mirror of
https://github.com/farcasclaudiu/Flowise.git
synced 2026-06-28 17:01:00 +03:00
Chore/update deprecating nodes (#2540)
* update deprecating nodes * add filters use cases to marketplace * update log level
This commit is contained in:
Henry Heng
-187
@@ -1,187 +0,0 @@
|
||||
import { flatten } from 'lodash'
|
||||
import { BaseMessage } from '@langchain/core/messages'
|
||||
import { ChainValues } from '@langchain/core/utils/types'
|
||||
import { AgentStep } from '@langchain/core/agents'
|
||||
import { RunnableSequence } from '@langchain/core/runnables'
|
||||
import { ChatOpenAI, formatToOpenAIFunction } from '@langchain/openai'
|
||||
import { ChatPromptTemplate, MessagesPlaceholder } from '@langchain/core/prompts'
|
||||
import { OpenAIFunctionsAgentOutputParser } from 'langchain/agents/openai/output_parser'
|
||||
import { FlowiseMemory, ICommonObject, INode, INodeData, INodeParams } from '../../../src/Interface'
|
||||
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.`
|
||||
|
||||
class ConversationalRetrievalAgent_Agents implements INode {
|
||||
label: string
|
||||
name: string
|
||||
version: number
|
||||
description: string
|
||||
type: string
|
||||
icon: string
|
||||
category: string
|
||||
baseClasses: string[]
|
||||
inputs: INodeParams[]
|
||||
badge?: string
|
||||
sessionId?: string
|
||||
|
||||
constructor(fields?: { sessionId?: string }) {
|
||||
this.label = 'Conversational Retrieval Agent'
|
||||
this.name = 'conversationalRetrievalAgent'
|
||||
this.version = 4.0
|
||||
this.type = 'AgentExecutor'
|
||||
this.category = 'Agents'
|
||||
this.badge = 'DEPRECATING'
|
||||
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/Azure Chat Model',
|
||||
name: 'model',
|
||||
type: 'BaseChatModel'
|
||||
},
|
||||
{
|
||||
label: 'System Message',
|
||||
name: 'systemMessage',
|
||||
type: 'string',
|
||||
default: defaultMessage,
|
||||
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
|
||||
},
|
||||
{
|
||||
label: 'Max Iterations',
|
||||
name: 'maxIterations',
|
||||
type: 'number',
|
||||
optional: true,
|
||||
additionalParams: true
|
||||
}
|
||||
]
|
||||
this.sessionId = fields?.sessionId
|
||||
}
|
||||
|
||||
async init(nodeData: INodeData, input: string, options: ICommonObject): Promise<any> {
|
||||
return prepareAgent(nodeData, options, { sessionId: this.sessionId, chatId: options.chatId, input })
|
||||
}
|
||||
|
||||
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, options, { sessionId: this.sessionId, chatId: options.chatId, input })
|
||||
|
||||
const loggerHandler = new ConsoleCallbackHandler(options.logger)
|
||||
const callbacks = await additionalCallbacks(nodeData, options)
|
||||
|
||||
let res: ChainValues = {}
|
||||
|
||||
if (options.socketIO && options.socketIOClientId) {
|
||||
const handler = new CustomChainHandler(options.socketIO, options.socketIOClientId)
|
||||
res = await executor.invoke({ input }, { callbacks: [loggerHandler, handler, ...callbacks] })
|
||||
} else {
|
||||
res = await executor.invoke({ input }, { callbacks: [loggerHandler, ...callbacks] })
|
||||
}
|
||||
|
||||
await memory.addChatMessages(
|
||||
[
|
||||
{
|
||||
text: input,
|
||||
type: 'userMessage'
|
||||
},
|
||||
{
|
||||
text: res?.output,
|
||||
type: 'apiMessage'
|
||||
}
|
||||
],
|
||||
this.sessionId
|
||||
)
|
||||
|
||||
return res?.output
|
||||
}
|
||||
}
|
||||
|
||||
const prepareAgent = (nodeData: INodeData, options: ICommonObject, flowObj: { sessionId?: string; chatId?: string; input?: string }) => {
|
||||
const model = nodeData.inputs?.model as ChatOpenAI
|
||||
const memory = nodeData.inputs?.memory as FlowiseMemory
|
||||
const systemMessage = nodeData.inputs?.systemMessage as string
|
||||
const maxIterations = nodeData.inputs?.maxIterations as string
|
||||
let tools = nodeData.inputs?.tools
|
||||
tools = flatten(tools)
|
||||
const memoryKey = memory.memoryKey ? memory.memoryKey : 'chat_history'
|
||||
const inputKey = memory.inputKey ? memory.inputKey : 'input'
|
||||
const prependMessages = options?.prependMessages
|
||||
|
||||
const prompt = ChatPromptTemplate.fromMessages([
|
||||
['ai', systemMessage ? systemMessage : defaultMessage],
|
||||
new MessagesPlaceholder(memoryKey),
|
||||
