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Fix merge conflict

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This commit is contained in:
Ilango
2024-03-12 12:46:25 +05:30
parent de2ac1d13c 305a585cf4
commit 7313891ea1
62 changed files with 1243 additions and 198 deletions
Download Patch File Download Diff File Expand all files Collapse all files
packages/components/nodes/agents/AirtableAgent/AirtableAgent.ts
+24 -2
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@@ -6,6 +6,8 @@ import { ICommonObject, INode, INodeData, INodeParams, PromptTemplate } from '..
import { getBaseClasses, getCredentialData, getCredentialParam } from '../../../src/utils'
import { ConsoleCallbackHandler, CustomChainHandler, additionalCallbacks } from '../../../src/handler'
import { LoadPyodide, finalSystemPrompt, systemPrompt } from './core'
import { checkInputs, Moderation } from '../../moderation/Moderation'
import { formatResponse } from '../../outputparsers/OutputParserHelpers'
class Airtable_Agents implements INode {
label: string
@@ -22,7 +24,7 @@ class Airtable_Agents implements INode {
constructor() {
this.label = 'Airtable Agent'
this.name = 'airtableAgent'
this.version = 1.0
this.version = 2.0
this.type = 'AgentExecutor'
this.category = 'Agents'
this.icon = 'airtable.svg'
@@ -71,6 +73,14 @@ class Airtable_Agents implements INode {
default: 100,
additionalParams: true,
description: 'Number of results to return'
},
{
label: 'Input Moderation',
description: 'Detect text that could generate harmful output and prevent it from being sent to the language model',
name: 'inputModeration',
type: 'Moderation',
optional: true,
list: true
}
]
}
@@ -80,12 +90,24 @@ class Airtable_Agents implements INode {
return undefined
}
async run(nodeData: INodeData, input: string, options: ICommonObject): Promise<string> {
async run(nodeData: INodeData, input: string, options: ICommonObject): Promise<string | object> {
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 moderations = nodeData.inputs?.inputModeration as Moderation[]
if (moderations && moderations.length > 0) {
try {
// Use the output of the moderation chain as input for the Vectara chain
input = await checkInputs(moderations, input)
} catch (e) {
await new Promise((resolve) => setTimeout(resolve, 500))
//streamResponse(options.socketIO && options.socketIOClientId, e.message, options.socketIO, options.socketIOClientId)
return formatResponse(e.message)
}
}
const credentialData = await getCredentialData(nodeData.credential ?? '', options)
const accessToken = getCredentialParam('accessToken', credentialData, nodeData)
packages/components/nodes/agents/AutoGPT/AutoGPT.ts
+24 -2
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@@ -7,6 +7,8 @@ import { PromptTemplate } from '@langchain/core/prompts'
import { AutoGPT } from 'langchain/experimental/autogpt'
import { LLMChain } from 'langchain/chains'
import { INode, INodeData, INodeParams } from '../../../src/Interface'
import { checkInputs, Moderation } from '../../moderation/Moderation'
import { formatResponse } from '../../outputparsers/OutputParserHelpers'
type ObjectTool = StructuredTool
const FINISH_NAME = 'finish'
@@ -25,7 +27,7 @@ class AutoGPT_Agents implements INode {
constructor() {
this.label = 'AutoGPT'
this.name = 'autoGPT'
this.version = 1.0
this.version = 2.0
this.type = 'AutoGPT'
this.category = 'Agents'
this.icon = 'autogpt.svg'
@@ -68,6 +70,14 @@ class AutoGPT_Agents implements INode {
type: 'number',
default: 5,
optional: true
},
{
label: 'Input Moderation',
description: 'Detect text that could generate harmful output and prevent it from being sent to the language model',
name: 'inputModeration',
type: 'Moderation',
optional: true,
list: true
}
]
}
@@ -92,9 +102,21 @@ class AutoGPT_Agents implements INode {
return autogpt
}
async run(nodeData: INodeData, input: string): Promise<string> {
async run(nodeData: INodeData, input: string): Promise<string | object> {
const executor = nodeData.instance as AutoGPT
const model = nodeData.inputs?.model as BaseChatModel
const moderations = nodeData.inputs?.inputModeration as Moderation[]
if (moderations && moderations.length > 0) {
try {
// Use the output of the moderation chain as input for the AutoGPT agent
input = await checkInputs(moderations, input)
} catch (e) {
await new Promise((resolve) => setTimeout(resolve, 500))
//streamResponse(options.socketIO && options.socketIOClientId, e.message, options.socketIO, options.socketIOClientId)
return formatResponse(e.message)
}
}
try {
let totalAssistantReply = ''
packages/components/nodes/agents/BabyAGI/BabyAGI.ts
+25 -2
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@@ -2,6 +2,8 @@ import { BaseChatModel } from '@langchain/core/language_models/chat_models'
import { VectorStore } from '@langchain/core/vectorstores'
import { INode, INodeData, INodeParams } from '../../../src/Interface'
import { BabyAGI } from './core'
import { checkInputs, Moderation } from '../../moderation/Moderation'
import { formatResponse } from '../../outputparsers/OutputParserHelpers'
class BabyAGI_Agents implements INode {
label: string
@@ -17,7 +19,7 @@ class BabyAGI_Agents implements INode {
constructor() {
this.label = 'BabyAGI'
this.name = 'babyAGI'
this.version = 1.0
this.version = 2.0
this.type = 'BabyAGI'
this.category = 'Agents'
this.icon = 'babyagi.svg'
@@ -39,6 +41,14 @@ class BabyAGI_Agents implements INode {
name: 'taskLoop',
type: 'number',
default: 3
},
{
label: 'Input Moderation',
description: 'Detect text that could generate harmful output and prevent it from being sent to the language model',
name: 'inputModeration',
type: 'Moderation',
optional: true,
list: true
}
]
}
@@ -53,8 +63,21 @@ class BabyAGI_Agents implements INode {
return babyAgi
}
async run(nodeData: INodeData, input: string): Promise<string> {
async run(nodeData: INodeData, input: string): Promise<string | object> {
const executor = nodeData.instance as BabyAGI
const moderations = nodeData.inputs?.inputModeration as Moderation[]
if (moderations && moderations.length > 0) {
try {
// Use the output of the moderation chain as input for the BabyAGI agent
input = await checkInputs(moderations, input)
} catch (e) {
await new Promise((resolve) => setTimeout(resolve, 500))
//streamResponse(options.socketIO && options.socketIOClientId, e.message, options.socketIO, options.socketIOClientId)
return formatResponse(e.message)
}
}
const objective = input
const res = await executor.call({ objective })
packages/components/nodes/agents/CSVAgent/CSVAgent.ts
+24 -2
View File
@@ -5,6 +5,8 @@ import { ConsoleCallbackHandler, CustomChainHandler, additionalCallbacks } from
import { ICommonObject, INode, INodeData, INodeParams, PromptTemplate } from '../../../src/Interface'
import { getBaseClasses } from '../../../src/utils'
import { LoadPyodide, finalSystemPrompt, systemPrompt } from './core'
import { checkInputs, Moderation } from '../../moderation/Moderation'
import { formatResponse } from '../../outputparsers/OutputParserHelpers'
class CSV_Agents implements INode {
label: string
@@ -20,7 +22,7 @@ class CSV_Agents implements INode {
constructor() {
this.label = 'CSV Agent'
this.name = 'csvAgent'
this.version = 1.0
this.version = 2.0
this.type = 'AgentExecutor'
this.category = 'Agents'
this.icon = 'CSVagent.svg'
@@ -47,6 +49,14 @@ class CSV_Agents 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: 'Input Moderation',
description: 'Detect text that could generate harmful output and prevent it from being sent to the language model',
name: 'inputModeration',
type: 'Moderation',
optional: true,
list: true
}
]
}
@@ -56,10 +66,22 @@ class CSV_Agents implements INode {
return undefined
}
async run(nodeData: INodeData, input: string, options: ICommonObject): Promise<string> {
async run(nodeData: INodeData, input: string, options: ICommonObject): Promise<string | object> {
const csvFileBase64 = nodeData.inputs?.csvFile as string
const model = nodeData.inputs?.model as BaseLanguageModel
const systemMessagePrompt = nodeData.inputs?.systemMessagePrompt as string
const moderations = nodeData.inputs?.inputModeration as Moderation[]
if (moderations && moderations.length > 0) {
try {
// Use the output of the moderation chain as input for the CSV agent
input = await checkInputs(moderations, input)
} catch (e) {
await new Promise((resolve) => setTimeout(resolve, 500))
//streamResponse(options.socketIO && options.socketIOClientId, e.message, options.socketIO, options.socketIOClientId)
return formatResponse(e.message)
}
}
const loggerHandler = new ConsoleCallbackHandler(options.logger)
const handler = new CustomChainHandler(options.socketIO, options.socketIOClientId)
packages/components/nodes/agents/ConversationalAgent/ConversationalAgent.ts
+42 -23
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@@ -4,15 +4,16 @@ import { BaseChatModel } from '@langchain/core/language_models/chat_models'
import { AIMessage, BaseMessage, HumanMessage } from '@langchain/core/messages'
import { ChainValues } from '@langchain/core/utils/types'
import { AgentStep } from '@langchain/core/agents'
import { renderTemplate, MessagesPlaceholder } from '@langchain/core/prompts'
import { renderTemplate, MessagesPlaceholder, HumanMessagePromptTemplate, PromptTemplate } from '@langchain/core/prompts'
import { RunnableSequence } from '@langchain/core/runnables'
import { ChatConversationalAgent } from 'langchain/agents'
import { getBaseClasses } from '../../../src/utils'
import { ConsoleCallbackHandler, CustomChainHandler, additionalCallbacks } from '../../../src/handler'
import { FlowiseMemory, ICommonObject, IMessage, INode, INodeData, INodeParams } from '../../../src/Interface'
import { IVisionChatModal, FlowiseMemory, ICommonObject, IMessage, INode, INodeData, INodeParams } from '../../../src/Interface'
import { AgentExecutor } from '../../../src/agents'
import { ChatOpenAI } from '../../chatmodels/ChatOpenAI/FlowiseChatOpenAI'
import { addImagesToMessages } from '../../../src/multiModalUtils'
import { addImagesToMessages, llmSupportsVision } from '../../../src/multiModalUtils'
import { checkInputs, Moderation } from '../../moderation/Moderation'
import { formatResponse } from '../../outputparsers/OutputParserHelpers'
const DEFAULT_PREFIX = `Assistant is a large language model trained by OpenAI.
