mirror of
https://github.com/farcasclaudiu/Flowise.git
synced 2026-06-22 09:01:09 +03:00
Feature/seq agents (#2798)
* update build functions * sequential agents * update langchain to 0.2, added sequential agent nodes * add marketplace templates * update howto wordings * Merge branch 'main' into feature/Seq-Agents # Conflicts: # pnpm-lock.yaml * update deprecated functions and add new sequential nodes * add marketplace templates * update marketplace templates, add structured output to llm node * add multi agents template * update llm node with bindmodels * update cypress version * update templates sticky note wordings * update tool node to include human in loop action * update structured outputs error from models * update cohere package to resolve google genai pipeThrough bug * update mistral package version, added message reconstruction before invoke seq agent * add HITL to agent * update state messages restructuring * update load and split methods for s3 directory
This commit is contained in:
Henry Heng
@@ -0,0 +1,605 @@
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import { flatten, uniq } from 'lodash'
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import { DataSource } from 'typeorm'
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import { z } from 'zod'
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import { RunnableSequence, RunnablePassthrough, RunnableConfig } from '@langchain/core/runnables'
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import { ChatPromptTemplate, MessagesPlaceholder, HumanMessagePromptTemplate, BaseMessagePromptTemplateLike } from '@langchain/core/prompts'
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import { BaseChatModel } from '@langchain/core/language_models/chat_models'
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import { AIMessage, AIMessageChunk } from '@langchain/core/messages'
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import {
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INode,
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INodeData,
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INodeParams,
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ISeqAgentsState,
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ICommonObject,
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MessageContentImageUrl,
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INodeOutputsValue,
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ISeqAgentNode,
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IDatabaseEntity
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} from '../../../src/Interface'
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import { AgentExecutor } from '../../../src/agents'
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import { getInputVariables, getVars, handleEscapeCharacters, prepareSandboxVars } from '../../../src/utils'
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import {
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ExtractTool,
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convertStructuredSchemaToZod,
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customGet,
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getVM,
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processImageMessage,
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transformObjectPropertyToFunction,
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restructureMessages
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} from '../commonUtils'
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import { ChatGoogleGenerativeAI } from '../../chatmodels/ChatGoogleGenerativeAI/FlowiseChatGoogleGenerativeAI'
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const TAB_IDENTIFIER = 'selectedUpdateStateMemoryTab'
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const customOutputFuncDesc = `This is only applicable when you have a custom State at the START node. After agent execution, you might want to update the State values`
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const howToUseCode = `
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1. Return the key value JSON object. For example: if you have the following State:
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\`\`\`json
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{
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"user": null
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}
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\`\`\`
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You can update the "user" value by returning the following:
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\`\`\`js
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return {
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"user": "john doe"
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}
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\`\`\`
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2. If you want to use the LLM Node's output as the value to update state, it is available as \`$flow.output\` with the following structure:
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\`\`\`json
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{
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"content": 'Hello! How can I assist you today?',
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"name": "",
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"additional_kwargs": {},
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"response_metadata": {},
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"tool_calls": [],
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"invalid_tool_calls": [],
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"usage_metadata": {}
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}
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\`\`\`
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For example, if the output \`content\` is the value you want to update the state with, you can return the following:
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\`\`\`js
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return {
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"user": $flow.output.content
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}
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\`\`\`
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3. You can also get default flow config, including the current "state":
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- \`$flow.sessionId\`
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- \`$flow.chatId\`
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- \`$flow.chatflowId\`
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- \`$flow.input\`
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- \`$flow.state\`
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4. You can get custom variables: \`$vars.<variable-name>\`
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`
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const howToUse = `
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1. Key and value pair to be updated. For example: if you have the following State:
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| Key | Operation | Default Value |
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|-----------|---------------|-------------------|
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| user | Replace | |
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You can update the "user" value with the following:
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| Key | Value |
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|-----------|-----------|
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| user | john doe |
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2. If you want to use the agent's output as the value to update state, it is available as available as \`$flow.output\` with the following structure:
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\`\`\`json
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{
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"content": 'Hello! How can I assist you today?',
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"name": "",
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"additional_kwargs": {},
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"response_metadata": {},
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"tool_calls": [],
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"invalid_tool_calls": [],
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"usage_metadata": {}
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}
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\`\`\`