['human', `{${inputKey}}`],
|
||||
new MessagesPlaceholder('agent_scratchpad')
|
||||
])
|
||||
|
||||
const modelWithFunctions = model.bind({
|
||||
functions: [...tools.map((tool: any) => formatToOpenAIFunction(tool))]
|
||||
})
|
||||
|
||||
const runnableAgent = RunnableSequence.from([
|
||||
{
|
||||
[inputKey]: (i: { input: string; steps: AgentStep[] }) => i.input,
|
||||
agent_scratchpad: (i: { input: string; steps: AgentStep[] }) => formatAgentSteps(i.steps),
|
||||
[memoryKey]: async (_: { input: string; steps: AgentStep[] }) => {
|
||||
const messages = (await memory.getChatMessages(flowObj?.sessionId, true, prependMessages)) as BaseMessage[]
|
||||
return messages ?? []
|
||||
}
|
||||
},
|
||||
prompt,
|
||||
modelWithFunctions,
|
||||
new OpenAIFunctionsAgentOutputParser()
|
||||
])
|
||||
|
||||
const executor = AgentExecutor.fromAgentAndTools({
|
||||
agent: runnableAgent,
|
||||
tools,
|
||||
sessionId: flowObj?.sessionId,
|
||||
chatId: flowObj?.chatId,
|
||||
input: flowObj?.input,
|
||||
returnIntermediateSteps: true,
|
||||
verbose: process.env.DEBUG === 'true' ? true : false,
|
||||
maxIterations: maxIterations ? parseFloat(maxIterations) : undefined
|
||||
})
|
||||
|
||||
return executor
|
||||
}
|
||||
|
||||
module.exports = { nodeClass: ConversationalRetrievalAgent_Agents }
|
||||
@@ -1,7 +0,0 @@
|
||||
<svg width="32" height="32" viewBox="0 0 32 32" fill="none" xmlns="http://www.w3.org/2000/svg">
|
||||
<path d="M10 6C10 5.44772 10.4477 5 11 5H21C21.5523 5 22 5.44772 22 6V11C22 13.2091 20.2091 15 18 15H14C11.7909 15 10 13.2091 10 11V6Z" stroke="black" stroke-width="2" stroke-linejoin="round"/>
|
||||
<path d="M16 5V3" stroke="black" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"/>
|
||||
<circle cx="14" cy="9" r="1.5" fill="black"/>
|
||||
<circle cx="18" cy="9" r="1.5" fill="black"/>
|
||||
<path d="M26 27C26 22.0294 21.5228 18 16 18C10.4772 18 6 22.0294 6 27" stroke="black" stroke-width="2" stroke-linecap="round"/>
|
||||
</svg>
|
||||
|
Before Width: | Height: | Size: 616 B |
@@ -1 +0,0 @@
|
||||
<svg width="32" height="32" fill="none" xmlns="http://www.w3.org/2000/svg"><path d="M5 6H4v19.5h1m8-7.5v3h1m7-11.5V6h1m-5 7.5V10h1" stroke="#000" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"/><mask id="MistralAI__a" style="mask-type:alpha" maskUnits="userSpaceOnUse" x="5" y="6" width="22" height="20"><path d="M5 6v19.5h5v-8h4V21h4v-3.5h4V25h5V6h-4.5v4H18v3.5h-4v-4h-4V6H5Z" fill="#FD7000"/></mask><g mask="url(#MistralAI__a)"><path fill="#FFCD00" d="M4 6h25v4H4z"/></g><mask id="MistralAI__b" style="mask-type:alpha" maskUnits="userSpaceOnUse" x="5" y="6" width="22" height="20"><path d="M5 6v19.5h5v-8h4V21h4v-3.5h4V25h5V6h-4.5v4H18v3.5h-4v-4h-4V6H5Z" fill="#FD7000"/></mask><g mask="url(#MistralAI__b)"><path fill="#FFA200" d="M4 10h25v4H4z"/></g><mask id="MistralAI__c" style="mask-type:alpha" maskUnits="userSpaceOnUse" x="5" y="6" width="22" height="20"><path d="M5 6v19.5h5v-8h4V21h4v-3.5h4V25h5V6h-4.5v4H18v3.5h-4v-4h-4V6H5Z" fill="#FD7000"/></mask><g mask="url(#MistralAI__c)"><path fill="#FF6E00" d="M4 14h25v4H4z"/></g><mask id="MistralAI__d" style="mask-type:alpha" maskUnits="userSpaceOnUse" x="5" y="6" width="22" height="20"><path d="M5 6v19.5h5v-8h4V21h4v-3.5h4V25h5V6h-4.5v4H18v3.5h-4v-4h-4V6H5Z" fill="#FD7000"/></mask><g mask="url(#MistralAI__d)"><path fill="#FF4A09" d="M4 18h25v4H4z"/></g><mask id="MistralAI__e" style="mask-type:alpha" maskUnits="userSpaceOnUse" x="5" y="6" width="22" height="20"><path d="M5 6v19.5h5v-8h4V21h4v-3.5h4V25h5V6h-4.5v4H18v3.5h-4v-4h-4V6H5Z" fill="#FD7000"/></mask><g mask="url(#MistralAI__e)"><path fill="#FE060F" d="M4 22h25v4H4z"/></g><path d="M21 18v7h1" stroke="#000" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"/><path d="M5 6v19.5h5v-8h4V21h4v-3.5h4V25h5V6h-4.5v4H18v3.5h-4v-4h-4V6H5Z" stroke="#000" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"/></svg>
|
||||
|
Before Width: | Height: | Size: 1.8 KiB |
@@ -1,213 +0,0 @@
|
||||
import { flatten } from 'lodash'
|
||||
import { BaseMessage } from '@langchain/core/messages'
|
||||
import { ChainValues } from '@langchain/core/utils/types'
|
||||
import { AgentStep } from '@langchain/core/agents'
|
||||
import { RunnableSequence } from '@langchain/core/runnables'
