implements INode {
+    label: string
+    name: string
+    version: number
+    type: string
+    icon: string
+    category: string
+    baseClasses: string[]
+    description: string
+    inputs: INodeParams[]
+
+    constructor() {
+        this.label = 'GET API Chain'
+        this.name = 'getApiChain'
+        this.version = 1.0
+        this.type = 'GETApiChain'
+        this.icon = 'apichain.svg'
+        this.category = 'Chains'
+        this.description = 'Chain to run queries against GET API'
+        this.baseClasses = [this.type, ...getBaseClasses(APIChain)]
+        this.inputs = [
+            {
+                label: 'Language Model',
+                name: 'model',
+                type: 'BaseLanguageModel'
+            },
+            {
+                label: 'API Documentation',
+                name: 'apiDocs',
+                type: 'string',
+                description:
+                    'Description of how API works. Please refer to more <a target="_blank" href="https://github.com/hwchase17/langchain/blob/master/langchain/chains/api/open_meteo_docs.py">examples</a>',
+                rows: 4
+            },
+            {
+                label: 'Headers',
+                name: 'headers',
+                type: 'json',
+                additionalParams: true,
+                optional: true
+            },
+            {
+                label: 'URL Prompt',
+                name: 'urlPrompt',
+                type: 'string',
+                description: 'Prompt used to tell LLMs how to construct the URL. Must contains {api_docs} and {question}',
+                default: API_URL_RAW_PROMPT_TEMPLATE,
+                rows: 4,
+                additionalParams: true
+            },
+            {
+                label: 'Answer Prompt',
+                name: 'ansPrompt',
+                type: 'string',
+                description:
+                    'Prompt used to tell LLMs how to return the API response. Must contains {api_response}, {api_url}, and {question}',
+                default: API_RESPONSE_RAW_PROMPT_TEMPLATE,
+                rows: 4,
+                additionalParams: true
+            }
+        ]
+    }
+
+    async init(nodeData: INodeData): Promise<any> {
+        const model = nodeData.inputs?.model as BaseLanguageModel
+        const apiDocs = nodeData.inputs?.apiDocs as string
+        const headers = nodeData.inputs?.headers as string
+        const urlPrompt = nodeData.inputs?.urlPrompt as string
+        const ansPrompt = nodeData.inputs?.ansPrompt as string
+
+        const chain = await getAPIChain(apiDocs, model, headers, urlPrompt, ansPrompt)
+        return chain
+    }
+
+    async run(nodeData: INodeData, input: string, options: ICommonObject): Promise<string> {
+        const model = nodeData.inputs?.model as BaseLanguageModel
+        const apiDocs = nodeData.inputs?.apiDocs as string
+        const headers = nodeData.inputs?.headers as string
+        const urlPrompt = nodeData.inputs?.urlPrompt as string
+        const ansPrompt = nodeData.inputs?.ansPrompt as string
+
+        const chain = await getAPIChain(apiDocs, model, headers, urlPrompt, ansPrompt)
+        const loggerHandler = new ConsoleCallbackHandler(options.logger)
+
+        if (options.socketIO && options.socketIOClientId) {
+            const handler = new CustomChainHandler(options.socketIO, options.socketIOClientId, 2)
+            const res = await chain.run(input, [loggerHandler, handler])
+            return res
+        } else {
+            const res = await chain.run(input, [loggerHandler])
+            return res
+        }
+    }
+}
+
+const getAPIChain = async (documents: string, llm: BaseLanguageModel, headers: string, urlPrompt: string, ansPrompt: string) => {
+    const apiUrlPrompt = new PromptTemplate({
+        inputVariables: ['api_docs', 'question'],
+        template: urlPrompt ? urlPrompt : API_URL_RAW_PROMPT_TEMPLATE
+    })
+
+    const apiResponsePrompt = new PromptTemplate({
+        inputVariables: ['api_docs', 'question', 'api_url', 'api_response'],
+        template: ansPrompt ? ansPrompt : API_RESPONSE_RAW_PROMPT_TEMPLATE
+    })
+
+    const chain = APIChain.fromLLMAndAPIDocs(llm, documents, {
+        apiUrlPrompt,
+        apiResponsePrompt,
+        verbose: process.env.DEBUG === 'true' ? true : false,
+        headers: typeof headers === 'object' ? headers : headers ? JSON.parse(headers) : {}
+    })
+    return chain
+}
+
+module.exports = { nodeClass: GETApiChain_Chains }

packages/components/nodes/chains/ApiChain/OpenAPIChain.ts

@@ -0,0 +1,100 @@
+import { ICommonObject, INode, INodeData, INodeParams } from '../../../src/Interface'
+import { APIChain, createOpenAPIChain } from 'langchain/chains'
+import { getBaseClasses } from '../../../src/utils'
+import { ChatOpenAI } from 'langchain/chat_models/openai'
+import { ConsoleCallbackHandler, CustomChainHandler } from '../../../src/handler'
+
+class OpenApiChain_Chains implements INode {
+    label: string
+    name: string
+    version: number
+    type: string
+    icon: string
+    category: string
+    baseClasses: string[]
+    description: string
+    inputs: INodeParams[]
+
+    constructor() {
+        this.label = 'OpenAPI Chain'
+        this.name = 'openApiChain'
+        this.version = 1.0
+        this.type = 'OpenAPIChain'
+        this.icon = 'openapi.png'
+        this.category = 'Chains'
+        this.description = 'Chain that automatically select and call APIs based only on an OpenAPI spec'
+        this.baseClasses = [this.type, ...getBaseClasses(APIChain)]
+        this.inputs = [
+            {
+                label: 'ChatOpenAI Model',
+                name: 'model',
+                type: 'ChatOpenAI'
+            },
+            {
+                label: 'YAML Link',
+                name: 'yamlLink',
+                type: 'string',
+                placeholder: 'https://api.speak.com/openapi.yaml',
+                description: 'If YAML link is provided, uploaded YAML File will be ignored and YAML link will be used instead'
+            },
+            {
+                label: 'YAML File',
+                name: 'yamlFile',
+                type: 'file',
+                fileType: '.yaml',
+                description: 'If YAML link is provided, uploaded YAML File will be ignored and YAML link will be used instead'
+            },
+            {
+                label: 'Headers',
+                name: 'headers',
+                type: 'json',
+                additionalParams: true,
+                optional: true
+            }
+        ]
+    }
+
+    async init(nodeData: INodeData): Promise<any> {
+        return await initChain(nodeData)
+    }
+
+    async run(nodeData: INodeData, input: string, options: ICommonObject): Promise<string> {
+        const chain = await initChain(nodeData)
+        const loggerHandler = new ConsoleCallbackHandler(options.logger)
+
+        if (options.socketIO && options.socketIOClientId) {
+            const handler = new CustomChainHandler(options.socketIO, options.socketIOClientId)
+            const res = await chain.run(input, [loggerHandler, handler])
+            return res
+        } else {
+            const res = await chain.run(input, [loggerHandler])
+            return res
+        }
+    }
+}
+
+const initChain = async (nodeData: INodeData) => {
+    const model = nodeData.inputs?.model as ChatOpenAI
+    const headers = nodeData.inputs?.headers as string
+    const yamlLink = nodeData.inputs?.yamlLink as string
+    const yamlFileBase64 = nodeData.inputs?.yamlFile as string
+
+    let yamlString = ''
+
+    if (yamlLink) {
+        yamlString = yamlLink
+    } else {
+        const splitDataURI = yamlFileBase64.split(',')
+        splitDataURI.pop()
+        const bf = Buffer.from(splitDataURI.pop() || '', 'base64')
+        yamlString = bf.toString('utf-8')
+    }
+
+    return await createOpenAPIChain(yamlString, {
+        llm: model,
+        headers: typeof headers === 'object' ? headers : headers ? JSON.parse(headers) : {},
+        verbose: process.env.DEBUG === 'true' ? true : false
+    })
+}
+
+module.exports = { nodeClass: OpenApiChain_Chains }

packages/components/nodes/chains/ApiChain/POSTApiChain.ts

@@ -0,0 +1,123 @@
+import { ICommonObject, INode, INodeData, INodeParams } from '../../../src/Interface'
+import { getBaseClasses } from '../../../src/utils'
+import { BaseLanguageModel } from 'langchain/base_language'
+import { PromptTemplate } from 'langchain/prompts'
+import { API_RESPONSE_RAW_PROMPT_TEMPLATE, API_URL_RAW_PROMPT_TEMPLATE, APIChain } from './postCore'
+import { ConsoleCallbackHandler, CustomChainHandler } from '../../../src/handler'
+
+class POSTApiChain_Chains implements INode {
+    label: string
+    name: string
+    version: number
+    type: string
+    icon: string
+    category: string
+    baseClasses: string[]
+    description: string
+    inputs: INodeParams[]
+
+    constructor() {
+        this.label = 'POST API Chain'
+        this.name = 'postApiChain'
+        this.version = 1.0
+        this.type = 'POSTApiChain'
+        this.icon = 'apichain.svg'
+        this.category = 'Chains'
+        this.description = 'Chain to run queries against POST API'
+        this.baseClasses = [this.type, ...getBaseClasses(APIChain)]
+        this.inputs = [
+            {
+                label: 'Language Model',
+                name: 'model',
+                type: 'BaseLanguageModel'
+            },
+            {
+                label: 'API Documentation',
+                name: 'apiDocs',
+                type: 'string',
+                description:
+                    'Description of how API works. Please refer to more <a target="_blank" href="https://github.com/hwchase17/langchain/blob/master/langchain/chains/api/open_meteo_docs.py">examples</a>',
+                rows: 4
+            },
+            {
+                label: 'Headers',
+                name: 'headers',
+                type: 'json',
+                additionalParams: true,
+                optional: true
+            },
+            {
+                label: 'URL Prompt',
+                name: 'urlPrompt',
+                type: 'string',
+                description: 'Prompt used to tell LLMs how to construct the URL. Must contains {api_docs} and {question}',
+                default: API_URL_RAW_PROMPT_TEMPLATE,
+                rows: 4,
+                additionalParams: true
+            },
+            {
+                label: 'Answer Prompt',
+                name: 'ansPrompt',
+                type: 'string',
+                description:
+                    'Prompt used to tell LLMs how to return the API response. Must contains {api_response}, {api_url}, and {question}',
+                default: API_RESPONSE_RAW_PROMPT_TEMPLATE,
+                rows: 4,
+                additionalParams: true
+            }
+        ]
+    }
+
+    async init(nodeData: INodeData): Promise<any> {
+        const model = nodeData.inputs?.model as BaseLanguageModel
+        const apiDocs = nodeData.inputs?.apiDocs as string
+        const headers = nodeData.inputs?.headers as string
+        const urlPrompt = nodeData.inputs?.urlPrompt as string
+        const ansPrompt = nodeData.inputs?.ansPrompt as string
+
+        const chain = await getAPIChain(apiDocs, model, headers, urlPrompt, ansPrompt)
+        return chain
+    }
+
+    async run(nodeData: INodeData, input: string, options: ICommonObject): Promise<string> {
+        const model = nodeData.inputs?.model as BaseLanguageModel
+        const apiDocs = nodeData.inputs?.apiDocs as string
+        const headers = nodeData.inputs?.headers as string
+        const urlPrompt = nodeData.inputs?.urlPrompt as string
+        const ansPrompt = nodeData.inputs?.ansPrompt as string
+
+        const chain = await getAPIChain(apiDocs, model, headers, urlPrompt, ansPrompt)
+        const loggerHandler = new ConsoleCallbackHandler(options.logger)
+
+        if (options.socketIO && options.socketIOClientId) {
+            const handler = new CustomChainHandler(options.socketIO, options.socketIOClientId, 2)
+            const res = await chain.run(input, [loggerHandler, handler])
+            return res
+        } else {
+            const res = await chain.run(input, [loggerHandler])
+            return res
+        }
+    }
+}
+
+const getAPIChain = async (documents: string, llm: BaseLanguageModel, headers: string, urlPrompt: string, ansPrompt: string) => {
+    const apiUrlPrompt = new PromptTemplate({
+        inputVariables: ['api_docs', 'question'],
+        template: urlPrompt ? urlPrompt : API_URL_RAW_PROMPT_TEMPLATE
+    })
+
+    const apiResponsePrompt = new PromptTemplate({
+        inputVariables: ['api_docs', 'question', 'api_url_body', 'api_response'],
+        template: ansPrompt ? ansPrompt : API_RESPONSE_RAW_PROMPT_TEMPLATE
+    })
+
+    const chain = APIChain.fromLLMAndAPIDocs(llm, documents, {
+        apiUrlPrompt,
+        apiResponsePrompt,
+        verbose: process.env.DEBUG === 'true' ? true : false,
+        headers: typeof headers === 'object' ? headers : headers ? JSON.parse(headers) : {}
+    })
+    return chain
+}
+
+module.exports = { nodeClass: POSTApiChain_Chains }

packages/components/nodes/chains/ApiChain/apichain.svg

@@ -0,0 +1,3 @@
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+	.st0{fill-rule:evenodd;clip-rule:evenodd;}
+]]></style><g><path class="st0" d="M100.09,6.87l2.5,3.3c0.66,0.87,0.49,2.12-0.38,2.77l-2.66,2.02c0.48,1.29,0.79,2.65,0.92,4.06l3.03,0.41 c1.08,0.15,1.84,1.15,1.69,2.23l-0.56,4.1c-0.15,1.08-1.15,1.84-2.23,1.69l-3.3-0.45c-0.59,1.28-1.34,2.46-2.22,3.52l1.85,2.43 c0.66,0.87,0.49,2.12-0.38,2.78l-3.3,2.5c-0.87,0.66-2.12,0.49-2.78-0.38l-2.02-2.66c-1.29,0.48-2.66,0.79-4.06,0.92l-0.41,3.03 c-0.15,1.08-1.15,1.84-2.23,1.69l-4.1-0.56c-1.08-0.15-1.84-1.15-1.69-2.23l0.45-3.3c-1.28-0.59-2.46-1.34-3.52-2.23l-2.43,1.85 c-0.87,0.66-2.12,0.49-2.78-0.38l-2.5-3.3c-0.66-0.87-0.49-2.12,0.38-2.77l2.66-2.02c-0.48-1.29-0.79-2.65-0.92-4.06l-3.03-0.41 c-1.08-0.15-1.84-1.15-1.69-2.23l0.56-4.1c0.15-1.08,1.15-1.84,2.23-1.69l3.3,0.45c0.59-1.28,1.34-2.46,2.23-3.52L70.84,7.9 c-0.66-0.87-0.49-2.12,0.38-2.78l3.3-2.5c0.87-0.66,2.12-0.49,2.78,0.38l2.02,2.66c1.29-0.48,2.66-0.79,4.06-0.92l0.41-3.02 c0.15-1.08,1.15-1.84,2.23-1.69l4.1,0.56c1.08,0.15,1.84,1.15,1.69,2.23l-0.45,3.3c1.28,0.59,2.46,1.34,3.52,2.23l2.43-1.85 C98.19,5.83,99.44,6,100.09,6.87L100.09,6.87L100.09,6.87z M55.71,13.75c-0.23,0.02-0.46,0.04-0.69,0.06 c-5.63,0.54-11.1,2.59-15.62,6.1c-5.23,4.05-9.2,10.11-10.73,18.14l-0.48,2.51L25.69,41c-2.45,0.43-4.64,1.02-6.56,1.77 c-1.86,0.72-3.52,1.61-4.97,2.66c-1.16,0.84-2.16,1.78-3.01,2.8c-2.63,3.15-3.85,7.1-3.82,11.1c0.03,4.06,1.35,8.16,3.79,11.53 c0.91,1.25,1.96,2.4,3.16,3.4l0.03,0.02l-2.68,7.13c-0.71-0.47-1.4-0.98-2.04-1.52c-1.68-1.4-3.15-2.99-4.4-4.72 C1.84,70.57,0.04,64.95,0,59.35c-0.04-5.66,1.72-11.29,5.52-15.85c1.23-1.48,2.68-2.84,4.34-4.04c1.93-1.4,4.14-2.58,6.64-3.55 c1.72-0.67,3.56-1.23,5.5-1.68c2.2-8.74,6.89-15.47,12.92-20.14c5.64-4.37,12.43-6.92,19.42-7.59c2.13-0.21,4.29-0.24,6.43-0.09 c-0.47,0.25-0.91,0.53-1.33,0.85l-0.03,0.02c-1.93,1.47-3.32,3.7-3.68,6.3L55.71,13.75L55.71,13.75z M43.85,87.38H31.99l-1.7,5.58 H19.6l12.75-33.87h11.46l12.7,33.87H45.55L43.85,87.38L43.85,87.38z M41.63,80.04l-3.7-12.17l-3.71,12.17H41.63L41.63,80.04z M59.78,59.09h17.41c3.79,0,6.64,0.9,8.52,2.7c1.88,1.8,2.83,4.38,2.83,7.71c0,3.42-1.04,6.1-3.09,8.03 c-2.06,1.93-5.21,2.89-9.43,2.89h-5.74v12.54h-10.5V59.09L59.78,59.09z M70.28,73.56h2.58c2.03,0,3.46-0.35,4.28-1.06 c0.82-0.7,1.23-1.6,1.23-2.7c0-1.06-0.36-1.96-1.07-2.7c-0.71-0.74-2.05-1.11-4.02-1.11h-3V73.56L70.28,73.56z M92.77,59.09h10.5 v33.87h-10.5V59.09L92.77,59.09z M112.01,31.74c1.07,0.83,2.09,1.77,3.07,2.82c1.07,1.15,2.08,2.45,3.03,3.9 c3.2,4.92,4.84,11.49,4.77,17.92c-0.07,6.31-1.77,12.59-5.25,17.21c-0.84,1.11-1.77,2.15-2.78,3.12V62.15 c0.43-1.87,0.65-3.84,0.67-5.83c0.06-5.07-1.18-10.16-3.59-13.86c-0.69-1.07-1.45-2.03-2.25-2.89c-0.65-0.7-1.34-1.34-2.05-1.9 c0.07-0.3,0.13-0.6,0.17-0.9c0.08-0.62,0.11-1.25,0.07-1.88c0.82-0.32,1.58-0.75,2.26-1.27l0.03-0.02 C110.86,33.06,111.48,32.44,112.01,31.74L112.01,31.74z M85.89,12.37c4.45,0.61,7.57,4.71,6.96,9.17 c-0.61,4.45-4.71,7.57-9.17,6.96c-4.45-0.61-7.57-4.71-6.96-9.17S81.44,11.76,85.89,12.37L85.89,12.37L85.89,12.37z"/></g></svg>

packages/components/nodes/chains/ApiChain/postCore.ts

@@ -0,0 +1,162 @@
+import { BaseLanguageModel } from 'langchain/base_language'
+import { CallbackManagerForChainRun } from 'langchain/callbacks'
+import { BaseChain, ChainInputs, LLMChain, SerializedAPIChain } from 'langchain/chains'
+import { BasePromptTemplate, PromptTemplate } from 'langchain/prompts'
+import { ChainValues } from 'langchain/schema'
+import fetch from 'node-fetch'
+
+export const API_URL_RAW_PROMPT_TEMPLATE = `You are given the below API Documentation:
+{api_docs}
+Using this documentation, generate a json string with two keys: "url" and "data".
+The value of "url" should be a string, which is the API url to call for answering the user question.
+The value of "data" should be a dictionary of key-value pairs you want to POST to the url as a JSON body.
+Be careful to always use double quotes for strings in the json string.
+You should build the json string in order to get a response that is as short as possible, while still getting the necessary information to answer the question. Pay attention to deliberately exclude any unnecessary pieces of data in the API call.
+
+Question:{question}
+json string:`
+
+export const API_RESPONSE_RAW_PROMPT_TEMPLATE = `${API_URL_RAW_PROMPT_TEMPLATE} {api_url_body}
+
+Here is the response from the API:
+
+{api_response}
+
+Summarize this response to answer the original question.
+
+Summary:`
+
+const defaultApiUrlPrompt = new PromptTemplate({
+    inputVariables: ['api_docs', 'question'],
+    template: API_URL_RAW_PROMPT_TEMPLATE
+})
+
+const defaultApiResponsePrompt = new PromptTemplate({
+    inputVariables: ['api_docs', 'question', 'api_url_body', 'api_response'],
+    template: API_RESPONSE_RAW_PROMPT_TEMPLATE
+})
+
+export interface APIChainInput extends Omit<ChainInputs, 'memory'> {
+    apiAnswerChain: LLMChain
+    apiRequestChain: LLMChain
+    apiDocs: string
+    inputKey?: string
+    headers?: Record<string, string>
+    /** Key to use for output, defaults to `output` */
+    outputKey?: string
+}
+
+export type APIChainOptions = {
+    headers?: Record<string, string>
+    apiUrlPrompt?: BasePromptTemplate
+    apiResponsePrompt?: BasePromptTemplate
+}
+
+export class APIChain extends BaseChain implements APIChainInput {
+    apiAnswerChain: LLMChain
+
+    apiRequestChain: LLMChain
+
+    apiDocs: string
+
+    headers = {}
+
+    inputKey = 'question'
+
+    outputKey = 'output'
+
+    get inputKeys() {
+        return [this.inputKey]
+    }
+
+    get outputKeys() {
+        return [this.outputKey]
+    }
+
+    constructor(fields: APIChainInput) {
+        super(fields)
+        this.apiRequestChain = fields.apiRequestChain
+        this.apiAnswerChain = fields.apiAnswerChain
+        this.apiDocs = fields.apiDocs
+        this.inputKey = fields.inputKey ?? this.inputKey
+        this.outputKey = fields.outputKey ?? this.outputKey
+        this.headers = fields.headers ?? this.headers
+    }
+
+    /** @ignore */
+    async _call(values: ChainValues, runManager?: CallbackManagerForChainRun): Promise<ChainValues> {
+        try {
+            const question: string = values[this.inputKey]
+
+            const api_url_body = await this.apiRequestChain.predict({ question, api_docs: this.apiDocs }, runManager?.getChild())
+
+            const { url, data } = JSON.parse(api_url_body)
+
+            const res = await fetch(url, {
+                method: 'POST',
+                headers: this.headers,
+                body: JSON.stringify(data)
+            })
+
+            const api_response = await res.text()
+
+            const answer = await this.apiAnswerChain.predict(
+                { question, api_docs: this.apiDocs, api_url_body, api_response },
+                runManager?.getChild()
+            )
+
+            return { [this.outputKey]: answer }
+        } catch (error) {
+            return { [this.outputKey]: error }
+        }
+    }
+
+    _chainType() {
+        return 'api_chain' as const
+    }
+
+    static async deserialize(data: SerializedAPIChain) {
+        const { api_request_chain, api_answer_chain, api_docs } = data
+
+        if (!api_request_chain) {
+            throw new Error('LLMChain must have api_request_chain')
+        }
+        if (!api_answer_chain) {
+            throw new Error('LLMChain must have api_answer_chain')
+        }
+        if (!api_docs) {
+            throw new Error('LLMChain must have api_docs')
+        }
+
+        return new APIChain({
+            apiAnswerChain: await LLMChain.deserialize(api_answer_chain),
+            apiRequestChain: await LLMChain.deserialize(api_request_chain),
+            apiDocs: api_docs
+        })
+    }
+
+    serialize(): SerializedAPIChain {
+        return {
+            _type: this._chainType(),
+            api_answer_chain: this.apiAnswerChain.serialize(),
+            api_request_chain: this.apiRequestChain.serialize(),
+            api_docs: this.apiDocs
+        }
+    }
+
+    static fromLLMAndAPIDocs(
+        llm: BaseLanguageModel,
+        apiDocs: string,
+        options: APIChainOptions & Omit<APIChainInput, 'apiAnswerChain' | 'apiRequestChain' | 'apiDocs'> = {}
+    ): APIChain {
+        const { apiUrlPrompt = defaultApiUrlPrompt, apiResponsePrompt = defaultApiResponsePrompt } = options
+        const apiRequestChain = new LLMChain({ prompt: apiUrlPrompt, llm })
+        const apiAnswerChain = new LLMChain({ prompt: apiResponsePrompt, llm })
+        return new this({
+            apiAnswerChain,
+            apiRequestChain,
+            apiDocs,
+            ...options
+        })
+    }
+}

packages/components/nodes/chains/ConversationChain/ConversationChain.ts

@@ -1,16 +1,19 @@
-import { ICommonObject, IMessage, INode, INodeData, INodeParams } from '../../../src/Interface'
+import { ICommonObject, INode, INodeData, INodeParams } from '../../../src/Interface'
 import { ConversationChain } from 'langchain/chains'
-import { getBaseClasses } from '../../../src/utils'
+import { getBaseClasses, mapChatHistory } from '../../../src/utils'
 import { ChatPromptTemplate, HumanMessagePromptTemplate, MessagesPlaceholder, SystemMessagePromptTemplate } from 'langchain/prompts'
-import { BufferMemory, ChatMessageHistory } from 'langchain/memory'
+import { BufferMemory } from 'langchain/memory'
 import { BaseChatModel } from 'langchain/chat_models/base'
-import { AIChatMessage, HumanChatMessage } from 'langchain/schema'
+import { ConsoleCallbackHandler, CustomChainHandler } from '../../../src/handler'
+import { flatten } from 'lodash'
+import { Document } from 'langchain/document'
 
-const systemMessage = `The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.`
+let systemMessage = `The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.`
 
 class ConversationChain_Chains implements INode {
     label: string
     name: string
+    version: number
     type: string
     icon: string
     category: string
@@ -21,6 +24,7 @@ class ConversationChain_Chains implements INode {
     constructor() {
         this.label = 'Conversation Chain'
         this.name = 'conversationChain'
+        this.version = 1.0
         this.type = 'ConversationChain'
         this.icon = 'chain.svg'
         this.category = 'Chains'
@@ -37,6 +41,15 @@ class ConversationChain_Chains implements INode {
                 name: 'memory',
                 type: 'BaseMemory'
             },
+            {
+                label: 'Document',
+                name: 'document',
+                type: 'Document',
+                description:
+                    'Include whole document into the context window, if you get maximum context length error, please use model with higher context window like Claude 100k, or gpt4 32k',
+                optional: true,
+                list: true
+            },
             {
                 label: 'System Message',
                 name: 'systemMessagePrompt',
@@ -53,10 +66,28 @@ class ConversationChain_Chains implements INode {
         const model = nodeData.inputs?.model as BaseChatModel
         const memory = nodeData.inputs?.memory as BufferMemory
         const prompt = nodeData.inputs?.systemMessagePrompt as string
+        const docs = nodeData.inputs?.document as Document[]
+
+        const flattenDocs = docs && docs.length ? flatten(docs) : []
+        const finalDocs = []
+        for (let i = 0; i < flattenDocs.length; i += 1) {
+            finalDocs.push(new Document(flattenDocs[i]))
+        }
+
+        let finalText = ''
+        for (let i = 0; i < finalDocs.length; i += 1) {
+            finalText += finalDocs[i].pageContent
+        }
+
+        const replaceChar: string[] = ['{', '}']
+        for (const char of replaceChar) finalText = finalText.replaceAll(char, '')
+
+        if (finalText) systemMessage = `${systemMessage}\nThe AI has the following context:\n${finalText}`
 
         const obj: any = {
             llm: model,
-            memory
+            memory,
+            verbose: process.env.DEBUG === 'true' ? true : false
         }
 
         const chatPrompt = ChatPromptTemplate.fromPromptMessages([
@@ -75,22 +106,20 @@ class ConversationChain_Chains implements INode {
         const memory = nodeData.inputs?.memory as BufferMemory
 
         if (options && options.chatHistory) {
-            const chatHistory = []
-            const histories: IMessage[] = options.chatHistory
-
-            for (const message of histories) {
-                if (message.type === 'apiMessage') {
-                    chatHistory.push(new AIChatMessage(message.message))
-                } else if (message.type === 'userMessage') {
-                    chatHistory.push(new HumanChatMessage(message.message))
-                }
-            }
-            memory.chatHistory = new ChatMessageHistory(chatHistory)
+            memory.chatHistory = mapChatHistory(options)
             chain.memory = memory
         }
 
-        const res = await chain.call({ input })
-        return res?.response
+        const loggerHandler = new ConsoleCallbackHandler(options.logger)
+
+        if (options.socketIO && options.socketIOClientId) {
+            const handler = new CustomChainHandler(options.socketIO, options.socketIOClientId)
+            const res = await chain.call({ input }, [loggerHandler, handler])
+            return res?.response
+        } else {
+            const res = await chain.call({ input }, [loggerHandler])
+            return res?.response
+        }
     }
 }
 

packages/components/nodes/chains/ConversationalRetrievalQAChain/ConversationalRetrievalQAChain.ts

@@ -1,26 +1,25 @@
 import { BaseLanguageModel } from 'langchain/base_language'
-import { ICommonObject, IMessage, INode, INodeData, INodeParams } from '../../../src/Interface'
-import { getBaseClasses } from '../../../src/utils'
-import { ConversationalRetrievalQAChain } from 'langchain/chains'
-import { BaseRetriever } from 'langchain/schema'
-
-const default_qa_template = `Use the following pieces of context to answer the question at the end. If you don't know the answer, just say that you don't know, don't try to make up an answer.
-
-{context}
-
-Question: {question}
-Helpful Answer:`
-
-const qa_template = `Use the following pieces of context to answer the question at the end.
-
-{context}
-
-Question: {question}
-Helpful Answer:`
+import { ICommonObject, INode, INodeData, INodeParams } from '../../../src/Interface'
+import { getBaseClasses, mapChatHistory } from '../../../src/utils'
+import { ConversationalRetrievalQAChain, QAChainParams } from 'langchain/chains'
+import { BaseRetriever } from 'langchain/schema/retriever'
+import { BufferMemory, BufferMemoryInput } from 'langchain/memory'
+import { PromptTemplate } from 'langchain/prompts'
+import { ConsoleCallbackHandler, CustomChainHandler } from '../../../src/handler'
+import {
+    default_map_reduce_template,
+    default_qa_template,
+    qa_template,
+    map_reduce_template,
+    CUSTOM_QUESTION_GENERATOR_CHAIN_PROMPT,
+    refine_question_template,
+    refine_template
+} from './prompts'
 
 class ConversationalRetrievalQAChain_Chains implements INode {
     label: string
     name: string
+    version: number
     type: string
     icon: string
     category: string
@@ -31,6 +30,7 @@ class ConversationalRetrievalQAChain_Chains implements INode {
     constructor() {
         this.label = 'Conversational Retrieval QA Chain'
         this.name = 'conversationalRetrievalQAChain'
+        this.version = 1.0
         this.type = 'ConversationalRetrievalQAChain'
         this.icon = 'chain.svg'
         this.category = 'Chains'
@@ -47,6 +47,19 @@ class ConversationalRetrievalQAChain_Chains implements INode {
                 name: 'vectorStoreRetriever',
                 type: 'BaseRetriever'
             },
+            {
+                label: 'Memory',
+                name: 'memory',
+                type: 'BaseMemory',
+                optional: true,
+                description: 'If left empty, a default BufferMemory will be used'
+            },
+            {
+                label: 'Return Source Documents',
+                name: 'returnSourceDocuments',
+                type: 'boolean',
+                optional: true
+            },
             {
                 label: 'System Message',
                 name: 'systemMessagePrompt',
@@ -56,6 +69,31 @@ class ConversationalRetrievalQAChain_Chains implements INode {
                 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.'
+            },
+            {
+                label: 'Chain Option',
+                name: 'chainOption',
+                type: 'options',
+                options: [
+                    {
+                        label: 'MapReduceDocumentsChain',
+                        name: 'map_reduce',
+                        description:
+                            'Suitable for QA tasks over larger documents and can run the preprocessing step in parallel, reducing the running time'
+                    },
+                    {
+                        label: 'RefineDocumentsChain',
+                        name: 'refine',
+                        description: 'Suitable for QA tasks over a large number of documents.'
+                    },
+                    {
+                        label: 'StuffDocumentsChain',
+                        name: 'stuff',
+                        description: 'Suitable for QA tasks over a small number of documents.'
+                    }
+                ],
+                additionalParams: true,
+                optional: true
             }
         ]
     }
@@ -64,35 +102,112 @@ class ConversationalRetrievalQAChain_Chains implements INode {
         const model = nodeData.inputs?.model as BaseLanguageModel
         const vectorStoreRetriever = nodeData.inputs?.vectorStoreRetriever as BaseRetriever
         const systemMessagePrompt = nodeData.inputs?.systemMessagePrompt as string
+        const returnSourceDocuments = nodeData.inputs?.returnSourceDocuments as boolean
+        const chainOption = nodeData.inputs?.chainOption as string
+        const externalMemory = nodeData.inputs?.memory
 