@@ -46,7 +47,7 @@ class ConversationalAgent_Agents implements INode {
constructor(fields?: { sessionId?: string }) {
this.label = 'Conversational Agent'
this.name = 'conversationalAgent'
this.version = 2.0
this.version = 3.0
this.type = 'AgentExecutor'
this.category = 'Agents'
this.icon = 'agent.svg'
@@ -77,6 +78,14 @@ class ConversationalAgent_Agents implements INode {
default: DEFAULT_PREFIX,
optional: true,
additionalParams: true
},
{
label: 'Input Moderation',
description: 'Detect text that could generate harmful output and prevent it from being sent to the language model',
name: 'inputModeration',
type: 'Moderation',
optional: true,
list: true
}
]
this.sessionId = fields?.sessionId
@@ -86,9 +95,20 @@ class ConversationalAgent_Agents implements INode {
return prepareAgent(nodeData, options, { sessionId: this.sessionId, chatId: options.chatId, input }, options.chatHistory)
}
async run(nodeData: INodeData, input: string, options: ICommonObject): Promise<string> {
async run(nodeData: INodeData, input: string, options: ICommonObject): Promise<string | object> {
const memory = nodeData.inputs?.memory as FlowiseMemory
const moderations = nodeData.inputs?.inputModeration as Moderation[]
if (moderations && moderations.length > 0) {
try {
// Use the output of the moderation chain as input for the BabyAGI agent
input = await checkInputs(moderations, input)
} catch (e) {
await new Promise((resolve) => setTimeout(resolve, 500))
//streamResponse(options.socketIO && options.socketIOClientId, e.message, options.socketIO, options.socketIOClientId)
return formatResponse(e.message)
}
}
const executor = await prepareAgent(
nodeData,
options,
@@ -150,33 +170,32 @@ const prepareAgent = async (
outputParser
})
if (model instanceof ChatOpenAI) {
let humanImageMessages: HumanMessage[] = []
if (llmSupportsVision(model)) {
const visionChatModel = model as IVisionChatModal
const messageContent = addImagesToMessages(nodeData, options, model.multiModalOption)
if (messageContent?.length) {
// Change model to gpt-4-vision
model.modelName = 'gpt-4-vision-preview'
// Change default max token to higher when using gpt-4-vision
model.maxTokens = 1024
for (const msg of messageContent) {
humanImageMessages.push(new HumanMessage({ content: [msg] }))
}
visionChatModel.setVisionModel()
// Pop the `agent_scratchpad` MessagePlaceHolder
let messagePlaceholder = prompt.promptMessages.pop() as MessagesPlaceholder
// Add the HumanMessage for images
prompt.promptMessages.push(...humanImageMessages)
if (prompt.promptMessages.at(-1) instanceof HumanMessagePromptTemplate) {
const lastMessage = prompt.promptMessages.pop() as HumanMessagePromptTemplate
const template = (lastMessage.prompt as PromptTemplate).template as string
const msg = HumanMessagePromptTemplate.fromTemplate([
...messageContent,
{
text: template
}
])
msg.inputVariables = lastMessage.inputVariables
prompt.promptMessages.push(msg)
}
// Add the `agent_scratchpad` MessagePlaceHolder back
prompt.promptMessages.push(messagePlaceholder)
} else {
// revert to previous values if image upload is empty
model.modelName = model.configuredModel
model.maxTokens = model.configuredMaxToken
visionChatModel.revertToOriginalModel()
}
}
packages/components/nodes/agents/ConversationalRetrievalAgent/ConversationalRetrievalAgent.ts
+25 -2
View File
@@ -10,6 +10,8 @@ import { FlowiseMemory, ICommonObject, IMessage, INode, INodeData, INodeParams }
import { getBaseClasses } from '../../../src/utils'
import { ConsoleCallbackHandler, CustomChainHandler, additionalCallbacks } from '../../../src/handler'
import { AgentExecutor, formatAgentSteps } from '../../../src/agents'
import { checkInputs, Moderation } from '../../moderation/Moderation'
import { formatResponse } from '../../outputparsers/OutputParserHelpers'
const defaultMessage = `Do your best to answer the questions. Feel free to use any tools available to look up relevant information, only if necessary.`
@@ -28,7 +30,7 @@ class ConversationalRetrievalAgent_Agents implements INode {
constructor(fields?: { sessionId?: string }) {
this.label = 'Conversational Retrieval Agent'
this.name = 'conversationalRetrievalAgent'
this.version = 3.0
this.version = 4.0
this.type = 'AgentExecutor'
this.category = 'Agents'
this.icon = 'agent.svg'
@@ -59,6 +61,14 @@ class ConversationalRetrievalAgent_Agents implements INode {
rows: 4,
optional: true,
additionalParams: true
},
{
label: 'Input Moderation',
description: 'Detect text that could generate harmful output and prevent it from being sent to the language model',
name: 'inputModeration',
type: 'Moderation',
optional: true,
list: true
}
]
this.sessionId = fields?.sessionId
@@ -68,8 +78,21 @@ class ConversationalRetrievalAgent_Agents implements INode {
return prepareAgent(nodeData, { sessionId: this.sessionId, chatId: options.chatId, input }, options.chatHistory)
}
async run(nodeData: INodeData, input: string, options: ICommonObject): Promise<string> {
async run(nodeData: INodeData, input: string, options: ICommonObject): Promise<string | object> {
const memory = nodeData.inputs?.memory as FlowiseMemory
const moderations = nodeData.inputs?.inputModeration as Moderation[]
if (moderations && moderations.length > 0) {
try {
// Use the output of the moderation chain as input for the BabyAGI agent
input = await checkInputs(moderations, input)
} catch (e) {
await new Promise((resolve) => setTimeout(resolve, 500))
//streamResponse(options.socketIO && options.socketIOClientId, e.message, options.socketIO, options.socketIOClientId)
return formatResponse(e.message)
}
}
const executor = prepareAgent(nodeData, { sessionId: this.sessionId, chatId: options.chatId, input }, options.chatHistory)
const loggerHandler = new ConsoleCallbackHandler(options.logger)
packages/components/nodes/agents/MRKLAgentChat/MRKLAgentChat.ts
+40 -17
View File
@@ -1,17 +1,17 @@
import { flatten } from 'lodash'
import { AgentExecutor } from 'langchain/agents'
import { HumanMessage } from '@langchain/core/messages'
import { ChatPromptTemplate, HumanMessagePromptTemplate } from '@langchain/core/prompts'
import { Tool } from '@langchain/core/tools'
import type { PromptTemplate } from '@langchain/core/prompts'
import { BaseChatModel } from '@langchain/core/language_models/chat_models'
import { pull } from 'langchain/hub'
import { additionalCallbacks } from '../../../src/handler'
import { FlowiseMemory, ICommonObject, IMessage, INode, INodeData, INodeParams } from '../../../src/Interface'
import { IVisionChatModal, FlowiseMemory, ICommonObject, IMessage, INode, INodeData, INodeParams } from '../../../src/Interface'
import { getBaseClasses } from '../../../src/utils'
import { createReactAgent } from '../../../src/agents'
import { ChatOpenAI } from '../../chatmodels/ChatOpenAI/FlowiseChatOpenAI'
import { addImagesToMessages } from '../../../src/multiModalUtils'
import { addImagesToMessages, llmSupportsVision } from '../../../src/multiModalUtils'
import { checkInputs, Moderation } from '../../moderation/Moderation'
import { formatResponse } from '../../outputparsers/OutputParserHelpers'
class MRKLAgentChat_Agents implements INode {
label: string
@@ -28,7 +28,7 @@ class MRKLAgentChat_Agents implements INode {
constructor(fields?: { sessionId?: string }) {
this.label = 'ReAct Agent for Chat Models'
this.name = 'mrklAgentChat'
this.version = 3.0
this.version = 4.0
this.type = 'AgentExecutor'
this.category = 'Agents'
this.icon = 'agent.svg'
@@ -50,6 +50,14 @@ class MRKLAgentChat_Agents implements INode {
label: 'Memory',
name: 'memory',
type: 'BaseChatMemory'
},
{
label: 'Input Moderation',
description: 'Detect text that could generate harmful output and prevent it from being sent to the language model',
name: 'inputModeration',
type: 'Moderation',
optional: true,