|
||||
import { ChatOpenAI } from '@langchain/openai'
|
||||
import { convertToOpenAITool } from '@langchain/core/utils/function_calling'
|
||||
import { ChatPromptTemplate, MessagesPlaceholder } from '@langchain/core/prompts'
|
||||
import { OpenAIToolsAgentOutputParser } from 'langchain/agents/openai/output_parser'
|
||||
import { getBaseClasses } from '../../../src/utils'
|
||||
import { FlowiseMemory, ICommonObject, INode, INodeData, INodeParams, IUsedTool } from '../../../src/Interface'
|
||||
import { ConsoleCallbackHandler, CustomChainHandler, additionalCallbacks } from '../../../src/handler'
|
||||
import { AgentExecutor, formatAgentSteps } from '../../../src/agents'
|
||||
import { Moderation, checkInputs, streamResponse } from '../../moderation/Moderation'
|
||||
import { formatResponse } from '../../outputparsers/OutputParserHelpers'
|
||||
|
||||
class MistralAIToolAgent_Agents implements INode {
|
||||
label: string
|
||||
name: string
|
||||
version: number
|
||||
description: string
|
||||
type: string
|
||||
icon: string
|
||||
category: string
|
||||
baseClasses: string[]
|
||||
inputs: INodeParams[]
|
||||
sessionId?: string
|
||||
badge?: string
|
||||
|
||||
constructor(fields?: { sessionId?: string }) {
|
||||
this.label = 'MistralAI Tool Agent'
|
||||
this.name = 'mistralAIToolAgent'
|
||||
this.version = 1.0
|
||||
this.type = 'AgentExecutor'
|
||||
this.category = 'Agents'
|
||||
this.icon = 'MistralAI.svg'
|
||||
this.badge = 'DEPRECATING'
|
||||
this.description = `Agent that uses MistralAI Function Calling to pick the tools and args to call`
|
||||
this.baseClasses = [this.type, ...getBaseClasses(AgentExecutor)]
|
||||
this.inputs = [
|
||||
{
|
||||
label: 'Tools',
|
||||
name: 'tools',
|
||||
type: 'Tool',
|
||||
list: true
|
||||
},
|
||||
{
|
||||
label: 'Memory',
|
||||
name: 'memory',
|
||||
type: 'BaseChatMemory'
|
||||
},
|
||||
{
|
||||
label: 'MistralAI Chat Model',
|
||||
name: 'model',
|
||||
type: 'BaseChatModel'
|
||||
},
|
||||
{
|
||||
label: 'System Message',
|
||||
name: 'systemMessage',
|
||||
type: 'string',
|
||||
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
|
||||
},
|
||||
{
|
||||
label: 'Max Iterations',
|
||||
name: 'maxIterations',
|
||||
type: 'number',
|
||||
optional: true,
|
||||
additionalParams: true
|
||||
}
|
||||
]
|
||||
this.sessionId = fields?.sessionId
|
||||
}
|
||||
|
||||
async init(nodeData: INodeData, input: string, options: ICommonObject): Promise<any> {
|
||||
return prepareAgent(nodeData, options, { sessionId: this.sessionId, chatId: options.chatId, input })
|
||||
}
|
||||
|
||||
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, options, { sessionId: this.sessionId, chatId: options.chatId, input })
|
||||
|
||||
const loggerHandler = new ConsoleCallbackHandler(options.logger)
|
||||
const callbacks = await additionalCallbacks(nodeData, options)
|
||||
|
||||
let res: ChainValues = {}
|
||||
let sourceDocuments: ICommonObject[] = []
|
||||
let usedTools: IUsedTool[] = []
|
||||
|
||||
if (options.socketIO && options.socketIOClientId) {
|
||||
const handler = new CustomChainHandler(options.socketIO, options.socketIOClientId)
|
||||
res = await executor.invoke({ input }, { callbacks: [loggerHandler, handler, ...callbacks] })
|
||||
if (res.sourceDocuments) {
|
||||
options.socketIO.to(options.socketIOClientId).emit('sourceDocuments', flatten(res.sourceDocuments))
|
||||
sourceDocuments = res.sourceDocuments
|
||||
}
|
||||
if (res.usedTools) {
|
||||
options.socketIO.to(options.socketIOClientId).emit('usedTools', res.usedTools)
|
||||
usedTools = res.usedTools
|
||||
}
|
||||
} else {
|
||||
res = await executor.invoke({ input }, { callbacks: [loggerHandler, ...callbacks] })
|
||||
if (res.sourceDocuments) {
|
||||
sourceDocuments = res.sourceDocuments
|
||||
}
|
||||
if (res.usedTools) {
|
||||
usedTools = res.usedTools
|
||||
}
|
||||
}
|
||||
|
||||
await memory.addChatMessages(
|
||||
[
|
||||
{
|
||||
text: input,
|
||||
type: 'userMessage'
|
||||
},
|
||||
{
|
||||
text: res?.output,
|
||||
type: 'apiMessage'
|
||||
}
|
||||
],
|
||||
this.sessionId
|
||||
)
|
||||
|
||||
let finalRes = res?.output
|
||||
|
||||
if (sourceDocuments.length || usedTools.length) {