-        const chain = ConversationalRetrievalQAChain.fromLLM(model, vectorStoreRetriever, {
+        const obj: any = {
             verbose: process.env.DEBUG === 'true' ? true : false,
-            qaTemplate: systemMessagePrompt ? `${systemMessagePrompt}\n${qa_template}` : default_qa_template
-        })
+            questionGeneratorChainOptions: {
+                template: CUSTOM_QUESTION_GENERATOR_CHAIN_PROMPT
+            }
+        }
+
+        if (returnSourceDocuments) obj.returnSourceDocuments = returnSourceDocuments
+
+        if (chainOption === 'map_reduce') {
+            obj.qaChainOptions = {
+                type: 'map_reduce',
+                combinePrompt: PromptTemplate.fromTemplate(
+                    systemMessagePrompt ? `${systemMessagePrompt}\n${map_reduce_template}` : default_map_reduce_template
+                )
+            } as QAChainParams
+        } else if (chainOption === 'refine') {
+            const qprompt = new PromptTemplate({
+                inputVariables: ['context', 'question'],
+                template: refine_question_template(systemMessagePrompt)
+            })
+            const rprompt = new PromptTemplate({
+                inputVariables: ['context', 'question', 'existing_answer'],
+                template: refine_template
+            })
+            obj.qaChainOptions = {
+                type: 'refine',
+                questionPrompt: qprompt,
+                refinePrompt: rprompt
+            } as QAChainParams
+        } else {
+            obj.qaChainOptions = {
+                type: 'stuff',
+                prompt: PromptTemplate.fromTemplate(systemMessagePrompt ? `${systemMessagePrompt}\n${qa_template}` : default_qa_template)
+            } as QAChainParams
+        }
+
+        if (externalMemory) {
+            externalMemory.memoryKey = 'chat_history'
+            externalMemory.inputKey = 'question'
+            externalMemory.outputKey = 'text'
+            externalMemory.returnMessages = true
+            if (chainOption === 'refine') externalMemory.outputKey = 'output_text'
+            obj.memory = externalMemory
+        } else {
+            const fields: BufferMemoryInput = {
+                memoryKey: 'chat_history',
+                inputKey: 'question',
+                outputKey: 'text',
+                returnMessages: true
+            }
+            if (chainOption === 'refine') fields.outputKey = 'output_text'
+            obj.memory = new BufferMemory(fields)
+        }
+
+        const chain = ConversationalRetrievalQAChain.fromLLM(model, vectorStoreRetriever, obj)
         return chain
     }
 
-    async run(nodeData: INodeData, input: string, options: ICommonObject): Promise<string> {
+    async run(nodeData: INodeData, input: string, options: ICommonObject): Promise<string | ICommonObject> {
         const chain = nodeData.instance as ConversationalRetrievalQAChain
-        let chatHistory = ''
+        const returnSourceDocuments = nodeData.inputs?.returnSourceDocuments as boolean
+        const chainOption = nodeData.inputs?.chainOption as string
 
-        if (options && options.chatHistory) {
-            const histories: IMessage[] = options.chatHistory
-            chatHistory = histories
-                .map((item) => {
-                    return item.message
-                })
-                .join('')
+        let model = nodeData.inputs?.model
+
+        // Temporary fix: https://github.com/hwchase17/langchainjs/issues/754
+        model.streaming = false
+        chain.questionGeneratorChain.llm = model
+
+        const obj = { question: input }
+
+        if (options && options.chatHistory && chain.memory) {
+            ;(chain.memory as any).chatHistory = mapChatHistory(options)
         }
 
-        const obj = {
-            question: input,
-            chat_history: chatHistory ? chatHistory : []
+        const loggerHandler = new ConsoleCallbackHandler(options.logger)
+
+        if (options.socketIO && options.socketIOClientId) {
+            const handler = new CustomChainHandler(
+                options.socketIO,
+                options.socketIOClientId,
+                chainOption === 'refine' ? 4 : undefined,
+                returnSourceDocuments
+            )
+            const res = await chain.call(obj, [loggerHandler, handler])
+            if (chainOption === 'refine') {
+                if (res.output_text && res.sourceDocuments) {
+                    return {
+                        text: res.output_text,
+                        sourceDocuments: res.sourceDocuments
+                    }
+                }
+                return res?.output_text
+            }
+            if (res.text && res.sourceDocuments) return res
+            return res?.text
+        } else {
+            const res = await chain.call(obj, [loggerHandler])
+            if (res.text && res.sourceDocuments) return res
+            return res?.text
         }
-
-        const res = await chain.call(obj)
-
-        return res?.text
     }
 }
 

packages/components/nodes/chains/ConversationalRetrievalQAChain/prompts.ts

@@ -0,0 +1,64 @@
+export const default_qa_template = `Use the following pieces of context to answer the question at the end. If you don't know the answer, just say that you don't know, don't try to make up an answer.
+
+{context}
+
+Question: {question}
+Helpful Answer:`
+
+export const qa_template = `Use the following pieces of context to answer the question at the end.
+
+{context}
+
+Question: {question}
+Helpful Answer:`
+
+export const default_map_reduce_template = `Given the following extracted parts of a long document and a question, create a final answer. 
+If you don't know the answer, just say that you don't know. Don't try to make up an answer.
+
+{summaries}
+
+Question: {question}
+Helpful Answer:`
+
+export const map_reduce_template = `Given the following extracted parts of a long document and a question, create a final answer. 
+
+{summaries}
+
+Question: {question}
+Helpful Answer:`
+
+export const refine_question_template = (sysPrompt?: string) => {
+    let returnPrompt = ''
+    if (sysPrompt)
+        returnPrompt = `Context information is below. 
+---------------------
+{context}
+---------------------
+Given the context information and not prior knowledge, ${sysPrompt}
+Answer the question: {question}.
+Answer:`
+    if (!sysPrompt)
+        returnPrompt = `Context information is below. 
+---------------------
+{context}
+---------------------
+Given the context information and not prior knowledge, answer the question: {question}.
+Answer:`
+    return returnPrompt
+}
+
+export const refine_template = `The original question is as follows: {question}
+We have provided an existing answer: {existing_answer}
+We have the opportunity to refine the existing answer (only if needed) with some more context below.
+------------
+{context}
+------------
+Given the new context, refine the original answer to better answer the question. 
+If you can't find answer from the context, return the original answer.`
+
+export const CUSTOM_QUESTION_GENERATOR_CHAIN_PROMPT = `Given the following conversation and a follow up question, rephrase the follow up question to be a standalone question, answer in the same language as the follow up question. include it in the standalone question.
+
+Chat History:
+{chat_history}
+Follow Up Input: {question}
+Standalone question:`

packages/components/nodes/chains/LLMChain/LLMChain.ts

@@ -1,11 +1,13 @@
 import { ICommonObject, INode, INodeData, INodeOutputsValue, INodeParams } from '../../../src/Interface'
-import { getBaseClasses } from '../../../src/utils'
+import { getBaseClasses, handleEscapeCharacters } from '../../../src/utils'
 import { LLMChain } from 'langchain/chains'
 import { BaseLanguageModel } from 'langchain/base_language'
+import { ConsoleCallbackHandler, CustomChainHandler } from '../../../src/handler'
 
 class LLMChain_Chains implements INode {
     label: string
     name: string
+    version: number
     type: string
     icon: string
     category: string
@@ -17,6 +19,7 @@ class LLMChain_Chains implements INode {
     constructor() {
         this.label = 'LLM Chain'
         this.name = 'llmChain'
+        this.version = 1.0
         this.type = 'LLMChain'
         this.icon = 'chain.svg'
         this.category = 'Chains'
@@ -50,12 +53,12 @@ class LLMChain_Chains implements INode {
             {
                 label: 'Output Prediction',
                 name: 'outputPrediction',
-                baseClasses: ['string']
+                baseClasses: ['string', 'json']
             }
         ]
     }
 
-    async init(nodeData: INodeData, input: string): Promise<any> {
+    async init(nodeData: INodeData, input: string, options: ICommonObject): Promise<any> {
         const model = nodeData.inputs?.model as BaseLanguageModel
         const prompt = nodeData.inputs?.prompt
         const output = nodeData.outputs?.output as string
@@ -67,21 +70,25 @@ class LLMChain_Chains implements INode {
         } else if (output === 'outputPrediction') {
             const chain = new LLMChain({ llm: model, prompt, verbose: process.env.DEBUG === 'true' ? true : false })
             const inputVariables = chain.prompt.inputVariables as string[] // ["product"]
-            const res = await runPrediction(inputVariables, chain, input, promptValues)
+            const res = await runPrediction(inputVariables, chain, input, promptValues, options)
             // eslint-disable-next-line no-console
             console.log('\x1b[92m\x1b[1m\n*****OUTPUT PREDICTION*****\n\x1b[0m\x1b[0m')
             // eslint-disable-next-line no-console
             console.log(res)
-            return res
+            /**
+             * Apply string transformation to convert special chars:
+             * FROM: hello i am ben\n\n\thow are you?
+             * TO: hello i am benFLOWISE_NEWLINEFLOWISE_NEWLINEFLOWISE_TABhow are you?
+             */
+            return handleEscapeCharacters(res, false)
         }
     }
 
-    async run(nodeData: INodeData, input: string): Promise<string> {
+    async run(nodeData: INodeData, input: string, options: ICommonObject): Promise<string> {
         const inputVariables = nodeData.instance.prompt.inputVariables as string[] // ["product"]
         const chain = nodeData.instance as LLMChain
         const promptValues = nodeData.inputs?.prompt.promptValues as ICommonObject
-
-        const res = await runPrediction(inputVariables, chain, input, promptValues)
+        const res = await runPrediction(inputVariables, chain, input, promptValues, options)
         // eslint-disable-next-line no-console
         console.log('\x1b[93m\x1b[1m\n*****FINAL RESULT*****\n\x1b[0m\x1b[0m')
         // eslint-disable-next-line no-console
@@ -90,11 +97,26 @@ class LLMChain_Chains implements INode {
     }
 }
 
-const runPrediction = async (inputVariables: string[], chain: LLMChain, input: string, promptValues: ICommonObject) => {
-    if (inputVariables.length === 1) {
-        const res = await chain.run(input)
-        return res
-    } else if (inputVariables.length > 1) {
+const runPrediction = async (
+    inputVariables: string[],
+    chain: LLMChain,
+    input: string,
+    promptValuesRaw: ICommonObject,
+    options: ICommonObject
+) => {
+    const loggerHandler = new ConsoleCallbackHandler(options.logger)
+    const isStreaming = options.socketIO && options.socketIOClientId
+    const socketIO = isStreaming ? options.socketIO : undefined
+    const socketIOClientId = isStreaming ? options.socketIOClientId : ''
+
+    /**
+     * Apply string transformation to reverse converted special chars:
+     * FROM: { "value": "hello i am benFLOWISE_NEWLINEFLOWISE_NEWLINEFLOWISE_TABhow are you?" }
+     * TO: { "value": "hello i am ben\n\n\thow are you?" }
+     */
+    const promptValues = handleEscapeCharacters(promptValuesRaw, true)
+
+    if (promptValues && inputVariables.length > 0) {
         let seen: string[] = []
 
         for (const variable of inputVariables) {
@@ -106,11 +128,15 @@ const runPrediction = async (inputVariables: string[], chain: LLMChain, input: s
 
         if (seen.length === 0) {
             // All inputVariables have fixed values specified
-            const options = {
-                ...promptValues
+            const options = { ...promptValues }
+            if (isStreaming) {
+                const handler = new CustomChainHandler(socketIO, socketIOClientId)
+                const res = await chain.call(options, [loggerHandler, handler])
+                return res?.text
+            } else {
+                const res = await chain.call(options, [loggerHandler])
+                return res?.text
             }
-            const res = await chain.call(options)
-            return res?.text
         } else if (seen.length === 1) {
             // If one inputVariable is not specify, use input (user's question) as value
             const lastValue = seen.pop()
@@ -119,14 +145,26 @@ const runPrediction = async (inputVariables: string[], chain: LLMChain, input: s
                 ...promptValues,
                 [lastValue]: input
             }
-            const res = await chain.call(options)
-            return res?.text
+            if (isStreaming) {
+                const handler = new CustomChainHandler(socketIO, socketIOClientId)
+                const res = await chain.call(options, [loggerHandler, handler])
+                return res?.text
+            } else {
+                const res = await chain.call(options, [loggerHandler])
+                return res?.text
+            }
         } else {
             throw new Error(`Please provide Prompt Values for: ${seen.join(', ')}`)
         }
     } else {
-        const res = await chain.run(input)
-        return res
+        if (isStreaming) {
+            const handler = new CustomChainHandler(socketIO, socketIOClientId)
+            const res = await chain.run(input, [loggerHandler, handler])
+            return res
+        } else {
+            const res = await chain.run(input, [loggerHandler])
+            return res
+        }
     }
 }
 

packages/components/nodes/chains/MultiPromptChain/MultiPromptChain.ts

@@ -0,0 +1,82 @@
+import { BaseLanguageModel } from 'langchain/base_language'
+import { ICommonObject, INode, INodeData, INodeParams, PromptRetriever } from '../../../src/Interface'
+import { getBaseClasses } from '../../../src/utils'
+import { MultiPromptChain } from 'langchain/chains'
+import { ConsoleCallbackHandler, CustomChainHandler } from '../../../src/handler'
+
+class MultiPromptChain_Chains implements INode {
+    label: string
+    name: string
+    version: number
+    type: string
+    icon: string
+    category: string
+    baseClasses: string[]
+    description: string
+    inputs: INodeParams[]
+
+    constructor() {
+        this.label = 'Multi Prompt Chain'
+        this.name = 'multiPromptChain'
+        this.version = 1.0
+        this.type = 'MultiPromptChain'
+        this.icon = 'chain.svg'
+        this.category = 'Chains'
+        this.description = 'Chain automatically picks an appropriate prompt from multiple prompt templates'
+        this.baseClasses = [this.type, ...getBaseClasses(MultiPromptChain)]
+        this.inputs = [
+            {
+                label: 'Language Model',
+                name: 'model',
+                type: 'BaseLanguageModel'
+            },
+            {
+                label: 'Prompt Retriever',
+                name: 'promptRetriever',
+                type: 'PromptRetriever',
+                list: true
+            }
+        ]
+    }
+
+    async init(nodeData: INodeData): Promise<any> {
+        const model = nodeData.inputs?.model as BaseLanguageModel
+        const promptRetriever = nodeData.inputs?.promptRetriever as PromptRetriever[]
+        const promptNames = []
+        const promptDescriptions = []
+        const promptTemplates = []
+
+        for (const prompt of promptRetriever) {
+            promptNames.push(prompt.name)
+            promptDescriptions.push(prompt.description)
+            promptTemplates.push(prompt.systemMessage)
+        }
+
+        const chain = MultiPromptChain.fromLLMAndPrompts(model, {
+            promptNames,
+            promptDescriptions,
+            promptTemplates,
+            llmChainOpts: { verbose: process.env.DEBUG === 'true' ? true : false }
+        })
+
+        return chain
+    }
+
+    async run(nodeData: INodeData, input: string, options: ICommonObject): Promise<string> {
+        const chain = nodeData.instance as MultiPromptChain
+        const obj = { input }
+
+        const loggerHandler = new ConsoleCallbackHandler(options.logger)
+
+        if (options.socketIO && options.socketIOClientId) {
+            const handler = new CustomChainHandler(options.socketIO, options.socketIOClientId, 2)
+            const res = await chain.call(obj, [loggerHandler, handler])
+            return res?.text
+        } else {
+            const res = await chain.call(obj, [loggerHandler])
+            return res?.text
+        }
+    }
+}
+
+module.exports = { nodeClass: MultiPromptChain_Chains }

packages/components/nodes/chains/MultiPromptChain/chain.svg

@@ -0,0 +1,6 @@
+<svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-dna" 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="M14.828 14.828a4 4 0 1 0 -5.656 -5.656a4 4 0 0 0 5.656 5.656z"></path>
+   <path d="M9.172 20.485a4 4 0 1 0 -5.657 -5.657"></path>
+   <path d="M14.828 3.515a4 4 0 0 0 5.657 5.657"></path>
+</svg>

packages/components/nodes/chains/MultiRetrievalQAChain/MultiRetrievalQAChain.ts

@@ -0,0 +1,92 @@
+import { BaseLanguageModel } from 'langchain/base_language'
+import { ICommonObject, INode, INodeData, INodeParams, VectorStoreRetriever } from '../../../src/Interface'
+import { getBaseClasses } from '../../../src/utils'
+import { MultiRetrievalQAChain } from 'langchain/chains'
+import { ConsoleCallbackHandler, CustomChainHandler } from '../../../src/handler'
+
+class MultiRetrievalQAChain_Chains implements INode {
+    label: string
+    name: string
+    version: number
+    type: string
+    icon: string
+    category: string
+    baseClasses: string[]
+    description: string
+    inputs: INodeParams[]
+
+    constructor() {
+        this.label = 'Multi Retrieval QA Chain'
+        this.name = 'multiRetrievalQAChain'
+        this.version = 1.0
+        this.type = 'MultiRetrievalQAChain'
+        this.icon = 'chain.svg'
+        this.category = 'Chains'
+        this.description = 'QA Chain that automatically picks an appropriate vector store from multiple retrievers'
+        this.baseClasses = [this.type, ...getBaseClasses(MultiRetrievalQAChain)]
+        this.inputs = [
+            {
+                label: 'Language Model',
+                name: 'model',
+                type: 'BaseLanguageModel'
+            },
+            {
+                label: 'Vector Store Retriever',
+                name: 'vectorStoreRetriever',
+                type: 'VectorStoreRetriever',
+                list: true
+            },
+            {
+                label: 'Return Source Documents',
+                name: 'returnSourceDocuments',
+                type: 'boolean',
+                optional: true
+            }
+        ]
+    }
+
+    async init(nodeData: INodeData): Promise<any> {
+        const model = nodeData.inputs?.model as BaseLanguageModel
+        const vectorStoreRetriever = nodeData.inputs?.vectorStoreRetriever as VectorStoreRetriever[]
+        const returnSourceDocuments = nodeData.inputs?.returnSourceDocuments as boolean
+
+        const retrieverNames = []
+        const retrieverDescriptions = []
+        const retrievers = []
+
+        for (const vs of vectorStoreRetriever) {
+            retrieverNames.push(vs.name)
+            retrieverDescriptions.push(vs.description)
+            retrievers.push(vs.vectorStore.asRetriever((vs.vectorStore as any).k ?? 4))
+        }
+
+        const chain = MultiRetrievalQAChain.fromLLMAndRetrievers(model, {
+            retrieverNames,
+            retrieverDescriptions,
+            retrievers,
+            retrievalQAChainOpts: { verbose: process.env.DEBUG === 'true' ? true : false, returnSourceDocuments }
+        })
+        return chain
+    }
+
+    async run(nodeData: INodeData, input: string, options: ICommonObject): Promise<string | ICommonObject> {
+        const chain = nodeData.instance as MultiRetrievalQAChain
+        const returnSourceDocuments = nodeData.inputs?.returnSourceDocuments as boolean
+
+        const obj = { input }
+        const loggerHandler = new ConsoleCallbackHandler(options.logger)
+
+        if (options.socketIO && options.socketIOClientId) {
+            const handler = new CustomChainHandler(options.socketIO, options.socketIOClientId, 2, returnSourceDocuments)
+            const res = await chain.call(obj, [loggerHandler, handler])
+            if (res.text && res.sourceDocuments) return res
+            return res?.text
+        } else {
+            const res = await chain.call(obj, [loggerHandler])
+            if (res.text && res.sourceDocuments) return res
+            return res?.text
+        }
+    }
+}
+
+module.exports = { nodeClass: MultiRetrievalQAChain_Chains }

packages/components/nodes/chains/MultiRetrievalQAChain/chain.svg

@@ -0,0 +1,6 @@
+<svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-dna" 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="M14.828 14.828a4 4 0 1 0 -5.656 -5.656a4 4 0 0 0 5.656 5.656z"></path>
+   <path d="M9.172 20.485a4 4 0 1 0 -5.657 -5.657"></path>
+   <path d="M14.828 3.515a4 4 0 0 0 5.657 5.657"></path>
+</svg>

packages/components/nodes/chains/RetrievalQAChain/RetrievalQAChain.ts

@@ -1,12 +1,14 @@
-import { INode, INodeData, INodeParams } from '../../../src/Interface'
+import { ICommonObject, INode, INodeData, INodeParams } from '../../../src/Interface'
 import { RetrievalQAChain } from 'langchain/chains'
-import { BaseRetriever } from 'langchain/schema'
+import { BaseRetriever } from 'langchain/schema/retriever'
 import { getBaseClasses } from '../../../src/utils'
 import { BaseLanguageModel } from 'langchain/base_language'
+import { ConsoleCallbackHandler, CustomChainHandler } from '../../../src/handler'
 
 class RetrievalQAChain_Chains implements INode {
     label: string
     name: string
+    version: number
     type: string
     icon: string
     category: string
@@ -17,6 +19,7 @@ class RetrievalQAChain_Chains implements INode {
     constructor() {
         this.label = 'Retrieval QA Chain'
         this.name = 'retrievalQAChain'
+        this.version = 1.0
         this.type = 'RetrievalQAChain'
         this.icon = 'chain.svg'
         this.category = 'Chains'
@@ -44,13 +47,21 @@ class RetrievalQAChain_Chains implements INode {
         return chain
     }
 
-    async run(nodeData: INodeData, input: string): Promise<string> {
+    async run(nodeData: INodeData, input: string, options: ICommonObject): Promise<string> {
         const chain = nodeData.instance as RetrievalQAChain
         const obj = {
             query: input
         }
-        const res = await chain.call(obj)
-        return res?.text
+        const loggerHandler = new ConsoleCallbackHandler(options.logger)
+
+        if (options.socketIO && options.socketIOClientId) {
+            const handler = new CustomChainHandler(options.socketIO, options.socketIOClientId)
+            const res = await chain.call(obj, [loggerHandler, handler])
+            return res?.text
+        } else {
+            const res = await chain.call(obj, [loggerHandler])
+            return res?.text
+        }
     }
 }
 

packages/components/nodes/chains/SqlDatabaseChain/SqlDatabaseChain.ts

@@ -1,13 +1,18 @@
-import { INode, INodeData, INodeParams } from '../../../src/Interface'
-import { SqlDatabaseChain, SqlDatabaseChainInput } from 'langchain/chains'
+import { ICommonObject, INode, INodeData, INodeParams } from '../../../src/Interface'
+import { SqlDatabaseChain, SqlDatabaseChainInput } from 'langchain/chains/sql_db'
 import { getBaseClasses } from '../../../src/utils'
 import { DataSource } from 'typeorm'
 import { SqlDatabase } from 'langchain/sql_db'
 import { BaseLanguageModel } from 'langchain/base_language'
+import { ConsoleCallbackHandler, CustomChainHandler } from '../../../src/handler'
+import { DataSourceOptions } from 'typeorm/data-source'
+
+type DatabaseType = 'sqlite' | 'postgres' | 'mssql' | 'mysql'
 
 class SqlDatabaseChain_Chains implements INode {
     label: string
     name: string
+    version: number
     type: string
     icon: string
     category: string
@@ -18,6 +23,7 @@ class SqlDatabaseChain_Chains implements INode {
     constructor() {
         this.label = 'Sql Database Chain'
         this.name = 'sqlDatabaseChain'
+        this.version = 1.0
         this.type = 'SqlDatabaseChain'
         this.icon = 'sqlchain.svg'
         this.category = 'Chains'
@@ -35,46 +41,73 @@ class SqlDatabaseChain_Chains implements INode {
                 type: 'options',
                 options: [
                     {
-                        label: 'SQlite',
+                        label: 'SQLite',
                         name: 'sqlite'
+                    },
+                    {
+                        label: 'PostgreSQL',
+                        name: 'postgres'
+                    },
+                    {
+                        label: 'MSSQL',
+                        name: 'mssql'
+                    },
+                    {
+                        label: 'MySQL',
+                        name: 'mysql'
                     }
                 ],
                 default: 'sqlite'
             },
             {
-                label: 'Database File Path',
-                name: 'dbFilePath',
+                label: 'Connection string or file path (sqlite only)',
+                name: 'url',
                 type: 'string',
-                placeholder: 'C:/Users/chinook.db'
+                placeholder: '1270.0.0.1:5432/chinook'
             }
         ]
     }
 
     async init(nodeData: INodeData): Promise<any> {
-        const databaseType = nodeData.inputs?.database as 'sqlite'
+        const databaseType = nodeData.inputs?.database as DatabaseType
         const model = nodeData.inputs?.model as BaseLanguageModel
-        const dbFilePath = nodeData.inputs?.dbFilePath
+        const url = nodeData.inputs?.url
 
-        const chain = await getSQLDBChain(databaseType, dbFilePath, model)
+        const chain = await getSQLDBChain(databaseType, url, model)
         return chain
     }
 
-    async run(nodeData: INodeData, input: string): Promise<string> {
-        const databaseType = nodeData.inputs?.database as 'sqlite'
+    async run(nodeData: INodeData, input: string, options: ICommonObject): Promise<string> {
+        const databaseType = nodeData.inputs?.database as DatabaseType
         const model = nodeData.inputs?.model as BaseLanguageModel
-        const dbFilePath = nodeData.inputs?.dbFilePath
+        const url = nodeData.inputs?.url
 
-        const chain = await getSQLDBChain(databaseType, dbFilePath, model)
-        const res = await chain.run(input)
-        return res
+        const chain = await getSQLDBChain(databaseType, url, model)
+        const loggerHandler = new ConsoleCallbackHandler(options.logger)
+
+        if (options.socketIO && options.socketIOClientId) {
+            const handler = new CustomChainHandler(options.socketIO, options.socketIOClientId, 2)
+            const res = await chain.run(input, [loggerHandler, handler])
+            return res
+        } else {
+            const res = await chain.run(input, [loggerHandler])
+            return res
+        }
     }
 }
 
-const getSQLDBChain = async (databaseType: 'sqlite', dbFilePath: string, llm: BaseLanguageModel) => {
-    const datasource = new DataSource({
-        type: databaseType,
-        database: dbFilePath
-    })
+const getSQLDBChain = async (databaseType: DatabaseType, url: string, llm: BaseLanguageModel) => {
+    const datasource = new DataSource(
+        databaseType === 'sqlite'
+            ? {
+                  type: databaseType,
+                  database: url
+              }
+            : ({
+                  type: databaseType,
+                  url: url
+              } as DataSourceOptions)
+    )
 
     const db = await SqlDatabase.fromDataSourceParams({
         appDataSource: datasource

packages/components/nodes/chains/VectorDBQAChain/VectorDBQAChain.ts

@@ -1,12 +1,14 @@
-import { INode, INodeData, INodeParams } from '../../../src/Interface'
+import { ICommonObject, INode, INodeData, INodeParams } from '../../../src/Interface'
 import { getBaseClasses } from '../../../src/utils'
 import { VectorDBQAChain } from 'langchain/chains'
 import { BaseLanguageModel } from 'langchain/base_language'
 import { VectorStore } from 'langchain/vectorstores'
+import { ConsoleCallbackHandler, CustomChainHandler } from '../../../src/handler'
 
 class VectorDBQAChain_Chains implements INode {
     label: string
     name: string
+    version: number
     type: string
     icon: string
     category: string
@@ -17,6 +19,7 @@ class VectorDBQAChain_Chains implements INode {
     constructor() {
         this.label = 'VectorDB QA Chain'
         this.name = 'vectorDBQAChain'
+        this.version = 1.0
         this.type = 'VectorDBQAChain'
         this.icon = 'chain.svg'
         this.category = 'Chains'
@@ -40,17 +43,29 @@ class VectorDBQAChain_Chains implements INode {
         const model = nodeData.inputs?.model as BaseLanguageModel
         const vectorStore = nodeData.inputs?.vectorStore as VectorStore
 
-        const chain = VectorDBQAChain.fromLLM(model, vectorStore, { verbose: process.env.DEBUG === 'true' ? true : false })
+        const chain = VectorDBQAChain.fromLLM(model, vectorStore, {
+            k: (vectorStore as any)?.k ?? 4,
+            verbose: process.env.DEBUG === 'true' ? true : false
+        })
         return chain
     }
 
-    async run(nodeData: INodeData, input: string): Promise<string> {
+    async run(nodeData: INodeData, input: string, options: ICommonObject): Promise<string> {
         const chain = nodeData.instance as VectorDBQAChain
         const obj = {
             query: input
         }
-        const res = await chain.call(obj)
-        return res?.text
+
+        const loggerHandler = new ConsoleCallbackHandler(options.logger)
+
+        if (options.socketIO && options.socketIOClientId) {
+            const handler = new CustomChainHandler(options.socketIO, options.socketIOClientId)
+            const res = await chain.call(obj, [loggerHandler, handler])
+            return res?.text
+        } else {
+            const res = await chain.call(obj, [loggerHandler])
+            return res?.text
+        }
     }
 }
 

packages/components/nodes/chatmodels/AzureChatOpenAI/Azure.svg

@@ -1,5 +1 @@
-<svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-brand-azure" 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="M6 7.5l-4 9.5h4l6 -15z"></path>
-   <path d="M22 20l-7 -15l-3 7l4 5l-8 3z"></path>
-</svg>
+<svg xmlns="http://www.w3.org/2000/svg"  viewBox="0 0 48 48" width="96px" height="96px"><path fill="#035bda" d="M46 40L29.317 10.852 22.808 23.96 34.267 37.24 13 39.655zM13.092 18.182L2 36.896 11.442 35.947 28.033 5.678z"/></svg>

packages/components/nodes/chatmodels/AzureChatOpenAI/AzureChatOpenAI.ts

@@ -1,32 +1,36 @@
 import { OpenAIBaseInput } from 'langchain/dist/types/openai-types'
-import { INode, INodeData, INodeParams } from '../../../src/Interface'
-import { getBaseClasses } from '../../../src/utils'
+import { ICommonObject, INode, INodeData, INodeParams } from '../../../src/Interface'
+import { getBaseClasses, getCredentialData, getCredentialParam } from '../../../src/utils'
 import { AzureOpenAIInput, ChatOpenAI } from 'langchain/chat_models/openai'
 
 class AzureChatOpenAI_ChatModels implements INode {
     label: string
     name: string
+    version: number
     type: string
     icon: string
     category: string
     description: string
     baseClasses: string[]
+    credential: INodeParams
     inputs: INodeParams[]
 
     constructor() {
         this.label = 'Azure ChatOpenAI'
         this.name = 'azureChatOpenAI'
+        this.version = 1.0
         this.type = 'AzureChatOpenAI'
         this.icon = 'Azure.svg'
         this.category = 'Chat Models'
         this.description = 'Wrapper around Azure OpenAI large language models that use the Chat endpoint'
         this.baseClasses = [this.type, ...getBaseClasses(ChatOpenAI)]
+        this.credential = {
+            label: 'Connect Credential',
+            name: 'credential',
+            type: 'credential',
+            credentialNames: ['azureOpenAIApi']
+        }
         this.inputs = [
-            {
-                label: 'Azure OpenAI Api Key',
-                name: 'azureOpenAIApiKey',
-                type: 'password'
-            },
             {
                 label: 'Model Name',
                 name: 'modelName',
@@ -43,6 +47,10 @@ class AzureChatOpenAI_ChatModels implements INode {
                     {
                         label: 'gpt-35-turbo',
                         name: 'gpt-35-turbo'
+                    },
+                    {
+                        label: 'gpt-35-turbo-16k',
+                        name: 'gpt-35-turbo-16k'
                     }
                 ],
                 default: 'gpt-35-turbo',
@@ -52,37 +60,15 @@ class AzureChatOpenAI_ChatModels implements INode {
                 label: 'Temperature',
                 name: 'temperature',
                 type: 'number',
+                step: 0.1,
                 default: 0.9,
                 optional: true
             },
-            {
-                label: 'Azure OpenAI Api Instance Name',
-                name: 'azureOpenAIApiInstanceName',
-                type: 'string',
-                placeholder: 'YOUR-INSTANCE-NAME'
-            },
-            {
-                label: 'Azure OpenAI Api Deployment Name',
-                name: 'azureOpenAIApiDeploymentName',
-                type: 'string',
-                placeholder: 'YOUR-DEPLOYMENT-NAME'
-            },
-            {
-                label: 'Azure OpenAI Api Version',
-                name: 'azureOpenAIApiVersion',
-                type: 'options',
-                options: [
-                    {
-                        label: '2023-03-15-preview',
-                        name: '2023-03-15-preview'
-                    }
-                ],
-                default: '2023-03-15-preview'
-            },
             {
                 label: 'Max Tokens',
                 name: 'maxTokens',
                 type: 'number',
+                step: 1,
                 optional: true,
                 additionalParams: true
             },
@@ -90,6 +76,7 @@ class AzureChatOpenAI_ChatModels implements INode {
                 label: 'Frequency Penalty',
                 name: 'frequencyPenalty',
                 type: 'number',
+                step: 0.1,
                 optional: true,
                 additionalParams: true
             },
@@ -97,6 +84,7 @@ class AzureChatOpenAI_ChatModels implements INode {
                 label: 'Presence Penalty',
                 name: 'presencePenalty',
                 type: 'number',
+                step: 0.1,
                 optional: true,
                 additionalParams: true
             },
@@ -104,36 +92,41 @@ class AzureChatOpenAI_ChatModels implements INode {
                 label: 'Timeout',
                 name: 'timeout',
                 type: 'number',
+                step: 1,
                 optional: true,
                 additionalParams: true
             }
         ]
     }
 