list: true
}
]
this.sessionId = fields?.sessionId
@@ -59,32 +67,47 @@ class MRKLAgentChat_Agents implements INode {
return null
}
async run(nodeData: INodeData, input: string, options: ICommonObject): Promise<string> {
async run(nodeData: INodeData, input: string, options: ICommonObject): Promise<string | object> {
const memory = nodeData.inputs?.memory as FlowiseMemory
const model = nodeData.inputs?.model as BaseChatModel
let tools = nodeData.inputs?.tools as Tool[]
const moderations = nodeData.inputs?.inputModeration as Moderation[]
if (moderations && moderations.length > 0) {
try {
// Use the output of the moderation chain as input for the ReAct Agent for Chat Models
input = await checkInputs(moderations, input)
} catch (e) {
await new Promise((resolve) => setTimeout(resolve, 500))
//streamResponse(options.socketIO && options.socketIOClientId, e.message, options.socketIO, options.socketIOClientId)
return formatResponse(e.message)
}
}
tools = flatten(tools)
const prompt = await pull<PromptTemplate>('hwchase17/react-chat')
let chatPromptTemplate = undefined
if (model instanceof ChatOpenAI) {
if (llmSupportsVision(model)) {
const visionChatModel = model as IVisionChatModal
const messageContent = addImagesToMessages(nodeData, options, model.multiModalOption)
if (messageContent?.length) {
// Change model to gpt-4-vision
model.modelName = 'gpt-4-vision-preview'
// Change default max token to higher when using gpt-4-vision
model.maxTokens = 1024
// Change model to vision supported
visionChatModel.setVisionModel()
const oldTemplate = prompt.template as string
chatPromptTemplate = ChatPromptTemplate.fromMessages([HumanMessagePromptTemplate.fromTemplate(oldTemplate)])
chatPromptTemplate.promptMessages.push(new HumanMessage({ content: messageContent }))
const msg = HumanMessagePromptTemplate.fromTemplate([
...messageContent,
{
text: oldTemplate
}
])
msg.inputVariables = prompt.inputVariables
chatPromptTemplate = ChatPromptTemplate.fromMessages([msg])
} else {
// revert to previous values if image upload is empty
model.modelName = model.configuredModel
model.maxTokens = model.configuredMaxToken
visionChatModel.revertToOriginalModel()
}
}
packages/components/nodes/agents/MRKLAgentLLM/MRKLAgentLLM.ts
+25 -2
View File
@@ -8,6 +8,8 @@ import { additionalCallbacks } from '../../../src/handler'
import { getBaseClasses } from '../../../src/utils'
import { ICommonObject, INode, INodeData, INodeParams } from '../../../src/Interface'
import { createReactAgent } from '../../../src/agents'
import { checkInputs, Moderation } from '../../moderation/Moderation'
import { formatResponse } from '../../outputparsers/OutputParserHelpers'
class MRKLAgentLLM_Agents implements INode {
label: string
@@ -23,7 +25,7 @@ class MRKLAgentLLM_Agents implements INode {
constructor() {
this.label = 'ReAct Agent for LLMs'
this.name = 'mrklAgentLLM'
this.version = 1.0
this.version = 2.0
this.type = 'AgentExecutor'
this.category = 'Agents'
this.icon = 'agent.svg'
@@ -40,6 +42,14 @@ class MRKLAgentLLM_Agents implements INode {
label: 'Language Model',
name: 'model',
type: 'BaseLanguageModel'
},
{
label: 'Input Moderation',
description: 'Detect text that could generate harmful output and prevent it from being sent to the language model',
name: 'inputModeration',
type: 'Moderation',
optional: true,
list: true
}
]
}
@@ -48,9 +58,22 @@ class MRKLAgentLLM_Agents implements INode {
return null
}
async run(nodeData: INodeData, input: string, options: ICommonObject): Promise<string> {
async run(nodeData: INodeData, input: string, options: ICommonObject): Promise<string | object> {
const model = nodeData.inputs?.model as BaseLanguageModel
let tools = nodeData.inputs?.tools as Tool[]
const moderations = nodeData.inputs?.inputModeration as Moderation[]
if (moderations && moderations.length > 0) {
try {
// Use the output of the moderation chain as input for the ReAct Agent for LLMs
input = await checkInputs(moderations, input)
} catch (e) {
await new Promise((resolve) => setTimeout(resolve, 500))
//streamResponse(options.socketIO && options.socketIOClientId, e.message, options.socketIO, options.socketIOClientId)
return formatResponse(e.message)
}
}
tools = flatten(tools)
const prompt = await pull<PromptTemplate>('hwchase17/react')
packages/components/nodes/agents/OpenAIFunctionAgent/OpenAIFunctionAgent.ts
+24 -1
View File
@@ -10,6 +10,8 @@ import { getBaseClasses } from '../../../src/utils'
import { FlowiseMemory, ICommonObject, IMessage, INode, INodeData, INodeParams } from '../../../src/Interface'
import { ConsoleCallbackHandler, CustomChainHandler, additionalCallbacks } from '../../../src/handler'
import { AgentExecutor, formatAgentSteps } from '../../../src/agents'
import { Moderation, checkInputs } from '../../moderation/Moderation'
import { formatResponse } from '../../outputparsers/OutputParserHelpers'
class OpenAIFunctionAgent_Agents implements INode {
label: string
@@ -26,7 +28,7 @@ class OpenAIFunctionAgent_Agents implements INode {
constructor(fields?: { sessionId?: string }) {
this.label = 'OpenAI Function Agent'
this.name = 'openAIFunctionAgent'
this.version = 3.0
this.version = 4.0
this.type = 'AgentExecutor'
this.category = 'Agents'
this.icon = 'function.svg'
@@ -56,6 +58,14 @@ class OpenAIFunctionAgent_Agents implements INode {
rows: 4,
optional: true,
additionalParams: true
},
{
label: 'Input Moderation',
description: 'Detect text that could generate harmful output and prevent it from being sent to the language model',
name: 'inputModeration',
type: 'Moderation',
optional: true,
list: true
}
]
this.sessionId = fields?.sessionId
@@ -67,6 +77,19 @@ class OpenAIFunctionAgent_Agents implements INode {
async run(nodeData: INodeData, input: string, options: ICommonObject): Promise<string | ICommonObject> {
const memory = nodeData.inputs?.memory as FlowiseMemory
const moderations = nodeData.inputs?.inputModeration as Moderation[]
if (moderations && moderations.length > 0) {
try {
// Use the output of the moderation chain as input for the OpenAI Function Agent
input = await checkInputs(moderations, input)
} catch (e) {
await new Promise((resolve) => setTimeout(resolve, 500))
//streamResponse(options.socketIO && options.socketIOClientId, e.message, options.socketIO, options.socketIOClientId)
return formatResponse(e.message)
}
}
const executor = prepareAgent(nodeData, { sessionId: this.sessionId, chatId: options.chatId, input }, options.chatHistory)
const loggerHandler = new ConsoleCallbackHandler(options.logger)
packages/components/nodes/agents/XMLAgent/XMLAgent.ts
+25 -4
View File
@@ -1,8 +1,8 @@
import { flatten } from 'lodash'
import { ChainValues } from '@langchain/core/utils/types'
import { AgentStep } from '@langchain/core/agents'
import { BaseChatModel } from '@langchain/core/language_models/chat_models'
import { RunnableSequence } from '@langchain/core/runnables'
import { ChatOpenAI } from '@langchain/openai'
import { Tool } from '@langchain/core/tools'
import { ChatPromptTemplate, HumanMessagePromptTemplate, MessagesPlaceholder } from '@langchain/core/prompts'
import { XMLAgentOutputParser } from 'langchain/agents/xml/output_parser'
@@ -11,7 +11,8 @@ import { getBaseClasses } from '../../../src/utils'
import { FlowiseMemory, ICommonObject, IMessage, INode, INodeData, INodeParams } from '../../../src/Interface'
import { ConsoleCallbackHandler, CustomChainHandler, additionalCallbacks } from '../../../src/handler'
import { AgentExecutor } from '../../../src/agents'
//import { AgentExecutor } from "langchain/agents";
import { Moderation, checkInputs } from '../../moderation/Moderation'
import { formatResponse } from '../../outputparsers/OutputParserHelpers'
const defaultSystemMessage = `You are a helpful assistant. Help the user answer any questions.