|
||||
finalRes = { text: res?.output }
|
||||
if (sourceDocuments.length) {
|
||||
finalRes.sourceDocuments = flatten(sourceDocuments)
|
||||
}
|
||||
if (usedTools.length) {
|
||||
finalRes.usedTools = usedTools
|
||||
}
|
||||
return finalRes
|
||||
}
|
||||
|
||||
return finalRes
|
||||
}
|
||||
}
|
||||
|
||||
const prepareAgent = (nodeData: INodeData, options: ICommonObject, flowObj: { sessionId?: string; chatId?: string; input?: string }) => {
|
||||
const model = nodeData.inputs?.model as ChatOpenAI
|
||||
const memory = nodeData.inputs?.memory as FlowiseMemory
|
||||
const maxIterations = nodeData.inputs?.maxIterations as string
|
||||
const systemMessage = nodeData.inputs?.systemMessage as string
|
||||
let tools = nodeData.inputs?.tools
|
||||
tools = flatten(tools)
|
||||
const memoryKey = memory.memoryKey ? memory.memoryKey : 'chat_history'
|
||||
const inputKey = memory.inputKey ? memory.inputKey : 'input'
|
||||
const prependMessages = options?.prependMessages
|
||||
|
||||
const prompt = ChatPromptTemplate.fromMessages([
|
||||
['system', systemMessage ? systemMessage : `You are a helpful AI assistant.`],
|
||||
new MessagesPlaceholder(memoryKey),
|
||||
['human', `{${inputKey}}`],
|
||||
new MessagesPlaceholder('agent_scratchpad')
|
||||
])
|
||||
|
||||
const llmWithTools = model.bind({
|
||||
tools: tools.map(convertToOpenAITool)
|
||||
})
|
||||
|
||||
const runnableAgent = RunnableSequence.from([
|
||||
{
|
||||
[inputKey]: (i: { input: string; steps: AgentStep[] }) => i.input,
|
||||
agent_scratchpad: (i: { input: string; steps: AgentStep[] }) => formatAgentSteps(i.steps),
|
||||
[memoryKey]: async (_: { input: string; steps: AgentStep[] }) => {
|
||||
const messages = (await memory.getChatMessages(flowObj?.sessionId, true, prependMessages)) as BaseMessage[]
|
||||
return messages ?? []
|
||||
}
|
||||
},
|
||||
prompt,
|
||||
llmWithTools,
|
||||
new OpenAIToolsAgentOutputParser()
|
||||
])
|
||||
|
||||
const executor = AgentExecutor.fromAgentAndTools({
|
||||
agent: runnableAgent,
|
||||
tools,
|
||||
sessionId: flowObj?.sessionId,
|
||||
chatId: flowObj?.chatId,
|
||||
input: flowObj?.input,
|
||||
verbose: process.env.DEBUG === 'true' ? true : false,
|
||||
maxIterations: maxIterations ? parseFloat(maxIterations) : undefined
|
||||
})
|
||||
|
||||
return executor
|
||||
}
|
||||
|
||||
module.exports = { nodeClass: MistralAIToolAgent_Agents }
|
||||
@@ -1,212 +0,0 @@
|
||||
import { flatten } from 'lodash'
|
||||
import { BaseMessage } from '@langchain/core/messages'
|
||||
import { ChainValues } from '@langchain/core/utils/types'
|
||||
import { AgentStep } from '@langchain/core/agents'
|
||||
import { RunnableSequence } from '@langchain/core/runnables'
|
||||
import { ChatOpenAI, formatToOpenAIFunction } from '@langchain/openai'
|
||||
import { ChatPromptTemplate, MessagesPlaceholder } from '@langchain/core/prompts'
|
||||
import { OpenAIFunctionsAgentOutputParser } from 'langchain/agents/openai/output_parser'
|
||||
import { getBaseClasses } from '../../../src/utils'
|
||||
import { FlowiseMemory, ICommonObject, INode, INodeData, INodeParams, IUsedTool } 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
|
||||
name: string
|
||||
version: number
|
||||
description: string
|
||||
type: string
|
||||
icon: string
|
||||
category: string
|
||||
baseClasses: string[]
|
||||
inputs: INodeParams[]
|
||||
badge?: string
|
||||
sessionId?: string
|
||||
|
||||
constructor(fields?: { sessionId?: string }) {
|
||||
this.label = 'OpenAI Function Agent'
|
||||
this.name = 'openAIFunctionAgent'
|
||||
this.version = 4.0
|
||||
this.type = 'AgentExecutor'
|
||||
this.category = 'Agents'
|
||||
this.icon = 'function.svg'
|
||||
this.description = `An agent that uses OpenAI Function Calling to pick the tool and args to call`
|
||||
this.baseClasses = [this.type, ...getBaseClasses(AgentExecutor)]
|
||||
this.badge = 'DEPRECATING'
|
||||
this.inputs = [
|
||||
{
|
||||
label: 'Allowed Tools',
|
||||
name: 'tools',
|
||||
type: 'Tool',
|
||||
list: true
|
||||
},
|
||||
{
|
||||
label: 'Memory',
|
||||
name: 'memory',
|
||||
type: 'BaseChatMemory'
|
||||
},
|
||||
{
|
||||
label: 'OpenAI/Azure Chat Model',
|
||||
name: 'model',
|
||||
type: 'BaseChatModel'