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For example, if the output \`content\` is the value you want to update the state with, you can do the following:
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| Key | Value |
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|-----------|---------------------------|
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| user | \`$flow.output.content\` |
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3. You can get default flow config, including the current "state":
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- \`$flow.sessionId\`
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- \`$flow.chatId\`
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- \`$flow.chatflowId\`
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- \`$flow.input\`
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- \`$flow.state\`
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4. You can get custom variables: \`$vars.<variable-name>\`
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`
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const defaultFunc = `const result = $flow.output;
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/* Suppose we have a custom State schema like this:
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* {
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aggregate: {
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value: (x, y) => x.concat(y),
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default: () => []
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}
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}
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*/
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return {
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aggregate: [result.content]
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};`
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class LLMNode_SeqAgents implements INode {
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label: string
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name: string
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version: number
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description: string
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type: string
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icon: string
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category: string
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baseClasses: string[]
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inputs?: INodeParams[]
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badge?: string
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outputs: INodeOutputsValue[]
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constructor() {
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this.label = 'LLM Node'
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this.name = 'seqLLMNode'
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this.version = 1.0
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this.type = 'LLMNode'
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this.icon = 'llmNode.svg'
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this.category = 'Sequential Agents'
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this.description = 'Run Chat Model and return the output'
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this.baseClasses = [this.type]
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this.inputs = [
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{
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label: 'Name',
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name: 'llmNodeName',
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type: 'string',
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placeholder: 'LLM'
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},
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{
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label: 'System Prompt',
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name: 'systemMessagePrompt',
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type: 'string',
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rows: 4,
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optional: true,
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additionalParams: true
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},
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{
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label: 'Human Prompt',
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name: 'humanMessagePrompt',
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type: 'string',
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description: 'This prompt will be added at the end of the messages as human message',
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rows: 4,
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optional: true,
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additionalParams: true
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},
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{
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label: 'Start | Agent | LLM | Tool Node',
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name: 'sequentialNode',
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type: 'Start | Agent | LLMNode | ToolNode',
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list: true
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},
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{
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label: 'Chat Model',
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name: 'model',
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type: 'BaseChatModel',
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optional: true,
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description: `Overwrite model to be used for this node`
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},
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{
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label: 'Format Prompt Values',
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name: 'promptValues',
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description: 'Assign values to the prompt variables. You can also use $flow.state.<variable-name> to get the state value',
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type: 'json',
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optional: true,
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acceptVariable: true,
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list: true,
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additionalParams: true
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},
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{
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label: 'JSON Structured Output',
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name: 'llmStructuredOutput',
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type: 'datagrid',
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description: 'Instruct the LLM to give output in a JSON structured schema',
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datagrid: [
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{ field: 'key', headerName: 'Key', editable: true },
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{
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field: 'type',
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headerName: 'Type',
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type: 'singleSelect',
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valueOptions: ['String', 'String Array', 'Number', 'Boolean', 'Enum'],
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editable: true
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},
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{ field: 'enumValues', headerName: 'Enum Values', editable: true },
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{ field: 'description', headerName: 'Description', flex: 1, editable: true }
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],
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optional: true,
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additionalParams: true
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},
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{
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label: 'Update State',
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name: 'updateStateMemory',
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type: 'tabs',