@@ -52,7 +53,7 @@ class XMLAgent_Agents implements INode {
constructor(fields?: { sessionId?: string }) {
this.label = 'XML Agent'
this.name = 'xmlAgent'
this.version = 1.0
this.version = 2.0
this.type = 'XMLAgent'
this.category = 'Agents'
this.icon = 'xmlagent.svg'
@@ -83,6 +84,14 @@ class XMLAgent_Agents implements INode {
rows: 4,
default: defaultSystemMessage,
additionalParams: true
},
{
label: 'Input Moderation',
description: 'Detect text that could generate harmful output and prevent it from being sent to the language model',
name: 'inputModeration',
type: 'Moderation',
optional: true,
list: true
}
]
this.sessionId = fields?.sessionId
@@ -94,6 +103,18 @@ class XMLAgent_Agents implements INode {
async run(nodeData: INodeData, input: string, options: ICommonObject): Promise<string | ICommonObject> {
const memory = nodeData.inputs?.memory as FlowiseMemory
const moderations = nodeData.inputs?.inputModeration as Moderation[]
if (moderations && moderations.length > 0) {
try {
// Use the output of the moderation chain as input for the OpenAI Function Agent
input = await checkInputs(moderations, input)
} catch (e) {
await new Promise((resolve) => setTimeout(resolve, 500))
//streamResponse(options.socketIO && options.socketIOClientId, e.message, options.socketIO, options.socketIOClientId)
return formatResponse(e.message)
}
}
const executor = await prepareAgent(nodeData, { sessionId: this.sessionId, chatId: options.chatId, input }, options.chatHistory)
const loggerHandler = new ConsoleCallbackHandler(options.logger)
@@ -139,7 +160,7 @@ const prepareAgent = async (
flowObj: { sessionId?: string; chatId?: string; input?: string },
chatHistory: IMessage[] = []
) => {
const model = nodeData.inputs?.model as ChatOpenAI
const model = nodeData.inputs?.model as BaseChatModel
const memory = nodeData.inputs?.memory as FlowiseMemory
const systemMessage = nodeData.inputs?.systemMessage as string
let tools = nodeData.inputs?.tools
packages/components/nodes/chains/ApiChain/OpenAPIChain.ts
+23 -3
View File
@@ -3,6 +3,8 @@ import { APIChain, createOpenAPIChain } from 'langchain/chains'
import { ICommonObject, INode, INodeData, INodeParams } from '../../../src/Interface'
import { getBaseClasses } from '../../../src/utils'
import { ConsoleCallbackHandler, CustomChainHandler, additionalCallbacks } from '../../../src/handler'
import { checkInputs, Moderation, streamResponse } from '../../moderation/Moderation'
import { formatResponse } from '../../outputparsers/OutputParserHelpers'
class OpenApiChain_Chains implements INode {
label: string
@@ -18,7 +20,7 @@ class OpenApiChain_Chains implements INode {
constructor() {
this.label = 'OpenAPI Chain'
this.name = 'openApiChain'
this.version = 1.0
this.version = 2.0
this.type = 'OpenAPIChain'
this.icon = 'openapi.svg'
this.category = 'Chains'
@@ -50,6 +52,14 @@ class OpenApiChain_Chains implements INode {
type: 'json',
additionalParams: true,
optional: true
},
{
label: 'Input Moderation',
description: 'Detect text that could generate harmful output and prevent it from being sent to the language model',
name: 'inputModeration',
type: 'Moderation',
optional: true,
list: true
}
]
}
@@ -58,11 +68,21 @@ class OpenApiChain_Chains implements INode {
return await initChain(nodeData)
}
async run(nodeData: INodeData, input: string, options: ICommonObject): Promise<string> {
async run(nodeData: INodeData, input: string, options: ICommonObject): Promise<string | object> {
const chain = await initChain(nodeData)
const loggerHandler = new ConsoleCallbackHandler(options.logger)
const callbacks = await additionalCallbacks(nodeData, options)
const moderations = nodeData.inputs?.inputModeration as Moderation[]
if (moderations && moderations.length > 0) {
try {
// Use the output of the moderation chain as input for the OpenAPI chain
input = await checkInputs(moderations, input)
} catch (e) {
await new Promise((resolve) => setTimeout(resolve, 500))
streamResponse(options.socketIO && options.socketIOClientId, e.message, options.socketIO, options.socketIOClientId)
return formatResponse(e.message)
}
}
if (options.socketIO && options.socketIOClientId) {
const handler = new CustomChainHandler(options.socketIO, options.socketIOClientId)
const res = await chain.run(input, [loggerHandler, handler, ...callbacks])
packages/components/nodes/chains/ConversationChain/ConversationChain.ts
+56 -27
View File
@@ -1,14 +1,30 @@
import { ConversationChain } from 'langchain/chains'
import { ChatPromptTemplate, HumanMessagePromptTemplate, MessagesPlaceholder, SystemMessagePromptTemplate } from '@langchain/core/prompts'
import {
ChatPromptTemplate,
HumanMessagePromptTemplate,
MessagesPlaceholder,
SystemMessagePromptTemplate,
BaseMessagePromptTemplateLike,
PromptTemplate
} from '@langchain/core/prompts'
import { RunnableSequence } from '@langchain/core/runnables'
import { StringOutputParser } from '@langchain/core/output_parsers'
import { BaseChatModel } from '@langchain/core/language_models/chat_models'
import { HumanMessage } from '@langchain/core/messages'
import { ConsoleCallbackHandler as LCConsoleCallbackHandler } from '@langchain/core/tracers/console'
import { checkInputs, Moderation, streamResponse } from '../../moderation/Moderation'
import { formatResponse } from '../../outputparsers/OutputParserHelpers'
import { addImagesToMessages } from '../../../src/multiModalUtils'
import { addImagesToMessages, llmSupportsVision } from '../../../src/multiModalUtils'
import { ChatOpenAI } from '../../chatmodels/ChatOpenAI/FlowiseChatOpenAI'
import { FlowiseMemory, ICommonObject, INode, INodeData, INodeParams } from '../../../src/Interface'
import {
IVisionChatModal,
FlowiseMemory,
ICommonObject,
INode,
INodeData,
INodeParams,
MessageContentImageUrl
} from '../../../src/Interface'
import { ConsoleCallbackHandler, CustomChainHandler, additionalCallbacks } from '../../../src/handler'
import { getBaseClasses, handleEscapeCharacters } from '../../../src/utils'
@@ -145,16 +161,32 @@ class ConversationChain_Chains implements INode {
}
}
const prepareChatPrompt = (nodeData: INodeData, humanImageMessages: HumanMessage[]) => {
const prepareChatPrompt = (nodeData: INodeData, humanImageMessages: MessageContentImageUrl[]) => {
const memory = nodeData.inputs?.memory as FlowiseMemory
const prompt = nodeData.inputs?.systemMessagePrompt as string
const chatPromptTemplate = nodeData.inputs?.chatPromptTemplate as ChatPromptTemplate
let model = nodeData.inputs?.model as BaseChatModel
if (chatPromptTemplate && chatPromptTemplate.promptMessages.length) {
const sysPrompt = chatPromptTemplate.promptMessages[0]
const humanPrompt = chatPromptTemplate.promptMessages[chatPromptTemplate.promptMessages.length - 1]
const messages = [sysPrompt, new MessagesPlaceholder(memory.memoryKey ?? 'chat_history'), humanPrompt]
if (humanImageMessages.length) messages.push(...humanImageMessages)
// OpenAI works better when separate images into standalone human messages
if (model instanceof ChatOpenAI && humanImageMessages.length) {
messages.push(new HumanMessage({ content: [...humanImageMessages] }))
} else if (humanImageMessages.length) {
const lastMessage = messages.pop() as HumanMessagePromptTemplate
const template = (lastMessage.prompt as PromptTemplate).template as string
const msg = HumanMessagePromptTemplate.fromTemplate([
...humanImageMessages,
{
text: template
}
])
msg.inputVariables = lastMessage.inputVariables
messages.push(msg)
}
const chatPrompt = ChatPromptTemplate.fromMessages(messages)
if ((chatPromptTemplate as any).promptValues) {
@@ -165,12 +197,18 @@ const prepareChatPrompt = (nodeData: INodeData, humanImageMessages: HumanMessage
return chatPrompt
}
const messages = [
const messages: BaseMessagePromptTemplateLike[] = [
SystemMessagePromptTemplate.fromTemplate(prompt ? prompt : systemMessage),
new MessagesPlaceholder(memory.memoryKey ?? 'chat_history'),