|
||||
},
|
||||
{
|
||||
label: 'System Message',
|
||||
name: 'systemMessage',
|
||||
type: 'string',
|
||||
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
|
||||
},
|
||||
{
|
||||
label: 'Max Iterations',
|
||||
name: 'maxIterations',
|
||||
type: 'number',
|
||||
optional: true,
|
||||
additionalParams: true
|
||||
}
|
||||
]
|
||||
this.sessionId = fields?.sessionId
|
||||
}
|
||||
|
||||
async init(nodeData: INodeData, input: string, options: ICommonObject): Promise<any> {
|
||||
return prepareAgent(nodeData, options, { sessionId: this.sessionId, chatId: options.chatId, input })
|
||||
}
|
||||
|
||||
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, options, { sessionId: this.sessionId, chatId: options.chatId, input })
|
||||
|
||||
const loggerHandler = new ConsoleCallbackHandler(options.logger)
|
||||
const callbacks = await additionalCallbacks(nodeData, options)
|
||||
|
||||
let res: ChainValues = {}
|
||||
let sourceDocuments: ICommonObject[] = []
|
||||
let usedTools: IUsedTool[] = []
|
||||
|
||||
if (options.socketIO && options.socketIOClientId) {
|
||||
const handler = new CustomChainHandler(options.socketIO, options.socketIOClientId)
|
||||
res = await executor.invoke({ input }, { callbacks: [loggerHandler, handler, ...callbacks] })
|
||||
if (res.sourceDocuments) {
|
||||
options.socketIO.to(options.socketIOClientId).emit('sourceDocuments', flatten(res.sourceDocuments))
|
||||
sourceDocuments = res.sourceDocuments
|
||||
}
|
||||
if (res.usedTools) {
|
||||
options.socketIO.to(options.socketIOClientId).emit('usedTools', res.usedTools)
|
||||
usedTools = res.usedTools
|
||||
}
|
||||
} else {
|
||||
res = await executor.invoke({ input }, { callbacks: [loggerHandler, ...callbacks] })
|
||||
if (res.sourceDocuments) {
|
||||
sourceDocuments = res.sourceDocuments
|
||||
}
|
||||
if (res.usedTools) {
|
||||
usedTools = res.usedTools
|
||||
}
|
||||
}
|
||||
|
||||
await memory.addChatMessages(
|
||||
[
|
||||
{
|
||||
text: input,
|
||||
type: 'userMessage'
|
||||
},
|
||||
{
|
||||
text: res?.output,
|
||||
type: 'apiMessage'
|
||||
}
|
||||
],
|
||||
this.sessionId
|
||||
)
|
||||
|
||||
let finalRes = res?.output
|
||||
|
||||
if (sourceDocuments.length || usedTools.length) {
|
||||
finalRes = { text: res?.output }
|
||||
if (sourceDocuments.length) {
|
||||
finalRes.sourceDocuments = flatten(sourceDocuments)
|
||||
}
|
||||
if (usedTools.length) {
|
||||
finalRes.usedTools = usedTools
|
||||
}
|
||||
return finalRes
|
||||
}
|
||||
|
||||
return finalRes
|
||||
}
|
||||
}
|
||||
|
||||
const prepareAgent = (nodeData: INodeData, options: ICommonObject, flowObj: { sessionId?: string; chatId?: string; input?: string }) => {
|
||||
const model = nodeData.inputs?.model as ChatOpenAI
|
||||
const maxIterations = nodeData.inputs?.maxIterations as string
|
||||
const memory = nodeData.inputs?.memory as FlowiseMemory
|
||||
const systemMessage = nodeData.inputs?.systemMessage as string
|
||||
let tools = nodeData.inputs?.tools
|
||||
tools = flatten(tools)
|
||||
const memoryKey = memory.memoryKey ? memory.memoryKey : 'chat_history'
|
||||
const inputKey = memory.inputKey ? memory.inputKey : 'input'
|
||||
const prependMessages = options?.prependMessages
|
||||
|
||||
const prompt = ChatPromptTemplate.fromMessages([
|
||||
['system', systemMessage ? systemMessage : `You are a helpful AI assistant.`],
|
||||
new MessagesPlaceholder(memoryKey),
|
||||
['human', `{${inputKey}}`],
|
||||
new MessagesPlaceholder('agent_scratchpad')
|
||||
])
|
||||
|
||||
const modelWithFunctions = model.bind({
|
||||
functions: [...tools.map((tool: any) => formatToOpenAIFunction(tool))]
|
||||
})
|
||||
|
||||
const runnableAgent = RunnableSequence.from([
|
||||
{
|
||||
[inputKey]: (i: { input: string; steps: AgentStep[] }) => i.input,
|
||||
agent_scratchpad: (i: { input: string; steps: AgentStep[] }) => formatAgentSteps(i.steps),
|
||||
[memoryKey]: async (_: { input: string; steps: AgentStep[] }) => {
|
||||
const messages = (await memory.getChatMessages(flowObj?.sessionId, true, prependMessages)) as BaseMessage[]
|
||||
return messages ?? []
|
||||
}
|
||||
},
|
||||
prompt,
|
||||
modelWithFunctions,
|
||||
new OpenAIFunctionsAgentOutputParser()
|
||||
])
|
||||