-    async init(nodeData: INodeData): Promise<any> {
-        const azureOpenAIApiKey = nodeData.inputs?.azureOpenAIApiKey as string
+    async init(nodeData: INodeData, _: string, options: ICommonObject): Promise<any> {
         const modelName = nodeData.inputs?.modelName as string
         const temperature = nodeData.inputs?.temperature as string
-        const azureOpenAIApiInstanceName = nodeData.inputs?.azureOpenAIApiInstanceName as string
-        const azureOpenAIApiDeploymentName = nodeData.inputs?.azureOpenAIApiDeploymentName as string
-        const azureOpenAIApiVersion = nodeData.inputs?.azureOpenAIApiVersion as string
         const maxTokens = nodeData.inputs?.maxTokens as string
         const frequencyPenalty = nodeData.inputs?.frequencyPenalty as string
         const presencePenalty = nodeData.inputs?.presencePenalty as string
         const timeout = nodeData.inputs?.timeout as string
+        const streaming = nodeData.inputs?.streaming as boolean
+
+        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<AzureOpenAIInput> & Partial<OpenAIBaseInput> = {
-            temperature: parseInt(temperature, 10),
+            temperature: parseFloat(temperature),
             modelName,
             azureOpenAIApiKey,
             azureOpenAIApiInstanceName,
             azureOpenAIApiDeploymentName,
-            azureOpenAIApiVersion
+            azureOpenAIApiVersion,
+            streaming: streaming ?? true
         }
 
         if (maxTokens) obj.maxTokens = parseInt(maxTokens, 10)
-        if (frequencyPenalty) obj.frequencyPenalty = parseInt(frequencyPenalty, 10)
-        if (presencePenalty) obj.presencePenalty = parseInt(presencePenalty, 10)
+        if (frequencyPenalty) obj.frequencyPenalty = parseFloat(frequencyPenalty)
+        if (presencePenalty) obj.presencePenalty = parseFloat(presencePenalty)
         if (timeout) obj.timeout = parseInt(timeout, 10)
 
         const model = new ChatOpenAI(obj)

packages/components/nodes/chatmodels/ChatAnthropic/ChatAnthropic.ts

@@ -1,36 +1,50 @@
-import { INode, INodeData, INodeParams } from '../../../src/Interface'
-import { getBaseClasses } from '../../../src/utils'
+import { ICommonObject, INode, INodeData, INodeParams } from '../../../src/Interface'
+import { getBaseClasses, getCredentialData, getCredentialParam } from '../../../src/utils'
 import { AnthropicInput, ChatAnthropic } from 'langchain/chat_models/anthropic'
 
 class ChatAnthropic_ChatModels implements INode {
     label: string
     name: string
+    version: number
     type: string
     icon: string
     category: string
     description: string
     baseClasses: string[]
+    credential: INodeParams
     inputs: INodeParams[]
 
     constructor() {
         this.label = 'ChatAnthropic'
         this.name = 'chatAnthropic'
+        this.version = 1.0
         this.type = 'ChatAnthropic'
         this.icon = 'chatAnthropic.png'
         this.category = 'Chat Models'
         this.description = 'Wrapper around ChatAnthropic large language models that use the Chat endpoint'
         this.baseClasses = [this.type, ...getBaseClasses(ChatAnthropic)]
+        this.credential = {
+            label: 'Connect Credential',
+            name: 'credential',
+            type: 'credential',
+            credentialNames: ['anthropicApi']
+        }
         this.inputs = [
-            {
-                label: 'ChatAnthropic Api Key',
-                name: 'anthropicApiKey',
-                type: 'password'
-            },
             {
                 label: 'Model Name',
                 name: 'modelName',
                 type: 'options',
                 options: [
+                    {
+                        label: 'claude-2',
+                        name: 'claude-2',
+                        description: 'Claude 2 latest major version, automatically get updates to the model as they are released'
+                    },
+                    {
+                        label: 'claude-instant-1',
+                        name: 'claude-instant-1',
+                        description: 'Claude Instant latest major version, automatically get updates to the model as they are released'
+                    },
                     {
                         label: 'claude-v1',
                         name: 'claude-v1'
@@ -76,13 +90,14 @@ class ChatAnthropic_ChatModels implements INode {
                         name: 'claude-instant-v1.1-100k'
                     }
                 ],
-                default: 'claude-v1',
+                default: 'claude-2',
                 optional: true
             },
             {
                 label: 'Temperature',
                 name: 'temperature',
                 type: 'number',
+                step: 0.1,
                 default: 0.9,
                 optional: true
             },
@@ -90,6 +105,7 @@ class ChatAnthropic_ChatModels implements INode {
                 label: 'Max Tokens',
                 name: 'maxTokensToSample',
                 type: 'number',
+                step: 1,
                 optional: true,
                 additionalParams: true
             },
@@ -97,6 +113,7 @@ class ChatAnthropic_ChatModels implements INode {
                 label: 'Top P',
                 name: 'topP',
                 type: 'number',
+                step: 0.1,
                 optional: true,
                 additionalParams: true
             },
@@ -104,29 +121,34 @@ class ChatAnthropic_ChatModels implements INode {
                 label: 'Top K',
                 name: 'topK',
                 type: 'number',
+                step: 0.1,
                 optional: true,
                 additionalParams: true
             }
         ]
     }
 
-    async init(nodeData: INodeData): Promise<any> {
+    async init(nodeData: INodeData, _: string, options: ICommonObject): Promise<any> {
         const temperature = nodeData.inputs?.temperature as string
         const modelName = nodeData.inputs?.modelName as string
-        const anthropicApiKey = nodeData.inputs?.anthropicApiKey as string
         const maxTokensToSample = nodeData.inputs?.maxTokensToSample as string
         const topP = nodeData.inputs?.topP as string
         const topK = nodeData.inputs?.topK as string
+        const streaming = nodeData.inputs?.streaming as boolean
+
+        const credentialData = await getCredentialData(nodeData.credential ?? '', options)
+        const anthropicApiKey = getCredentialParam('anthropicApiKey', credentialData, nodeData)
 
         const obj: Partial<AnthropicInput> & { anthropicApiKey?: string } = {
-            temperature: parseInt(temperature, 10),
+            temperature: parseFloat(temperature),
             modelName,
-            anthropicApiKey
+            anthropicApiKey,
+            streaming: streaming ?? true
         }
 
         if (maxTokensToSample) obj.maxTokensToSample = parseInt(maxTokensToSample, 10)
-        if (topP) obj.topP = parseInt(topP, 10)
-        if (topK) obj.topK = parseInt(topK, 10)
+        if (topP) obj.topP = parseFloat(topP)
+        if (topK) obj.topK = parseFloat(topK)
 
         const model = new ChatAnthropic(obj)
         return model

packages/components/nodes/chatmodels/ChatHuggingFace/ChatHuggingFace.ts

@@ -0,0 +1,126 @@
+import { ICommonObject, INode, INodeData, INodeParams } from '../../../src/Interface'
+import { getBaseClasses, getCredentialData, getCredentialParam } from '../../../src/utils'
+import { HFInput, HuggingFaceInference } from './core'
+
+class ChatHuggingFace_ChatModels implements INode {
+    label: string
+    name: string
+    version: number
+    type: string
+    icon: string
+    category: string
+    description: string
+    baseClasses: string[]
+    credential: INodeParams
+    inputs: INodeParams[]
+
+    constructor() {
+        this.label = 'ChatHuggingFace'
+        this.name = 'chatHuggingFace'
+        this.version = 1.0
+        this.type = 'ChatHuggingFace'
+        this.icon = 'huggingface.png'
+        this.category = 'Chat Models'
+        this.description = 'Wrapper around HuggingFace large language models'
+        this.baseClasses = [this.type, 'BaseChatModel', ...getBaseClasses(HuggingFaceInference)]
+        this.credential = {
+            label: 'Connect Credential',
+            name: 'credential',
+            type: 'credential',
+            credentialNames: ['huggingFaceApi']
+        }
+        this.inputs = [
+            {
+                label: 'Model',
+                name: 'model',
+                type: 'string',
+                description: 'If using own inference endpoint, leave this blank',
+                placeholder: 'gpt2',
+                optional: true
+            },
+            {
+                label: 'Endpoint',
+                name: 'endpoint',
+                type: 'string',
+                placeholder: 'https://xyz.eu-west-1.aws.endpoints.huggingface.cloud/gpt2',
+                description: 'Using your own inference endpoint',
+                optional: true
+            },
+            {
+                label: 'Temperature',
+                name: 'temperature',
+                type: 'number',
+                step: 0.1,
+                description: 'Temperature parameter may not apply to certain model. Please check available model parameters',
+                optional: true,
+                additionalParams: true
+            },
+            {
+                label: 'Max Tokens',
+                name: 'maxTokens',
+                type: 'number',
+                step: 1,
+                description: 'Max Tokens parameter may not apply to certain model. Please check available model parameters',
+                optional: true,
+                additionalParams: true
+            },
+            {
+                label: 'Top Probability',
+                name: 'topP',
+                type: 'number',
+                step: 0.1,
+                description: 'Top Probability parameter may not apply to certain model. Please check available model parameters',
+                optional: true,
+                additionalParams: true
+            },
+            {
+                label: 'Top K',
+                name: 'hfTopK',
+                type: 'number',
+                step: 0.1,
+                description: 'Top K parameter may not apply to certain model. Please check available model parameters',
+                optional: true,
+                additionalParams: true
+            },
+            {
+                label: 'Frequency Penalty',
+                name: 'frequencyPenalty',
+                type: 'number',
+                step: 0.1,
+                description: 'Frequency Penalty parameter may not apply to certain model. Please check available model parameters',
+                optional: true,
+                additionalParams: true
+            }
+        ]
+    }
+
+    async init(nodeData: INodeData, _: string, options: ICommonObject): Promise<any> {
+        const model = nodeData.inputs?.model as string
+        const temperature = nodeData.inputs?.temperature as string
+        const maxTokens = nodeData.inputs?.maxTokens as string
+        const topP = nodeData.inputs?.topP as string
+        const hfTopK = nodeData.inputs?.hfTopK as string
+        const frequencyPenalty = nodeData.inputs?.frequencyPenalty as string
+        const endpoint = nodeData.inputs?.endpoint as string
+
+        const credentialData = await getCredentialData(nodeData.credential ?? '', options)
+        const huggingFaceApiKey = getCredentialParam('huggingFaceApiKey', credentialData, nodeData)
+
+        const obj: Partial<HFInput> = {
+            model,
+            apiKey: huggingFaceApiKey
+        }
+
+        if (temperature) obj.temperature = parseFloat(temperature)
+        if (maxTokens) obj.maxTokens = parseInt(maxTokens, 10)
+        if (topP) obj.topP = parseFloat(topP)
+        if (hfTopK) obj.topK = parseFloat(hfTopK)
+        if (frequencyPenalty) obj.frequencyPenalty = parseFloat(frequencyPenalty)
+        if (endpoint) obj.endpoint = endpoint
+
+        const huggingFace = new HuggingFaceInference(obj)
+        return huggingFace
+    }
+}
+
+module.exports = { nodeClass: ChatHuggingFace_ChatModels }

packages/components/nodes/chatmodels/ChatHuggingFace/core.ts

@@ -0,0 +1,113 @@
+import { getEnvironmentVariable } from '../../../src/utils'
+import { LLM, BaseLLMParams } from 'langchain/llms/base'
+
+export interface HFInput {
+    /** Model to use */
+    model: string
+
+    /** Sampling temperature to use */
+    temperature?: number
+
+    /**
+     * Maximum number of tokens to generate in the completion.
+     */
+    maxTokens?: number
+
+    /** Total probability mass of tokens to consider at each step */
+    topP?: number
+
+    /** Integer to define the top tokens considered within the sample operation to create new text. */
+    topK?: number
+
+    /** Penalizes repeated tokens according to frequency */
+    frequencyPenalty?: number
+
+    /** API key to use. */
+    apiKey?: string
+
+    /** Private endpoint to use. */
+    endpoint?: string
+}
+
+export class HuggingFaceInference extends LLM implements HFInput {
+    get lc_secrets(): { [key: string]: string } | undefined {
+        return {
+            apiKey: 'HUGGINGFACEHUB_API_KEY'
+        }
+    }
+
+    model = 'gpt2'
+
+    temperature: number | undefined = undefined
+
+    maxTokens: number | undefined = undefined
+
+    topP: number | undefined = undefined
+
+    topK: number | undefined = undefined
+
+    frequencyPenalty: number | undefined = undefined
+
+    apiKey: string | undefined = undefined
+
+    endpoint: string | undefined = undefined
+
+    constructor(fields?: Partial<HFInput> & BaseLLMParams) {
+        super(fields ?? {})
+
+        this.model = fields?.model ?? this.model
+        this.temperature = fields?.temperature ?? this.temperature
+        this.maxTokens = fields?.maxTokens ?? this.maxTokens
+        this.topP = fields?.topP ?? this.topP
+        this.topK = fields?.topK ?? this.topK
+        this.frequencyPenalty = fields?.frequencyPenalty ?? this.frequencyPenalty
+        this.endpoint = fields?.endpoint ?? ''
+        this.apiKey = fields?.apiKey ?? getEnvironmentVariable('HUGGINGFACEHUB_API_KEY')
+        if (!this.apiKey) {
+            throw new Error(
+                'Please set an API key for HuggingFace Hub in the environment variable HUGGINGFACEHUB_API_KEY or in the apiKey field of the HuggingFaceInference constructor.'
+            )
+        }
+    }
+
+    _llmType() {
+        return 'hf'
+    }
+
+    /** @ignore */
+    async _call(prompt: string, options: this['ParsedCallOptions']): Promise<string> {
+        const { HfInference } = await HuggingFaceInference.imports()
+        const hf = new HfInference(this.apiKey)
+        const obj: any = {
+            parameters: {
+                // make it behave similar to openai, returning only the generated text
+                return_full_text: false,
+                temperature: this.temperature,
+                max_new_tokens: this.maxTokens,
+                top_p: this.topP,
+                top_k: this.topK,
+                repetition_penalty: this.frequencyPenalty
+            },
+            inputs: prompt
+        }
+        if (this.endpoint) {
+            hf.endpoint(this.endpoint)
+        } else {
+            obj.model = this.model
+        }
+        const res = await this.caller.callWithOptions({ signal: options.signal }, hf.textGeneration.bind(hf), obj)
+        return res.generated_text
+    }
+
+    /** @ignore */
+    static async imports(): Promise<{
+        HfInference: typeof import('@huggingface/inference').HfInference
+    }> {
+        try {
+            const { HfInference } = await import('@huggingface/inference')
+            return { HfInference }
+        } catch (e) {
+            throw new Error('Please install huggingface as a dependency with, e.g. `yarn add @huggingface/inference`')
+        }
+    }
+}

packages/components/nodes/chatmodels/ChatLocalAI/ChatLocalAI.ts

@@ -6,6 +6,7 @@ import { OpenAIChatInput } from 'langchain/chat_models/openai'
 class ChatLocalAI_ChatModels implements INode {
     label: string
     name: string
+    version: number
     type: string
     icon: string
     category: string
@@ -16,6 +17,7 @@ class ChatLocalAI_ChatModels implements INode {
     constructor() {
         this.label = 'ChatLocalAI'
         this.name = 'chatLocalAI'
+        this.version = 1.0
         this.type = 'ChatLocalAI'
         this.icon = 'localai.png'
         this.category = 'Chat Models'
@@ -38,6 +40,7 @@ class ChatLocalAI_ChatModels implements INode {
                 label: 'Temperature',
                 name: 'temperature',
                 type: 'number',
+                step: 0.1,
                 default: 0.9,
                 optional: true
             },
@@ -45,6 +48,7 @@ class ChatLocalAI_ChatModels implements INode {
                 label: 'Max Tokens',
                 name: 'maxTokens',
                 type: 'number',
+                step: 1,
                 optional: true,
                 additionalParams: true
             },
@@ -52,6 +56,7 @@ class ChatLocalAI_ChatModels implements INode {
                 label: 'Top Probability',
                 name: 'topP',
                 type: 'number',
+                step: 0.1,
                 optional: true,
                 additionalParams: true
             },
@@ -59,6 +64,7 @@ class ChatLocalAI_ChatModels implements INode {
                 label: 'Timeout',
                 name: 'timeout',
                 type: 'number',
+                step: 1,
                 optional: true,
                 additionalParams: true
             }
@@ -74,13 +80,13 @@ class ChatLocalAI_ChatModels implements INode {
         const basePath = nodeData.inputs?.basePath as string
 
         const obj: Partial<OpenAIChatInput> & { openAIApiKey?: string } = {
-            temperature: parseInt(temperature, 10),
+            temperature: parseFloat(temperature),
             modelName,
             openAIApiKey: 'sk-'
         }
 
         if (maxTokens) obj.maxTokens = parseInt(maxTokens, 10)
-        if (topP) obj.topP = parseInt(topP, 10)
+        if (topP) obj.topP = parseFloat(topP)
         if (timeout) obj.timeout = parseInt(timeout, 10)
 
         const model = new OpenAIChat(obj, { basePath })

packages/components/nodes/chatmodels/ChatOpenAI/ChatOpenAI.ts

@@ -1,31 +1,35 @@
-import { INode, INodeData, INodeParams } from '../../../src/Interface'
-import { getBaseClasses } from '../../../src/utils'
+import { ICommonObject, INode, INodeData, INodeParams } from '../../../src/Interface'
+import { getBaseClasses, getCredentialData, getCredentialParam } from '../../../src/utils'
 import { ChatOpenAI, OpenAIChatInput } from 'langchain/chat_models/openai'
 
 class ChatOpenAI_ChatModels implements INode {
     label: string
     name: string
+    version: number
     type: string
     icon: string
     category: string
     description: string
     baseClasses: string[]
+    credential: INodeParams
     inputs: INodeParams[]
 
     constructor() {
         this.label = 'ChatOpenAI'
         this.name = 'chatOpenAI'
+        this.version = 1.0
         this.type = 'ChatOpenAI'
         this.icon = 'openai.png'
         this.category = 'Chat Models'
         this.description = 'Wrapper around OpenAI large language models that use the Chat endpoint'
         this.baseClasses = [this.type, ...getBaseClasses(ChatOpenAI)]
+        this.credential = {
+            label: 'Connect Credential',
+            name: 'credential',
+            type: 'credential',
+            credentialNames: ['openAIApi']
+        }
         this.inputs = [
-            {
-                label: 'OpenAI Api Key',
-                name: 'openAIApiKey',
-                type: 'password'
-            },
             {
                 label: 'Model Name',
                 name: 'modelName',
@@ -36,20 +40,32 @@ class ChatOpenAI_ChatModels implements INode {
                         name: 'gpt-4'
                     },
                     {
-                        label: 'gpt-4-0314',
-                        name: 'gpt-4-0314'
+                        label: 'gpt-4-0613',
+                        name: 'gpt-4-0613'
                     },
                     {
-                        label: 'gpt-4-32k-0314',
-                        name: 'gpt-4-32k-0314'
+                        label: 'gpt-4-32k',
+                        name: 'gpt-4-32k'
+                    },
+                    {
+                        label: 'gpt-4-32k-0613',
+                        name: 'gpt-4-32k-0613'
                     },
                     {
                         label: 'gpt-3.5-turbo',
                         name: 'gpt-3.5-turbo'
                     },
                     {
-                        label: 'gpt-3.5-turbo-0301',
-                        name: 'gpt-3.5-turbo-0301'
+                        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',
@@ -59,6 +75,7 @@ class ChatOpenAI_ChatModels implements INode {
                 label: 'Temperature',
                 name: 'temperature',
                 type: 'number',
+                step: 0.1,
                 default: 0.9,
                 optional: true
             },
@@ -66,6 +83,7 @@ class ChatOpenAI_ChatModels implements INode {
                 label: 'Max Tokens',
                 name: 'maxTokens',
                 type: 'number',
+                step: 1,
                 optional: true,
                 additionalParams: true
             },
@@ -73,6 +91,7 @@ class ChatOpenAI_ChatModels implements INode {
                 label: 'Top Probability',
                 name: 'topP',
                 type: 'number',
+                step: 0.1,
                 optional: true,
                 additionalParams: true
             },
@@ -80,6 +99,7 @@ class ChatOpenAI_ChatModels implements INode {
                 label: 'Frequency Penalty',
                 name: 'frequencyPenalty',
                 type: 'number',
+                step: 0.1,
                 optional: true,
                 additionalParams: true
             },
@@ -87,6 +107,7 @@ class ChatOpenAI_ChatModels implements INode {
                 label: 'Presence Penalty',
                 name: 'presencePenalty',
                 type: 'number',
+                step: 0.1,
                 optional: true,
                 additionalParams: true
             },
@@ -94,35 +115,68 @@ class ChatOpenAI_ChatModels implements INode {
                 label: 'Timeout',
                 name: 'timeout',
                 type: 'number',
+                step: 1,
+                optional: true,
+                additionalParams: true
+            },
+            {
+                label: 'BasePath',
+                name: 'basepath',
+                type: 'string',
+                optional: true,
+                additionalParams: true
+            },
+            {
+                label: 'BaseOptions',
+                name: 'baseOptions',
+                type: 'json',
                 optional: true,
                 additionalParams: true
             }
         ]
     }
 
-    async init(nodeData: INodeData): Promise<any> {
+    async init(nodeData: INodeData, _: string, options: ICommonObject): Promise<any> {
         const temperature = nodeData.inputs?.temperature as string
         const modelName = nodeData.inputs?.modelName as string
-        const openAIApiKey = nodeData.inputs?.openAIApiKey as string
         const maxTokens = nodeData.inputs?.maxTokens as string
         const topP = nodeData.inputs?.topP as string
         const frequencyPenalty = nodeData.inputs?.frequencyPenalty as string
         const presencePenalty = nodeData.inputs?.presencePenalty as string
         const timeout = nodeData.inputs?.timeout as string
+        const streaming = nodeData.inputs?.streaming as boolean
+        const basePath = nodeData.inputs?.basepath as string
+        const baseOptions = nodeData.inputs?.baseOptions
+
+        const credentialData = await getCredentialData(nodeData.credential ?? '', options)
+        const openAIApiKey = getCredentialParam('openAIApiKey', credentialData, nodeData)
 
         const obj: Partial<OpenAIChatInput> & { openAIApiKey?: string } = {
-            temperature: parseInt(temperature, 10),
+            temperature: parseFloat(temperature),
             modelName,
-            openAIApiKey
+            openAIApiKey,
+            streaming: streaming ?? true
         }
 
         if (maxTokens) obj.maxTokens = parseInt(maxTokens, 10)
-        if (topP) obj.topP = parseInt(topP, 10)
-        if (frequencyPenalty) obj.frequencyPenalty = parseInt(frequencyPenalty, 10)
-        if (presencePenalty) obj.presencePenalty = parseInt(presencePenalty, 10)
+        if (topP) obj.topP = parseFloat(topP)
+        if (frequencyPenalty) obj.frequencyPenalty = parseFloat(frequencyPenalty)
+        if (presencePenalty) obj.presencePenalty = parseFloat(presencePenalty)
         if (timeout) obj.timeout = parseInt(timeout, 10)
 
-        const model = new ChatOpenAI(obj)
+        let parsedBaseOptions: any | undefined = undefined
+
+        if (baseOptions) {
+            try {
+                parsedBaseOptions = typeof baseOptions === 'object' ? baseOptions : JSON.parse(baseOptions)
+            } catch (exception) {
+                throw new Error("Invalid JSON in the ChatOpenAI's BaseOptions: " + exception)
+            }
+        }
+        const model = new ChatOpenAI(obj, {
+            basePath,
+            baseOptions: parsedBaseOptions
+        })
         return model
     }
 }

packages/components/nodes/chatmodels/GoogleVertexAI/ChatGoogleVertexAI.ts

@@ -0,0 +1,115 @@
+import { ICommonObject, INode, INodeData, INodeParams } from '../../../src/Interface'
+import { getBaseClasses, getCredentialData, getCredentialParam } from '../../../src/utils'
+import { ChatGoogleVertexAI, GoogleVertexAIChatInput } from 'langchain/chat_models/googlevertexai'
+import { GoogleAuthOptions } from 'google-auth-library'
+
+class GoogleVertexAI_ChatModels implements INode {
+    label: string
+    name: string
+    version: number
+    type: string
+    icon: string
+    category: string
+    description: string
+    baseClasses: string[]
+    credential: INodeParams
+    inputs: INodeParams[]
+
+    constructor() {
+        this.label = 'ChatGoogleVertexAI'
+        this.name = 'chatGoogleVertexAI'
+        this.version = 1.0
+        this.type = 'ChatGoogleVertexAI'
+        this.icon = 'vertexai.svg'
+        this.category = 'Chat Models'
+        this.description = 'Wrapper around VertexAI large language models that use the Chat endpoint'
+        this.baseClasses = [this.type, ...getBaseClasses(ChatGoogleVertexAI)]
+        this.credential = {
+            label: 'Connect Credential',
+            name: 'credential',
+            type: 'credential',
+            credentialNames: ['googleVertexAuth']
+        }
+        this.inputs = [
+            {
+                label: 'Model Name',
+                name: 'modelName',
+                type: 'options',
+                options: [
+                    {
+                        label: 'chat-bison',
+                        name: 'chat-bison'
+                    },
+                    {
+                        label: 'codechat-bison',
+                        name: 'codechat-bison'
+                    }
+                ],
+                default: 'chat-bison',
+                optional: true
+            },
+            {
+                label: 'Temperature',
+                name: 'temperature',
+                type: 'number',
+                step: 0.1,
+                default: 0.9,
+                optional: true
+            },
+            {
+                label: 'Max Output Tokens',
+                name: 'maxOutputTokens',
+                type: 'number',
+                step: 1,
+                optional: true,
+                additionalParams: true
+            },
+            {
+                label: 'Top Probability',
+                name: 'topP',
+                type: 'number',
+                step: 0.1,
+                optional: true,
+                additionalParams: true
+            }
+        ]
+    }
+
+    async init(nodeData: INodeData, _: string, options: ICommonObject): Promise<any> {
+        const credentialData = await getCredentialData(nodeData.credential ?? '', options)
+        const googleApplicationCredentialFilePath = getCredentialParam('googleApplicationCredentialFilePath', credentialData, nodeData)
+        const googleApplicationCredential = getCredentialParam('googleApplicationCredential', credentialData, nodeData)
+        const projectID = getCredentialParam('projectID', credentialData, nodeData)
+
+        if (!googleApplicationCredentialFilePath && !googleApplicationCredential)
+            throw new Error('Please specify your Google Application Credential')
+        if (googleApplicationCredentialFilePath && googleApplicationCredential)
+            throw new Error('Please use either Google Application Credential File Path or Google Credential JSON Object')
+
+        const authOptions: GoogleAuthOptions = {}
+        if (googleApplicationCredentialFilePath && !googleApplicationCredential) authOptions.keyFile = googleApplicationCredentialFilePath
+        else if (!googleApplicationCredentialFilePath && googleApplicationCredential)
+            authOptions.credentials = JSON.parse(googleApplicationCredential)
+
+        if (projectID) authOptions.projectId = projectID
+
+        const temperature = nodeData.inputs?.temperature as string
+        const modelName = nodeData.inputs?.modelName as string
+        const maxOutputTokens = nodeData.inputs?.maxOutputTokens as string
+        const topP = nodeData.inputs?.topP as string
+
+        const obj: Partial<GoogleVertexAIChatInput> = {
+            temperature: parseFloat(temperature),
+            model: modelName,
+            authOptions
+        }
+
+        if (maxOutputTokens) obj.maxOutputTokens = parseInt(maxOutputTokens, 10)
+        if (topP) obj.topP = parseFloat(topP)
+
+        const model = new ChatGoogleVertexAI(obj)
+        return model
+    }
+}
+
+module.exports = { nodeClass: GoogleVertexAI_ChatModels }

packages/components/nodes/chatmodels/GoogleVertexAI/vertexai.svg

@@ -0,0 +1,2 @@
+<!-- from  https://cloud.google.com/icons-->
+<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" width="24px" height="24px"><path d="M20,13.89A.77.77,0,0,0,19,13.73l-7,5.14v.22a.72.72,0,1,1,0,1.43v0a.74.74,0,0,0,.45-.15l7.41-5.47A.76.76,0,0,0,20,13.89Z" style="fill:#669df6"/><path d="M12,20.52a.72.72,0,0,1,0-1.43h0v-.22L5,13.73a.76.76,0,0,0-1,.16.74.74,0,0,0,.16,1l7.41,5.47a.73.73,0,0,0,.44.15v0Z" style="fill:#aecbfa"/><path d="M12,18.34a1.47,1.47,0,1,0,1.47,1.47A1.47,1.47,0,0,0,12,18.34Zm0,2.18a.72.72,0,1,1,.72-.71A.71.71,0,0,1,12,20.52Z" style="fill:#4285f4"/><path d="M6,6.11a.76.76,0,0,1-.75-.75V3.48a.76.76,0,1,1,1.51,0V5.36A.76.76,0,0,1,6,6.11Z" style="fill:#aecbfa"/><circle cx="5.98" cy="12" r="0.76" style="fill:#aecbfa"/><circle cx="5.98" cy="9.79" r="0.76" style="fill:#aecbfa"/><circle cx="5.98" cy="7.57" r="0.76" style="fill:#aecbfa"/><path d="M18,8.31a.76.76,0,0,1-.75-.76V5.67a.75.75,0,1,1,1.5,0V7.55A.75.75,0,0,1,18,8.31Z" style="fill:#4285f4"/><circle cx="18.02" cy="12.01" r="0.76" style="fill:#4285f4"/><circle cx="18.02" cy="9.76" r="0.76" style="fill:#4285f4"/><circle cx="18.02" cy="3.48" r="0.76" style="fill:#4285f4"/><path d="M12,15a.76.76,0,0,1-.75-.75V12.34a.76.76,0,0,1,1.51,0v1.89A.76.76,0,0,1,12,15Z" style="fill:#669df6"/><circle cx="12" cy="16.45" r="0.76" style="fill:#669df6"/><circle cx="12" cy="10.14" r="0.76" style="fill:#669df6"/><circle cx="12" cy="7.92" r="0.76" style="fill:#669df6"/><path d="M15,10.54a.76.76,0,0,1-.75-.75V7.91a.76.76,0,1,1,1.51,0V9.79A.76.76,0,0,1,15,10.54Z" style="fill:#4285f4"/><circle cx="15.01" cy="5.69" r="0.76" style="fill:#4285f4"/><circle cx="15.01" cy="14.19" r="0.76" style="fill:#4285f4"/><circle cx="15.01" cy="11.97" r="0.76" style="fill:#4285f4"/><circle cx="8.99" cy="14.19" r="0.76" style="fill:#aecbfa"/><circle cx="8.99" cy="7.92" r="0.76" style="fill:#aecbfa"/><circle cx="8.99" cy="5.69" r="0.76" style="fill:#aecbfa"/><path d="M9,12.73A.76.76,0,0,1,8.24,12V10.1a.75.75,0,1,1,1.5,0V12A.75.75,0,0,1,9,12.73Z" style="fill:#aecbfa"/></svg>