HumanMessagePromptTemplate.fromTemplate(`{${inputKey}}`)
new MessagesPlaceholder(memory.memoryKey ?? 'chat_history')
]
if (humanImageMessages.length) messages.push(...(humanImageMessages as any[]))
// OpenAI works better when separate images into standalone human messages
if (model instanceof ChatOpenAI && humanImageMessages.length) {
messages.push(HumanMessagePromptTemplate.fromTemplate(`{${inputKey}}`))
messages.push(new HumanMessage({ content: [...humanImageMessages] }))
} else if (humanImageMessages.length) {
messages.push(HumanMessagePromptTemplate.fromTemplate([`{${inputKey}}`, ...humanImageMessages]))
}
const chatPrompt = ChatPromptTemplate.fromMessages(messages)
@@ -179,32 +217,23 @@ const prepareChatPrompt = (nodeData: INodeData, humanImageMessages: HumanMessage
const prepareChain = (nodeData: INodeData, options: ICommonObject, sessionId?: string) => {
const chatHistory = options.chatHistory
let model = nodeData.inputs?.model as ChatOpenAI
let model = nodeData.inputs?.model as BaseChatModel
const memory = nodeData.inputs?.memory as FlowiseMemory
const memoryKey = memory.memoryKey ?? 'chat_history'
let humanImageMessages: HumanMessage[] = []
if (model instanceof ChatOpenAI) {
const messageContent = addImagesToMessages(nodeData, options, model.multiModalOption)
let messageContent: MessageContentImageUrl[] = []
if (llmSupportsVision(model)) {
messageContent = addImagesToMessages(nodeData, options, model.multiModalOption)
const visionChatModel = model as IVisionChatModal
if (messageContent?.length) {
// Change model to gpt-4-vision
model.modelName = 'gpt-4-vision-preview'
// Change default max token to higher when using gpt-4-vision
model.maxTokens = 1024
for (const msg of messageContent) {
humanImageMessages.push(new HumanMessage({ content: [msg] }))
}
visionChatModel.setVisionModel()
} else {
// revert to previous values if image upload is empty
model.modelName = model.configuredModel
model.maxTokens = model.configuredMaxToken
visionChatModel.revertToOriginalModel()
}
}
const chatPrompt = prepareChatPrompt(nodeData, humanImageMessages)
const chatPrompt = prepareChatPrompt(nodeData, messageContent)
let promptVariables = {}
const promptValuesRaw = (chatPrompt as any).promptValues
if (promptValuesRaw) {
@@ -228,7 +257,7 @@ const prepareChain = (nodeData: INodeData, options: ICommonObject, sessionId?: s
},
...promptVariables
},
prepareChatPrompt(nodeData, humanImageMessages),
prepareChatPrompt(nodeData, messageContent),
model,
new StringOutputParser()
])
packages/components/nodes/chains/ConversationalRetrievalQAChain/ConversationalRetrievalQAChain.ts
+22 -1
View File
@@ -5,6 +5,8 @@ import { PromptTemplate, ChatPromptTemplate, MessagesPlaceholder } from '@langch
import { Runnable, RunnableSequence, RunnableMap, RunnableBranch, RunnableLambda } from '@langchain/core/runnables'
import { BaseMessage, HumanMessage, AIMessage } from '@langchain/core/messages'
import { ConsoleCallbackHandler as LCConsoleCallbackHandler } from '@langchain/core/tracers/console'
import { checkInputs, Moderation, streamResponse } from '../../moderation/Moderation'
import { formatResponse } from '../../outputparsers/OutputParserHelpers'
import { StringOutputParser } from '@langchain/core/output_parsers'
import type { Document } from '@langchain/core/documents'
import { BufferMemoryInput } from 'langchain/memory'
@@ -36,7 +38,7 @@ class ConversationalRetrievalQAChain_Chains implements INode {
constructor(fields?: { sessionId?: string }) {
this.label = 'Conversational Retrieval QA Chain'
this.name = 'conversationalRetrievalQAChain'
this.version = 2.0
this.version = 3.0
this.type = 'ConversationalRetrievalQAChain'
this.icon = 'qa.svg'
this.category = 'Chains'
@@ -87,6 +89,14 @@ class ConversationalRetrievalQAChain_Chains implements INode {
additionalParams: true,
optional: true,
default: RESPONSE_TEMPLATE
},
{
label: 'Input Moderation',
description: 'Detect text that could generate harmful output and prevent it from being sent to the language model',
name: 'inputModeration',
type: 'Moderation',
optional: true,
list: true
}
/** Deprecated
{
@@ -163,6 +173,7 @@ class ConversationalRetrievalQAChain_Chains implements INode {
}
let memory: FlowiseMemory | undefined = externalMemory
const moderations = nodeData.inputs?.inputModeration as Moderation[]
if (!memory) {
memory = new BufferMemory({
returnMessages: true,
@@ -171,6 +182,16 @@ class ConversationalRetrievalQAChain_Chains implements INode {
})
}
if (moderations && moderations.length > 0) {
try {
// Use the output of the moderation chain as input for the Conversational Retrieval QA Chain
input = await checkInputs(moderations, input)
} catch (e) {
await new Promise((resolve) => setTimeout(resolve, 500))
streamResponse(options.socketIO && options.socketIOClientId, e.message, options.socketIO, options.socketIOClientId)
return formatResponse(e.message)
}
}
const answerChain = createChain(model, vectorStoreRetriever, rephrasePrompt, customResponsePrompt)
const history = ((await memory.getChatMessages(this.sessionId, false, options.chatHistory)) as IMessage[]) ?? []
packages/components/nodes/chains/LLMChain/LLMChain.ts
+30 -18
View File
@@ -1,16 +1,15 @@
import { BaseLanguageModel, BaseLanguageModelCallOptions } from '@langchain/core/language_models/base'
import { BaseLLMOutputParser, BaseOutputParser } from '@langchain/core/output_parsers'
import { HumanMessage } from '@langchain/core/messages'
import { ChatPromptTemplate, FewShotPromptTemplate, PromptTemplate, HumanMessagePromptTemplate } from '@langchain/core/prompts'
import { ChatPromptTemplate, FewShotPromptTemplate, HumanMessagePromptTemplate, PromptTemplate } from '@langchain/core/prompts'
import { OutputFixingParser } from 'langchain/output_parsers'
import { LLMChain } from 'langchain/chains'
import { ICommonObject, INode, INodeData, INodeOutputsValue, INodeParams } from '../../../src/Interface'
import { ConsoleCallbackHandler, CustomChainHandler, additionalCallbacks } from '../../../src/handler'
import { IVisionChatModal, ICommonObject, INode, INodeData, INodeOutputsValue, INodeParams } from '../../../src/Interface'
import { additionalCallbacks, ConsoleCallbackHandler, CustomChainHandler } from '../../../src/handler'
import { getBaseClasses, handleEscapeCharacters } from '../../../src/utils'
import { checkInputs, Moderation, streamResponse } from '../../moderation/Moderation'
import { formatResponse, injectOutputParser } from '../../outputparsers/OutputParserHelpers'
import { ChatOpenAI } from '../../chatmodels/ChatOpenAI/FlowiseChatOpenAI'
import { addImagesToMessages } from '../../../src/multiModalUtils'
import { addImagesToMessages, llmSupportsVision } from '../../../src/multiModalUtils'
class LLMChain_Chains implements INode {
label: string
@@ -164,7 +163,6 @@ const runPrediction = async (
const socketIO = isStreaming ? options.socketIO : undefined
const socketIOClientId = isStreaming ? options.socketIOClientId : ''
const moderations = nodeData.inputs?.inputModeration as Moderation[]
let model = nodeData.inputs?.model as ChatOpenAI
if (moderations && moderations.length > 0) {
try {
@@ -183,24 +181,39 @@ const runPrediction = async (
* TO: { "value": "hello i am ben\n\n\thow are you?" }
*/
const promptValues = handleEscapeCharacters(promptValuesRaw, true)
const messageContent = addImagesToMessages(nodeData, options, model.multiModalOption)
if (chain.llm instanceof ChatOpenAI) {
const chatOpenAI = chain.llm as ChatOpenAI
if (llmSupportsVision(chain.llm)) {
const visionChatModel = chain.llm as IVisionChatModal
const messageContent = addImagesToMessages(nodeData, options, visionChatModel.multiModalOption)
if (messageContent?.length) {
// Change model to gpt-4-vision && max token to higher when using gpt-4-vision
chatOpenAI.modelName = 'gpt-4-vision-preview'
chatOpenAI.maxTokens = 1024
visionChatModel.setVisionModel()
// Add image to the message