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tabIdentifier: TAB_IDENTIFIER,
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default: 'updateStateMemoryUI',
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additionalParams: true,
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tabs: [
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{
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label: 'Update State (Table)',
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name: 'updateStateMemoryUI',
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type: 'datagrid',
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hint: {
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label: 'How to use',
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value: howToUse
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},
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description: customOutputFuncDesc,
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datagrid: [
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{
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field: 'key',
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headerName: 'Key',
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type: 'asyncSingleSelect',
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loadMethod: 'loadStateKeys',
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flex: 0.5,
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editable: true
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},
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{
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field: 'value',
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headerName: 'Value',
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type: 'freeSolo',
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valueOptions: [
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{
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label: 'LLM Node Output (string)',
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value: '$flow.output.content'
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},
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{
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label: `LLM JSON Output Key (string)`,
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value: '$flow.output.<replace-with-key>'
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},
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{
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label: `Global variable (string)`,
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value: '$vars.<variable-name>'
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},
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{
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label: 'Input Question (string)',
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value: '$flow.input'
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},
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{
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label: 'Session Id (string)',
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value: '$flow.sessionId'
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},
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{
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label: 'Chat Id (string)',
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value: '$flow.chatId'
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},
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{
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label: 'Chatflow Id (string)',
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value: '$flow.chatflowId'
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}
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],
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editable: true,
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flex: 1
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}
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],
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optional: true,
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additionalParams: true
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},
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{
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label: 'Update State (Code)',
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name: 'updateStateMemoryCode',
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type: 'code',
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hint: {
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label: 'How to use',
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value: howToUseCode
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},
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description: `${customOutputFuncDesc}. Must return an object representing the state`,
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hideCodeExecute: true,
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codeExample: defaultFunc,
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optional: true,
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additionalParams: true
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}
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]
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}
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]
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}
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async init(nodeData: INodeData, input: string, options: ICommonObject): Promise<any> {
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// Tools can be connected through ToolNodes
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let tools = nodeData.inputs?.tools
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tools = flatten(tools)
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let systemPrompt = nodeData.inputs?.systemMessagePrompt as string
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let humanPrompt = nodeData.inputs?.humanMessagePrompt as string
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const llmNodeLabel = nodeData.inputs?.llmNodeName as string
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const sequentialNodes = nodeData.inputs?.sequentialNode as ISeqAgentNode[]
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const model = nodeData.inputs?.model as BaseChatModel
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const promptValuesStr = nodeData.inputs?.promptValues
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const output = nodeData.outputs?.output as string
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const llmStructuredOutput = nodeData.inputs?.llmStructuredOutput
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if (!llmNodeLabel) throw new Error('LLM Node name is required!')
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const llmNodeName = llmNodeLabel.toLowerCase().replace(/\s/g, '_').trim()
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||||
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if (!sequentialNodes || !sequentialNodes.length) throw new Error('Agent must have a predecessor!')
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||||
|
||||
let llmNodeInputVariablesValues: ICommonObject = {}
|
||||
if (promptValuesStr) {
|
||||
try {
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llmNodeInputVariablesValues = typeof promptValuesStr === 'object' ? promptValuesStr : JSON.parse(promptValuesStr)
|
||||
} catch (exception) {
|
||||
throw new Error("Invalid JSON in the LLM Node's Prompt Input Values: " + exception)
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||||
}
|
||||
}
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llmNodeInputVariablesValues = handleEscapeCharacters(llmNodeInputVariablesValues, true)
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||||
|
||||
const startLLM = sequentialNodes[0].startLLM
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const llm = model || startLLM
|
||||
if (nodeData.inputs) nodeData.inputs.model = llm
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||||
|
||||
const multiModalMessageContent = sequentialNodes[0]?.multiModalMessageContent || (await processImageMessage(llm, nodeData, options))
|
||||
const abortControllerSignal = options.signal as AbortController
|
||||
const llmNodeInputVariables = uniq([...getInputVariables(systemPrompt), ...getInputVariables(humanPrompt)])
|
||||
|
||||
if (!llmNodeInputVariables.every((element) => Object.keys(llmNodeInputVariablesValues).includes(element))) {
|
||||
throw new Error('LLM Node input variables values are not provided!')