packages/components/nodes/documentloaders/API/APILoader.ts

@@ -0,0 +1,200 @@
+import { ICommonObject, INode, INodeData, INodeParams } from '../../../src/Interface'
+import { TextSplitter } from 'langchain/text_splitter'
+import { BaseDocumentLoader } from 'langchain/document_loaders/base'
+import { Document } from 'langchain/document'
+import axios, { AxiosRequestConfig } from 'axios'
+
+class API_DocumentLoaders implements INode {
+    label: string
+    name: string
+    version: number
+    description: string
+    type: string
+    icon: string
+    category: string
+    baseClasses: string[]
+    inputs?: INodeParams[]
+
+    constructor() {
+        this.label = 'API Loader'
+        this.name = 'apiLoader'
+        this.version = 1.0
+        this.type = 'Document'
+        this.icon = 'api-loader.png'
+        this.category = 'Document Loaders'
+        this.description = `Load data from an API`
+        this.baseClasses = [this.type]
+        this.inputs = [
+            {
+                label: 'Text Splitter',
+                name: 'textSplitter',
+                type: 'TextSplitter',
+                optional: true
+            },
+            {
+                label: 'Method',
+                name: 'method',
+                type: 'options',
+                options: [
+                    {
+                        label: 'GET',
+                        name: 'GET'
+                    },
+                    {
+                        label: 'POST',
+                        name: 'POST'
+                    }
+                ]
+            },
+            {
+                label: 'URL',
+                name: 'url',
+                type: 'string'
+            },
+            {
+                label: 'Headers',
+                name: 'headers',
+                type: 'json',
+                additionalParams: true,
+                optional: true
+            },
+            {
+                label: 'Body',
+                name: 'body',
+                type: 'json',
+                description:
+                    'JSON body for the POST request. If not specified, agent will try to figure out itself from AIPlugin if provided',
+                additionalParams: true,
+                optional: true
+            }
+        ]
+    }
+    async init(nodeData: INodeData): Promise<any> {
+        const headers = nodeData.inputs?.headers as string
+        const url = nodeData.inputs?.url as string
+        const body = nodeData.inputs?.body as string
+        const method = nodeData.inputs?.method as string
+        const textSplitter = nodeData.inputs?.textSplitter as TextSplitter
+        const metadata = nodeData.inputs?.metadata
+
+        const options: ApiLoaderParams = {
+            url,
+            method
+        }
+
+        if (headers) {
+            const parsedHeaders = typeof headers === 'object' ? headers : JSON.parse(headers)
+            options.headers = parsedHeaders
+        }
+
+        if (body) {
+            const parsedBody = typeof body === 'object' ? body : JSON.parse(body)
+            options.body = parsedBody
+        }
+
+        const loader = new ApiLoader(options)
+
+        let docs = []
+
+        if (textSplitter) {
+            docs = await loader.loadAndSplit(textSplitter)
+        } else {
+            docs = await loader.load()
+        }
+
+        if (metadata) {
+            const parsedMetadata = typeof metadata === 'object' ? metadata : JSON.parse(metadata)
+            let finaldocs = []
+            for (const doc of docs) {
+                const newdoc = {
+                    ...doc,
+                    metadata: {
+                        ...doc.metadata,
+                        ...parsedMetadata
+                    }
+                }
+                finaldocs.push(newdoc)
+            }
+            return finaldocs
+        }
+
+        return docs
+    }
+}
+
+interface ApiLoaderParams {
+    url: string
+    method: string
+    headers?: ICommonObject
+    body?: ICommonObject
+}
+
+class ApiLoader extends BaseDocumentLoader {
+    public readonly url: string
+
+    public readonly headers?: ICommonObject
+
+    public readonly body?: ICommonObject
+
+    public readonly method: string
+
+    constructor({ url, headers, body, method }: ApiLoaderParams) {
+        super()
+        this.url = url
+        this.headers = headers
+        this.body = body
+        this.method = method
+    }
+
+    public async load(): Promise<Document[]> {
+        if (this.method === 'POST') {
+            return this.executePostRequest(this.url, this.headers, this.body)
+        } else {
+            return this.executeGetRequest(this.url, this.headers)
+        }
+    }
+
+    protected async executeGetRequest(url: string, headers?: ICommonObject): Promise<Document[]> {
+        try {
+            const config: AxiosRequestConfig = {}
+            if (headers) {
+                config.headers = headers
+            }
+            const response = await axios.get(url, config)
+            const responseJsonString = JSON.stringify(response.data, null, 2)
+            const doc = new Document({
+                pageContent: responseJsonString,
+                metadata: {
+                    url
+                }
+            })
+            return [doc]
+        } catch (error) {
+            throw new Error(`Failed to fetch ${url}: ${error}`)
+        }
+    }
+
+    protected async executePostRequest(url: string, headers?: ICommonObject, body?: ICommonObject): Promise<Document[]> {
+        try {
+            const config: AxiosRequestConfig = {}
+            if (headers) {
+                config.headers = headers
+            }
+            const response = await axios.post(url, body ?? {}, config)
+            const responseJsonString = JSON.stringify(response.data, null, 2)
+            const doc = new Document({
+                pageContent: responseJsonString,
+                metadata: {
+                    url
+                }
+            })
+            return [doc]
+        } catch (error) {
+            throw new Error(`Failed to post ${url}: ${error}`)
+        }
+    }
+}
+
+module.exports = {
+    nodeClass: API_DocumentLoaders
+}

packages/components/nodes/documentloaders/Airtable/Airtable.ts

@@ -0,0 +1,230 @@
+import { ICommonObject, INode, INodeData, INodeParams } from '../../../src/Interface'
+import { TextSplitter } from 'langchain/text_splitter'
+import { BaseDocumentLoader } from 'langchain/document_loaders/base'
+import { Document } from 'langchain/document'
+import axios from 'axios'
+import { getCredentialData, getCredentialParam } from '../../../src/utils'
+
+class Airtable_DocumentLoaders implements INode {
+    label: string
+    name: string
+    version: number
+    description: string
+    type: string
+    icon: string
+    category: string
+    baseClasses: string[]
+    credential: INodeParams
+    inputs?: INodeParams[]
+
+    constructor() {
+        this.label = 'Airtable'
+        this.name = 'airtable'
+        this.version = 1.0
+        this.type = 'Document'
+        this.icon = 'airtable.svg'
+        this.category = 'Document Loaders'
+        this.description = `Load data from Airtable table`
+        this.baseClasses = [this.type]
+        this.credential = {
+            label: 'Connect Credential',
+            name: 'credential',
+            type: 'credential',
+            credentialNames: ['airtableApi']
+        }
+        this.inputs = [
+            {
+                label: 'Text Splitter',
+                name: 'textSplitter',
+                type: 'TextSplitter',
+                optional: true
+            },
+            {
+                label: 'Base Id',
+                name: 'baseId',
+                type: 'string',
+                placeholder: 'app11RobdGoX0YNsC',
+                description:
+                    'If your table URL looks like: https://airtable.com/app11RobdGoX0YNsC/tblJdmvbrgizbYICO/viw9UrP77Id0CE4ee, app11RovdGoX0YNsC is the base id'
+            },
+            {
+                label: 'Table Id',
+                name: 'tableId',
+                type: 'string',
+                placeholder: 'tblJdmvbrgizbYICO',
+                description:
+                    'If your table URL looks like: https://airtable.com/app11RobdGoX0YNsC/tblJdmvbrgizbYICO/viw9UrP77Id0CE4ee, tblJdmvbrgizbYICO is the table id'
+            },
+            {
+                label: 'Return All',
+                name: 'returnAll',
+                type: 'boolean',
+                default: true,
+                additionalParams: true,
+                description: 'If all results should be returned or only up to a given limit'
+            },
+            {
+                label: 'Limit',
+                name: 'limit',
+                type: 'number',
+                default: 100,
+                additionalParams: true,
+                description: 'Number of results to return'
+            },
+            {
+                label: 'Metadata',
+                name: 'metadata',
+                type: 'json',
+                optional: true,
+                additionalParams: true
+            }
+        ]
+    }
+    async init(nodeData: INodeData, _: string, options: ICommonObject): Promise<any> {
+        const baseId = nodeData.inputs?.baseId as string
+        const tableId = nodeData.inputs?.tableId as string
+        const returnAll = nodeData.inputs?.returnAll as boolean
+        const limit = nodeData.inputs?.limit as string
+        const textSplitter = nodeData.inputs?.textSplitter as TextSplitter
+        const metadata = nodeData.inputs?.metadata
+
+        const credentialData = await getCredentialData(nodeData.credential ?? '', options)
+        const accessToken = getCredentialParam('accessToken', credentialData, nodeData)
+
+        const airtableOptions: AirtableLoaderParams = {
+            baseId,
+            tableId,
+            returnAll,
+            accessToken,
+            limit: limit ? parseInt(limit, 10) : 100
+        }
+
+        const loader = new AirtableLoader(airtableOptions)
+
+        let docs = []
+
+        if (textSplitter) {
+            docs = await loader.loadAndSplit(textSplitter)
+        } else {
+            docs = await loader.load()
+        }
+
+        if (metadata) {
+            const parsedMetadata = typeof metadata === 'object' ? metadata : JSON.parse(metadata)
+            let finaldocs = []
+            for (const doc of docs) {
+                const newdoc = {
+                    ...doc,
+                    metadata: {
+                        ...doc.metadata,
+                        ...parsedMetadata
+                    }
+                }
+                finaldocs.push(newdoc)
+            }
+            return finaldocs
+        }
+
+        return docs
+    }
+}
+
+interface AirtableLoaderParams {
+    baseId: string
+    tableId: string
+    accessToken: string
+    limit?: number
+    returnAll?: boolean
+}
+
+interface AirtableLoaderResponse {
+    records: AirtableLoaderPage[]
+    offset?: string
+}
+
+interface AirtableLoaderPage {
+    id: string
+    createdTime: string
+    fields: ICommonObject
+}
+
+class AirtableLoader extends BaseDocumentLoader {
+    public readonly baseId: string
+
+    public readonly tableId: string
+
+    public readonly accessToken: string
+
+    public readonly limit: number
+
+    public readonly returnAll: boolean
+
+    constructor({ baseId, tableId, accessToken, limit = 100, returnAll = false }: AirtableLoaderParams) {
+        super()
+        this.baseId = baseId
+        this.tableId = tableId
+        this.accessToken = accessToken
+        this.limit = limit
+        this.returnAll = returnAll
+    }
+
+    public async load(): Promise<Document[]> {
+        if (this.returnAll) {
+            return this.loadAll()
+        }
+        return this.loadLimit()
+    }
+
+    protected async fetchAirtableData(url: string, params: ICommonObject): Promise<AirtableLoaderResponse> {
+        try {
+            const headers = {
+                Authorization: `Bearer ${this.accessToken}`,
+                'Content-Type': 'application/json',
+                Accept: 'application/json'
+            }
+            const response = await axios.get(url, { params, headers })
+            return response.data
+        } catch (error) {
+            throw new Error(`Failed to fetch ${url} from Airtable: ${error}`)
+        }
+    }
+
+    private createDocumentFromPage(page: AirtableLoaderPage): Document {
+        // Generate the URL
+        const pageUrl = `https://api.airtable.com/v0/${this.baseId}/${this.tableId}/${page.id}`
+
+        // Return a langchain document
+        return new Document({
+            pageContent: JSON.stringify(page.fields, null, 2),
+            metadata: {
+                url: pageUrl
+            }
+        })
+    }
+
+    private async loadLimit(): Promise<Document[]> {
+        const params = { maxRecords: this.limit }
+        const data = await this.fetchAirtableData(`https://api.airtable.com/v0/${this.baseId}/${this.tableId}`, params)
+        if (data.records.length === 0) {
+            return []
+        }
+        return data.records.map((page) => this.createDocumentFromPage(page))
+    }
+
+    private async loadAll(): Promise<Document[]> {
+        const params: ICommonObject = { pageSize: 100 }
+        let data: AirtableLoaderResponse
+        let returnPages: AirtableLoaderPage[] = []
+
+        do {
+            data = await this.fetchAirtableData(`https://api.airtable.com/v0/${this.baseId}/${this.tableId}`, params)
+            returnPages.push.apply(returnPages, data.records)
+            params.offset = data.offset
+        } while (data.offset !== undefined)
+        return returnPages.map((page) => this.createDocumentFromPage(page))
+    }
+}
+
+module.exports = {
+    nodeClass: Airtable_DocumentLoaders
+}

packages/components/nodes/documentloaders/Airtable/airtable.svg

@@ -0,0 +1,9 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<svg width="256px" height="215px" viewBox="0 0 256 215" version="1.1" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" preserveAspectRatio="xMidYMid">
+	<g>
+		<path d="M114.25873,2.70101695 L18.8604023,42.1756384 C13.5552723,44.3711638 13.6102328,51.9065311 18.9486282,54.0225085 L114.746142,92.0117514 C123.163769,95.3498757 132.537419,95.3498757 140.9536,92.0117514 L236.75256,54.0225085 C242.08951,51.9065311 242.145916,44.3711638 236.83934,42.1756384 L141.442459,2.70101695 C132.738459,-0.900338983 122.961284,-0.900338983 114.25873,2.70101695" fill="#FFBF00"></path>
+		<path d="M136.349071,112.756863 L136.349071,207.659101 C136.349071,212.173089 140.900664,215.263892 145.096461,213.600615 L251.844122,172.166219 C254.281184,171.200072 255.879376,168.845451 255.879376,166.224705 L255.879376,71.3224678 C255.879376,66.8084791 251.327783,63.7176768 247.131986,65.3809537 L140.384325,106.815349 C137.94871,107.781496 136.349071,110.136118 136.349071,112.756863" fill="#26B5F8"></path>
+		<path d="M111.422771,117.65355 L79.742409,132.949912 L76.5257763,134.504714 L9.65047684,166.548104 C5.4112904,168.593211 0.000578531073,165.503855 0.000578531073,160.794612 L0.000578531073,71.7210757 C0.000578531073,70.0173017 0.874160452,68.5463864 2.04568588,67.4384994 C2.53454463,66.9481944 3.08848814,66.5446689 3.66412655,66.2250305 C5.26231864,65.2661153 7.54173107,65.0101153 9.47981017,65.7766689 L110.890522,105.957098 C116.045234,108.002206 116.450206,115.225166 111.422771,117.65355" fill="#ED3049"></path>
+		<path d="M111.422771,117.65355 L79.742409,132.949912 L2.04568588,67.4384994 C2.53454463,66.9481944 3.08848814,66.5446689 3.66412655,66.2250305 C5.26231864,65.2661153 7.54173107,65.0101153 9.47981017,65.7766689 L110.890522,105.957098 C116.045234,108.002206 116.450206,115.225166 111.422771,117.65355" fill-opacity="0.25" fill="#000000"></path>
+	</g>
+</svg>

packages/components/nodes/documentloaders/ApifyWebsiteContentCrawler/ApifyWebsiteContentCrawler.ts

@@ -0,0 +1,139 @@
+import { INode, INodeData, INodeParams, ICommonObject } from '../../../src/Interface'
+import { getCredentialData, getCredentialParam } from '../../../src/utils'
+import { TextSplitter } from 'langchain/text_splitter'
+import { ApifyDatasetLoader } from 'langchain/document_loaders/web/apify_dataset'
+import { Document } from 'langchain/document'
+
+class ApifyWebsiteContentCrawler_DocumentLoaders implements INode {
+    label: string
+    name: string
+    description: string
+    type: string
+    icon: string
+    version: number
+    category: string
+    baseClasses: string[]
+    inputs: INodeParams[]
+    credential: INodeParams
+
+    constructor() {
+        this.label = 'Apify Website Content Crawler'
+        this.name = 'apifyWebsiteContentCrawler'
+        this.type = 'Document'
+        this.icon = 'apify-symbol-transparent.svg'
+        this.version = 1.0
+        this.category = 'Document Loaders'
+        this.description = 'Load data from Apify Website Content Crawler'
+        this.baseClasses = [this.type]
+        this.inputs = [
+            {
+                label: 'Start URLs',
+                name: 'urls',
+                type: 'string',
+                description: 'One or more URLs of pages where the crawler will start, separated by commas.',
+                placeholder: 'https://js.langchain.com/docs/'
+            },
+            {
+                label: 'Crawler type',
+                type: 'options',
+                name: 'crawlerType',
+                options: [
+                    {
+                        label: 'Headless web browser (Chrome+Playwright)',
+                        name: 'playwright:chrome'
+                    },
+                    {
+                        label: 'Stealthy web browser (Firefox+Playwright)',
+                        name: 'playwright:firefox'
+                    },
+                    {
+                        label: 'Raw HTTP client (Cheerio)',
+                        name: 'cheerio'
+                    },
+                    {
+                        label: 'Raw HTTP client with JavaScript execution (JSDOM) [experimental]',
+                        name: 'jsdom'
+                    }
+                ],
+                description:
+                    'Select the crawling engine, see <a target="_blank" href="https://apify.com/apify/website-content-crawler#crawling">documentation</a> for additional information.',
+                default: 'playwright:firefox'
+            },
+            {
+                label: 'Max crawling depth',
+                name: 'maxCrawlDepth',
+                type: 'number',
+                optional: true,
+                default: 1
+            },
+            {
+                label: 'Max crawl pages',
+                name: 'maxCrawlPages',
+                type: 'number',
+                optional: true,
+                default: 3
+            },
+            {
+                label: 'Additional input',
+                name: 'additionalInput',
+                type: 'json',
+                default: JSON.stringify({}),
+                description:
+                    'For additional input options for the crawler see <a target="_blank" href="https://apify.com/apify/website-content-crawler/input-schema">documentation</a>.',
+                optional: true
+            },
+            {
+                label: 'Text Splitter',
+                name: 'textSplitter',
+                type: 'TextSplitter',
+                optional: true
+            }
+        ]
+        this.credential = {
+            label: 'Connect Apify API',
+            name: 'credential',
+            type: 'credential',
+            credentialNames: ['apifyApi']
+        }
+    }
+
+    async init(nodeData: INodeData, _: string, options: ICommonObject): Promise<any> {
+        const textSplitter = nodeData.inputs?.textSplitter as TextSplitter
+
+        // Get input options and merge with additional input
+        const urls = nodeData.inputs?.urls as string
+        const crawlerType = nodeData.inputs?.crawlerType as string
+        const maxCrawlDepth = nodeData.inputs?.maxCrawlDepth as string
+        const maxCrawlPages = nodeData.inputs?.maxCrawlPages as string
+        const additionalInput =
+            typeof nodeData.inputs?.additionalInput === 'object'
+                ? nodeData.inputs?.additionalInput
+                : JSON.parse(nodeData.inputs?.additionalInput as string)
+        const input = {
+            startUrls: urls.split(',').map((url) => ({ url: url.trim() })),
+            crawlerType,
+            maxCrawlDepth: parseInt(maxCrawlDepth, 10),
+            maxCrawlPages: parseInt(maxCrawlPages, 10),
+            ...additionalInput
+        }
+
+        // Get Apify API token from credential data
+        const credentialData = await getCredentialData(nodeData.credential ?? '', options)
+        const apifyApiToken = getCredentialParam('apifyApiToken', credentialData, nodeData)
+
+        const loader = await ApifyDatasetLoader.fromActorCall('apify/website-content-crawler', input, {
+            datasetMappingFunction: (item) =>
+                new Document({
+                    pageContent: (item.text || '') as string,
+                    metadata: { source: item.url }
+                }),
+            clientOptions: {
+                token: apifyApiToken
+            }
+        })
+
+        return textSplitter ? loader.loadAndSplit(textSplitter) : loader.load()
+    }
+}
+
+module.exports = { nodeClass: ApifyWebsiteContentCrawler_DocumentLoaders }

packages/components/nodes/documentloaders/ApifyWebsiteContentCrawler/apify-symbol-transparent.svg

@@ -0,0 +1 @@
+<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 512 512"><defs><style>.cls-1{fill:none;}.cls-2{fill:#97d700;}.cls-3{fill:#71c5e8;}.cls-4{fill:#ff9013;}</style></defs><g id="Trmplate"><rect class="cls-1" width="512" height="512"/><path class="cls-2" d="M163.14,152.65a36.06,36.06,0,0,0-30.77,40.67v0l21.34,152.33,89.74-204.23Z"/><path class="cls-3" d="M379.69,279.56l-8.38-117.1a36.12,36.12,0,0,0-38.53-33.36,17.61,17.61,0,0,0-2.4.26l-34.63,4.79,76.08,170.57A35.94,35.94,0,0,0,379.69,279.56Z"/><path class="cls-4" d="M186.43,382.69a35.88,35.88,0,0,0,18-2.63l130.65-55.13L273,185.65Z"/></g></svg>

packages/components/nodes/documentloaders/Cheerio/Cheerio.ts

@@ -2,11 +2,12 @@ import { INode, INodeData, INodeParams } from '../../../src/Interface'
 import { TextSplitter } from 'langchain/text_splitter'
 import { CheerioWebBaseLoader } from 'langchain/document_loaders/web/cheerio'
 import { test } from 'linkifyjs'
-import { getAvailableURLs } from '../../../src'
+import { webCrawl, xmlScrape } from '../../../src'
 
 class Cheerio_DocumentLoaders implements INode {
     label: string
     name: string
+    version: number
     description: string
     type: string
     icon: string
@@ -17,6 +18,7 @@ class Cheerio_DocumentLoaders implements INode {
     constructor() {
         this.label = 'Cheerio Web Scraper'
         this.name = 'cheerioWebScraper'
+        this.version = 1.0
         this.type = 'Document'
         this.icon = 'cheerio.svg'
         this.category = 'Document Loaders'
@@ -35,19 +37,34 @@ class Cheerio_DocumentLoaders implements INode {
                 optional: true
             },
             {
-                label: 'Web Scrap for Relative Links',
-                name: 'webScrap',
-                type: 'boolean',
+                label: 'Get Relative Links Method',
+                name: 'relativeLinksMethod',
+                type: 'options',
+                description: 'Select a method to retrieve relative links',
+                options: [
+                    {
+                        label: 'Web Crawl',
+                        name: 'webCrawl',
+                        description: 'Crawl relative links from HTML URL'
+                    },
+                    {
+                        label: 'Scrape XML Sitemap',
+                        name: 'scrapeXMLSitemap',
+                        description: 'Scrape relative links from XML sitemap URL'
+                    }
+                ],
                 optional: true,
                 additionalParams: true
             },
             {
-                label: 'Web Scrap Links Limit',
+                label: 'Get Relative Links Limit',
                 name: 'limit',
                 type: 'number',
-                default: 10,
                 optional: true,
-                additionalParams: true
+                additionalParams: true,
+                description:
+                    'Only used when "Get Relative Links Method" is selected. Set 0 to retrieve all relative links, default limit is 10.',
+                warning: `Retrieving all links might take long time, and all links will be upserted again if the flow's state changed (eg: different URL, chunk size, etc)`
             },
             {
                 label: 'Metadata',
@@ -62,7 +79,7 @@ class Cheerio_DocumentLoaders implements INode {
     async init(nodeData: INodeData): Promise<any> {
         const textSplitter = nodeData.inputs?.textSplitter as TextSplitter
         const metadata = nodeData.inputs?.metadata
-        const webScrap = nodeData.inputs?.webScrap as boolean
+        const relativeLinksMethod = nodeData.inputs?.relativeLinksMethod as string
         let limit = nodeData.inputs?.limit as string
 
         let url = nodeData.inputs?.url as string
@@ -71,25 +88,34 @@ class Cheerio_DocumentLoaders implements INode {
             throw new Error('Invalid URL')
         }
 
-        const cheerioLoader = async (url: string): Promise<any> => {
-            let docs = []
-            const loader = new CheerioWebBaseLoader(url)
-            if (textSplitter) {
-                docs = await loader.loadAndSplit(textSplitter)
-            } else {
-                docs = await loader.load()
+        async function cheerioLoader(url: string): Promise<any> {
+            try {
+                let docs = []
+                const loader = new CheerioWebBaseLoader(url)
+                if (textSplitter) {
+                    docs = await loader.loadAndSplit(textSplitter)
+                } else {
+                    docs = await loader.load()
+                }
+                return docs
+            } catch (err) {
+                if (process.env.DEBUG === 'true') console.error(`error in CheerioWebBaseLoader: ${err.message}, on page: ${url}`)
             }
-            return docs
         }
 
-        let availableUrls: string[]
         let docs = []
-        if (webScrap) {
+        if (relativeLinksMethod) {
+            if (process.env.DEBUG === 'true') console.info(`Start ${relativeLinksMethod}`)
             if (!limit) limit = '10'
-            availableUrls = await getAvailableURLs(url, parseInt(limit))
-            for (let i = 0; i < availableUrls.length; i++) {
-                docs.push(...(await cheerioLoader(availableUrls[i])))
+            else if (parseInt(limit) < 0) throw new Error('Limit cannot be less than 0')
+            const pages: string[] =
+                relativeLinksMethod === 'webCrawl' ? await webCrawl(url, parseInt(limit)) : await xmlScrape(url, parseInt(limit))
+            if (process.env.DEBUG === 'true') console.info(`pages: ${JSON.stringify(pages)}, length: ${pages.length}`)
+            if (!pages || pages.length === 0) throw new Error('No relative links found')
+            for (const page of pages) {
+                docs.push(...(await cheerioLoader(page)))
             }
+            if (process.env.DEBUG === 'true') console.info(`Finish ${relativeLinksMethod}`)
         } else {
             docs = await cheerioLoader(url)
         }

packages/components/nodes/documentloaders/Confluence/Confluence.ts

@@ -0,0 +1,120 @@
+import { ICommonObject, INode, INodeData, INodeParams } from '../../../src/Interface'
+import { TextSplitter } from 'langchain/text_splitter'
+import { ConfluencePagesLoader, ConfluencePagesLoaderParams } from 'langchain/document_loaders/web/confluence'
+import { getCredentialData, getCredentialParam } from '../../../src'
+
+class Confluence_DocumentLoaders implements INode {
+    label: string
+    name: string
+    version: number
+    description: string
+    type: string
+    icon: string
+    category: string
+    baseClasses: string[]
+    credential: INodeParams
+    inputs: INodeParams[]
+
+    constructor() {
+        this.label = 'Confluence'
+        this.name = 'confluence'
+        this.version = 1.0
+        this.type = 'Document'
+        this.icon = 'confluence.png'
+        this.category = 'Document Loaders'
+        this.description = `Load data from a Confluence Document`
+        this.baseClasses = [this.type]
+        this.credential = {
+            label: 'Connect Credential',
+            name: 'credential',
+            type: 'credential',
+            credentialNames: ['confluenceApi']
+        }
+        this.inputs = [
+            {
+                label: 'Text Splitter',
+                name: 'textSplitter',
+                type: 'TextSplitter',
+                optional: true
+            },
+            {
+                label: 'Base URL',
+                name: 'baseUrl',
+                type: 'string',
+                placeholder: 'https://example.atlassian.net/wiki'
+            },
+            {
+                label: 'Space Key',
+                name: 'spaceKey',
+                type: 'string',
+                placeholder: '~EXAMPLE362906de5d343d49dcdbae5dEXAMPLE',
+                description:
+                    'Refer to <a target="_blank" href="https://community.atlassian.com/t5/Confluence-questions/How-to-find-the-key-for-a-space/qaq-p/864760">official guide</a> on how to get Confluence Space Key'
+            },
+            {
+                label: 'Limit',
+                name: 'limit',
+                type: 'number',
+                default: 0,
+                optional: true
+            },
+            {
+                label: 'Metadata',
+                name: 'metadata',
+                type: 'json',
+                optional: true,
+                additionalParams: true
+            }
+        ]
+    }
+
+    async init(nodeData: INodeData, _: string, options: ICommonObject): Promise<any> {
+        const spaceKey = nodeData.inputs?.spaceKey as string
+        const baseUrl = nodeData.inputs?.baseUrl as string
+        const limit = nodeData.inputs?.limit as number
+        const textSplitter = nodeData.inputs?.textSplitter as TextSplitter
+        const metadata = nodeData.inputs?.metadata
+
+        const credentialData = await getCredentialData(nodeData.credential ?? '', options)
+        const accessToken = getCredentialParam('accessToken', credentialData, nodeData)
+        const username = getCredentialParam('username', credentialData, nodeData)
+
+        const confluenceOptions: ConfluencePagesLoaderParams = {
+            username,
+            accessToken,
+            baseUrl,
+            spaceKey,
+            limit
+        }
+
+        const loader = new ConfluencePagesLoader(confluenceOptions)
+
+        let docs = []
+
+        if (textSplitter) {
+            docs = await loader.loadAndSplit(textSplitter)
+        } else {
+            docs = await loader.load()
+        }
+
+        if (metadata) {
+            const parsedMetadata = typeof metadata === 'object' ? metadata : JSON.parse(metadata)
+            let finaldocs = []
+            for (const doc of docs) {
+                const newdoc = {
+                    ...doc,
+                    metadata: {
+                        ...doc.metadata,
+                        ...parsedMetadata
+                    }
+                }
+                finaldocs.push(newdoc)
+            }
+            return finaldocs
+        }
+
+        return docs
+    }
+}
+
+module.exports = { nodeClass: Confluence_DocumentLoaders }

packages/components/nodes/documentloaders/Csv/Csv.ts

@@ -5,6 +5,7 @@ import { CSVLoader } from 'langchain/document_loaders/fs/csv'
 class Csv_DocumentLoaders implements INode {
     label: string
     name: string
+    version: number
     description: string
     type: string
     icon: string
@@ -15,6 +16,7 @@ class Csv_DocumentLoaders implements INode {
     constructor() {
         this.label = 'Csv File'
         this.name = 'csvFile'
+        this.version = 1.0
         this.type = 'Document'
         this.icon = 'Csv.png'
         this.category = 'Document Loaders'

packages/components/nodes/documentloaders/Docx/Docx.ts

@@ -5,6 +5,7 @@ import { DocxLoader } from 'langchain/document_loaders/fs/docx'
 class Docx_DocumentLoaders implements INode {
     label: string
     name: string
+    version: number
     description: string
     type: string
     icon: string
@@ -15,6 +16,7 @@ class Docx_DocumentLoaders implements INode {
     constructor() {
         this.label = 'Docx File'
         this.name = 'docxFile'
+        this.version = 1.0
         this.type = 'Document'
         this.icon = 'Docx.png'
         this.category = 'Document Loaders'

packages/components/nodes/documentloaders/Figma/Figma.ts

@@ -0,0 +1,91 @@
+import { getCredentialData, getCredentialParam } from '../../../src'
+import { ICommonObject, INode, INodeData, INodeParams } from '../../../src/Interface'
+import { FigmaFileLoader, FigmaLoaderParams } from 'langchain/document_loaders/web/figma'
+
+class Figma_DocumentLoaders implements INode {
+    label: string
+    name: string
+    version: number
+    description: string
+    type: string
+    icon: string
+    category: string
+    baseClasses: string[]
+    credential: INodeParams
+    inputs: INodeParams[]
+
+    constructor() {
+        this.label = 'Figma'
+        this.name = 'figma'
+        this.version = 1.0
+        this.type = 'Document'
+        this.icon = 'figma.svg'
+        this.category = 'Document Loaders'
+        this.description = 'Load data from a Figma file'
+        this.baseClasses = [this.type]
+        this.credential = {
+            label: 'Connect Credential',
+            name: 'credential',
+            type: 'credential',
+            credentialNames: ['figmaApi']
+        }
+        this.inputs = [
+            {
+                label: 'File Key',
+                name: 'fileKey',
+                type: 'string',
+                placeholder: 'key',
+                description:
+                    'The file key can be read from any Figma file URL: https://www.figma.com/file/:key/:title. For example, in https://www.figma.com/file/12345/Website, the file key is 12345'
+            },
+            {
+                label: 'Node IDs',
+                name: 'nodeIds',
+                type: 'string',
+                placeholder: '0, 1, 2',
+                description:
+                    'A list of Node IDs, seperated by comma. Refer to <a target="_blank" href="https://www.figma.com/community/plugin/758276196886757462/Node-Inspector">official guide</a> on how to get Node IDs'
+            },
+            {
+                label: 'Recursive',
+                name: 'recursive',
+                type: 'boolean',
+                optional: true
+            },
+            {
+                label: 'Text Splitter',
+                name: 'textSplitter',
+                type: 'TextSplitter',
+                optional: true
+            },
+            {
+                label: 'Metadata',
+                name: 'metadata',
+                type: 'json',
+                optional: true,
+                additionalParams: true
+            }
+        ]
+    }
+
+    async init(nodeData: INodeData, _: string, options: ICommonObject): Promise<any> {
+        const nodeIds = (nodeData.inputs?.nodeIds as string)?.trim().split(',') || []
+        const fileKey = nodeData.inputs?.fileKey as string
+
+        const credentialData = await getCredentialData(nodeData.credential ?? '', options)
+        const accessToken = getCredentialParam('accessToken', credentialData, nodeData)
+
+        const figmaOptions: FigmaLoaderParams = {
+            accessToken,
+            nodeIds,
+            fileKey
+        }
+
+        const loader = new FigmaFileLoader(figmaOptions)
+        const docs = await loader.load()
+
+        return docs
+    }
+}
+
+module.exports = { nodeClass: Figma_DocumentLoaders }

packages/components/nodes/documentloaders/Figma/figma.svg

@@ -0,0 +1 @@
+<svg xmlns="http://www.w3.org/2000/svg"  viewBox="0 0 48 48" width="96px" height="96px"><path fill="#e64a19" d="M26,17h-8c-3.866,0-7-3.134-7-7v0c0-3.866,3.134-7,7-7h8V17z"/><path fill="#7c4dff" d="M25,31h-7c-3.866,0-7-3.134-7-7v0c0-3.866,3.134-7,7-7h7V31z"/><path fill="#66bb6a" d="M18,45L18,45c-3.866,0-7-3.134-7-7v0c0-3.866,3.134-7,7-7h7v7C25,41.866,21.866,45,18,45z"/><path fill="#ff7043" d="M32,17h-7V3h7c3.866,0,7,3.134,7,7v0C39,13.866,35.866,17,32,17z"/><circle cx="32" cy="24" r="7" fill="#29b6f6"/></svg>