if (chain.prompt instanceof PromptTemplate) {
const existingPromptTemplate = chain.prompt.template as string
let newChatPromptTemplate = ChatPromptTemplate.fromMessages([
HumanMessagePromptTemplate.fromTemplate(existingPromptTemplate)
const msg = HumanMessagePromptTemplate.fromTemplate([
...messageContent,
{
text: existingPromptTemplate
}
])
newChatPromptTemplate.promptMessages.push(new HumanMessage({ content: messageContent }))
chain.prompt = newChatPromptTemplate
msg.inputVariables = chain.prompt.inputVariables
chain.prompt = ChatPromptTemplate.fromMessages([msg])
} else if (chain.prompt instanceof ChatPromptTemplate) {
chain.prompt.promptMessages.push(new HumanMessage({ content: messageContent }))
if (chain.prompt.promptMessages.at(-1) instanceof HumanMessagePromptTemplate) {
const lastMessage = chain.prompt.promptMessages.pop() as HumanMessagePromptTemplate
const template = (lastMessage.prompt as PromptTemplate).template as string
const msg = HumanMessagePromptTemplate.fromTemplate([
...messageContent,
{
text: template
}
])
msg.inputVariables = lastMessage.inputVariables
chain.prompt.promptMessages.push(msg)
} else {
chain.prompt.promptMessages.push(new HumanMessage({ content: messageContent }))
}
} else if (chain.prompt instanceof FewShotPromptTemplate) {
let existingFewShotPromptTemplate = chain.prompt.examplePrompt.template as string
let newFewShotPromptTemplate = ChatPromptTemplate.fromMessages([
@@ -212,8 +225,7 @@ const runPrediction = async (
}
} else {
// revert to previous values if image upload is empty
chatOpenAI.modelName = model.configuredModel
chatOpenAI.maxTokens = model.configuredMaxToken
visionChatModel.revertToOriginalModel()
}
}
packages/components/nodes/chains/MultiPromptChain/MultiPromptChain.ts
+23 -2
View File
@@ -3,6 +3,8 @@ import { MultiPromptChain } from 'langchain/chains'
import { ICommonObject, INode, INodeData, INodeParams, PromptRetriever } from '../../../src/Interface'
import { getBaseClasses } from '../../../src/utils'
import { ConsoleCallbackHandler, CustomChainHandler, additionalCallbacks } from '../../../src/handler'
import { checkInputs, Moderation, streamResponse } from '../../moderation/Moderation'
import { formatResponse } from '../../outputparsers/OutputParserHelpers'
class MultiPromptChain_Chains implements INode {
label: string
@@ -18,7 +20,7 @@ class MultiPromptChain_Chains implements INode {
constructor() {
this.label = 'Multi Prompt Chain'
this.name = 'multiPromptChain'
this.version = 1.0
this.version = 2.0
this.type = 'MultiPromptChain'
this.icon = 'prompt.svg'
this.category = 'Chains'
@@ -35,6 +37,14 @@ class MultiPromptChain_Chains implements INode {
name: 'promptRetriever',
type: 'PromptRetriever',
list: true
},
{
label: 'Input Moderation',
description: 'Detect text that could generate harmful output and prevent it from being sent to the language model',
name: 'inputModeration',
type: 'Moderation',
optional: true,
list: true
}
]
}
@@ -62,8 +72,19 @@ class MultiPromptChain_Chains implements INode {
return chain
}
async run(nodeData: INodeData, input: string, options: ICommonObject): Promise<string> {
async run(nodeData: INodeData, input: string, options: ICommonObject): Promise<string | object> {
const chain = nodeData.instance as MultiPromptChain
const moderations = nodeData.inputs?.inputModeration as Moderation[]
if (moderations && moderations.length > 0) {
try {
// Use the output of the moderation chain as input for the Multi Prompt Chain
input = await checkInputs(moderations, input)
} catch (e) {
await new Promise((resolve) => setTimeout(resolve, 500))
streamResponse(options.socketIO && options.socketIOClientId, e.message, options.socketIO, options.socketIOClientId)
return formatResponse(e.message)
}
}
const obj = { input }
const loggerHandler = new ConsoleCallbackHandler(options.logger)
packages/components/nodes/chains/MultiRetrievalQAChain/MultiRetrievalQAChain.ts
+22 -2
View File
@@ -3,6 +3,8 @@ import { MultiRetrievalQAChain } from 'langchain/chains'
import { ICommonObject, INode, INodeData, INodeParams, VectorStoreRetriever } from '../../../src/Interface'
import { getBaseClasses } from '../../../src/utils'
import { ConsoleCallbackHandler, CustomChainHandler, additionalCallbacks } from '../../../src/handler'
import { checkInputs, Moderation, streamResponse } from '../../moderation/Moderation'
import { formatResponse } from '../../outputparsers/OutputParserHelpers'
class MultiRetrievalQAChain_Chains implements INode {
label: string
@@ -18,7 +20,7 @@ class MultiRetrievalQAChain_Chains implements INode {
constructor() {
this.label = 'Multi Retrieval QA Chain'
this.name = 'multiRetrievalQAChain'
this.version = 1.0
this.version = 2.0
this.type = 'MultiRetrievalQAChain'
this.icon = 'qa.svg'
this.category = 'Chains'
@@ -41,6 +43,14 @@ class MultiRetrievalQAChain_Chains implements INode {
name: 'returnSourceDocuments',
type: 'boolean',
optional: true
},
{
label: 'Input Moderation',
description: 'Detect text that could generate harmful output and prevent it from being sent to the language model',
name: 'inputModeration',
type: 'Moderation',
optional: true,
list: true
}
]
}
@@ -72,7 +82,17 @@ class MultiRetrievalQAChain_Chains implements INode {
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 moderations = nodeData.inputs?.inputModeration as Moderation[]
if (moderations && moderations.length > 0) {
try {
// Use the output of the moderation chain as input for the Multi Retrieval QA Chain
input = await checkInputs(moderations, input)
} catch (e) {
await new Promise((resolve) => setTimeout(resolve, 500))
streamResponse(options.socketIO && options.socketIOClientId, e.message, options.socketIO, options.socketIOClientId)
return formatResponse(e.message)
}
}
const obj = { input }
const loggerHandler = new ConsoleCallbackHandler(options.logger)
const callbacks = await additionalCallbacks(nodeData, options)
packages/components/nodes/chains/RetrievalQAChain/RetrievalQAChain.ts
+23 -2
View File
@@ -4,6 +4,8 @@ import { RetrievalQAChain } from 'langchain/chains'
import { ConsoleCallbackHandler, CustomChainHandler, additionalCallbacks } from '../../../src/handler'
import { ICommonObject, INode, INodeData, INodeParams } from '../../../src/Interface'
import { getBaseClasses } from '../../../src/utils'
import { checkInputs, Moderation, streamResponse } from '../../moderation/Moderation'
import { formatResponse } from '../../outputparsers/OutputParserHelpers'
class RetrievalQAChain_Chains implements INode {
label: string
@@ -19,7 +21,7 @@ class RetrievalQAChain_Chains implements INode {
constructor() {
this.label = 'Retrieval QA Chain'
this.name = 'retrievalQAChain'
this.version = 1.0
this.version = 2.0
this.type = 'RetrievalQAChain'
this.icon = 'qa.svg'
this.category = 'Chains'
@@ -35,6 +37,14 @@ class RetrievalQAChain_Chains implements INode {
label: 'Vector Store Retriever',
name: 'vectorStoreRetriever',
type: 'BaseRetriever'
},
{
label: 'Input Moderation',
description: 'Detect text that could generate harmful output and prevent it from being sent to the language model',
name: 'inputModeration',
type: 'Moderation',
optional: true,
list: true
}
]
}
@@ -47,8 +57,19 @@ class RetrievalQAChain_Chains implements INode {
return chain
}
async run(nodeData: INodeData, input: string, options: ICommonObject): Promise<string> {
async run(nodeData: INodeData, input: string, options: ICommonObject): Promise<string | object> {
const chain = nodeData.instance as RetrievalQAChain
const moderations = nodeData.inputs?.inputModeration as Moderation[]
if (moderations && moderations.length > 0) {
try {
// Use the output of the moderation chain as input for the Retrieval QA Chain
input = await checkInputs(moderations, input)
} catch (e) {
await new Promise((resolve) => setTimeout(resolve, 500))
streamResponse(options.socketIO && options.socketIOClientId, e.message, options.socketIO, options.socketIOClientId)