|
||||
const executor = AgentExecutor.fromAgentAndTools({
|
||||
agent: runnableAgent,
|
||||
tools,
|
||||
sessionId: flowObj?.sessionId,
|
||||
chatId: flowObj?.chatId,
|
||||
input: flowObj?.input,
|
||||
verbose: process.env.DEBUG === 'true' ? true : false,
|
||||
maxIterations: maxIterations ? parseFloat(maxIterations) : undefined
|
||||
})
|
||||
|
||||
return executor
|
||||
}
|
||||
|
||||
module.exports = { nodeClass: OpenAIFunctionAgent_Agents }
|
||||
@@ -1,9 +0,0 @@
|
||||
<svg width="32" height="32" viewBox="0 0 32 32" fill="none" xmlns="http://www.w3.org/2000/svg">
|
||||
<path d="M16 12.6108L22 15.9608" stroke="black" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"/>
|
||||
<path d="M7.17701 19.5848C6.49568 20.4069 6.12505 21.4424 6.12993 22.5101C6.13481 23.5779 6.51489 24.6099 7.2037 25.4258C7.89252 26.2416 8.84622 26.7893 9.89802 26.9732C10.9498 27.157 12.0328 26.9653 12.9575 26.4314L15.4787 24.9657M18.6002 14.106V19.5848" stroke="black" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"/>
|
||||
<path d="M8.19877 9.98459C6.39026 9.67775 4.57524 10.4982 3.60403 12.1806C3.00524 13.2178 2.84295 14.4504 3.15284 15.6073C3.46273 16.7642 4.21943 17.7507 5.25652 18.3498L10.3049 21.3269C10.6109 21.5074 10.9898 21.5119 11.3001 21.3388L18.6 17.2655" stroke="black" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"/>
|
||||
<path d="M17.0172 6.06585C16.6456 5.06522 15.9342 4.227 15.0072 3.6977C14.0803 3.1684 12.9969 2.98168 11.9462 3.17018C10.8956 3.35869 9.94464 3.91042 9.25954 4.72895C8.57444 5.54747 8.19879 6.58074 8.19824 7.64814V13.6575C8.19824 14.0154 8.38951 14.346 8.69977 14.5244L15.9992 18.7215" stroke="black" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"/>
|
||||
<path d="M24.8216 11.7476C25.5029 10.9255 25.8735 9.89004 25.8687 8.8223C25.8638 7.75457 25.4837 6.72253 24.7949 5.90667C24.1061 5.09082 23.1524 4.54308 22.1006 4.35924C21.0488 4.17541 19.9658 4.36718 19.0411 4.90101L13.8942 7.90613C13.5872 8.08539 13.3984 8.41418 13.3984 8.76971V17.2265" stroke="black" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"/>
|
||||
<path d="M28.3944 19.0635C28.9932 18.0263 29.1555 16.7937 28.8456 15.6368C28.5357 14.4799 27.779 13.4934 26.7419 12.8943L21.6409 9.91752C21.3316 9.73703 20.9494 9.7357 20.6388 9.91405L13.3984 14.0723" stroke="black" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"/>
|
||||
<path d="M18 28.9997H18.8071C19.6909 28.9997 20.4526 28.3921 20.6297 27.546L21.991 21.4537C22.1681 20.6076 22.9299 20 23.8136 20H24.6207M20.0929 22.7023H23.8136M24 25.0214H24.5014C24.8438 25.0214 25.1586 25.2052 25.3207 25.5L27.3429 28.5213C27.5051 28.8161 27.8198 29 28.1622 29H28.6997M24.049 29C24.6261 29 25.1609 28.7041 25.4578 28.2205L27.2424 25.8009C27.5393 25.3173 28.0741 25.0214 28.6512 25.0214" stroke="black" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"/>
|
||||
</svg>
|
||||
|
Before Width: | Height: | Size: 2.3 KiB |
@@ -1,211 +0,0 @@
|
||||
import { flatten } from 'lodash'
|
||||
import { BaseMessage } from '@langchain/core/messages'
|
||||
import { ChainValues } from '@langchain/core/utils/types'
|
||||
import { RunnableSequence } from '@langchain/core/runnables'
|
||||
import { ChatOpenAI } from '@langchain/openai'
|
||||
import { ChatPromptTemplate, MessagesPlaceholder } from '@langchain/core/prompts'
|
||||
import { convertToOpenAITool } from '@langchain/core/utils/function_calling'
|
||||
import { formatToOpenAIToolMessages } from 'langchain/agents/format_scratchpad/openai_tools'
|
||||
import { OpenAIToolsAgentOutputParser, type ToolsAgentStep } from 'langchain/agents/openai/output_parser'
|
||||
import { getBaseClasses } from '../../../src/utils'
|
||||
import { FlowiseMemory, ICommonObject, INode, INodeData, INodeParams, IUsedTool } from '../../../src/Interface'
|
||||
import { ConsoleCallbackHandler, CustomChainHandler, additionalCallbacks } from '../../../src/handler'
|
||||
import { AgentExecutor } from '../../../src/agents'
|
||||
import { Moderation, checkInputs } from '../../moderation/Moderation'
|
||||
import { formatResponse } from '../../outputparsers/OutputParserHelpers'
|
||||
|
||||
class OpenAIToolAgent_Agents implements INode {
|
||||
label: string
|
||||
name: string
|
||||
version: number
|
||||