packages/components/nodes/documentloaders/Folder/Folder.ts

@@ -10,6 +10,7 @@ import { DocxLoader } from 'langchain/document_loaders/fs/docx'
 class Folder_DocumentLoaders implements INode {
     label: string
     name: string
+    version: number
     description: string
     type: string
     icon: string
@@ -20,6 +21,7 @@ class Folder_DocumentLoaders implements INode {
     constructor() {
         this.label = 'Folder with Files'
         this.name = 'folderFiles'
+        this.version = 1.0
         this.type = 'Document'
         this.icon = 'folder.svg'
         this.category = 'Document Loaders'
@@ -59,7 +61,40 @@ class Folder_DocumentLoaders implements INode {
             '.csv': (path) => new CSVLoader(path),
             '.docx': (path) => new DocxLoader(path),
             // @ts-ignore
-            '.pdf': (path) => new PDFLoader(path, { pdfjs: () => import('pdf-parse/lib/pdf.js/v1.10.100/build/pdf.js') })
+            '.pdf': (path) => new PDFLoader(path, { pdfjs: () => import('pdf-parse/lib/pdf.js/v1.10.100/build/pdf.js') }),
+            '.aspx': (path) => new TextLoader(path),
+            '.asp': (path) => new TextLoader(path),
+            '.cpp': (path) => new TextLoader(path), // C++
+            '.c': (path) => new TextLoader(path),
+            '.cs': (path) => new TextLoader(path),
+            '.css': (path) => new TextLoader(path),
+            '.go': (path) => new TextLoader(path), // Go
+            '.h': (path) => new TextLoader(path), // C++ Header files
+            '.java': (path) => new TextLoader(path), // Java
+            '.js': (path) => new TextLoader(path), // JavaScript
+            '.less': (path) => new TextLoader(path), // Less files
+            '.ts': (path) => new TextLoader(path), // TypeScript
+            '.php': (path) => new TextLoader(path), // PHP
+            '.proto': (path) => new TextLoader(path), // Protocol Buffers
+            '.python': (path) => new TextLoader(path), // Python
+            '.py': (path) => new TextLoader(path), // Python
+            '.rst': (path) => new TextLoader(path), // reStructuredText
+            '.ruby': (path) => new TextLoader(path), // Ruby
+            '.rb': (path) => new TextLoader(path), // Ruby
+            '.rs': (path) => new TextLoader(path), // Rust
+            '.scala': (path) => new TextLoader(path), // Scala
+            '.sc': (path) => new TextLoader(path), // Scala
+            '.scss': (path) => new TextLoader(path), // Sass
+            '.sol': (path) => new TextLoader(path), // Solidity
+            '.sql': (path) => new TextLoader(path), //SQL
+            '.swift': (path) => new TextLoader(path), // Swift
+            '.markdown': (path) => new TextLoader(path), // Markdown
+            '.md': (path) => new TextLoader(path), // Markdown
+            '.tex': (path) => new TextLoader(path), // LaTeX
+            '.ltx': (path) => new TextLoader(path), // LaTeX
+            '.html': (path) => new TextLoader(path), // HTML
+            '.vb': (path) => new TextLoader(path), // Visual Basic
+            '.xml': (path) => new TextLoader(path) // XML
         })
         let docs = []
 

packages/components/nodes/documentloaders/Gitbook/Gitbook.ts

@@ -0,0 +1,84 @@
+import { INode, INodeData, INodeParams } from '../../../src/Interface'
+import { TextSplitter } from 'langchain/text_splitter'
+import { GitbookLoader } from 'langchain/document_loaders/web/gitbook'
+
+class Gitbook_DocumentLoaders implements INode {
+    label: string
+    name: string
+    version: number
+    description: string
+    type: string
+    icon: string
+    category: string
+    baseClasses: string[]
+    inputs?: INodeParams[]
+
+    constructor() {
+        this.label = 'GitBook'
+        this.name = 'gitbook'
+        this.version = 1.0
+        this.type = 'Document'
+        this.icon = 'gitbook.svg'
+        this.category = 'Document Loaders'
+        this.description = `Load data from GitBook`
+        this.baseClasses = [this.type]
+        this.inputs = [
+            {
+                label: 'Web Path',
+                name: 'webPath',
+                type: 'string',
+                placeholder: 'https://docs.gitbook.com/product-tour/navigation',
+                description: 'If want to load all paths from the GitBook provide only root path e.g.https://docs.gitbook.com/ '
+            },
+            {
+                label: 'Should Load All Paths',
+                name: 'shouldLoadAllPaths',
+                type: 'boolean',
+                description: 'Load from all paths in a given GitBook',
+                optional: true
+            },
+            {
+                label: 'Text Splitter',
+                name: 'textSplitter',
+                type: 'TextSplitter',
+                optional: true
+            },
+            {
+                label: 'Metadata',
+                name: 'metadata',
+                type: 'json',
+                optional: true,
+                additionalParams: true
+            }
+        ]
+    }
+    async init(nodeData: INodeData): Promise<any> {
+        const webPath = nodeData.inputs?.webPath as string
+        const shouldLoadAllPaths = nodeData.inputs?.shouldLoadAllPaths as boolean
+        const textSplitter = nodeData.inputs?.textSplitter as TextSplitter
+        const metadata = nodeData.inputs?.metadata
+
+        const loader = shouldLoadAllPaths ? new GitbookLoader(webPath, { shouldLoadAllPaths }) : new GitbookLoader(webPath)
+
+        const docs = textSplitter ? await loader.loadAndSplit() : await loader.load()
+
+        if (metadata) {
+            const parsedMetadata = typeof metadata === 'object' ? metadata : JSON.parse(metadata)
+            return docs.map((doc) => {
+                return {
+                    ...doc,
+                    metadata: {
+                        ...doc.metadata,
+                        ...parsedMetadata
+                    }
+                }
+            })
+        }
+
+        return docs
+    }
+}
+
+module.exports = {
+    nodeClass: Gitbook_DocumentLoaders
+}

packages/components/nodes/documentloaders/Gitbook/gitbook.svg

@@ -0,0 +1 @@
+<svg xmlns="http://www.w3.org/2000/svg" width="64" height="64"><switch><g><path d="M28.8 47.4c1 0 1.9.8 1.9 1.9 0 1-.8 1.9-1.9 1.9-1 0-1.9-.8-1.9-1.9 0-1.1.9-1.9 1.9-1.9m29.4-11.6c-1 0-1.9-.8-1.9-1.9 0-1 .8-1.9 1.9-1.9 1 0 1.9.8 1.9 1.9 0 1-.9 1.9-1.9 1.9m0-7.7c-3.2 0-5.8 2.6-5.8 5.8 0 .6.1 1.2.3 1.8L33.6 45.9c-1.1-1.6-2.9-2.5-4.8-2.5-2.2 0-4.2 1.3-5.2 3.2l-17.2-9c-1.8-1-3.2-3.9-3-6.7.1-1.4.6-2.5 1.3-2.9.5-.3 1-.2 1.7.1l.1.1c4.6 2.4 19.5 10.2 20.1 10.5 1 .4 1.5.6 3.2-.2l30.8-16c.5-.2 1-.6 1-1.3 0-.9-.9-1.3-.9-1.3-1.8-.8-4.5-2.1-7.1-3.3C48 14 41.6 11 38.8 9.5c-2.4-1.3-4.4-.2-4.7 0l-.7.3C20.7 16.2 3.9 24.5 2.9 25.1c-1.7 1-2.8 3.1-2.9 5.7-.2 4.1 1.9 8.4 4.9 9.9l18.2 9.4c.4 2.8 2.9 5 5.7 5 3.2 0 5.7-2.5 5.8-5.7l20-10.8c1 .8 2.3 1.2 3.6 1.2 3.2 0 5.8-2.6 5.8-5.8 0-3.3-2.6-5.9-5.8-5.9" fill="#4285fd"/></g></switch></svg>

packages/components/nodes/documentloaders/Github/Github.ts

@@ -1,25 +1,37 @@
-import { INode, INodeData, INodeParams } from '../../../src/Interface'
+import { ICommonObject, INode, INodeData, INodeParams } from '../../../src/Interface'
 import { TextSplitter } from 'langchain/text_splitter'
 import { GithubRepoLoader, GithubRepoLoaderParams } from 'langchain/document_loaders/web/github'
+import { getCredentialData, getCredentialParam } from '../../../src'
 
 class Github_DocumentLoaders implements INode {
     label: string
     name: string
+    version: number
     description: string
     type: string
     icon: string
     category: string
     baseClasses: string[]
+    credential: INodeParams
     inputs: INodeParams[]
 
     constructor() {
         this.label = 'Github'
         this.name = 'github'
+        this.version = 1.0
         this.type = 'Document'
         this.icon = 'github.png'
         this.category = 'Document Loaders'
         this.description = `Load data from a GitHub repository`
         this.baseClasses = [this.type]
+        this.credential = {
+            label: 'Connect Credential',
+            name: 'credential',
+            type: 'credential',
+            description: 'Only needed when accessing private repo',
+            optional: true,
+            credentialNames: ['githubApi']
+        }
         this.inputs = [
             {
                 label: 'Repo Link',
@@ -34,10 +46,9 @@ class Github_DocumentLoaders implements INode {
                 default: 'main'
             },
             {
-                label: 'Access Token',
-                name: 'accessToken',
-                type: 'password',
-                placeholder: '<GITHUB_ACCESS_TOKEN>',
+                label: 'Recursive',
+                name: 'recursive',
+                type: 'boolean',
                 optional: true
             },
             {
@@ -56,44 +67,38 @@ class Github_DocumentLoaders implements INode {
         ]
     }
 
-    async init(nodeData: INodeData): Promise<any> {
+    async init(nodeData: INodeData, _: string, options: ICommonObject): Promise<any> {
         const repoLink = nodeData.inputs?.repoLink as string
         const branch = nodeData.inputs?.branch as string
-        const accessToken = nodeData.inputs?.accessToken as string
+        const recursive = nodeData.inputs?.recursive as boolean
         const textSplitter = nodeData.inputs?.textSplitter as TextSplitter
         const metadata = nodeData.inputs?.metadata
 
-        const options: GithubRepoLoaderParams = {
+        const credentialData = await getCredentialData(nodeData.credential ?? '', options)
+        const accessToken = getCredentialParam('accessToken', credentialData, nodeData)
+
+        const githubOptions: GithubRepoLoaderParams = {
             branch,
-            recursive: false,
+            recursive,
             unknown: 'warn'
         }
 
-        if (accessToken) options.accessToken = accessToken
+        if (accessToken) githubOptions.accessToken = accessToken
 
-        const loader = new GithubRepoLoader(repoLink, options)
-        let docs = []
-
-        if (textSplitter) {
-            docs = await loader.loadAndSplit(textSplitter)
-        } else {
-            docs = await loader.load()
-        }
+        const loader = new GithubRepoLoader(repoLink, githubOptions)
+        const docs = textSplitter ? await loader.loadAndSplit(textSplitter) : await loader.load()
 
         if (metadata) {
             const parsedMetadata = typeof metadata === 'object' ? metadata : JSON.parse(metadata)
-            let finaldocs = []
-            for (const doc of docs) {
-                const newdoc = {
+            return docs.map((doc) => {
+                return {
                     ...doc,
                     metadata: {
                         ...doc.metadata,
                         ...parsedMetadata
                     }
                 }
-                finaldocs.push(newdoc)
-            }
-            return finaldocs
+            })
         }
 
         return docs

packages/components/nodes/documentloaders/Json/Json.ts

@@ -5,6 +5,7 @@ import { JSONLoader } from 'langchain/document_loaders/fs/json'
 class Json_DocumentLoaders implements INode {
     label: string
     name: string
+    version: number
     description: string
     type: string
     icon: string
@@ -15,6 +16,7 @@ class Json_DocumentLoaders implements INode {
     constructor() {
         this.label = 'Json File'
         this.name = 'jsonFile'
+        this.version = 1.0
         this.type = 'Document'
         this.icon = 'json.svg'
         this.category = 'Document Loaders'

packages/components/nodes/documentloaders/Jsonlines/Jsonlines.ts

@@ -0,0 +1,108 @@
+import { INode, INodeData, INodeParams } from '../../../src/Interface'
+import { TextSplitter } from 'langchain/text_splitter'
+import { JSONLinesLoader } from 'langchain/document_loaders/fs/json'
+
+class Jsonlines_DocumentLoaders implements INode {
+    label: string
+    name: string
+    version: number
+    description: string
+    type: string
+    icon: string
+    category: string
+    baseClasses: string[]
+    inputs: INodeParams[]
+
+    constructor() {
+        this.label = 'Json Lines File'
+        this.name = 'jsonlinesFile'
+        this.version = 1.0
+        this.type = 'Document'
+        this.icon = 'jsonlines.svg'
+        this.category = 'Document Loaders'
+        this.description = `Load data from JSON Lines files`
+        this.baseClasses = [this.type]
+        this.inputs = [
+            {
+                label: 'Jsonlines File',
+                name: 'jsonlinesFile',
+                type: 'file',
+                fileType: '.jsonl'
+            },
+            {
+                label: 'Text Splitter',
+                name: 'textSplitter',
+                type: 'TextSplitter',
+                optional: true
+            },
+            {
+                label: 'Pointer Extraction',
+                name: 'pointerName',
+                type: 'string',
+                placeholder: 'Enter pointer name',
+                optional: false
+            },
+            {
+                label: 'Metadata',
+                name: 'metadata',
+                type: 'json',
+                optional: true,
+                additionalParams: true
+            }
+        ]
+    }
+
+    async init(nodeData: INodeData): Promise<any> {
+        const textSplitter = nodeData.inputs?.textSplitter as TextSplitter
+        const jsonLinesFileBase64 = nodeData.inputs?.jsonlinesFile as string
+        const pointerName = nodeData.inputs?.pointerName as string
+        const metadata = nodeData.inputs?.metadata
+
+        let alldocs = []
+        let files: string[] = []
+
+        let pointer = '/' + pointerName.trim()
+
+        if (jsonLinesFileBase64.startsWith('[') && jsonLinesFileBase64.endsWith(']')) {
+            files = JSON.parse(jsonLinesFileBase64)
+        } else {
+            files = [jsonLinesFileBase64]
+        }
+
+        for (const file of files) {
+            const splitDataURI = file.split(',')
+            splitDataURI.pop()
+            const bf = Buffer.from(splitDataURI.pop() || '', 'base64')
+            const blob = new Blob([bf])
+            const loader = new JSONLinesLoader(blob, pointer)
+
+            if (textSplitter) {
+                const docs = await loader.loadAndSplit(textSplitter)
+                alldocs.push(...docs)
+            } else {
+                const docs = await loader.load()
+                alldocs.push(...docs)
+            }
+        }
+
+        if (metadata) {
+            const parsedMetadata = typeof metadata === 'object' ? metadata : JSON.parse(metadata)
+            let finaldocs = []
+            for (const doc of alldocs) {
+                const newdoc = {
+                    ...doc,
+                    metadata: {
+                        ...doc.metadata,
+                        ...parsedMetadata
+                    }
+                }
+                finaldocs.push(newdoc)
+            }
+            return finaldocs
+        }
+
+        return alldocs
+    }
+}
+
+module.exports = { nodeClass: Jsonlines_DocumentLoaders }

packages/components/nodes/documentloaders/Jsonlines/jsonlines.svg

@@ -0,0 +1,16 @@
+<svg width="24" height="24" xmlns="http://www.w3.org/2000/svg">
+ <!-- Created with Method Draw - http://github.com/duopixel/Method-Draw/ -->
+
+ <g>
+  <title>background</title>
+  <rect fill="none" id="canvas_background" height="26" width="26" y="-1" x="-1"/>
+  <g display="none" overflow="visible" y="0" x="0" height="100%" width="100%" id="canvasGrid">
+   <rect fill="url(#gridpattern)" stroke-width="0" y="0" x="0" height="100%" width="100%"/>
+  </g>
+ </g>
+ <g>
+  <title>Layer 1</title>
+  <text font-weight="bold" stroke="#000" transform="matrix(8.682896011956823,0,0,10.412942243751806,-30.866304860177404,-63.784276261342) " xml:space="preserve" text-anchor="start" font-family="Helvetica, Arial, sans-serif" font-size="1" id="svg_2" y="7.062874" x="3.579384" stroke-opacity="null" stroke-width="0" fill="#000000">JSON</text>
+  <text font-weight="bold" stroke="#000" transform="matrix(9.059566511875573,0,0,9.893934811310315,-1.3962337706973242,-106.08964247698567) " xml:space="preserve" text-anchor="start" font-family="Helvetica, Arial, sans-serif" font-size="1" id="svg_3" y="12.90427" x="0.172236" stroke-opacity="null" stroke-width="0" fill="#000000">Lines</text>
+ </g>
+</svg>

packages/components/nodes/documentloaders/Notion/NotionDB.ts

@@ -0,0 +1,100 @@
+import { ICommonObject, INode, INodeData, INodeParams } from '../../../src/Interface'
+import { TextSplitter } from 'langchain/text_splitter'
+import { NotionAPILoader, NotionAPILoaderOptions } from 'langchain/document_loaders/web/notionapi'
+import { getCredentialData, getCredentialParam } from '../../../src'
+
+class NotionDB_DocumentLoaders implements INode {
+    label: string
+    name: string
+    version: number
+    description: string
+    type: string
+    icon: string
+    category: string
+    baseClasses: string[]
+    credential: INodeParams
+    inputs: INodeParams[]
+
+    constructor() {
+        this.label = 'Notion Database'
+        this.name = 'notionDB'
+        this.version = 1.0
+        this.type = 'Document'
+        this.icon = 'notion.png'
+        this.category = 'Document Loaders'
+        this.description = 'Load data from Notion Database (each row is a separate document with all properties as metadata)'
+        this.baseClasses = [this.type]
+        this.credential = {
+            label: 'Connect Credential',
+            name: 'credential',
+            type: 'credential',
+            credentialNames: ['notionApi']
+        }
+        this.inputs = [
+            {
+                label: 'Text Splitter',
+                name: 'textSplitter',
+                type: 'TextSplitter',
+                optional: true
+            },
+            {
+                label: 'Notion Database Id',
+                name: 'databaseId',
+                type: 'string',
+                description: 'If your URL looks like - https://www.notion.so/abcdefh?v=long_hash_2, then abcdefh is the database ID'
+            },
+            {
+                label: 'Metadata',
+                name: 'metadata',
+                type: 'json',
+                optional: true,
+                additionalParams: true
+            }
+        ]
+    }
+
+    async init(nodeData: INodeData, _: string, options: ICommonObject): Promise<any> {
+        const textSplitter = nodeData.inputs?.textSplitter as TextSplitter
+        const databaseId = nodeData.inputs?.databaseId as string
+        const metadata = nodeData.inputs?.metadata
+
+        const credentialData = await getCredentialData(nodeData.credential ?? '', options)
+        const notionIntegrationToken = getCredentialParam('notionIntegrationToken', credentialData, nodeData)
+
+        const obj: NotionAPILoaderOptions = {
+            clientOptions: {
+                auth: notionIntegrationToken
+            },
+            id: databaseId,
+            type: 'database'
+        }
+        const loader = new NotionAPILoader(obj)
+
+        let docs = []
+        if (textSplitter) {
+            docs = await loader.loadAndSplit(textSplitter)
+        } else {
+            docs = await loader.load()
+        }
+
+        if (metadata) {
+            const parsedMetadata = typeof metadata === 'object' ? metadata : JSON.parse(metadata)
+            let finaldocs = []
+            for (const doc of docs) {
+                const newdoc = {
+                    ...doc,
+                    metadata: {
+                        ...doc.metadata,
+                        ...parsedMetadata
+                    }
+                }
+                finaldocs.push(newdoc)
+            }
+            return finaldocs
+        }
+
+        return docs
+    }
+}
+
+module.exports = { nodeClass: NotionDB_DocumentLoaders }

packages/components/nodes/documentloaders/Notion/NotionFolder.ts

@@ -2,9 +2,10 @@ import { INode, INodeData, INodeParams } from '../../../src/Interface'
 import { TextSplitter } from 'langchain/text_splitter'
 import { NotionLoader } from 'langchain/document_loaders/fs/notion'
 
-class Notion_DocumentLoaders implements INode {
+class NotionFolder_DocumentLoaders implements INode {
     label: string
     name: string
+    version: number
     description: string
     type: string
     icon: string
@@ -15,10 +16,11 @@ class Notion_DocumentLoaders implements INode {
     constructor() {
         this.label = 'Notion Folder'
         this.name = 'notionFolder'
+        this.version = 1.0
         this.type = 'Document'
         this.icon = 'notion.png'
         this.category = 'Document Loaders'
-        this.description = `Load data from Notion folder`
+        this.description = 'Load data from the exported and unzipped Notion folder'
         this.baseClasses = [this.type]
         this.inputs = [
             {
@@ -78,4 +80,4 @@ class Notion_DocumentLoaders implements INode {
     }
 }
 
-module.exports = { nodeClass: Notion_DocumentLoaders }
+module.exports = { nodeClass: NotionFolder_DocumentLoaders }

packages/components/nodes/documentloaders/Notion/NotionPage.ts

@@ -0,0 +1,101 @@
+import { ICommonObject, INode, INodeData, INodeParams } from '../../../src/Interface'
+import { TextSplitter } from 'langchain/text_splitter'
+import { NotionAPILoader, NotionAPILoaderOptions } from 'langchain/document_loaders/web/notionapi'
+import { getCredentialData, getCredentialParam } from '../../../src'
+
+class NotionPage_DocumentLoaders implements INode {
+    label: string
+    name: string
+    version: number
+    description: string
+    type: string
+    icon: string
+    category: string
+    baseClasses: string[]
+    credential: INodeParams
+    inputs: INodeParams[]
+
+    constructor() {
+        this.label = 'Notion Page'
+        this.name = 'notionPage'
+        this.version = 1.0
+        this.type = 'Document'
+        this.icon = 'notion.png'
+        this.category = 'Document Loaders'
+        this.description = 'Load data from Notion Page (including child pages all as separate documents)'
+        this.baseClasses = [this.type]
+        this.credential = {
+            label: 'Connect Credential',
+            name: 'credential',
+            type: 'credential',
+            credentialNames: ['notionApi']
+        }
+        this.inputs = [
+            {
+                label: 'Text Splitter',
+                name: 'textSplitter',
+                type: 'TextSplitter',
+                optional: true
+            },
+            {
+                label: 'Notion Page Id',
+                name: 'pageId',
+                type: 'string',
+                description:
+                    'The last The 32 char hex in the url path. For example: https://www.notion.so/skarard/LangChain-Notion-API-b34ca03f219c4420a6046fc4bdfdf7b4, b34ca03f219c4420a6046fc4bdfdf7b4 is the Page ID'
+            },
+            {
+                label: 'Metadata',
+                name: 'metadata',
+                type: 'json',
+                optional: true,
+                additionalParams: true
+            }
+        ]
+    }
+
+    async init(nodeData: INodeData, _: string, options: ICommonObject): Promise<any> {
+        const textSplitter = nodeData.inputs?.textSplitter as TextSplitter
+        const pageId = nodeData.inputs?.pageId as string
+        const metadata = nodeData.inputs?.metadata
+
+        const credentialData = await getCredentialData(nodeData.credential ?? '', options)
+        const notionIntegrationToken = getCredentialParam('notionIntegrationToken', credentialData, nodeData)
+
+        const obj: NotionAPILoaderOptions = {
+            clientOptions: {
+                auth: notionIntegrationToken
+            },
+            id: pageId,
+            type: 'page'
+        }
+        const loader = new NotionAPILoader(obj)
+
+        let docs = []
+        if (textSplitter) {
+            docs = await loader.loadAndSplit(textSplitter)
+        } else {
+            docs = await loader.load()
+        }
+
+        if (metadata) {
+            const parsedMetadata = typeof metadata === 'object' ? metadata : JSON.parse(metadata)
+            let finaldocs = []
+            for (const doc of docs) {
+                const newdoc = {
+                    ...doc,
+                    metadata: {
+                        ...doc.metadata,
+                        ...parsedMetadata
+                    }
+                }
+                finaldocs.push(newdoc)
+            }
+            return finaldocs
+        }
+
+        return docs
+    }
+}
+
+module.exports = { nodeClass: NotionPage_DocumentLoaders }

packages/components/nodes/documentloaders/Pdf/Pdf.ts

@@ -5,6 +5,7 @@ import { PDFLoader } from 'langchain/document_loaders/fs/pdf'
 class Pdf_DocumentLoaders implements INode {
     label: string
     name: string
+    version: number
     description: string
     type: string
     icon: string
@@ -15,6 +16,7 @@ class Pdf_DocumentLoaders implements INode {
     constructor() {
         this.label = 'Pdf File'
         this.name = 'pdfFile'
+        this.version = 1.0
         this.type = 'Document'
         this.icon = 'pdf.svg'
         this.category = 'Document Loaders'
@@ -49,6 +51,13 @@ class Pdf_DocumentLoaders implements INode {
                 ],
                 default: 'perPage'
             },
+            {
+                label: 'Use Legacy Build',
+                name: 'legacyBuild',
+                type: 'boolean',
+                optional: true,
+                additionalParams: true
+            },
             {
                 label: 'Metadata',
                 name: 'metadata',
@@ -64,6 +73,7 @@ class Pdf_DocumentLoaders implements INode {
         const pdfFileBase64 = nodeData.inputs?.pdfFile as string
         const usage = nodeData.inputs?.usage as string
         const metadata = nodeData.inputs?.metadata
+        const legacyBuild = nodeData.inputs?.legacyBuild as boolean
 
         let alldocs = []
         let files: string[] = []
@@ -81,8 +91,9 @@ class Pdf_DocumentLoaders implements INode {
             if (usage === 'perFile') {
                 const loader = new PDFLoader(new Blob([bf]), {
                     splitPages: false,
-                    // @ts-ignore
-                    pdfjs: () => import('pdf-parse/lib/pdf.js/v1.10.100/build/pdf.js')
+                    pdfjs: () =>
+                        // @ts-ignore
+                        legacyBuild ? import('pdfjs-dist/legacy/build/pdf.js') : import('pdf-parse/lib/pdf.js/v1.10.100/build/pdf.js')
                 })
                 if (textSplitter) {
                     const docs = await loader.loadAndSplit(textSplitter)
@@ -92,8 +103,11 @@ class Pdf_DocumentLoaders implements INode {
                     alldocs.push(...docs)
                 }
             } else {
-                // @ts-ignore
-                const loader = new PDFLoader(new Blob([bf]), { pdfjs: () => import('pdf-parse/lib/pdf.js/v1.10.100/build/pdf.js') })
+                const loader = new PDFLoader(new Blob([bf]), {
+                    pdfjs: () =>
+                        // @ts-ignore
+                        legacyBuild ? import('pdfjs-dist/legacy/build/pdf.js') : import('pdf-parse/lib/pdf.js/v1.10.100/build/pdf.js')
+                })
                 if (textSplitter) {
                     const docs = await loader.loadAndSplit(textSplitter)
                     alldocs.push(...docs)

packages/components/nodes/documentloaders/Playwright/Playwright.ts

@@ -0,0 +1,202 @@
+import { INode, INodeData, INodeParams } from '../../../src/Interface'
+import { TextSplitter } from 'langchain/text_splitter'
+import { Browser, Page, PlaywrightWebBaseLoader, PlaywrightWebBaseLoaderOptions } from 'langchain/document_loaders/web/playwright'
+import { test } from 'linkifyjs'
+import { webCrawl, xmlScrape } from '../../../src'
+
+class Playwright_DocumentLoaders implements INode {
+    label: string
+    name: string
+    version: number
+    description: string
+    type: string
+    icon: string
+    category: string
+    baseClasses: string[]
+    inputs: INodeParams[]
+
+    constructor() {
+        this.label = 'Playwright Web Scraper'
+        this.name = 'playwrightWebScraper'
+        this.version = 1.0
+        this.type = 'Document'
+        this.icon = 'playwright.svg'
+        this.category = 'Document Loaders'
+        this.description = `Load data from webpages`
+        this.baseClasses = [this.type]
+        this.inputs = [
+            {
+                label: 'URL',
+                name: 'url',
+                type: 'string'
+            },
+            {
+                label: 'Text Splitter',
+                name: 'textSplitter',
+                type: 'TextSplitter',
+                optional: true
+            },
+            {
+                label: 'Get Relative Links Method',
+                name: 'relativeLinksMethod',
+                type: 'options',
+                description: 'Select a method to retrieve relative links',
+                options: [
+                    {
+                        label: 'Web Crawl',
+                        name: 'webCrawl',
+                        description: 'Crawl relative links from HTML URL'
+                    },
+                    {
+                        label: 'Scrape XML Sitemap',
+                        name: 'scrapeXMLSitemap',
+                        description: 'Scrape relative links from XML sitemap URL'
+                    }
+                ],
+                optional: true,
+                additionalParams: true
+            },
+            {
+                label: 'Get Relative Links Limit',
+                name: 'limit',
+                type: 'number',
+                optional: true,
+                additionalParams: true,
+                description:
+                    'Only used when "Get Relative Links Method" is selected. Set 0 to retrieve all relative links, default limit is 10.',
+                warning: `Retrieving all links might take long time, and all links will be upserted again if the flow's state changed (eg: different URL, chunk size, etc)`
+            },
+            {
+                label: 'Wait Until',
+                name: 'waitUntilGoToOption',
+                type: 'options',
+                description: 'Select a go to wait until option',
+                options: [
+                    {
+                        label: 'Load',
+                        name: 'load',
+                        description: 'Consider operation to be finished when the load event is fired.'
+                    },
+                    {
+                        label: 'DOM Content Loaded',
+                        name: 'domcontentloaded',
+                        description: 'Consider operation to be finished when the DOMContentLoaded event is fired.'
+                    },
+                    {
+                        label: 'Network Idle',
+                        name: 'networkidle',
+                        description: 'Navigation is finished when there are no more connections for at least 500 ms.'
+                    },
+                    {
+                        label: 'Commit',
+                        name: 'commit',
+                        description: 'Consider operation to be finished when network response is received and the document started loading.'
+                    }
+                ],
+                optional: true,
+                additionalParams: true
+            },
+            {
+                label: 'Wait for selector to load',
+                name: 'waitForSelector',
+                type: 'string',
+                optional: true,
+                additionalParams: true,
+                description: 'CSS selectors like .div or #div'
+            },
+            {
+                label: 'Metadata',
+                name: 'metadata',
+                type: 'json',
+                optional: true,
+                additionalParams: true
+            }
+        ]
+    }
+
+    async init(nodeData: INodeData): Promise<any> {
+        const textSplitter = nodeData.inputs?.textSplitter as TextSplitter
+        const metadata = nodeData.inputs?.metadata
+        const relativeLinksMethod = nodeData.inputs?.relativeLinksMethod as string
+        let limit = nodeData.inputs?.limit as string
+        let waitUntilGoToOption = nodeData.inputs?.waitUntilGoToOption as 'load' | 'domcontentloaded' | 'networkidle' | 'commit' | undefined
+        let waitForSelector = nodeData.inputs?.waitForSelector as string
+
+        let url = nodeData.inputs?.url as string
+        url = url.trim()
+        if (!test(url)) {
+            throw new Error('Invalid URL')
+        }
+
+        async function playwrightLoader(url: string): Promise<any> {
+            try {
+                let docs = []
+                const config: PlaywrightWebBaseLoaderOptions = {
+                    launchOptions: {
+                        args: ['--no-sandbox'],
+                        headless: true
+                    }
+                }
+                if (waitUntilGoToOption) {
+                    config['gotoOptions'] = {
+                        waitUntil: waitUntilGoToOption
+                    }
+                }
+                if (waitForSelector) {
+                    config['evaluate'] = async (page: Page, _: Browser): Promise<string> => {
+                        await page.waitForSelector(waitForSelector)
+
+                        const result = await page.evaluate(() => document.body.innerHTML)
+                        return result
+                    }
+                }
+                const loader = new PlaywrightWebBaseLoader(url, config)
+                if (textSplitter) {
+                    docs = await loader.loadAndSplit(textSplitter)
+                } else {
+                    docs = await loader.load()
+                }
+                return docs
+            } catch (err) {
+                if (process.env.DEBUG === 'true') console.error(`error in PlaywrightWebBaseLoader: ${err.message}, on page: ${url}`)
+            }
+        }
+
+        let docs = []
+        if (relativeLinksMethod) {
+            if (process.env.DEBUG === 'true') console.info(`Start ${relativeLinksMethod}`)
+            if (!limit) limit = '10'
+            else if (parseInt(limit) < 0) throw new Error('Limit cannot be less than 0')
+            const pages: string[] =
+                relativeLinksMethod === 'webCrawl' ? await webCrawl(url, parseInt(limit)) : await xmlScrape(url, parseInt(limit))
+            if (process.env.DEBUG === 'true') console.info(`pages: ${JSON.stringify(pages)}, length: ${pages.length}`)
+            if (!pages || pages.length === 0) throw new Error('No relative links found')
+            for (const page of pages) {
+                docs.push(...(await playwrightLoader(page)))
+            }
+            if (process.env.DEBUG === 'true') console.info(`Finish ${relativeLinksMethod}`)
+        } else {
+            docs = await playwrightLoader(url)
+        }
+
+        if (metadata) {
+            const parsedMetadata = typeof metadata === 'object' ? metadata : JSON.parse(metadata)
+            let finaldocs = []
+            for (const doc of docs) {
+                const newdoc = {
+                    ...doc,
+                    metadata: {
+                        ...doc.metadata,
+                        ...parsedMetadata
+                    }
+                }
+                finaldocs.push(newdoc)
+            }
+            return finaldocs
+        }
+
+        return docs
+    }
+}
+
+module.exports = { nodeClass: Playwright_DocumentLoaders }