return formatResponse(e.message)
}
}
const obj = {
query: input
}
packages/components/nodes/chains/SqlDatabaseChain/SqlDatabaseChain.ts
+23 -2
View File
@@ -7,6 +7,8 @@ import { SqlDatabase } from 'langchain/sql_db'
import { ICommonObject, INode, INodeData, INodeParams } from '../../../src/Interface'
import { ConsoleCallbackHandler, CustomChainHandler, additionalCallbacks } from '../../../src/handler'
import { getBaseClasses, getInputVariables } from '../../../src/utils'
import { checkInputs, Moderation, streamResponse } from '../../moderation/Moderation'
import { formatResponse } from '../../outputparsers/OutputParserHelpers'
type DatabaseType = 'sqlite' | 'postgres' | 'mssql' | 'mysql'
@@ -24,7 +26,7 @@ class SqlDatabaseChain_Chains implements INode {
constructor() {
this.label = 'Sql Database Chain'
this.name = 'sqlDatabaseChain'
this.version = 4.0
this.version = 5.0
this.type = 'SqlDatabaseChain'
this.icon = 'sqlchain.svg'
this.category = 'Chains'
@@ -115,6 +117,14 @@ class SqlDatabaseChain_Chains implements INode {
placeholder: DEFAULT_SQL_DATABASE_PROMPT.template + DEFAULT_SQL_DATABASE_PROMPT.templateFormat,
additionalParams: true,
optional: true
},
{
label: 'Input Moderation',
description: 'Detect text that could generate harmful output and prevent it from being sent to the language model',
name: 'inputModeration',
type: 'Moderation',
optional: true,
list: true
}
]
}
@@ -144,7 +154,7 @@ class SqlDatabaseChain_Chains implements INode {
return chain
}
async run(nodeData: INodeData, input: string, options: ICommonObject): Promise<string> {
async run(nodeData: INodeData, input: string, options: ICommonObject): Promise<string | object> {
const databaseType = nodeData.inputs?.database as DatabaseType
const model = nodeData.inputs?.model as BaseLanguageModel
const url = nodeData.inputs?.url as string
@@ -155,6 +165,17 @@ class SqlDatabaseChain_Chains implements INode {
const sampleRowsInTableInfo = nodeData.inputs?.sampleRowsInTableInfo as number
const topK = nodeData.inputs?.topK as number
const customPrompt = nodeData.inputs?.customPrompt as string
const moderations = nodeData.inputs?.inputModeration as Moderation[]
if (moderations && moderations.length > 0) {
try {
// Use the output of the moderation chain as input for the Sql Database Chain
input = await checkInputs(moderations, input)
} catch (e) {
await new Promise((resolve) => setTimeout(resolve, 500))
streamResponse(options.socketIO && options.socketIOClientId, e.message, options.socketIO, options.socketIOClientId)
return formatResponse(e.message)
}
}
const chain = await getSQLDBChain(
databaseType,
packages/components/nodes/chains/VectaraChain/VectaraChain.ts
+24 -2
View File
@@ -4,6 +4,8 @@ import { VectaraStore } from '@langchain/community/vectorstores/vectara'
import { VectorDBQAChain } from 'langchain/chains'
import { INode, INodeData, INodeParams } from '../../../src/Interface'
import { getBaseClasses } from '../../../src/utils'
import { checkInputs, Moderation } from '../../moderation/Moderation'
import { formatResponse } from '../../outputparsers/OutputParserHelpers'
// functionality based on https://github.com/vectara/vectara-answer
const reorderCitations = (unorderedSummary: string) => {
@@ -48,7 +50,7 @@ class VectaraChain_Chains implements INode {
constructor() {
this.label = 'Vectara QA Chain'
this.name = 'vectaraQAChain'
this.version = 1.0
this.version = 2.0
this.type = 'VectaraQAChain'
this.icon = 'vectara.png'
this.category = 'Chains'
@@ -219,6 +221,14 @@ class VectaraChain_Chains implements INode {
description: 'Maximum results used to build the summarized response',
type: 'number',
default: 7
},
{
label: 'Input Moderation',
description: 'Detect text that could generate harmful output and prevent it from being sent to the language model',
name: 'inputModeration',
type: 'Moderation',
optional: true,
list: true
}
]
}
@@ -227,7 +237,7 @@ class VectaraChain_Chains implements INode {
return null
}
async run(nodeData: INodeData, input: string): Promise<object> {
async run(nodeData: INodeData, input: string): Promise<string | object> {
const vectorStore = nodeData.inputs?.vectaraStore as VectaraStore
const responseLang = (nodeData.inputs?.responseLang as string) ?? 'eng'
const summarizerPromptName = nodeData.inputs?.summarizerPromptName as string
@@ -252,6 +262,18 @@ class VectaraChain_Chains implements INode {
const mmrRerankerId = 272725718
const mmrEnabled = vectaraFilter?.mmrConfig?.enabled
const moderations = nodeData.inputs?.inputModeration as Moderation[]
if (moderations && moderations.length > 0) {
try {
// Use the output of the moderation chain as input for the Vectara chain
input = await checkInputs(moderations, input)
} catch (e) {
await new Promise((resolve) => setTimeout(resolve, 500))
//streamResponse(options.socketIO && options.socketIOClientId, e.message, options.socketIO, options.socketIOClientId)
return formatResponse(e.message)
}
}
const data = {
query: [
{
packages/components/nodes/chains/VectorDBQAChain/VectorDBQAChain.ts
+24 -2
View File
@@ -4,6 +4,8 @@ import { VectorDBQAChain } from 'langchain/chains'
import { ConsoleCallbackHandler, CustomChainHandler, additionalCallbacks } from '../../../src/handler'
import { ICommonObject, INode, INodeData, INodeParams } from '../../../src/Interface'
import { getBaseClasses } from '../../../src/utils'
import { checkInputs, Moderation } from '../../moderation/Moderation'
import { formatResponse } from '../../outputparsers/OutputParserHelpers'
class VectorDBQAChain_Chains implements INode {
label: string
@@ -19,7 +21,7 @@ class VectorDBQAChain_Chains implements INode {
constructor() {
this.label = 'VectorDB QA Chain'
this.name = 'vectorDBQAChain'
this.version = 1.0
this.version = 2.0
this.type = 'VectorDBQAChain'
this.icon = 'vectordb.svg'
this.category = 'Chains'
@@ -35,6 +37,14 @@ class VectorDBQAChain_Chains implements INode {
label: 'Vector Store',
name: 'vectorStore',
type: 'VectorStore'
},
{
label: 'Input Moderation',
description: 'Detect text that could generate harmful output and prevent it from being sent to the language model',
name: 'inputModeration',
type: 'Moderation',
optional: true,
list: true
}
]
}
@@ -50,8 +60,20 @@ class VectorDBQAChain_Chains implements INode {
return chain
}
async run(nodeData: INodeData, input: string, options: ICommonObject): Promise<string> {
async run(nodeData: INodeData, input: string, options: ICommonObject): Promise<string | object> {
const chain = nodeData.instance as VectorDBQAChain
const moderations = nodeData.inputs?.inputModeration as Moderation[]
if (moderations && moderations.length > 0) {
try {
// Use the output of the moderation chain as input for the VectorDB QA Chain
input = await checkInputs(moderations, input)
} catch (e) {
await new Promise((resolve) => setTimeout(resolve, 500))
//streamResponse(options.socketIO && options.socketIOClientId, e.message, options.socketIO, options.socketIOClientId)
return formatResponse(e.message)
}
}
const obj = {
query: input
}
packages/components/nodes/chatmodels/AWSBedrock/AWSChatBedrock.ts
+1 -1
View File
@@ -1,7 +1,7 @@
import { BedrockChat } from '@langchain/community/chat_models/bedrock'
import { BaseCache } from '@langchain/core/caches'
import { BaseChatModelParams } from '@langchain/core/language_models/chat_models'
import { BaseBedrockInput } from 'langchain/dist/util/bedrock'
import { BaseBedrockInput } from '@langchain/community/dist/utils/bedrock'
import { ICommonObject, INode, INodeData, INodeParams } from '../../../src/Interface'
import { getBaseClasses, getCredentialData, getCredentialParam } from '../../../src/utils'
packages/components/nodes/chatmodels/ChatAnthropic/ChatAnthropic.ts
+24 -5
View File
@@ -1,8 +1,9 @@
import { AnthropicInput, ChatAnthropic } from '@langchain/anthropic'
import { AnthropicInput, ChatAnthropic as LangchainChatAnthropic } from '@langchain/anthropic'