description: string
|
||||
type: string
|
||||
icon: string
|
||||
category: string
|
||||
baseClasses: string[]
|
||||
inputs: INodeParams[]
|
||||
sessionId?: string
|
||||
badge?: string
|
||||
|
||||
constructor(fields?: { sessionId?: string }) {
|
||||
this.label = 'OpenAI Tool Agent'
|
||||
this.name = 'openAIToolAgent'
|
||||
this.version = 1.0
|
||||
this.type = 'AgentExecutor'
|
||||
this.category = 'Agents'
|
||||
this.icon = 'function.svg'
|
||||
this.description = `Agent that uses OpenAI Function Calling to pick the tools and args to call`
|
||||
this.baseClasses = [this.type, ...getBaseClasses(AgentExecutor)]
|
||||
this.badge = 'DEPRECATING'
|
||||
this.inputs = [
|
||||
{
|
||||
label: 'Tools',
|
||||
name: 'tools',
|
||||
type: 'Tool',
|
||||
list: true
|
||||
},
|
||||
{
|
||||
label: 'Memory',
|
||||
name: 'memory',
|
||||
type: 'BaseChatMemory'
|
||||
},
|
||||
{
|
||||
label: 'OpenAI/Azure Chat Model',
|
||||
name: 'model',
|
||||
type: 'BaseChatModel'
|
||||
},
|
||||
{
|
||||
label: 'System Message',
|
||||
name: 'systemMessage',
|
||||
type: 'string',
|
||||
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
|
||||
},
|
||||
{
|
||||
label: 'Max Iterations',
|
||||
name: 'maxIterations',
|
||||
type: 'number',
|
||||
optional: true,
|
||||
additionalParams: true
|
||||
}
|
||||
]
|
||||
this.sessionId = fields?.sessionId
|
||||
}
|
||||
|
||||
async init(nodeData: INodeData, input: string, options: ICommonObject): Promise<any> {
|
||||
return prepareAgent(nodeData, options, { sessionId: this.sessionId, chatId: options.chatId, input })
|
||||
}
|
||||
|
||||
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, options, { sessionId: this.sessionId, chatId: options.chatId, input })
|
||||
|
||||
const loggerHandler = new ConsoleCallbackHandler(options.logger)
|
||||
const callbacks = await additionalCallbacks(nodeData, options)
|
||||
|
||||
let res: ChainValues = {}
|
||||
let sourceDocuments: ICommonObject[] = []
|
||||
let usedTools: IUsedTool[] = []
|
||||
|
||||
if (options.socketIO && options.socketIOClientId) {
|
||||
const handler = new CustomChainHandler(options.socketIO, options.socketIOClientId)
|
||||
res = await executor.invoke({ input }, { callbacks: [loggerHandler, handler, ...callbacks] })
|
||||
if (res.sourceDocuments) {
|
||||
options.socketIO.to(options.socketIOClientId).emit('sourceDocuments', flatten(res.sourceDocuments))
|
||||
sourceDocuments = res.sourceDocuments
|
||||
}
|
||||
if (res.usedTools) {
|
||||
options.socketIO.to(options.socketIOClientId).emit('usedTools', res.usedTools)
|
||||
usedTools = res.usedTools
|
||||
}
|
||||
} else {
|
||||
res = await executor.invoke({ input }, { callbacks: [loggerHandler, ...callbacks] })
|
||||
if (res.sourceDocuments) {
|
||||
sourceDocuments = res.sourceDocuments
|
||||
}
|
||||
if (res.usedTools) {
|
||||
usedTools = res.usedTools
|
||||
}
|
||||
}
|
||||
|
||||
await memory.addChatMessages(
|
||||
[
|
||||
{
|
||||
text: input,
|
||||
type: 'userMessage'
|
||||
},
|
||||
{
|
||||
text: res?.output,
|
||||
type: 'apiMessage'
|
||||
}
|
||||
],
|
||||
this.sessionId
|
||||
)
|
||||
|
||||
let finalRes = res?.output
|
||||
|
||||
if (sourceDocuments.length || usedTools.length) {
|
||||
finalRes = { text: res?.output }
|
||||
if (sourceDocuments.length) {
|
||||
finalRes.sourceDocuments = flatten(sourceDocuments)
|
||||
}
|
||||
if (usedTools.length) {
|
||||
finalRes.usedTools = usedTools
|
||||
}
|
||||
return finalRes
|
||||
}
|
||||
|
||||
return finalRes
|
||||
}
|
||||
}
|
||||
|
||||
const prepareAgent = (nodeData: INodeData, options: ICommonObject, flowObj: { sessionId?: string; chatId?: string; input?: string }) => {
|
||||
const model = nodeData.inputs?.model as ChatOpenAI
|
||||
const maxIterations = nodeData.inputs?.maxIterations as string
|
||||
const memory = nodeData.inputs?.memory as FlowiseMemory
|
||||
const systemMessage = nodeData.inputs?.systemMessage as string
|
||||
let tools = nodeData.inputs?.tools
|
||||
tools = flatten(tools)
|
||||
const memoryKey = memory.memoryKey ? memory.memoryKey : 'chat_history'
|
||||
const inputKey = memory.inputKey ? memory.inputKey : 'input'
|
||||
const prependMessages = options?.prependMessages
|
||||
|
||||