packages/components/nodes/documentloaders/Playwright/playwright.svg

@@ -0,0 +1,9 @@
+<svg xmlns="http://www.w3.org/2000/svg" width="400" height="400" viewBox="0 0 400 400" fill="none">
+<path d="M136.444 221.556C123.558 225.213 115.104 231.625 109.535 238.032C114.869 233.364 122.014 229.08 131.652 226.348C141.51 223.554 149.92 223.574 156.869 224.915V219.481C150.941 218.939 144.145 219.371 136.444 221.556ZM108.946 175.876L61.0895 188.484C61.0895 188.484 61.9617 189.716 63.5767 191.36L104.153 180.668C104.153 180.668 103.578 188.077 98.5847 194.705C108.03 187.559 108.946 175.876 108.946 175.876ZM149.005 288.347C81.6582 306.486 46.0272 228.438 35.2396 187.928C30.2556 169.229 28.0799 155.067 27.5 145.928C27.4377 144.979 27.4665 144.179 27.5336 143.446C24.04 143.657 22.3674 145.473 22.7077 150.721C23.2876 159.855 25.4633 174.016 30.4473 192.721C41.2301 233.225 76.8659 311.273 144.213 293.134C158.872 289.185 169.885 281.992 178.152 272.81C170.532 279.692 160.995 285.112 149.005 288.347ZM161.661 128.11V132.903H188.077C187.535 131.206 186.989 129.677 186.447 128.11H161.661Z" fill="#2D4552"/>
+<path d="M193.981 167.584C205.861 170.958 212.144 179.287 215.465 186.658L228.711 190.42C228.711 190.42 226.904 164.623 203.57 157.995C181.741 151.793 168.308 170.124 166.674 172.496C173.024 167.972 182.297 164.268 193.981 167.584ZM299.422 186.777C277.573 180.547 264.145 198.916 262.535 201.255C268.89 196.736 278.158 193.031 289.837 196.362C301.698 199.741 307.976 208.06 311.307 215.436L324.572 219.212C324.572 219.212 322.736 193.41 299.422 186.777ZM286.262 254.795L176.072 223.99C176.072 223.99 177.265 230.038 181.842 237.869L274.617 263.805C282.255 259.386 286.262 254.795 286.262 254.795ZM209.867 321.102C122.618 297.71 133.166 186.543 147.284 133.865C153.097 112.156 159.073 96.0203 164.029 85.204C161.072 84.5953 158.623 86.1529 156.203 91.0746C150.941 101.747 144.212 119.124 137.7 143.45C123.586 196.127 113.038 307.29 200.283 330.682C241.406 341.699 273.442 324.955 297.323 298.659C274.655 319.19 245.714 330.701 209.867 321.102Z" fill="#2D4552"/>
+<path d="M161.661 262.296V239.863L99.3324 257.537C99.3324 257.537 103.938 230.777 136.444 221.556C146.302 218.762 154.713 218.781 161.661 220.123V128.11H192.869C189.471 117.61 186.184 109.526 183.423 103.909C178.856 94.612 174.174 100.775 163.545 109.665C156.059 115.919 137.139 129.261 108.668 136.933C80.1966 144.61 57.179 142.574 47.5752 140.911C33.9601 138.562 26.8387 135.572 27.5049 145.928C28.0847 155.062 30.2605 169.224 35.2445 187.928C46.0272 228.433 81.663 306.481 149.01 288.342C166.602 283.602 179.019 274.233 187.626 262.291H161.661V262.296ZM61.0848 188.484L108.946 175.876C108.946 175.876 107.551 194.288 89.6087 199.018C71.6614 203.743 61.0848 188.484 61.0848 188.484Z" fill="#E2574C"/>
+<path d="M341.786 129.174C329.345 131.355 299.498 134.072 262.612 124.185C225.716 114.304 201.236 97.0224 191.537 88.8994C177.788 77.3834 171.74 69.3802 165.788 81.4857C160.526 92.163 153.797 109.54 147.284 133.866C133.171 186.543 122.623 297.706 209.867 321.098C297.093 344.47 343.53 242.92 357.644 190.238C364.157 165.917 367.013 147.5 367.799 135.625C368.695 122.173 359.455 126.078 341.786 129.174ZM166.497 172.756C166.497 172.756 180.246 151.372 203.565 158C226.899 164.628 228.706 190.425 228.706 190.425L166.497 172.756ZM223.42 268.713C182.403 256.698 176.077 223.99 176.077 223.99L286.262 254.796C286.262 254.791 264.021 280.578 223.42 268.713ZM262.377 201.495C262.377 201.495 276.107 180.126 299.422 186.773C322.736 193.411 324.572 219.208 324.572 219.208L262.377 201.495Z" fill="#2EAD33"/>
+<path d="M139.88 246.04L99.3324 257.532C99.3324 257.532 103.737 232.44 133.607 222.496L110.647 136.33L108.663 136.933C80.1918 144.611 57.1742 142.574 47.5704 140.911C33.9554 138.563 26.834 135.572 27.5001 145.929C28.08 155.063 30.2557 169.224 35.2397 187.929C46.0225 228.433 81.6583 306.481 149.005 288.342L150.989 287.719L139.88 246.04ZM61.0848 188.485L108.946 175.876C108.946 175.876 107.551 194.288 89.6087 199.018C71.6615 203.743 61.0848 188.485 61.0848 188.485Z" fill="#D65348"/>
+<path d="M225.27 269.163L223.415 268.712C182.398 256.698 176.072 223.99 176.072 223.99L232.89 239.872L262.971 124.281L262.607 124.185C225.711 114.304 201.232 97.0224 191.532 88.8994C177.783 77.3834 171.735 69.3802 165.783 81.4857C160.526 92.163 153.797 109.54 147.284 133.866C133.171 186.543 122.623 297.706 209.867 321.097L211.655 321.5L225.27 269.163ZM166.497 172.756C166.497 172.756 180.246 151.372 203.565 158C226.899 164.628 228.706 190.425 228.706 190.425L166.497 172.756Z" fill="#1D8D22"/>
+<path d="M141.946 245.451L131.072 248.537C133.641 263.019 138.169 276.917 145.276 289.195C146.513 288.922 147.74 288.687 149 288.342C152.302 287.451 155.364 286.348 158.312 285.145C150.371 273.361 145.118 259.789 141.946 245.451ZM137.7 143.451C132.112 164.307 127.113 194.326 128.489 224.436C130.952 223.367 133.554 222.371 136.444 221.551L138.457 221.101C136.003 188.939 141.308 156.165 147.284 133.866C148.799 128.225 150.318 122.978 151.832 118.085C149.393 119.637 146.767 121.228 143.776 122.867C141.759 129.093 139.722 135.898 137.7 143.451Z" fill="#C04B41"/>
+</svg>

packages/components/nodes/documentloaders/Puppeteer/Puppeteer.ts

@@ -0,0 +1,203 @@
+import { INode, INodeData, INodeParams } from '../../../src/Interface'
+import { TextSplitter } from 'langchain/text_splitter'
+import { Browser, Page, PuppeteerWebBaseLoader, PuppeteerWebBaseLoaderOptions } from 'langchain/document_loaders/web/puppeteer'
+import { test } from 'linkifyjs'
+import { webCrawl, xmlScrape } from '../../../src'
+import { PuppeteerLifeCycleEvent } from 'puppeteer'
+
+class Puppeteer_DocumentLoaders implements INode {
+    label: string
+    name: string
+    version: number
+    description: string
+    type: string
+    icon: string
+    category: string
+    baseClasses: string[]
+    inputs: INodeParams[]
+
+    constructor() {
+        this.label = 'Puppeteer Web Scraper'
+        this.name = 'puppeteerWebScraper'
+        this.version = 1.0
+        this.type = 'Document'
+        this.icon = 'puppeteer.svg'
+        this.category = 'Document Loaders'
+        this.description = `Load data from webpages`
+        this.baseClasses = [this.type]
+        this.inputs = [
+            {
+                label: 'URL',
+                name: 'url',
+                type: 'string'
+            },
+            {
+                label: 'Text Splitter',
+                name: 'textSplitter',
+                type: 'TextSplitter',
+                optional: true
+            },
+            {
+                label: 'Get Relative Links Method',
+                name: 'relativeLinksMethod',
+                type: 'options',
+                description: 'Select a method to retrieve relative links',
+                options: [
+                    {
+                        label: 'Web Crawl',
+                        name: 'webCrawl',
+                        description: 'Crawl relative links from HTML URL'
+                    },
+                    {
+                        label: 'Scrape XML Sitemap',
+                        name: 'scrapeXMLSitemap',
+                        description: 'Scrape relative links from XML sitemap URL'
+                    }
+                ],
+                optional: true,
+                additionalParams: true
+            },
+            {
+                label: 'Get Relative Links Limit',
+                name: 'limit',
+                type: 'number',
+                optional: true,
+                additionalParams: true,
+                description:
+                    'Only used when "Get Relative Links Method" is selected. Set 0 to retrieve all relative links, default limit is 10.',
+                warning: `Retrieving all links might take long time, and all links will be upserted again if the flow's state changed (eg: different URL, chunk size, etc)`
+            },
+            {
+                label: 'Wait Until',
+                name: 'waitUntilGoToOption',
+                type: 'options',
+                description: 'Select a go to wait until option',
+                options: [
+                    {
+                        label: 'Load',
+                        name: 'load',
+                        description: `When the initial HTML document's DOM has been loaded and parsed`
+                    },
+                    {
+                        label: 'DOM Content Loaded',
+                        name: 'domcontentloaded',
+                        description: `When the complete HTML document's DOM has been loaded and parsed`
+                    },
+                    {
+                        label: 'Network Idle 0',
+                        name: 'networkidle0',
+                        description: 'Navigation is finished when there are no more than 0 network connections for at least 500 ms'
+                    },
+                    {
+                        label: 'Network Idle 2',
+                        name: 'networkidle2',
+                        description: 'Navigation is finished when there are no more than 2 network connections for at least 500 ms'
+                    }
+                ],
+                optional: true,
+                additionalParams: true
+            },
+            {
+                label: 'Wait for selector to load',
+                name: 'waitForSelector',
+                type: 'string',
+                optional: true,
+                additionalParams: true,
+                description: 'CSS selectors like .div or #div'
+            },
+            {
+                label: 'Metadata',
+                name: 'metadata',
+                type: 'json',
+                optional: true,
+                additionalParams: true
+            }
+        ]
+    }
+
+    async init(nodeData: INodeData): Promise<any> {
+        const textSplitter = nodeData.inputs?.textSplitter as TextSplitter
+        const metadata = nodeData.inputs?.metadata
+        const relativeLinksMethod = nodeData.inputs?.relativeLinksMethod as string
+        let limit = nodeData.inputs?.limit as string
+        let waitUntilGoToOption = nodeData.inputs?.waitUntilGoToOption as PuppeteerLifeCycleEvent
+        let waitForSelector = nodeData.inputs?.waitForSelector as string
+
+        let url = nodeData.inputs?.url as string
+        url = url.trim()
+        if (!test(url)) {
+            throw new Error('Invalid URL')
+        }
+
+        async function puppeteerLoader(url: string): Promise<any> {
+            try {
+                let docs = []
+                const config: PuppeteerWebBaseLoaderOptions = {
+                    launchOptions: {
+                        args: ['--no-sandbox'],
+                        headless: 'new'
+                    }
+                }
+                if (waitUntilGoToOption) {
+                    config['gotoOptions'] = {
+                        waitUntil: waitUntilGoToOption
+                    }
+                }
+                if (waitForSelector) {
+                    config['evaluate'] = async (page: Page, _: Browser): Promise<string> => {
+                        await page.waitForSelector(waitForSelector)
+
+                        const result = await page.evaluate(() => document.body.innerHTML)
+                        return result
+                    }
+                }
+                const loader = new PuppeteerWebBaseLoader(url, config)
+                if (textSplitter) {
+                    docs = await loader.loadAndSplit(textSplitter)
+                } else {
+                    docs = await loader.load()
+                }
+                return docs
+            } catch (err) {
+                if (process.env.DEBUG === 'true') console.error(`error in PuppeteerWebBaseLoader: ${err.message}, on page: ${url}`)
+            }
+        }
+
+        let docs = []
+        if (relativeLinksMethod) {
+            if (process.env.DEBUG === 'true') console.info(`Start ${relativeLinksMethod}`)
+            if (!limit) limit = '10'
+            else if (parseInt(limit) < 0) throw new Error('Limit cannot be less than 0')
+            const pages: string[] =
+                relativeLinksMethod === 'webCrawl' ? await webCrawl(url, parseInt(limit)) : await xmlScrape(url, parseInt(limit))
+            if (process.env.DEBUG === 'true') console.info(`pages: ${JSON.stringify(pages)}, length: ${pages.length}`)
+            if (!pages || pages.length === 0) throw new Error('No relative links found')
+            for (const page of pages) {
+                docs.push(...(await puppeteerLoader(page)))
+            }
+            if (process.env.DEBUG === 'true') console.info(`Finish ${relativeLinksMethod}`)
+        } else {
+            docs = await puppeteerLoader(url)
+        }
+
+        if (metadata) {
+            const parsedMetadata = typeof metadata === 'object' ? metadata : JSON.parse(metadata)
+            let finaldocs = []
+            for (const doc of docs) {
+                const newdoc = {
+                    ...doc,
+                    metadata: {
+                        ...doc.metadata,
+                        ...parsedMetadata
+                    }
+                }
+                finaldocs.push(newdoc)
+            }
+            return finaldocs
+        }
+
+        return docs
+    }
+}
+
+module.exports = { nodeClass: Puppeteer_DocumentLoaders }

packages/components/nodes/documentloaders/Puppeteer/puppeteer.svg

@@ -0,0 +1,14 @@
+<svg xmlns="http://www.w3.org/2000/svg" width="0.67em" height="1em" viewBox="0 0 256 383">
+    <path fill="#DFDEDF"
+        d="M253.422 214.246H2.192v-14.168c0-9.52 7.717-17.237 17.236-17.237h216.757c9.52 0 17.237 7.717 17.237 17.237v14.168Z"></path>
+    <path fill="#FFF"
+        d="M235.92 378.553H19.692c-9.666 0-17.502-7.836-17.502-17.502V214.246h251.23V361.05c0 9.666-7.835 17.502-17.502 17.502"></path>
+    <path
+        d="M250.302 211.398v-12.706c0-7.501-6.08-13.582-13.581-13.582H19.279c-7.501 0-13.581 6.08-13.581 13.582v12.706h244.604Zm0 151.469V217.096H5.698v145.77c0 7.502 6.08 13.582 13.581 13.582h217.442c7.501 0 13.581-6.08 13.581-13.581ZM20.554 179.413l.846-9.236l-3.592 9.31c.487-.037.974-.074 1.471-.074h1.275Zm213.401-15.57l1.254 15.57h1.512c.809 0 1.601.066 2.384.163l-5.15-15.733Zm17.247 22.164c2.977 3.394 4.798 7.826 4.798 12.685v164.175c0 10.63-8.649 19.279-19.279 19.279H19.279C8.649 382.146 0 373.498 0 362.866V198.693c0-5.152 2.043-9.827 5.347-13.29l19.22-49.817l8.381-91.554l9.456.865l-7.552 82.492l2.66 1.026l-3.61 9.358l-3.812 41.64h195.593L214.829 44.669l9.464-.763l6.402 79.469l.004-.001l20.503 62.634Zm-230.046 8.39a4.302 4.302 0 1 1 0 8.603a4.302 4.302 0 0 1 0-8.603Zm14.709 0a4.302 4.302 0 1 1 0 8.603a4.302 4.302 0 0 1 0-8.603Zm14.244 0a4.302 4.302 0 1 1 0 8.603a4.302 4.302 0 0 1 0-8.603Z"></path>
+    <path
+        d="M36.022 287.748c.739-.855 1.128-1.979 1.128-3.374c0-1.396-.39-2.475-1.128-3.33c-.778-.855-1.868-1.26-3.268-1.26h-6.847v9.223h6.847c1.4 0 2.49-.405 3.268-1.259Zm-3.268-13.497c2.956 0 5.29.9 7.04 2.744c1.752 1.844 2.608 4.318 2.608 7.38c0 3.102-.856 5.532-2.567 7.376c-1.751 1.846-4.085 2.79-7.081 2.79h-6.847v12.414h-5.291v-32.704h12.138Zm28.544 20.057v-9.54h5.698v21.54h-5.698v-1.942c-1.727 1.726-3.97 2.589-6.777 2.589c-2.59 0-4.662-.863-6.259-2.546c-1.597-1.683-2.374-3.842-2.374-6.518v-13.123h5.698v11.914c0 1.51.388 2.763 1.208 3.67c.777.906 1.813 1.38 3.151 1.38c3.583 0 5.353-2.46 5.353-7.424Zm27.926 5.914c1.122-1.166 1.683-2.72 1.683-4.662c0-1.942-.56-3.54-1.683-4.705c-1.166-1.166-2.59-1.77-4.36-1.77c-1.597 0-3.02.604-4.273 1.813c-1.252 1.209-1.856 2.762-1.856 4.662c0 1.899.604 3.454 1.856 4.618c1.252 1.21 2.676 1.814 4.273 1.814c1.77 0 3.194-.604 4.36-1.77Zm-3.367-16.101c2.806 0 5.31 1.08 7.425 3.238c2.115 2.2 3.15 4.92 3.15 8.2c0 3.28-1.035 6-3.15 8.159c-2.116 2.159-4.576 3.237-7.425 3.237c-2.806 0-5.18-.777-7.122-2.33v11.959h-5.698v-31.816h5.698v1.727c1.9-1.597 4.273-2.374 7.122-2.374Zm31.078 16.1c1.122-1.165 1.683-2.72 1.683-4.661c0-1.942-.561-3.54-1.683-4.705c-1.166-1.166-2.59-1.77-4.36-1.77c-1.597 0-3.021.604-4.273 1.813c-1.252 1.209-1.857 2.762-1.857 4.662c0 1.899.605 3.454 1.857 4.618c1.252 1.21 2.676 1.814 4.273 1.814c1.77 0 3.194-.604 4.36-1.77Zm-3.368-16.1c2.806 0 5.31 1.08 7.426 3.238c2.114 2.2 3.15 4.92 3.15 8.2c0 3.28-1.036 6-3.15 8.159c-2.116 2.159-4.576 3.237-7.426 3.237c-2.805 0-5.18-.777-7.122-2.33v11.959h-5.697v-31.816h5.697v1.727c1.9-1.597 4.274-2.374 7.122-2.374Zm18.991 8.935h10.403c-.345-1.338-.993-2.33-1.899-2.98c-.907-.646-1.985-.991-3.195-.991a5.684 5.684 0 0 0-3.28 1.036c-1.036.69-1.727 1.683-2.029 2.935Zm5.31-8.935c2.935 0 5.482 1.036 7.597 3.065c2.115 2.072 3.194 4.704 3.324 7.942v1.943h-16.403c.302 1.51.95 2.718 1.943 3.626c.992.906 2.114 1.38 3.452 1.38c2.461 0 4.231-.95 5.353-2.848l5.094 1.079c-.907 2.202-2.288 3.843-4.1 4.965c-1.814 1.122-3.929 1.682-6.347 1.682c-3.108 0-5.74-1.078-7.856-3.193c-2.115-2.115-3.195-4.878-3.195-8.202c0-3.323 1.08-6.086 3.238-8.245c2.115-2.114 4.75-3.194 7.9-3.194Zm26.632 17.526c1.079 0 2.288-.346 3.583-1.036l1.64 4.748c-1.986 1.079-3.842 1.597-5.612 1.597c-2.633 0-4.705-.734-6.216-2.16c-1.51-1.422-2.244-3.538-2.244-6.43v-8.936h-4.62v-4.662h4.62v-6.517h5.698v6.517h7.036v4.662h-7.036v8.504c0 2.46 1.036 3.713 3.15 3.713Zm11.954-8.591h10.403c-.345-1.338-.993-2.33-1.899-2.98c-.907-.646-1.985-.991-3.195-.991a5.684 5.684 0 0 0-3.28 1.036c-1.036.69-1.727 1.683-2.029 2.935Zm5.31-8.935c2.935 0 5.482 1.036 7.597 3.065c2.115 2.072 3.194 4.704 3.324 7.942v1.943h-16.403c.302 1.51.95 2.718 1.943 3.626c.992.906 2.114 1.38 3.452 1.38c2.461 0 4.231-.95 5.353-2.848l5.093 1.079c-.906 2.202-2.287 3.843-4.1 4.965c-1.813 1.122-3.928 1.682-6.346 1.682c-3.108 0-5.74-1.078-7.856-3.193c-2.115-2.115-3.195-4.878-3.195-8.202c0-3.323 1.08-6.086 3.238-8.245c2.115-2.114 4.75-3.194 7.9-3.194Zm20.285 8.935h10.403c-.345-1.338-.993-2.33-1.898-2.98c-.907-.646-1.986-.991-3.196-.991a5.684 5.684 0 0 0-3.28 1.036c-1.036.69-1.727 1.683-2.029 2.935Zm5.31-8.935c2.935 0 5.482 1.036 7.597 3.065c2.115 2.072 3.195 4.704 3.325 7.942v1.943h-16.404c.302 1.51.95 2.718 1.944 3.626c.992.906 2.114 1.38 3.452 1.38c2.46 0 4.23-.95 5.352-2.848l5.095 1.079c-.907 2.202-2.288 3.843-4.101 4.965c-1.814 1.122-3.929 1.682-6.346 1.682c-3.108 0-5.742-1.078-7.856-3.193c-2.115-2.115-3.196-4.878-3.196-8.202c0-3.323 1.08-6.086 3.238-8.245c2.115-2.114 4.75-3.194 7.9-3.194Zm28.66.345c.649 0 1.167.043 1.513.172l-.303 5.483h-1.251c-5.137 0-7.685 3.065-7.685 9.238v6.949h-5.697v-21.54h5.697v3.928c1.943-2.806 4.533-4.23 7.727-4.23Z"></path>
+    <path fill="#00D8A2"
+        d="m211.095 132.638l30.741-14.841v-14.84l-71.023-33.922l71.023-36.571v-14.31L212.155 4.373l-84.096 41.871l-85.51-41.871l-27.561 14.841v12.72L81.77 68.505l-66.782 33.922v15.37l28.621 14.841l84.803-43.462z"></path>
+    <path
+        d="M214.95 126.585v-6.392l23.14-11.991v7.18l-23.14 11.203Zm-198.1-18.551l22.36 12.836v5.11l-22.36-11.312v-6.634ZM131.383 81.04l76.92 38.005v7.642l-76.92-39.405V81.04Zm-85.526 39.117l78.88-39.11v6.233l-78.88 39.908v-7.031Zm55.852-63.905L21.887 17.714L42.561 7.207L128.06 51.53l84.067-44.304l21.404 10.963l-77.84 39.174c-3.113 1.566-3.108 6.01.007 7.57l77.038 38.557l-19.968 10.347l-84.708-43.403l-86.206 44.289l-19.689-11.303l79.59-39.562c3.152-1.567 3.126-6.075-.045-7.606ZM17.844 30.77v-7.014L92.29 60.899l-7.526 3.007l-66.92-33.135Zm152.654 33.548c-.102-.062-7.13-3.378-7.13-3.378l74.721-37.605v7.045L170.5 64.319Zm7.48 3.683l65.674-32.976a1.969 1.969 0 0 0 1.085-1.76v-15.6a1.97 1.97 0 0 0-1.07-1.752L213.032.222a1.97 1.97 0 0 0-1.788-.003l-82.303 41.764a1.965 1.965 0 0 1-1.769.006L43.42.207a1.97 1.97 0 0 0-1.77.007L11.28 15.648a1.969 1.969 0 0 0-1.078 1.755v15.855c0 .749.425 1.433 1.097 1.765L77.25 67.607l-65.646 32.595a1.968 1.968 0 0 0-1.092 1.727l-.287 15.59a1.969 1.969 0 0 0 1.08 1.793l31.536 15.956a1.97 1.97 0 0 0 1.772.002l82.558-41.455a1.968 1.968 0 0 1 1.775.004l81.53 41.454a1.97 1.97 0 0 0 1.748.017l31.402-15.203a1.969 1.969 0 0 0 1.111-1.771v-16.29a1.97 1.97 0 0 0-1.1-1.767l-65.66-32.257Z"></path>
+</svg>

packages/components/nodes/documentloaders/Subtitles/Subtitles.ts

@@ -0,0 +1,97 @@
+import { INode, INodeData, INodeParams } from '../../../src/Interface'
+import { TextSplitter } from 'langchain/text_splitter'
+import { SRTLoader } from 'langchain/document_loaders/fs/srt'
+
+class Subtitles_DocumentLoaders implements INode {
+    label: string
+    name: string
+    version: number
+    description: string
+    type: string
+    icon: string
+    category: string
+    baseClasses: string[]
+    inputs: INodeParams[]
+
+    constructor() {
+        this.label = 'Subtitles File'
+        this.name = 'subtitlesFile'
+        this.version = 1.0
+        this.type = 'Document'
+        this.icon = 'subtitlesFile.svg'
+        this.category = 'Document Loaders'
+        this.description = `Load data from subtitles files`
+        this.baseClasses = [this.type]
+        this.inputs = [
+            {
+                label: 'Subtitles File',
+                name: 'subtitlesFile',
+                type: 'file',
+                fileType: '.srt'
+            },
+            {
+                label: 'Text Splitter',
+                name: 'textSplitter',
+                type: 'TextSplitter',
+                optional: true
+            },
+            {
+                label: 'Metadata',
+                name: 'metadata',
+                type: 'json',
+                optional: true,
+                additionalParams: true
+            }
+        ]
+    }
+
+    async init(nodeData: INodeData): Promise<any> {
+        const textSplitter = nodeData.inputs?.textSplitter as TextSplitter
+        const subtitlesFileBase64 = nodeData.inputs?.subtitlesFile as string
+        const metadata = nodeData.inputs?.metadata
+
+        let alldocs = []
+        let files: string[] = []
+
+        if (subtitlesFileBase64.startsWith('[') && subtitlesFileBase64.endsWith(']')) {
+            files = JSON.parse(subtitlesFileBase64)
+        } else {
+            files = [subtitlesFileBase64]
+        }
+
+        for (const file of files) {
+            const splitDataURI = file.split(',')
+            splitDataURI.pop()
+            const bf = Buffer.from(splitDataURI.pop() || '', 'base64')
+            const blob = new Blob([bf])
+            const loader = new SRTLoader(blob)
+
+            if (textSplitter) {
+                const docs = await loader.loadAndSplit(textSplitter)
+                alldocs.push(...docs)
+            } else {
+                const docs = await loader.load()
+                alldocs.push(...docs)
+            }
+        }
+
+        if (metadata) {
+            const parsedMetadata = typeof metadata === 'object' ? metadata : JSON.parse(metadata)
+            let finaldocs = []
+            for (const doc of alldocs) {
+                const newdoc = {
+                    ...doc,
+                    metadata: {
+                        ...doc.metadata,
+                        ...parsedMetadata
+                    }
+                }
+                finaldocs.push(newdoc)
+            }
+            return finaldocs
+        }
+        return alldocs
+    }
+}
+
+module.exports = { nodeClass: Subtitles_DocumentLoaders }

packages/components/nodes/documentloaders/Subtitles/subtitlesFile.svg

@@ -0,0 +1 @@
+<svg xmlns="http://www.w3.org/2000/svg" data-name="Layer 1" viewBox="0 0 24 24" id="subtitles"><path d="M20,4.25H4A2.748,2.748,0,0,0,1.25,7V17A2.748,2.748,0,0,0,4,19.75H20A2.748,2.748,0,0,0,22.75,17V7A2.748,2.748,0,0,0,20,4.25Zm-14,5H8a.75.75,0,0,1,0,1.5H6a.75.75,0,0,1,0-1.5Zm7,5.5H6a.75.75,0,0,1,0-1.5h7a.75.75,0,0,1,0,1.5Zm5,0H16a.75.75,0,0,1,0-1.5h2a.75.75,0,0,1,0,1.5Zm0-4H11a.75.75,0,0,1,0-1.5h7a.75.75,0,0,1,0,1.5Z"></path></svg>

packages/components/nodes/documentloaders/Text/Text.ts

@@ -5,6 +5,7 @@ import { TextLoader } from 'langchain/document_loaders/fs/text'
 class Text_DocumentLoaders implements INode {
     label: string
     name: string
+    version: number
     description: string
     type: string
     icon: string
@@ -15,6 +16,7 @@ class Text_DocumentLoaders implements INode {
     constructor() {
         this.label = 'Text File'
         this.name = 'textFile'
+        this.version = 1.0
         this.type = 'Document'
         this.icon = 'textFile.svg'
         this.category = 'Document Loaders'

packages/components/nodes/documentloaders/VectorStoreToDocument/VectorStoreToDocument.ts

@@ -0,0 +1,87 @@
+import { VectorStore } from 'langchain/vectorstores/base'
+import { INode, INodeData, INodeOutputsValue, INodeParams } from '../../../src/Interface'
+import { handleEscapeCharacters } from '../../../src/utils'
+
+class VectorStoreToDocument_DocumentLoaders implements INode {
+    label: string
+    name: string
+    version: number
+    description: string
+    type: string
+    icon: string
+    category: string
+    baseClasses: string[]
+    inputs: INodeParams[]
+    outputs: INodeOutputsValue[]
+
+    constructor() {
+        this.label = 'VectorStore To Document'
+        this.name = 'vectorStoreToDocument'
+        this.version = 1.0
+        this.type = 'Document'
+        this.icon = 'vectorretriever.svg'
+        this.category = 'Document Loaders'
+        this.description = 'Search documents with scores from vector store'
+        this.baseClasses = [this.type]
+        this.inputs = [
+            {
+                label: 'Vector Store',
+                name: 'vectorStore',
+                type: 'VectorStore'
+            },
+            {
+                label: 'Minimum Score (%)',
+                name: 'minScore',
+                type: 'number',
+                optional: true,
+                placeholder: '75',
+                step: 1,
+                description: 'Minumum score for embeddings documents to be included'
+            }
+        ]
+        this.outputs = [
+            {
+                label: 'Document',
+                name: 'document',
+                baseClasses: this.baseClasses
+            },
+            {
+                label: 'Text',
+                name: 'text',
+                baseClasses: ['string', 'json']
+            }
+        ]
+    }
+
+    async init(nodeData: INodeData, input: string): Promise<any> {
+        const vectorStore = nodeData.inputs?.vectorStore as VectorStore
+        const minScore = nodeData.inputs?.minScore as number
+        const output = nodeData.outputs?.output as string
+
+        const topK = (vectorStore as any)?.k ?? 4
+
+        const docs = await vectorStore.similaritySearchWithScore(input, topK)
+        // eslint-disable-next-line no-console
+        console.log('\x1b[94m\x1b[1m\n*****VectorStore Documents*****\n\x1b[0m\x1b[0m')
+        // eslint-disable-next-line no-console
+        console.log(docs)
+
+        if (output === 'document') {
+            let finaldocs = []
+            for (const doc of docs) {
+                if (minScore && doc[1] < minScore / 100) continue
+                finaldocs.push(doc[0])
+            }
+            return finaldocs
+        } else {
+            let finaltext = ''
+            for (const doc of docs) {
+                if (minScore && doc[1] < minScore / 100) continue
+                finaltext += `${doc[0].pageContent}\n`
+            }
+            return handleEscapeCharacters(finaltext, false)
+        }
+    }
+}
+
+module.exports = { nodeClass: VectorStoreToDocument_DocumentLoaders }

packages/components/nodes/documentloaders/VectorStoreToDocument/vectorretriever.svg