import { BaseCache } from '@langchain/core/caches'
import { BaseLLMParams } from '@langchain/core/language_models/llms'
import { ICommonObject, INode, INodeData, INodeParams } from '../../../src/Interface'
import { ICommonObject, IMultiModalOption, INode, INodeData, INodeParams } from '../../../src/Interface'
import { getBaseClasses, getCredentialData, getCredentialParam } from '../../../src/utils'
import { ChatAnthropic } from './FlowiseChatAntrhopic'
class ChatAnthropic_ChatModels implements INode {
label: string
@@ -19,12 +20,12 @@ class ChatAnthropic_ChatModels implements INode {
constructor() {
this.label = 'ChatAnthropic'
this.name = 'chatAnthropic'
this.version = 3.0
this.version = 4.0
this.type = 'ChatAnthropic'
this.icon = 'Anthropic.svg'
this.category = 'Chat Models'
this.description = 'Wrapper around ChatAnthropic large language models that use the Chat endpoint'
this.baseClasses = [this.type, ...getBaseClasses(ChatAnthropic)]
this.baseClasses = [this.type, ...getBaseClasses(LangchainChatAnthropic)]
this.credential = {
label: 'Connect Credential',
name: 'credential',
@@ -147,6 +148,15 @@ class ChatAnthropic_ChatModels implements INode {
step: 0.1,
optional: true,
additionalParams: true
},
{
label: 'Allow Image Uploads',
name: 'allowImageUploads',
type: 'boolean',
description:
'Automatically uses claude-3-* models when image is being uploaded from chat. Only works with LLMChain, Conversation Chain, ReAct Agent, and Conversational Agent',
default: false,
optional: true
}
]
}
@@ -163,6 +173,8 @@ class ChatAnthropic_ChatModels implements INode {
const credentialData = await getCredentialData(nodeData.credential ?? '', options)
const anthropicApiKey = getCredentialParam('anthropicApiKey', credentialData, nodeData)
const allowImageUploads = nodeData.inputs?.allowImageUploads as boolean
const obj: Partial<AnthropicInput> & BaseLLMParams & { anthropicApiKey?: string } = {
temperature: parseFloat(temperature),
modelName,
@@ -175,7 +187,14 @@ class ChatAnthropic_ChatModels implements INode {
if (topK) obj.topK = parseFloat(topK)
if (cache) obj.cache = cache
const model = new ChatAnthropic(obj)
const multiModalOption: IMultiModalOption = {
image: {
allowImageUploads: allowImageUploads ?? false
}
}
const model = new ChatAnthropic(nodeData.id, obj)
model.setMultiModalOption(multiModalOption)
return model
}
}
packages/components/nodes/chatmodels/ChatAnthropic/FlowiseChatAntrhopic.ts
+33
View File
@@ -0,0 +1,33 @@
import { AnthropicInput, ChatAnthropic as LangchainChatAnthropic } from '@langchain/anthropic'
import { IVisionChatModal, IMultiModalOption } from '../../../src'
import { BaseLLMParams } from '@langchain/core/language_models/llms'
export class ChatAnthropic extends LangchainChatAnthropic implements IVisionChatModal {
configuredModel: string
configuredMaxToken: number
multiModalOption: IMultiModalOption
id: string
constructor(id: string, fields: Partial<AnthropicInput> & BaseLLMParams & { anthropicApiKey?: string }) {
super(fields)
this.id = id
this.configuredModel = fields?.modelName || 'claude-3-opus-20240229'
this.configuredMaxToken = fields?.maxTokens ?? 256
}
revertToOriginalModel(): void {
super.modelName = this.configuredModel
super.maxTokens = this.configuredMaxToken
}
setMultiModalOption(multiModalOption: IMultiModalOption): void {
this.multiModalOption = multiModalOption
}
setVisionModel(): void {
if (!this.modelName.startsWith('claude-3')) {
super.modelName = 'claude-3-opus-20240229'
super.maxTokens = 1024
}
}
}
packages/components/nodes/chatmodels/ChatOpenAI/ChatOpenAI.ts
+2 -3
View File
@@ -228,7 +228,7 @@ class ChatOpenAI_ChatModels implements INode {
const obj: Partial<OpenAIChatInput> &
Partial<AzureOpenAIInput> &
BaseChatModelParams & { configuration?: ClientOptions & LegacyOpenAIInput; multiModalOption?: IMultiModalOption } = {
BaseChatModelParams & { configuration?: ClientOptions & LegacyOpenAIInput } = {
temperature: parseFloat(temperature),
modelName,
openAIApiKey,
@@ -265,10 +265,9 @@ class ChatOpenAI_ChatModels implements INode {
imageResolution
}
}
obj.multiModalOption = multiModalOption
const model = new ChatOpenAI(nodeData.id, obj)
model.setMultiModalOption(multiModalOption)
return model
}
}
packages/components/nodes/chatmodels/ChatOpenAI/FlowiseChatOpenAI.ts
+19 -19
View File
@@ -1,39 +1,39 @@
import type { ClientOptions } from 'openai'
import {
ChatOpenAI as LangchainChatOpenAI,
OpenAIChatInput,
LegacyOpenAIInput,
AzureOpenAIInput,
ChatOpenAICallOptions
} from '@langchain/openai'
import { ChatOpenAI as LangchainChatOpenAI, OpenAIChatInput, LegacyOpenAIInput, AzureOpenAIInput } from '@langchain/openai'
import { BaseChatModelParams } from '@langchain/core/language_models/chat_models'
import { BaseMessageLike } from '@langchain/core/messages'
import { Callbacks } from '@langchain/core/callbacks/manager'
import { LLMResult } from '@langchain/core/outputs'
import { IMultiModalOption } from '../../../src'
import { IMultiModalOption, IVisionChatModal } from '../../../src'
export class ChatOpenAI extends LangchainChatOpenAI {
export class ChatOpenAI extends LangchainChatOpenAI implements IVisionChatModal {
configuredModel: string
configuredMaxToken?: number
multiModalOption?: IMultiModalOption
configuredMaxToken: number
multiModalOption: IMultiModalOption
id: string
constructor(
id: string,
fields?: Partial<OpenAIChatInput> &
Partial<AzureOpenAIInput> &
BaseChatModelParams & { configuration?: ClientOptions & LegacyOpenAIInput; multiModalOption?: IMultiModalOption },
BaseChatModelParams & { configuration?: ClientOptions & LegacyOpenAIInput },
/** @deprecated */
configuration?: ClientOptions & LegacyOpenAIInput
) {
super(fields, configuration)
this.id = id
this.multiModalOption = fields?.multiModalOption
this.configuredModel = fields?.modelName ?? 'gpt-3.5-turbo'
this.configuredMaxToken = fields?.maxTokens
this.configuredMaxToken = fields?.maxTokens ?? 256
}
async generate(messages: BaseMessageLike[][], options?: string[] | ChatOpenAICallOptions, callbacks?: Callbacks): Promise<LLMResult> {
return super.generate(messages, options, callbacks)
revertToOriginalModel(): void {
super.modelName = this.configuredModel
super.maxTokens = this.configuredMaxToken
}
setMultiModalOption(multiModalOption: IMultiModalOption): void {
this.multiModalOption = multiModalOption
}
setVisionModel(): void {
super.modelName = 'gpt-4-vision-preview'
super.maxTokens = 1024
}
}
packages/components/nodes/llms/AWSBedrock/AWSBedrock.ts
+1 -1
View File
@@ -1,9 +1,9 @@
import { Bedrock } from '@langchain/community/llms/bedrock'
import { BaseCache } from '@langchain/core/caches'
import { BaseLLMParams } from '@langchain/core/language_models/llms'
import { BaseBedrockInput } from 'langchain/dist/util/bedrock'
import { ICommonObject, INode, INodeData, INodeParams } from '../../../src/Interface'
import { getBaseClasses, getCredentialData, getCredentialParam } from '../../../src/utils'
import { BaseBedrockInput } from '@langchain/community/dist/utils/bedrock'
/**
* I had to run the following to build the component
packages/components/nodes/utilities/IfElseFunction/IfElseFunction.ts
+3 -2
View File
@@ -143,10 +143,11 @@ class IfElseFunction_Utilities implements INode {
const vm = new NodeVM(nodeVMOptions)
try {
const responseTrue = await vm.run(`module.exports = async function() {${ifFunction}}()`, __dirname)
if (responseTrue) return { output: responseTrue, type: true }
if (responseTrue)
return { output: typeof responseTrue === 'string' ? handleEscapeCharacters(responseTrue, false) : responseTrue, type: true }
const responseFalse = await vm.run(`module.exports = async function() {${elseFunction}}()`, __dirname)
return { output: responseFalse, type: false }
return { output: typeof responseFalse === 'string' ? handleEscapeCharacters(responseFalse, false) : responseFalse, type: false }
} catch (e) {
throw new Error(e)
}