const prompt = ChatPromptTemplate.fromMessages([
|
||||
['system', systemMessage ? systemMessage : `You are a helpful AI assistant.`],
|
||||
new MessagesPlaceholder(memoryKey),
|
||||
['human', `{${inputKey}}`],
|
||||
new MessagesPlaceholder('agent_scratchpad')
|
||||
])
|
||||
|
||||
const modelWithTools = model.bind({ tools: tools.map(convertToOpenAITool) })
|
||||
|
||||
const runnableAgent = RunnableSequence.from([
|
||||
{
|
||||
[inputKey]: (i: { input: string; steps: ToolsAgentStep[] }) => i.input,
|
||||
agent_scratchpad: (i: { input: string; steps: ToolsAgentStep[] }) => formatToOpenAIToolMessages(i.steps),
|
||||
[memoryKey]: async (_: { input: string; steps: ToolsAgentStep[] }) => {
|
||||
const messages = (await memory.getChatMessages(flowObj?.sessionId, true, prependMessages)) as BaseMessage[]
|
||||
return messages ?? []
|
||||
}
|
||||
},
|
||||
prompt,
|
||||
modelWithTools,
|
||||
new OpenAIToolsAgentOutputParser()
|
||||
])
|
||||
|
||||
const executor = AgentExecutor.fromAgentAndTools({
|
||||
agent: runnableAgent,
|
||||
tools,
|
||||
sessionId: flowObj?.sessionId,
|
||||
chatId: flowObj?.chatId,
|
||||
input: flowObj?.input,
|
||||
verbose: process.env.DEBUG === 'true' ? true : false,
|
||||
maxIterations: maxIterations ? parseFloat(maxIterations) : undefined
|
||||
})
|
||||
|
||||
return executor
|
||||
}
|
||||
|
||||
module.exports = { nodeClass: OpenAIToolAgent_Agents }
|
||||
@@ -1,9 +0,0 @@
|
||||
<svg width="32" height="32" viewBox="0 0 32 32" fill="none" xmlns="http://www.w3.org/2000/svg">
|
||||
<path d="M16 12.6108L22 15.9608" stroke="black" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"/>
|
||||
<path d="M7.17701 19.5848C6.49568 20.4069 6.12505 21.4424 6.12993 22.5101C6.13481 23.5779 6.51489 24.6099 7.2037 25.4258C7.89252 26.2416 8.84622 26.7893 9.89802 26.9732C10.9498 27.157 12.0328 26.9653 12.9575 26.4314L15.4787 24.9657M18.6002 14.106V19.5848" stroke="black" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"/>
|
||||
<path d="M8.19877 9.98459C6.39026 9.67775 4.57524 10.4982 3.60403 12.1806C3.00524 13.2178 2.84295 14.4504 3.15284 15.6073C3.46273 16.7642 4.21943 17.7507 5.25652 18.3498L10.3049 21.3269C10.6109 21.5074 10.9898 21.5119 11.3001 21.3388L18.6 17.2655" stroke="black" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"/>
|
||||
<path d="M17.0172 6.06585C16.6456 5.06522 15.9342 4.227 15.0072 3.6977C14.0803 3.1684 12.9969 2.98168 11.9462 3.17018C10.8956 3.35869 9.94464 3.91042 9.25954 4.72895C8.57444 5.54747 8.19879 6.58074 8.19824 7.64814V13.6575C8.19824 14.0154 8.38951 14.346 8.69977 14.5244L15.9992 18.7215" stroke="black" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"/>
|
||||
<path d="M24.8216 11.7476C25.5029 10.9255 25.8735 9.89004 25.8687 8.8223C25.8638 7.75457 25.4837 6.72253 24.7949 5.90667C24.1061 5.09082 23.1524 4.54308 22.1006 4.35924C21.0488 4.17541 19.9658 4.36718 19.0411 4.90101L13.8942 7.90613C13.5872 8.08539 13.3984 8.41418 13.3984 8.76971V17.2265" stroke="black" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"/>
|
||||
<path d="M28.3944 19.0635C28.9932 18.0263 29.1555 16.7937 28.8456 15.6368C28.5357 14.4799 27.779 13.4934 26.7419 12.8943L21.6409 9.91752C21.3316 9.73703 20.9494 9.7357 20.6388 9.91405L13.3984 14.0723" stroke="black" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"/>
|
||||
<path d="M18 28.9997H18.8071C19.6909 28.9997 20.4526 28.3921 20.6297 27.546L21.991 21.4537C22.1681 20.6076 22.9299 20 23.8136 20H24.6207M20.0929 22.7023H23.8136M24 25.0214H24.5014C24.8438 25.0214 25.1586 25.2052 25.3207 25.5L27.3429 28.5213C27.5051 28.8161 27.8198 29 28.1622 29H28.6997M24.049 29C24.6261 29 25.1609 28.7041 25.4578 28.2205L27.2424 25.8009C27.5393 25.3173 28.0741 25.0214 28.6512 25.0214" stroke="black" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"/>
|
||||
</svg>
|
||||
|
Before Width: | Height: | Size: 2.3 KiB |
@@ -36,7 +36,6 @@ class ToolAgent_Agents implements INode {
|
||||
this.icon = 'toolAgent.png'
|
||||
this.description = `Agent that uses Function Calling to pick the tools and args to call`
|
||||
this.baseClasses = [this.type, ...getBaseClasses(AgentExecutor)]
|
||||
this.badge = 'NEW'
|
||||
this.inputs = [
|
||||
{
|
||||
label: 'Tools',
|
||||
|
||||
Reference in New Issue
Block a user