@@ -0,0 +1,7 @@
+<svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-file-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 12.75m-4 0a4 1.75 0 1 0 8 0a4 1.75 0 1 0 -8 0"></path>
+   <path d="M8 12.5v3.75c0 .966 1.79 1.75 4 1.75s4 -.784 4 -1.75v-3.75"></path>
+   <path d="M14 3v4a1 1 0 0 0 1 1h4"></path>
+   <path d="M17 21h-10a2 2 0 0 1 -2 -2v-14a2 2 0 0 1 2 -2h7l5 5v11a2 2 0 0 1 -2 2z"></path>
+</svg>

packages/components/nodes/embeddings/AzureOpenAIEmbedding/Azure.svg

@@ -1,5 +1 @@
-<svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-brand-azure" 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="M6 7.5l-4 9.5h4l6 -15z"></path>
-   <path d="M22 20l-7 -15l-3 7l4 5l-8 3z"></path>
-</svg>
+<svg xmlns="http://www.w3.org/2000/svg"  viewBox="0 0 48 48" width="96px" height="96px"><path fill="#035bda" d="M46 40L29.317 10.852 22.808 23.96 34.267 37.24 13 39.655zM13.092 18.182L2 36.896 11.442 35.947 28.033 5.678z"/></svg>

packages/components/nodes/embeddings/AzureOpenAIEmbedding/AzureOpenAIEmbedding.ts

@@ -1,59 +1,43 @@
 import { AzureOpenAIInput } from 'langchain/chat_models/openai'
-import { INode, INodeData, INodeParams } from '../../../src/Interface'
-import { getBaseClasses } from '../../../src/utils'
+import { ICommonObject, INode, INodeData, INodeParams } from '../../../src/Interface'
+import { getBaseClasses, getCredentialData, getCredentialParam } from '../../../src/utils'
 import { OpenAIEmbeddings, OpenAIEmbeddingsParams } from 'langchain/embeddings/openai'
 
 class AzureOpenAIEmbedding_Embeddings implements INode {
     label: string
     name: string
+    version: number
     type: string
     icon: string
     category: string
     description: string
     baseClasses: string[]
+    credential: INodeParams
     inputs: INodeParams[]
 
     constructor() {
         this.label = 'Azure OpenAI Embeddings'
         this.name = 'azureOpenAIEmbeddings'
+        this.version = 1.0
         this.type = 'AzureOpenAIEmbeddings'
         this.icon = 'Azure.svg'
         this.category = 'Embeddings'
         this.description = 'Azure OpenAI API to generate embeddings for a given text'
         this.baseClasses = [this.type, ...getBaseClasses(OpenAIEmbeddings)]
+        this.credential = {
+            label: 'Connect Credential',
+            name: 'credential',
+            type: 'credential',
+            credentialNames: ['azureOpenAIApi']
+        }
         this.inputs = [
             {
-                label: 'Azure OpenAI Api Key',
-                name: 'azureOpenAIApiKey',
-                type: 'password'
-            },
-            {
-                label: 'Azure OpenAI Api Instance Name',
-                name: 'azureOpenAIApiInstanceName',
-                type: 'string',
-                placeholder: 'YOUR-INSTANCE-NAME'
-            },
-            {
-                label: 'Azure OpenAI Api Deployment Name',
-                name: 'azureOpenAIApiDeploymentName',
-                type: 'string',
-                placeholder: 'YOUR-DEPLOYMENT-NAME'
-            },
-            {
-                label: 'Azure OpenAI Api Version',
-                name: 'azureOpenAIApiVersion',
-                type: 'options',
-                options: [
-                    {
-                        label: '2023-03-15-preview',
-                        name: '2023-03-15-preview'
-                    },
-                    {
-                        label: '2022-12-01',
-                        name: '2022-12-01'
-                    }
-                ],
-                default: '2023-03-15-preview'
+                label: 'Batch Size',
+                name: 'batchSize',
+                type: 'number',
+                default: '1',
+                optional: true,
+                additionalParams: true
             },
             {
                 label: 'Timeout',
@@ -65,13 +49,16 @@ class AzureOpenAIEmbedding_Embeddings implements INode {
         ]
     }
 
-    async init(nodeData: INodeData): Promise<any> {
-        const azureOpenAIApiKey = nodeData.inputs?.azureOpenAIApiKey as string
-        const azureOpenAIApiInstanceName = nodeData.inputs?.azureOpenAIApiInstanceName as string
-        const azureOpenAIApiDeploymentName = nodeData.inputs?.azureOpenAIApiDeploymentName as string
-        const azureOpenAIApiVersion = nodeData.inputs?.azureOpenAIApiVersion as string
+    async init(nodeData: INodeData, _: string, options: ICommonObject): Promise<any> {
+        const batchSize = nodeData.inputs?.batchSize as string
         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<OpenAIEmbeddingsParams> & Partial<AzureOpenAIInput> = {
             azureOpenAIApiKey,
             azureOpenAIApiInstanceName,
@@ -79,6 +66,7 @@ class AzureOpenAIEmbedding_Embeddings implements INode {
             azureOpenAIApiVersion
         }
 
+        if (batchSize) obj.batchSize = parseInt(batchSize, 10)
         if (timeout) obj.timeout = parseInt(timeout, 10)
 
         const model = new OpenAIEmbeddings(obj)

packages/components/nodes/embeddings/CohereEmbedding/CohereEmbedding.ts

@@ -1,31 +1,35 @@
-import { INode, INodeData, INodeParams } from '../../../src/Interface'
-import { getBaseClasses } from '../../../src/utils'
+import { ICommonObject, INode, INodeData, INodeParams } from '../../../src/Interface'
+import { getBaseClasses, getCredentialData, getCredentialParam } from '../../../src/utils'
 import { CohereEmbeddings, CohereEmbeddingsParams } from 'langchain/embeddings/cohere'
 
 class CohereEmbedding_Embeddings implements INode {
     label: string
     name: string
+    version: number
     type: string
     icon: string
     category: string
     description: string
     baseClasses: string[]
+    credential: INodeParams
     inputs: INodeParams[]
 
     constructor() {
         this.label = 'Cohere Embeddings'
         this.name = 'cohereEmbeddings'
+        this.version = 1.0
         this.type = 'CohereEmbeddings'
         this.icon = 'cohere.png'
         this.category = 'Embeddings'
         this.description = 'Cohere API to generate embeddings for a given text'
         this.baseClasses = [this.type, ...getBaseClasses(CohereEmbeddings)]
+        this.credential = {
+            label: 'Connect Credential',
+            name: 'credential',
+            type: 'credential',
+            credentialNames: ['cohereApi']
+        }
         this.inputs = [
-            {
-                label: 'Cohere API Key',
-                name: 'cohereApiKey',
-                type: 'password'
-            },
             {
                 label: 'Model Name',
                 name: 'modelName',
@@ -50,12 +54,14 @@ class CohereEmbedding_Embeddings implements INode {
         ]
     }
 
-    async init(nodeData: INodeData): Promise<any> {
-        const apiKey = nodeData.inputs?.cohereApiKey as string
+    async init(nodeData: INodeData, _: string, options: ICommonObject): Promise<any> {
         const modelName = nodeData.inputs?.modelName as string
 
+        const credentialData = await getCredentialData(nodeData.credential ?? '', options)
+        const cohereApiKey = getCredentialParam('cohereApiKey', credentialData, nodeData)
+
         const obj: Partial<CohereEmbeddingsParams> & { apiKey?: string } = {
-            apiKey
+            apiKey: cohereApiKey
         }
 
         if (modelName) obj.modelName = modelName

packages/components/nodes/embeddings/GoogleVertexAIEmbedding/GoogleVertexAIEmbedding.ts

@@ -0,0 +1,63 @@
+import { GoogleVertexAIEmbeddings, GoogleVertexAIEmbeddingsParams } from 'langchain/embeddings/googlevertexai'
+import { ICommonObject, INode, INodeData, INodeParams } from '../../../src/Interface'
+import { getBaseClasses, getCredentialData, getCredentialParam } from '../../../src/utils'
+import { GoogleAuthOptions } from 'google-auth-library'
+
+class GoogleVertexAIEmbedding_Embeddings implements INode {
+    label: string
+    name: string
+    version: number
+    type: string
+    icon: string
+    category: string
+    description: string
+    baseClasses: string[]
+    credential: INodeParams
+    inputs: INodeParams[]
+
+    constructor() {
+        this.label = 'GoogleVertexAI Embeddings'
+        this.name = 'googlevertexaiEmbeddings'
+        this.version = 1.0
+        this.type = 'GoogleVertexAIEmbeddings'
+        this.icon = 'vertexai.svg'
+        this.category = 'Embeddings'
+        this.description = 'Google vertexAI API to generate embeddings for a given text'
+        this.baseClasses = [this.type, ...getBaseClasses(GoogleVertexAIEmbeddings)]
+        this.credential = {
+            label: 'Connect Credential',
+            name: 'credential',
+            type: 'credential',
+            credentialNames: ['googleVertexAuth']
+        }
+        this.inputs = []
+    }
+
+    async init(nodeData: INodeData, _: string, options: ICommonObject): Promise<any> {
+        const credentialData = await getCredentialData(nodeData.credential ?? '', options)
+        const googleApplicationCredentialFilePath = getCredentialParam('googleApplicationCredentialFilePath', credentialData, nodeData)
+        const googleApplicationCredential = getCredentialParam('googleApplicationCredential', credentialData, nodeData)
+        const projectID = getCredentialParam('projectID', credentialData, nodeData)
+
+        if (!googleApplicationCredentialFilePath && !googleApplicationCredential)
+            throw new Error('Please specify your Google Application Credential')
+        if (googleApplicationCredentialFilePath && googleApplicationCredential)
+            throw new Error('Please use either Google Application Credential File Path or Google Credential JSON Object')
+
+        const authOptions: GoogleAuthOptions = {}
+        if (googleApplicationCredentialFilePath && !googleApplicationCredential) authOptions.keyFile = googleApplicationCredentialFilePath
+        else if (!googleApplicationCredentialFilePath && googleApplicationCredential)
+            authOptions.credentials = JSON.parse(googleApplicationCredential)
+
+        if (projectID) authOptions.projectId = projectID
+
+        const obj: GoogleVertexAIEmbeddingsParams = {
+            authOptions
+        }
+
+        const model = new GoogleVertexAIEmbeddings(obj)
+        return model
+    }
+}
+
+module.exports = { nodeClass: GoogleVertexAIEmbedding_Embeddings }

packages/components/nodes/embeddings/GoogleVertexAIEmbedding/vertexai.svg

@@ -0,0 +1,2 @@
+<!-- from  https://cloud.google.com/icons-->
+<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" width="24px" height="24px"><path d="M20,13.89A.77.77,0,0,0,19,13.73l-7,5.14v.22a.72.72,0,1,1,0,1.43v0a.74.74,0,0,0,.45-.15l7.41-5.47A.76.76,0,0,0,20,13.89Z" style="fill:#669df6"/><path d="M12,20.52a.72.72,0,0,1,0-1.43h0v-.22L5,13.73a.76.76,0,0,0-1,.16.74.74,0,0,0,.16,1l7.41,5.47a.73.73,0,0,0,.44.15v0Z" style="fill:#aecbfa"/><path d="M12,18.34a1.47,1.47,0,1,0,1.47,1.47A1.47,1.47,0,0,0,12,18.34Zm0,2.18a.72.72,0,1,1,.72-.71A.71.71,0,0,1,12,20.52Z" style="fill:#4285f4"/><path d="M6,6.11a.76.76,0,0,1-.75-.75V3.48a.76.76,0,1,1,1.51,0V5.36A.76.76,0,0,1,6,6.11Z" style="fill:#aecbfa"/><circle cx="5.98" cy="12" r="0.76" style="fill:#aecbfa"/><circle cx="5.98" cy="9.79" r="0.76" style="fill:#aecbfa"/><circle cx="5.98" cy="7.57" r="0.76" style="fill:#aecbfa"/><path d="M18,8.31a.76.76,0,0,1-.75-.76V5.67a.75.75,0,1,1,1.5,0V7.55A.75.75,0,0,1,18,8.31Z" style="fill:#4285f4"/><circle cx="18.02" cy="12.01" r="0.76" style="fill:#4285f4"/><circle cx="18.02" cy="9.76" r="0.76" style="fill:#4285f4"/><circle cx="18.02" cy="3.48" r="0.76" style="fill:#4285f4"/><path d="M12,15a.76.76,0,0,1-.75-.75V12.34a.76.76,0,0,1,1.51,0v1.89A.76.76,0,0,1,12,15Z" style="fill:#669df6"/><circle cx="12" cy="16.45" r="0.76" style="fill:#669df6"/><circle cx="12" cy="10.14" r="0.76" style="fill:#669df6"/><circle cx="12" cy="7.92" r="0.76" style="fill:#669df6"/><path d="M15,10.54a.76.76,0,0,1-.75-.75V7.91a.76.76,0,1,1,1.51,0V9.79A.76.76,0,0,1,15,10.54Z" style="fill:#4285f4"/><circle cx="15.01" cy="5.69" r="0.76" style="fill:#4285f4"/><circle cx="15.01" cy="14.19" r="0.76" style="fill:#4285f4"/><circle cx="15.01" cy="11.97" r="0.76" style="fill:#4285f4"/><circle cx="8.99" cy="14.19" r="0.76" style="fill:#aecbfa"/><circle cx="8.99" cy="7.92" r="0.76" style="fill:#aecbfa"/><circle cx="8.99" cy="5.69" r="0.76" style="fill:#aecbfa"/><path d="M9,12.73A.76.76,0,0,1,8.24,12V10.1a.75.75,0,1,1,1.5,0V12A.75.75,0,0,1,9,12.73Z" style="fill:#aecbfa"/></svg>

packages/components/nodes/embeddings/HuggingFaceInferenceEmbedding/HuggingFaceInferenceEmbedding.ts

@@ -1,49 +1,67 @@
-import { INode, INodeData, INodeParams } from '../../../src/Interface'
-import { getBaseClasses } from '../../../src/utils'
-import { HuggingFaceInferenceEmbeddings, HuggingFaceInferenceEmbeddingsParams } from 'langchain/embeddings/hf'
+import { ICommonObject, INode, INodeData, INodeParams } from '../../../src/Interface'
+import { getBaseClasses, getCredentialData, getCredentialParam } from '../../../src/utils'
+import { HuggingFaceInferenceEmbeddings, HuggingFaceInferenceEmbeddingsParams } from './core'
 
 class HuggingFaceInferenceEmbedding_Embeddings implements INode {
     label: string
     name: string
+    version: number
     type: string
     icon: string
     category: string
     description: string
     baseClasses: string[]
+    credential: INodeParams
     inputs: INodeParams[]
 
     constructor() {
         this.label = 'HuggingFace Inference Embeddings'
         this.name = 'huggingFaceInferenceEmbeddings'
+        this.version = 1.0
         this.type = 'HuggingFaceInferenceEmbeddings'
         this.icon = 'huggingface.png'
         this.category = 'Embeddings'
         this.description = 'HuggingFace Inference API to generate embeddings for a given text'
         this.baseClasses = [this.type, ...getBaseClasses(HuggingFaceInferenceEmbeddings)]
+        this.credential = {
+            label: 'Connect Credential',
+            name: 'credential',
+            type: 'credential',
+            credentialNames: ['huggingFaceApi']
+        }
         this.inputs = [
-            {
-                label: 'HuggingFace Api Key',
-                name: 'apiKey',
-                type: 'password'
-            },
             {
                 label: 'Model',
                 name: 'modelName',
                 type: 'string',
+                description: 'If using own inference endpoint, leave this blank',
+                placeholder: 'sentence-transformers/distilbert-base-nli-mean-tokens',
+                optional: true
+            },
+            {
+                label: 'Endpoint',
+                name: 'endpoint',
+                type: 'string',
+                placeholder: 'https://xyz.eu-west-1.aws.endpoints.huggingface.cloud/sentence-transformers/all-MiniLM-L6-v2',
+                description: 'Using your own inference endpoint',
                 optional: true
             }
         ]
     }
 
-    async init(nodeData: INodeData): Promise<any> {
-        const apiKey = nodeData.inputs?.apiKey as string
+    async init(nodeData: INodeData, _: string, options: ICommonObject): Promise<any> {
         const modelName = nodeData.inputs?.modelName as string
+        const endpoint = nodeData.inputs?.endpoint as string
+
+        const credentialData = await getCredentialData(nodeData.credential ?? '', options)
+        const huggingFaceApiKey = getCredentialParam('huggingFaceApiKey', credentialData, nodeData)
 
         const obj: Partial<HuggingFaceInferenceEmbeddingsParams> = {
-            apiKey
+            apiKey: huggingFaceApiKey
         }
 
         if (modelName) obj.model = modelName
+        if (endpoint) obj.endpoint = endpoint
 
         const model = new HuggingFaceInferenceEmbeddings(obj)
         return model

packages/components/nodes/embeddings/HuggingFaceInferenceEmbedding/core.ts

@@ -0,0 +1,55 @@
+import { HfInference } from '@huggingface/inference'
+import { Embeddings, EmbeddingsParams } from 'langchain/embeddings/base'
+import { getEnvironmentVariable } from '../../../src/utils'
+
+export interface HuggingFaceInferenceEmbeddingsParams extends EmbeddingsParams {
+    apiKey?: string
+    model?: string
+    endpoint?: string
+}
+
+export class HuggingFaceInferenceEmbeddings extends Embeddings implements HuggingFaceInferenceEmbeddingsParams {
+    apiKey?: string
+
+    endpoint?: string
+
+    model: string
+
+    client: HfInference
+
+    constructor(fields?: HuggingFaceInferenceEmbeddingsParams) {
+        super(fields ?? {})
+
+        this.model = fields?.model ?? 'sentence-transformers/distilbert-base-nli-mean-tokens'
+        this.apiKey = fields?.apiKey ?? getEnvironmentVariable('HUGGINGFACEHUB_API_KEY')
+        this.endpoint = fields?.endpoint ?? ''
+        this.client = new HfInference(this.apiKey)
+        if (this.endpoint) this.client.endpoint(this.endpoint)
+    }
+
+    async _embed(texts: string[]): Promise<number[][]> {
+        // replace newlines, which can negatively affect performance.
+        const clean = texts.map((text) => text.replace(/\n/g, ' '))
+        const hf = new HfInference(this.apiKey)
+        const obj: any = {
+            inputs: clean
+        }
+        if (this.endpoint) {
+            hf.endpoint(this.endpoint)
+        } else {
+            obj.model = this.model
+        }
+
+        const res = await this.caller.callWithOptions({}, hf.featureExtraction.bind(hf), obj)
+        return res as number[][]
+    }
+
+    async embedQuery(document: string): Promise<number[]> {
+        const res = await this._embed([document])
+        return res[0]
+    }
+
+    async embedDocuments(documents: string[]): Promise<number[][]> {
+        return this._embed(documents)
+    }
+}

packages/components/nodes/embeddings/LocalAIEmbedding/LocalAIEmbedding.ts

@@ -0,0 +1,55 @@
+import { INode, INodeData, INodeParams } from '../../../src/Interface'
+import { OpenAIEmbeddings, OpenAIEmbeddingsParams } from 'langchain/embeddings/openai'
+
+class LocalAIEmbedding_Embeddings implements INode {
+    label: string
+    name: string
+    version: number
+    type: string
+    icon: string
+    category: string
+    description: string
+    baseClasses: string[]
+    inputs: INodeParams[]
+
+    constructor() {
+        this.label = 'LocalAI Embeddings'
+        this.name = 'localAIEmbeddings'
+        this.version = 1.0
+        this.type = 'LocalAI Embeddings'
+        this.icon = 'localai.png'
+        this.category = 'Embeddings'
+        this.description = 'Use local embeddings models like llama.cpp'
+        this.baseClasses = [this.type, 'Embeddings']
+        this.inputs = [
+            {
+                label: 'Base Path',
+                name: 'basePath',
+                type: 'string',
+                placeholder: 'http://localhost:8080/v1'
+            },
+            {
+                label: 'Model Name',
+                name: 'modelName',
+                type: 'string',
+                placeholder: 'text-embedding-ada-002'
+            }
+        ]
+    }
+
+    async init(nodeData: INodeData): Promise<any> {
+        const modelName = nodeData.inputs?.modelName as string
+        const basePath = nodeData.inputs?.basePath as string
+
+        const obj: Partial<OpenAIEmbeddingsParams> & { openAIApiKey?: string } = {
+            modelName,
+            openAIApiKey: 'sk-'
+        }
+
+        const model = new OpenAIEmbeddings(obj, { basePath })
+
+        return model
+    }
+}
+
+module.exports = { nodeClass: LocalAIEmbedding_Embeddings }

packages/components/nodes/embeddings/OpenAIEmbedding/OpenAIEmbedding.ts

@@ -1,31 +1,35 @@
-import { INode, INodeData, INodeParams } from '../../../src/Interface'
-import { getBaseClasses } from '../../../src/utils'
+import { ICommonObject, INode, INodeData, INodeParams } from '../../../src/Interface'
+import { getBaseClasses, getCredentialData, getCredentialParam } from '../../../src/utils'
 import { OpenAIEmbeddings, OpenAIEmbeddingsParams } from 'langchain/embeddings/openai'
 
 class OpenAIEmbedding_Embeddings implements INode {
     label: string
     name: string
+    version: number
     type: string
     icon: string
     category: string
     description: string
     baseClasses: string[]
+    credential: INodeParams
     inputs: INodeParams[]
 
     constructor() {
         this.label = 'OpenAI Embeddings'
         this.name = 'openAIEmbeddings'
+        this.version = 1.0
         this.type = 'OpenAIEmbeddings'
         this.icon = 'openai.png'
         this.category = 'Embeddings'
         this.description = 'OpenAI API to generate embeddings for a given text'
         this.baseClasses = [this.type, ...getBaseClasses(OpenAIEmbeddings)]
+        this.credential = {
+            label: 'Connect Credential',
+            name: 'credential',
+            type: 'credential',
+            credentialNames: ['openAIApi']
+        }
         this.inputs = [
-            {
-                label: 'OpenAI Api Key',
-                name: 'openAIApiKey',
-                type: 'password'
-            },
             {
                 label: 'Strip New Lines',
                 name: 'stripNewLines',
@@ -46,15 +50,25 @@ class OpenAIEmbedding_Embeddings implements INode {
                 type: 'number',
                 optional: true,
                 additionalParams: true
+            },
+            {
+                label: 'BasePath',
+                name: 'basepath',
+                type: 'string',
+                optional: true,
+                additionalParams: true
             }
         ]
     }
 
-    async init(nodeData: INodeData): Promise<any> {
-        const openAIApiKey = nodeData.inputs?.openAIApiKey as string
+    async init(nodeData: INodeData, _: string, options: ICommonObject): Promise<any> {
         const stripNewLines = nodeData.inputs?.stripNewLines as boolean
         const batchSize = nodeData.inputs?.batchSize as string
         const timeout = nodeData.inputs?.timeout as string
+        const basePath = nodeData.inputs?.basepath as string
+
+        const credentialData = await getCredentialData(nodeData.credential ?? '', options)
+        const openAIApiKey = getCredentialParam('openAIApiKey', credentialData, nodeData)
 
         const obj: Partial<OpenAIEmbeddingsParams> & { openAIApiKey?: string } = {
             openAIApiKey
@@ -64,7 +78,7 @@ class OpenAIEmbedding_Embeddings implements INode {
         if (batchSize) obj.batchSize = parseInt(batchSize, 10)
         if (timeout) obj.timeout = parseInt(timeout, 10)
 
-        const model = new OpenAIEmbeddings(obj)
+        const model = new OpenAIEmbeddings(obj, { basePath })
         return model
     }
 }

packages/components/nodes/llms/Azure OpenAI/Azure.svg

@@ -1,5 +1 @@
-<svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-brand-azure" 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="M6 7.5l-4 9.5h4l6 -15z"></path>
-   <path d="M22 20l-7 -15l-3 7l4 5l-8 3z"></path>
-</svg>
+<svg xmlns="http://www.w3.org/2000/svg"  viewBox="0 0 48 48" width="96px" height="96px"><path fill="#035bda" d="M46 40L29.317 10.852 22.808 23.96 34.267 37.24 13 39.655zM13.092 18.182L2 36.896 11.442 35.947 28.033 5.678z"/></svg>

packages/components/nodes/llms/Azure OpenAI/AzureOpenAI.ts

@@ -1,31 +1,35 @@
-import { INode, INodeData, INodeParams } from '../../../src/Interface'
-import { getBaseClasses } from '../../../src/utils'
+import { ICommonObject, INode, INodeData, INodeParams } from '../../../src/Interface'
+import { getBaseClasses, getCredentialData, getCredentialParam } from '../../../src/utils'
 import { AzureOpenAIInput, OpenAI, OpenAIInput } from 'langchain/llms/openai'
 
 class AzureOpenAI_LLMs implements INode {
     label: string
     name: string
+    version: number
     type: string
     icon: string
     category: string
     description: string
     baseClasses: string[]
+    credential: INodeParams
     inputs: INodeParams[]
 
     constructor() {
         this.label = 'Azure OpenAI'
         this.name = 'azureOpenAI'
+        this.version = 1.0
         this.type = 'AzureOpenAI'
         this.icon = 'Azure.svg'
         this.category = 'LLMs'
         this.description = 'Wrapper around Azure OpenAI large language models'
         this.baseClasses = [this.type, ...getBaseClasses(OpenAI)]
+        this.credential = {
+            label: 'Connect Credential',
+            name: 'credential',
+            type: 'credential',
+            credentialNames: ['azureOpenAIApi']
+        }
         this.inputs = [
-            {
-                label: 'Azure OpenAI Api Key',
-                name: 'azureOpenAIApiKey',
-                type: 'password'
-            },
             {
                 label: 'Model Name',
                 name: 'modelName',
@@ -87,41 +91,15 @@ class AzureOpenAI_LLMs implements INode {
                 label: 'Temperature',
                 name: 'temperature',
                 type: 'number',
+                step: 0.1,
                 default: 0.9,
                 optional: true
             },
-            {
-                label: 'Azure OpenAI Api Instance Name',
-                name: 'azureOpenAIApiInstanceName',
-                type: 'string',
-                placeholder: 'YOUR-INSTANCE-NAME'
-            },
-            {
-                label: 'Azure OpenAI Api Deployment Name',
-                name: 'azureOpenAIApiDeploymentName',
-                type: 'string',
-                placeholder: 'YOUR-DEPLOYMENT-NAME'
-            },
-            {
-                label: 'Azure OpenAI Api Version',
-                name: 'azureOpenAIApiVersion',
-                type: 'options',
-                options: [
-                    {
-                        label: '2023-03-15-preview',
-                        name: '2023-03-15-preview'
-                    },
-                    {
-                        label: '2022-12-01',
-                        name: '2022-12-01'
-                    }
-                ],
-                default: '2023-03-15-preview'
-            },
             {
                 label: 'Max Tokens',
                 name: 'maxTokens',
                 type: 'number',
+                step: 1,
                 optional: true,
                 additionalParams: true
             },
@@ -129,6 +107,7 @@ class AzureOpenAI_LLMs implements INode {
                 label: 'Top Probability',
                 name: 'topP',
                 type: 'number',
+                step: 0.1,
                 optional: true,
                 additionalParams: true
             },
@@ -136,6 +115,7 @@ class AzureOpenAI_LLMs implements INode {
                 label: 'Best Of',
                 name: 'bestOf',
                 type: 'number',
+                step: 1,
                 optional: true,
                 additionalParams: true
             },
@@ -143,6 +123,7 @@ class AzureOpenAI_LLMs implements INode {
                 label: 'Frequency Penalty',
                 name: 'frequencyPenalty',
                 type: 'number',
+                step: 0.1,
                 optional: true,
                 additionalParams: true
             },
@@ -150,6 +131,7 @@ class AzureOpenAI_LLMs implements INode {
                 label: 'Presence Penalty',
                 name: 'presencePenalty',
                 type: 'number',
+                step: 0.1,
                 optional: true,
                 additionalParams: true
             },
@@ -157,39 +139,44 @@ class AzureOpenAI_LLMs implements INode {
                 label: 'Timeout',
                 name: 'timeout',
                 type: 'number',
+                step: 1,
                 optional: true,
                 additionalParams: true
             }
         ]
     }
 
-    async init(nodeData: INodeData): Promise<any> {
-        const azureOpenAIApiKey = nodeData.inputs?.azureOpenAIApiKey as string
+    async init(nodeData: INodeData, _: string, options: ICommonObject): Promise<any> {
         const temperature = nodeData.inputs?.temperature as string
         const modelName = nodeData.inputs?.modelName as string
-        const azureOpenAIApiInstanceName = nodeData.inputs?.azureOpenAIApiInstanceName as string
-        const azureOpenAIApiDeploymentName = nodeData.inputs?.azureOpenAIApiDeploymentName as string
-        const azureOpenAIApiVersion = nodeData.inputs?.azureOpenAIApiVersion as string
         const maxTokens = nodeData.inputs?.maxTokens as string
         const topP = nodeData.inputs?.topP as string
         const frequencyPenalty = nodeData.inputs?.frequencyPenalty as string
         const presencePenalty = nodeData.inputs?.presencePenalty as string
         const timeout = nodeData.inputs?.timeout as string
         const bestOf = nodeData.inputs?.bestOf as string
+        const streaming = nodeData.inputs?.streaming as boolean
+
+        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<AzureOpenAIInput> & Partial<OpenAIInput> = {
-            temperature: parseInt(temperature, 10),
+            temperature: parseFloat(temperature),
             modelName,
             azureOpenAIApiKey,
             azureOpenAIApiInstanceName,
             azureOpenAIApiDeploymentName,
-            azureOpenAIApiVersion
+            azureOpenAIApiVersion,
+            streaming: streaming ?? true
         }
 
         if (maxTokens) obj.maxTokens = parseInt(maxTokens, 10)
-        if (topP) obj.topP = parseInt(topP, 10)
-        if (frequencyPenalty) obj.frequencyPenalty = parseInt(frequencyPenalty, 10)
-        if (presencePenalty) obj.presencePenalty = parseInt(presencePenalty, 10)
+        if (topP) obj.topP = parseFloat(topP)
+        if (frequencyPenalty) obj.frequencyPenalty = parseFloat(frequencyPenalty)
+        if (presencePenalty) obj.presencePenalty = parseFloat(presencePenalty)
         if (timeout) obj.timeout = parseInt(timeout, 10)
         if (bestOf) obj.bestOf = parseInt(bestOf, 10)
 

packages/components/nodes/llms/Cohere/Cohere.ts

@@ -1,31 +1,35 @@
-import { INode, INodeData, INodeParams } from '../../../src/Interface'
-import { getBaseClasses } from '../../../src/utils'
-import { Cohere, CohereInput } from 'langchain/llms/cohere'
+import { ICommonObject, INode, INodeData, INodeParams } from '../../../src/Interface'
+import { getBaseClasses, getCredentialData, getCredentialParam } from '../../../src/utils'
+import { Cohere, CohereInput } from './core'
 
 class Cohere_LLMs implements INode {
     label: string
     name: string
+    version: number
     type: string
     icon: string
     category: string
     description: string
     baseClasses: string[]
+    credential: INodeParams
     inputs: INodeParams[]
 
     constructor() {
         this.label = 'Cohere'
         this.name = 'cohere'
+        this.version = 1.0
         this.type = 'Cohere'
         this.icon = 'cohere.png'
         this.category = 'LLMs'
         this.description = 'Wrapper around Cohere large language models'
         this.baseClasses = [this.type, ...getBaseClasses(Cohere)]
+        this.credential = {
+            label: 'Connect Credential',
+            name: 'credential',
+            type: 'credential',
+            credentialNames: ['cohereApi']
+        }
         this.inputs = [
-            {
-                label: 'Cohere Api Key',
-                name: 'cohereApiKey',
-                type: 'password'
-            },
             {
                 label: 'Model Name',
                 name: 'modelName',
@@ -63,6 +67,7 @@ class Cohere_LLMs implements INode {
                 label: 'Temperature',
                 name: 'temperature',
                 type: 'number',
+                step: 0.1,
                 default: 0.7,
                 optional: true
             },
@@ -70,24 +75,27 @@ class Cohere_LLMs implements INode {
                 label: 'Max Tokens',
                 name: 'maxTokens',
                 type: 'number',
+                step: 1,
                 optional: true
             }
         ]
     }
 
-    async init(nodeData: INodeData): Promise<any> {
+    async init(nodeData: INodeData, _: string, options: ICommonObject): Promise<any> {
         const temperature = nodeData.inputs?.temperature as string
         const modelName = nodeData.inputs?.modelName as string
-        const apiKey = nodeData.inputs?.cohereApiKey as string
         const maxTokens = nodeData.inputs?.maxTokens as string
 
+        const credentialData = await getCredentialData(nodeData.credential ?? '', options)
+        const cohereApiKey = getCredentialParam('cohereApiKey', credentialData, nodeData)
+
         const obj: CohereInput = {
-            apiKey
+            apiKey: cohereApiKey
         }
 
         if (maxTokens) obj.maxTokens = parseInt(maxTokens, 10)
         if (modelName) obj.model = modelName
-        if (temperature) obj.temperature = parseInt(temperature, 10)
+        if (temperature) obj.temperature = parseFloat(temperature)
 
         const model = new Cohere(obj)
         return model

packages/components/nodes/llms/Cohere/core.ts

@@ -0,0 +1,78 @@
+import { LLM, BaseLLMParams } from 'langchain/llms/base'
+
+export interface CohereInput extends BaseLLMParams {
+    /** Sampling temperature to use */
+    temperature?: number
+
+    /**
+     * Maximum number of tokens to generate in the completion.
+     */
+    maxTokens?: number
+
+    /** Model to use */
+    model?: string
+
+    apiKey?: string
+}
+
+export class Cohere extends LLM implements CohereInput {
+    temperature = 0
+
+    maxTokens = 250
+
+    model: string
+
+    apiKey: string
+
+    constructor(fields?: CohereInput) {
+        super(fields ?? {})
+
+        const apiKey = fields?.apiKey ?? undefined
+
+        if (!apiKey) {
+            throw new Error('Please set the COHERE_API_KEY environment variable or pass it to the constructor as the apiKey field.')
+        }
+
+        this.apiKey = apiKey
+        this.maxTokens = fields?.maxTokens ?? this.maxTokens
+        this.temperature = fields?.temperature ?? this.temperature
+        this.model = fields?.model ?? this.model
+    }
+
+    _llmType() {
+        return 'cohere'
+    }
+
+    /** @ignore */
+    async _call(prompt: string, options: this['ParsedCallOptions']): Promise<string> {
+        const { cohere } = await Cohere.imports()
+
+        cohere.init(this.apiKey)
+
+        // Hit the `generate` endpoint on the `large` model
+        const generateResponse = await this.caller.callWithOptions({ signal: options.signal }, cohere.generate.bind(cohere), {
+            prompt,
+            model: this.model,
+            max_tokens: this.maxTokens,
+            temperature: this.temperature,
+            end_sequences: options.stop
+        })
+        try {
+            return generateResponse.body.generations[0].text
+        } catch {
+            throw new Error('Could not parse response.')
+        }
+    }
+
+    /** @ignore */
+    static async imports(): Promise<{
+        cohere: typeof import('cohere-ai')
+    }> {
+        try {
+            const { default: cohere } = await import('cohere-ai')
+            return { cohere }
+        } catch (e) {
+            throw new Error('Please install cohere-ai as a dependency with, e.g. `yarn add cohere-ai`')
+        }
+    }
+}

packages/components/nodes/llms/GoogleVertexAI/GoogleVertexAI.ts

@@ -0,0 +1,117 @@
+import { ICommonObject, INode, INodeData, INodeParams } from '../../../src/Interface'
+import { getBaseClasses, getCredentialData, getCredentialParam } from '../../../src/utils'
+import { GoogleVertexAI, GoogleVertexAITextInput } from 'langchain/llms/googlevertexai'
+import { GoogleAuthOptions } from 'google-auth-library'
+
+class GoogleVertexAI_LLMs implements INode {
+    label: string
+    name: string
+    version: number
+    type: string
+    icon: string
+    category: string
+    description: string
+    baseClasses: string[]
+    credential: INodeParams
+    inputs: INodeParams[]
+
+    constructor() {
+        this.label = 'GoogleVertexAI'
+        this.name = 'googlevertexai'
+        this.version = 1.0
+        this.type = 'GoogleVertexAI'
+        this.icon = 'vertexai.svg'
+        this.category = 'LLMs'
+        this.description = 'Wrapper around GoogleVertexAI large language models'
+        this.baseClasses = [this.type, ...getBaseClasses(GoogleVertexAI)]
+        this.credential = {
+            label: 'Connect Credential',
+            name: 'credential',
+            type: 'credential',
+            credentialNames: ['googleVertexAuth']
+        }
+        this.inputs = [
+            {
+                label: 'Model Name',
+                name: 'modelName',
+                type: 'options',
+                options: [
+                    {
+                        label: 'text-bison',
+                        name: 'text-bison'
+                    },
+                    {
+                        label: 'code-bison',
+                        name: 'code-bison'
+                    },
+                    {
+                        label: 'code-gecko',
+                        name: 'code-gecko'
+                    }
+                ],
+                default: 'text-bison'
+            },
+            {
+                label: 'Temperature',
+                name: 'temperature',
+                type: 'number',
+                step: 0.1,
+                default: 0.7,
+                optional: true
+            },
+            {
+                label: 'max Output Tokens',
+                name: 'maxOutputTokens',
+                type: 'number',
+                step: 1,
+                optional: true,
+                additionalParams: true
+            },
+            {
+                label: 'Top Probability',
+                name: 'topP',
+                type: 'number',
+                step: 0.1,
+                optional: true,
+                additionalParams: true
+            }
+        ]
+    }
+
+    async init(nodeData: INodeData, _: string, options: ICommonObject): Promise<any> {
+        const credentialData = await getCredentialData(nodeData.credential ?? '', options)
+        const googleApplicationCredentialFilePath = getCredentialParam('googleApplicationCredentialFilePath', credentialData, nodeData)
+        const googleApplicationCredential = getCredentialParam('googleApplicationCredential', credentialData, nodeData)
+        const projectID = getCredentialParam('projectID', credentialData, nodeData)
+
+        if (!googleApplicationCredentialFilePath && !googleApplicationCredential)
+            throw new Error('Please specify your Google Application Credential')
+        if (googleApplicationCredentialFilePath && googleApplicationCredential)
+            throw new Error('Please use either Google Application Credential File Path or Google Credential JSON Object')
+
+        const authOptions: GoogleAuthOptions = {}
+        if (googleApplicationCredentialFilePath && !googleApplicationCredential) authOptions.keyFile = googleApplicationCredentialFilePath
+        else if (!googleApplicationCredentialFilePath && googleApplicationCredential)
+            authOptions.credentials = JSON.parse(googleApplicationCredential)
+        if (projectID) authOptions.projectId = projectID
+
+        const temperature = nodeData.inputs?.temperature as string
+        const modelName = nodeData.inputs?.modelName as string
+        const maxOutputTokens = nodeData.inputs?.maxOutputTokens as string
+        const topP = nodeData.inputs?.topP as string
+
+        const obj: Partial<GoogleVertexAITextInput> = {
+            temperature: parseFloat(temperature),
+            model: modelName,
+            authOptions
+        }
+
+        if (maxOutputTokens) obj.maxOutputTokens = parseInt(maxOutputTokens, 10)
+        if (topP) obj.topP = parseFloat(topP)
+
+        const model = new GoogleVertexAI(obj)
+        return model
+    }
+}
+
+module.exports = { nodeClass: GoogleVertexAI_LLMs }

packages/components/nodes/llms/GoogleVertexAI/vertexai.svg

@@ -0,0 +1,2 @@
+<!-- from  https://cloud.google.com/icons-->
+<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" width="24px" height="24px"><path d="M20,13.89A.77.77,0,0,0,19,13.73l-7,5.14v.22a.72.72,0,1,1,0,1.43v0a.74.74,0,0,0,.45-.15l7.41-5.47A.76.76,0,0,0,20,13.89Z" style="fill:#669df6"/><path d="M12,20.52a.72.72,0,0,1,0-1.43h0v-.22L5,13.73a.76.76,0,0,0-1,.16.74.74,0,0,0,.16,1l7.41,5.47a.73.73,0,0,0,.44.15v0Z" style="fill:#aecbfa"/><path d="M12,18.34a1.47,1.47,0,1,0,1.47,1.47A1.47,1.47,0,0,0,12,18.34Zm0,2.18a.72.72,0,1,1,.72-.71A.71.71,0,0,1,12,20.52Z" style="fill:#4285f4"/><path d="M6,6.11a.76.76,0,0,1-.75-.75V3.48a.76.76,0,1,1,1.51,0V5.36A.76.76,0,0,1,6,6.11Z" style="fill:#aecbfa"/><circle cx="5.98" cy="12" r="0.76" style="fill:#aecbfa"/><circle cx="5.98" cy="9.79" r="0.76" style="fill:#aecbfa"/><circle cx="5.98" cy="7.57" r="0.76" style="fill:#aecbfa"/><path d="M18,8.31a.76.76,0,0,1-.75-.76V5.67a.75.75,0,1,1,1.5,0V7.55A.75.75,0,0,1,18,8.31Z" style="fill:#4285f4"/><circle cx="18.02" cy="12.01" r="0.76" style="fill:#4285f4"/><circle cx="18.02" cy="9.76" r="0.76" style="fill:#4285f4"/><circle cx="18.02" cy="3.48" r="0.76" style="fill:#4285f4"/><path d="M12,15a.76.76,0,0,1-.75-.75V12.34a.76.76,0,0,1,1.51,0v1.89A.76.76,0,0,1,12,15Z" style="fill:#669df6"/><circle cx="12" cy="16.45" r="0.76" style="fill:#669df6"/><circle cx="12" cy="10.14" r="0.76" style="fill:#669df6"/><circle cx="12" cy="7.92" r="0.76" style="fill:#669df6"/><path d="M15,10.54a.76.76,0,0,1-.75-.75V7.91a.76.76,0,1,1,1.51,0V9.79A.76.76,0,0,1,15,10.54Z" style="fill:#4285f4"/><circle cx="15.01" cy="5.69" r="0.76" style="fill:#4285f4"/><circle cx="15.01" cy="14.19" r="0.76" style="fill:#4285f4"/><circle cx="15.01" cy="11.97" r="0.76" style="fill:#4285f4"/><circle cx="8.99" cy="14.19" r="0.76" style="fill:#aecbfa"/><circle cx="8.99" cy="7.92" r="0.76" style="fill:#aecbfa"/><circle cx="8.99" cy="5.69" r="0.76" style="fill:#aecbfa"/><path d="M9,12.73A.76.76,0,0,1,8.24,12V10.1a.75.75,0,1,1,1.5,0V12A.75.75,0,0,1,9,12.73Z" style="fill:#aecbfa"/></svg>

packages/components/nodes/llms/HuggingFaceInference/HuggingFaceInference.ts

@@ -1,48 +1,124 @@
-import { INode, INodeData, INodeParams } from '../../../src/Interface'
-import { getBaseClasses } from '../../../src/utils'
-import { HuggingFaceInference } from 'langchain/llms/hf'
+import { ICommonObject, INode, INodeData, INodeParams } from '../../../src/Interface'
+import { getBaseClasses, getCredentialData, getCredentialParam } from '../../../src/utils'
+import { HFInput, HuggingFaceInference } from './core'
 
 class HuggingFaceInference_LLMs implements INode {
     label: string
     name: string
+    version: number
     type: string
     icon: string
     category: string
     description: string
     baseClasses: string[]
+    credential: INodeParams
     inputs: INodeParams[]
 
     constructor() {
         this.label = 'HuggingFace Inference'
         this.name = 'huggingFaceInference_LLMs'
+        this.version = 1.0
         this.type = 'HuggingFaceInference'
         this.icon = 'huggingface.png'
         this.category = 'LLMs'
         this.description = 'Wrapper around HuggingFace large language models'
         this.baseClasses = [this.type, ...getBaseClasses(HuggingFaceInference)]
+        this.credential = {
+            label: 'Connect Credential',
+            name: 'credential',
+            type: 'credential',
+            credentialNames: ['huggingFaceApi']
+        }
         this.inputs = [
             {
                 label: 'Model',
                 name: 'model',
                 type: 'string',
-                placeholder: 'gpt2'
+                description: 'If using own inference endpoint, leave this blank',
+                placeholder: 'gpt2',
+                optional: true
             },
             {
-                label: 'HuggingFace Api Key',
-                name: 'apiKey',
-                type: 'password'
+                label: 'Endpoint',
+                name: 'endpoint',
+                type: 'string',
+                placeholder: 'https://xyz.eu-west-1.aws.endpoints.huggingface.cloud/gpt2',
+                description: 'Using your own inference endpoint',
+                optional: true
+            },
+            {
+                label: 'Temperature',
+                name: 'temperature',
+                type: 'number',
+                step: 0.1,
+                description: 'Temperature parameter may not apply to certain model. Please check available model parameters',
+                optional: true,
+                additionalParams: true
+            },
+            {
+                label: 'Max Tokens',
+                name: 'maxTokens',
+                type: 'number',
+                step: 1,
+                description: 'Max Tokens parameter may not apply to certain model. Please check available model parameters',
+                optional: true,
+                additionalParams: true
+            },
+            {
+                label: 'Top Probability',
+                name: 'topP',
+                type: 'number',
+                step: 0.1,
+                description: 'Top Probability parameter may not apply to certain model. Please check available model parameters',
+                optional: true,
+                additionalParams: true
+            },
+            {
+                label: 'Top K',
+                name: 'hfTopK',
+                type: 'number',
+                step: 0.1,
+                description: 'Top K parameter may not apply to certain model. Please check available model parameters',
+                optional: true,
+                additionalParams: true
+            },
+            {
+                label: 'Frequency Penalty',
+                name: 'frequencyPenalty',
+                type: 'number',
+                step: 0.1,
+                description: 'Frequency Penalty parameter may not apply to certain model. Please check available model parameters',
+                optional: true,
+                additionalParams: true
             }
         ]
     }
 
-    async init(nodeData: INodeData): Promise<any> {
+    async init(nodeData: INodeData, _: string, options: ICommonObject): Promise<any> {
         const model = nodeData.inputs?.model as string
-        const apiKey = nodeData.inputs?.apiKey as string
+        const temperature = nodeData.inputs?.temperature as string
+        const maxTokens = nodeData.inputs?.maxTokens as string
+        const topP = nodeData.inputs?.topP as string
+        const hfTopK = nodeData.inputs?.hfTopK as string
+        const frequencyPenalty = nodeData.inputs?.frequencyPenalty as string
+        const endpoint = nodeData.inputs?.endpoint as string
 
-        const huggingFace = new HuggingFaceInference({
+        const credentialData = await getCredentialData(nodeData.credential ?? '', options)
+        const huggingFaceApiKey = getCredentialParam('huggingFaceApiKey', credentialData, nodeData)
+
+        const obj: Partial<HFInput> = {
             model,
-            apiKey
-        })
+            apiKey: huggingFaceApiKey
+        }
+
+        if (temperature) obj.temperature = parseFloat(temperature)
+        if (maxTokens) obj.maxTokens = parseInt(maxTokens, 10)
+        if (topP) obj.topP = parseFloat(topP)
+        if (hfTopK) obj.topK = parseFloat(hfTopK)
+        if (frequencyPenalty) obj.frequencyPenalty = parseFloat(frequencyPenalty)
+        if (endpoint) obj.endpoint = endpoint
+
+        const huggingFace = new HuggingFaceInference(obj)
         return huggingFace
     }
 }

packages/components/nodes/llms/HuggingFaceInference/core.ts

@@ -0,0 +1,113 @@
+import { getEnvironmentVariable } from '../../../src/utils'
+import { LLM, BaseLLMParams } from 'langchain/llms/base'
+
+export interface HFInput {
+    /** Model to use */
+    model: string
+
+    /** Sampling temperature to use */
+    temperature?: number
+
+    /**
+     * Maximum number of tokens to generate in the completion.
+     */
+    maxTokens?: number
+
+    /** Total probability mass of tokens to consider at each step */
+    topP?: number
+
+    /** Integer to define the top tokens considered within the sample operation to create new text. */
+    topK?: number
+
+    /** Penalizes repeated tokens according to frequency */
+    frequencyPenalty?: number
+
+    /** API key to use. */
+    apiKey?: string
+
+    /** Private endpoint to use. */
+    endpoint?: string
+}
+
+export class HuggingFaceInference extends LLM implements HFInput {
+    get lc_secrets(): { [key: string]: string } | undefined {
+        return {
+            apiKey: 'HUGGINGFACEHUB_API_KEY'
+        }
+    }
+
+    model = 'gpt2'
+
+    temperature: number | undefined = undefined
+
+    maxTokens: number | undefined = undefined
+
+    topP: number | undefined = undefined
+
+    topK: number | undefined = undefined
+
+    frequencyPenalty: number | undefined = undefined
+
+    apiKey: string | undefined = undefined
+
+    endpoint: string | undefined = undefined
+
+    constructor(fields?: Partial<HFInput> & BaseLLMParams) {
+        super(fields ?? {})
+
+        this.model = fields?.model ?? this.model
+        this.temperature = fields?.temperature ?? this.temperature
+        this.maxTokens = fields?.maxTokens ?? this.maxTokens
+        this.topP = fields?.topP ?? this.topP
+        this.topK = fields?.topK ?? this.topK
+        this.frequencyPenalty = fields?.frequencyPenalty ?? this.frequencyPenalty
+        this.endpoint = fields?.endpoint ?? ''
+        this.apiKey = fields?.apiKey ?? getEnvironmentVariable('HUGGINGFACEHUB_API_KEY')
+        if (!this.apiKey) {
+            throw new Error(
+                'Please set an API key for HuggingFace Hub in the environment variable HUGGINGFACEHUB_API_KEY or in the apiKey field of the HuggingFaceInference constructor.'
+            )
+        }
+    }
+
+    _llmType() {
+        return 'hf'
+    }
+
+    /** @ignore */
+    async _call(prompt: string, options: this['ParsedCallOptions']): Promise<string> {
+        const { HfInference } = await HuggingFaceInference.imports()
+        const hf = new HfInference(this.apiKey)
+        const obj: any = {
+            parameters: {
+                // make it behave similar to openai, returning only the generated text
+                return_full_text: false,
+                temperature: this.temperature,
+                max_new_tokens: this.maxTokens,
+                top_p: this.topP,
+                top_k: this.topK,
+                repetition_penalty: this.frequencyPenalty
+            },
+            inputs: prompt
+        }
+        if (this.endpoint) {
+            hf.endpoint(this.endpoint)
+        } else {
+            obj.model = this.model
+        }
+        const res = await this.caller.callWithOptions({ signal: options.signal }, hf.textGeneration.bind(hf), obj)
+        return res.generated_text
+    }
+
+    /** @ignore */
+    static async imports(): Promise<{
+        HfInference: typeof import('@huggingface/inference').HfInference
+    }> {
+        try {
+            const { HfInference } = await import('@huggingface/inference')
+            return { HfInference }
+        } catch (e) {
+            throw new Error('Please install huggingface as a dependency with, e.g. `yarn add @huggingface/inference`')
+        }
+    }
+}

packages/components/nodes/llms/OpenAI/OpenAI.ts

@@ -1,31 +1,35 @@
-import { INode, INodeData, INodeParams } from '../../../src/Interface'
-import { getBaseClasses } from '../../../src/utils'
+import { ICommonObject, INode, INodeData, INodeParams } from '../../../src/Interface'
+import { getBaseClasses, getCredentialData, getCredentialParam } from '../../../src/utils'
 import { OpenAI, OpenAIInput } from 'langchain/llms/openai'
 
 class OpenAI_LLMs implements INode {
     label: string
     name: string
+    version: number
     type: string
     icon: string
     category: string
     description: string
     baseClasses: string[]
+    credential: INodeParams
     inputs: INodeParams[]
 
     constructor() {
         this.label = 'OpenAI'
         this.name = 'openAI'
+        this.version = 1.0
         this.type = 'OpenAI'
         this.icon = 'openai.png'
         this.category = 'LLMs'
         this.description = 'Wrapper around OpenAI large language models'
         this.baseClasses = [this.type, ...getBaseClasses(OpenAI)]
+        this.credential = {
+            label: 'Connect Credential',
+            name: 'credential',
+            type: 'credential',
+            credentialNames: ['openAIApi']
+        }
         this.inputs = [
-            {
-                label: 'OpenAI Api Key',
-                name: 'openAIApiKey',
-                type: 'password'
-            },
             {
                 label: 'Model Name',
                 name: 'modelName',
@@ -55,6 +59,7 @@ class OpenAI_LLMs implements INode {
                 label: 'Temperature',
                 name: 'temperature',
                 type: 'number',
+                step: 0.1,
                 default: 0.7,
                 optional: true
             },
@@ -62,6 +67,7 @@ class OpenAI_LLMs implements INode {
                 label: 'Max Tokens',
                 name: 'maxTokens',
                 type: 'number',
+                step: 1,
                 optional: true,
                 additionalParams: true
             },
@@ -69,6 +75,7 @@ class OpenAI_LLMs implements INode {
                 label: 'Top Probability',
                 name: 'topP',
                 type: 'number',
+                step: 0.1,
                 optional: true,
                 additionalParams: true
             },
@@ -76,6 +83,7 @@ class OpenAI_LLMs implements INode {
                 label: 'Best Of',
                 name: 'bestOf',
                 type: 'number',
+                step: 1,
                 optional: true,
                 additionalParams: true
             },
@@ -83,6 +91,7 @@ class OpenAI_LLMs implements INode {
                 label: 'Frequency Penalty',
                 name: 'frequencyPenalty',
                 type: 'number',
+                step: 0.1,
                 optional: true,
                 additionalParams: true
             },
@@ -90,6 +99,7 @@ class OpenAI_LLMs implements INode {
                 label: 'Presence Penalty',
                 name: 'presencePenalty',
                 type: 'number',
+                step: 0.1,
                 optional: true,
                 additionalParams: true
             },
@@ -97,6 +107,7 @@ class OpenAI_LLMs implements INode {
                 label: 'Batch Size',
                 name: 'batchSize',
                 type: 'number',
+                step: 1,
                 optional: true,
                 additionalParams: true
             },
@@ -104,16 +115,23 @@ class OpenAI_LLMs implements INode {
                 label: 'Timeout',
                 name: 'timeout',
                 type: 'number',
+                step: 1,
+                optional: true,
+                additionalParams: true
+            },
+            {
+                label: 'BasePath',
+                name: 'basepath',
+                type: 'string',
                 optional: true,
                 additionalParams: true
             }
         ]
     }
 
-    async init(nodeData: INodeData): Promise<any> {
+    async init(nodeData: INodeData, _: string, options: ICommonObject): Promise<any> {
         const temperature = nodeData.inputs?.temperature as string
         const modelName = nodeData.inputs?.modelName as string
-        const openAIApiKey = nodeData.inputs?.openAIApiKey as string
         const maxTokens = nodeData.inputs?.maxTokens as string
         const topP = nodeData.inputs?.topP as string
         const frequencyPenalty = nodeData.inputs?.frequencyPenalty as string
@@ -121,22 +139,30 @@ class OpenAI_LLMs implements INode {
         const timeout = nodeData.inputs?.timeout as string
         const batchSize = nodeData.inputs?.batchSize as string
         const bestOf = nodeData.inputs?.bestOf as string
+        const streaming = nodeData.inputs?.streaming as boolean
+        const basePath = nodeData.inputs?.basepath as string
+
+        const credentialData = await getCredentialData(nodeData.credential ?? '', options)
+        const openAIApiKey = getCredentialParam('openAIApiKey', credentialData, nodeData)
 
         const obj: Partial<OpenAIInput> & { openAIApiKey?: string } = {
-            temperature: parseInt(temperature, 10),
+            temperature: parseFloat(temperature),
             modelName,
-            openAIApiKey
+            openAIApiKey,
+            streaming: streaming ?? true
         }
 
         if (maxTokens) obj.maxTokens = parseInt(maxTokens, 10)
-        if (topP) obj.topP = parseInt(topP, 10)
-        if (frequencyPenalty) obj.frequencyPenalty = parseInt(frequencyPenalty, 10)
-        if (presencePenalty) obj.presencePenalty = parseInt(presencePenalty, 10)
+        if (topP) obj.topP = parseFloat(topP)
+        if (frequencyPenalty) obj.frequencyPenalty = parseFloat(frequencyPenalty)
+        if (presencePenalty) obj.presencePenalty = parseFloat(presencePenalty)
         if (timeout) obj.timeout = parseInt(timeout, 10)
         if (batchSize) obj.batchSize = parseInt(batchSize, 10)
         if (bestOf) obj.bestOf = parseInt(bestOf, 10)
 
-        const model = new OpenAI(obj)
+        const model = new OpenAI(obj, {
+            basePath
+        })
         return model
     }
 }

packages/components/nodes/llms/Replicate/Replicate.ts

@@ -0,0 +1,128 @@
+import { ICommonObject, INode, INodeData, INodeParams } from '../../../src/Interface'
+import { getBaseClasses, getCredentialData, getCredentialParam } from '../../../src/utils'
+import { Replicate, ReplicateInput } from 'langchain/llms/replicate'
+
+class Replicate_LLMs implements INode {
+    label: string
+    name: string
+    version: number
+    type: string
+    icon: string
+    category: string
+    description: string
+    baseClasses: string[]
+    credential: INodeParams
+    inputs: INodeParams[]
+
+    constructor() {
+        this.label = 'Replicate'
+        this.name = 'replicate'
+        this.version = 1.0
+        this.type = 'Replicate'
+        this.icon = 'replicate.svg'
+        this.category = 'LLMs'
+        this.description = 'Use Replicate to run open source models on cloud'
+        this.baseClasses = [this.type, 'BaseChatModel', ...getBaseClasses(Replicate)]
+        this.credential = {
+            label: 'Connect Credential',
+            name: 'credential',
+            type: 'credential',
+            credentialNames: ['replicateApi']
+        }
+        this.inputs = [
+            {
+                label: 'Model',
+                name: 'model',
+                type: 'string',
+                placeholder: 'a16z-infra/llama13b-v2-chat:df7690f1994d94e96ad9d568eac121aecf50684a0b0963b25a41cc40061269e5',
+                optional: true
+            },
+            {
+                label: 'Temperature',
+                name: 'temperature',
+                type: 'number',
+                step: 0.1,
+                description:
+                    'Adjusts randomness of outputs, greater than 1 is random and 0 is deterministic, 0.75 is a good starting value.',
+                default: 0.7,
+                optional: true
+            },
+            {
+                label: 'Max Tokens',
+                name: 'maxTokens',
+                type: 'number',
+                step: 1,
+                description: 'Maximum number of tokens to generate. A word is generally 2-3 tokens',
+                optional: true,
+                additionalParams: true
+            },
+            {
+                label: 'Top Probability',
+                name: 'topP',
+                type: 'number',
+                step: 0.1,
+                description:
+                    'When decoding text, samples from the top p percentage of most likely tokens; lower to ignore less likely tokens',
+                optional: true,
+                additionalParams: true
+            },
+            {
+                label: 'Repetition Penalty',
+                name: 'repetitionPenalty',
+                type: 'number',
+                step: 0.1,
+                description:
+                    'Penalty for repeated words in generated text; 1 is no penalty, values greater than 1 discourage repetition, less than 1 encourage it. (minimum: 0.01; maximum: 5)',
+                optional: true,
+                additionalParams: true
+            },
+            {
+                label: 'Additional Inputs',
+                name: 'additionalInputs',
+                type: 'json',
+                description:
+                    'Each model has different parameters, refer to the specific model accepted inputs. For example: <a target="_blank" href="https://replicate.com/a16z-infra/llama13b-v2-chat/api#inputs">llama13b-v2</a>',
+                additionalParams: true,
+                optional: true
+            }
+        ]
+    }
+
+    async init(nodeData: INodeData, _: string, options: ICommonObject): Promise<any> {
+        const modelName = nodeData.inputs?.model as string
+        const temperature = nodeData.inputs?.temperature as string
+        const maxTokens = nodeData.inputs?.maxTokens as string
+        const topP = nodeData.inputs?.topP as string
+        const repetitionPenalty = nodeData.inputs?.repetitionPenalty as string
+        const additionalInputs = nodeData.inputs?.additionalInputs as string
+
+        const credentialData = await getCredentialData(nodeData.credential ?? '', options)
+        const apiKey = getCredentialParam('replicateApiKey', credentialData, nodeData)
+
+        const version = modelName.split(':').pop()
+        const name = modelName.split(':')[0].split('/').pop()
+        const org = modelName.split(':')[0].split('/')[0]
+
+        const obj: ReplicateInput = {
+            model: `${org}/${name}:${version}`,
+            apiKey
+        }
+
+        let inputs: any = {}
+        if (maxTokens) inputs.max_length = parseInt(maxTokens, 10)
+        if (temperature) inputs.temperature = parseFloat(temperature)
+        if (topP) inputs.top_p = parseFloat(topP)
+        if (repetitionPenalty) inputs.repetition_penalty = parseFloat(repetitionPenalty)
+        if (additionalInputs) {
+            const parsedInputs =
+                typeof additionalInputs === 'object' ? additionalInputs : additionalInputs ? JSON.parse(additionalInputs) : {}
+            inputs = { ...inputs, ...parsedInputs }
+        }
+        if (Object.keys(inputs).length) obj.input = inputs
+
+        const model = new Replicate(obj)
+        return model
+    }
+}
+
+module.exports = { nodeClass: Replicate_LLMs }

packages/components/nodes/llms/Replicate/replicate.svg

@@ -0,0 +1,7 @@
+<svg version="1.1" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" x="0px" y="0px" viewBox="0 0 1000 1000" class="logo" xml:space="preserve">
+  <g>
+    <polygon points="1000,427.6 1000,540.6 603.4,540.6 603.4,1000 477,1000 477,427.6 	"></polygon>
+    <polygon points="1000,213.8 1000,327 364.8,327 364.8,1000 238.4,1000 238.4,213.8 	"></polygon>
+    <polygon points="1000,0 1000,113.2 126.4,113.2 126.4,1000 0,1000 0,0 	"></polygon>
+  </g>
+</svg>

packages/components/nodes/memory/BufferMemory/BufferMemory.ts

@@ -5,6 +5,7 @@ import { BufferMemory } from 'langchain/memory'
 class BufferMemory_Memory implements INode {
     label: string
     name: string
+    version: number
     description: string
     type: string
     icon: string
@@ -15,6 +16,7 @@ class BufferMemory_Memory implements INode {
     constructor() {
         this.label = 'Buffer Memory'
         this.name = 'bufferMemory'
+        this.version = 1.0
         this.type = 'BufferMemory'
         this.icon = 'memory.svg'
         this.category = 'Memory'

packages/components/nodes/memory/BufferWindowMemory/BufferWindowMemory.ts

@@ -0,0 +1,64 @@
+import { INode, INodeData, INodeParams } from '../../../src/Interface'
+import { getBaseClasses } from '../../../src/utils'
+import { BufferWindowMemory, BufferWindowMemoryInput } from 'langchain/memory'
+
+class BufferWindowMemory_Memory implements INode {
+    label: string
+    name: string
+    version: number
+    description: string
+    type: string
+    icon: string
+    category: string
+    baseClasses: string[]
+    inputs: INodeParams[]
+
+    constructor() {
+        this.label = 'Buffer Window Memory'
+        this.name = 'bufferWindowMemory'
+        this.version = 1.0
+        this.type = 'BufferWindowMemory'
+        this.icon = 'memory.svg'
+        this.category = 'Memory'
+        this.description = 'Uses a window of size k to surface the last k back-and-forths to use as memory'
+        this.baseClasses = [this.type, ...getBaseClasses(BufferWindowMemory)]
+        this.inputs = [
+            {
+                label: 'Memory Key',
+                name: 'memoryKey',
+                type: 'string',
+                default: 'chat_history'
+            },
+            {
+                label: 'Input Key',
+                name: 'inputKey',
+                type: 'string',
+                default: 'input'
+            },
+            {
+                label: 'Size',
+                name: 'k',
+                type: 'number',
+                default: '4',
+                description: 'Window of size k to surface the last k back-and-forths to use as memory.'
+            }
+        ]
+    }
+
+    async init(nodeData: INodeData): Promise<any> {
+        const memoryKey = nodeData.inputs?.memoryKey as string
+        const inputKey = nodeData.inputs?.inputKey as string
+        const k = nodeData.inputs?.k as string
+
+        const obj: Partial<BufferWindowMemoryInput> = {
+            returnMessages: true,
+            memoryKey: memoryKey,
+            inputKey: inputKey,
+            k: parseInt(k, 10)
+        }
+
+        return new BufferWindowMemory(obj)
+    }
+}
+
+module.exports = { nodeClass: BufferWindowMemory_Memory }

packages/components/nodes/memory/BufferWindowMemory/memory.svg

@@ -0,0 +1,8 @@
+<svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-book" 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="M3 19a9 9 0 0 1 9 0a9 9 0 0 1 9 0"></path>
+   <path d="M3 6a9 9 0 0 1 9 0a9 9 0 0 1 9 0"></path>
+   <path d="M3 6l0 13"></path>
+   <path d="M12 6l0 13"></path>
+   <path d="M21 6l0 13"></path>
+</svg>