|
||||
}
|
||||
|
||||
const workerNode = async (state: ISeqAgentsState, config: RunnableConfig) => {
|
||||
const bindModel = config.configurable?.bindModel?.[nodeData.id]
|
||||
return await agentNode(
|
||||
{
|
||||
state,
|
||||
llm,
|
||||
agent: await createAgent(
|
||||
llmNodeName,
|
||||
state,
|
||||
bindModel || llm,
|
||||
[...tools],
|
||||
systemPrompt,
|
||||
humanPrompt,
|
||||
multiModalMessageContent,
|
||||
llmNodeInputVariablesValues,
|
||||
llmStructuredOutput
|
||||
),
|
||||
name: llmNodeName,
|
||||
abortControllerSignal,
|
||||
nodeData,
|
||||
input,
|
||||
options
|
||||
},
|
||||
config
|
||||
)
|
||||
}
|
||||
|
||||
const returnOutput: ISeqAgentNode = {
|
||||
id: nodeData.id,
|
||||
node: workerNode,
|
||||
name: llmNodeName,
|
||||
label: llmNodeLabel,
|
||||
type: 'llm',
|
||||
llm,
|
||||
startLLM,
|
||||
output,
|
||||
predecessorAgents: sequentialNodes,
|
||||
multiModalMessageContent,
|
||||
moderations: sequentialNodes[0]?.moderations
|
||||
}
|
||||
|
||||
return returnOutput
|
||||
}
|
||||
}
|
||||
|
||||
async function createAgent(
|
||||
llmNodeName: string,
|
||||
state: ISeqAgentsState,
|
||||
llm: BaseChatModel,
|
||||
tools: any[],
|
||||
systemPrompt: string,
|
||||
humanPrompt: string,
|
||||
multiModalMessageContent: MessageContentImageUrl[],
|
||||
llmNodeInputVariablesValues: ICommonObject,
|
||||
llmStructuredOutput: string
|
||||
): Promise<AgentExecutor | RunnableSequence> {
|
||||
if (tools.length) {
|
||||
if (llm.bindTools === undefined) {
|
||||
throw new Error(`LLM Node only compatible with function calling models.`)
|
||||
}
|
||||
// @ts-ignore
|
||||
llm = llm.bindTools(tools)
|
||||
}
|
||||
|
||||
if (llmStructuredOutput && llmStructuredOutput !== '[]') {
|
||||
try {
|
||||
const structuredOutput = z.object(convertStructuredSchemaToZod(llmStructuredOutput))
|
||||
|
||||
if (llm instanceof ChatGoogleGenerativeAI) {
|
||||
const tool = new ExtractTool({
|
||||
schema: structuredOutput
|
||||
})
|
||||
// @ts-ignore
|
||||
const modelWithTool = llm.bind({
|
||||
tools: [tool]
|
||||
}) as any
|
||||
llm = modelWithTool
|
||||
} else {
|
||||
// @ts-ignore
|
||||
llm = llm.withStructuredOutput(structuredOutput)
|
||||
}
|
||||
} catch (exception) {
|
||||
console.error(exception)
|
||||
}
|
||||
}
|
||||
|
||||
const promptArrays = [new MessagesPlaceholder('messages')] as BaseMessagePromptTemplateLike[]
|
||||
if (systemPrompt) promptArrays.unshift(['system', systemPrompt])
|
||||
if (humanPrompt) promptArrays.push(['human', humanPrompt])
|
||||
|
||||
const prompt = ChatPromptTemplate.fromMessages(promptArrays)
|
||||
if (multiModalMessageContent.length) {
|
||||
const msg = HumanMessagePromptTemplate.fromTemplate([...multiModalMessageContent])
|
||||
prompt.promptMessages.splice(1, 0, msg)
|
||||
}
|
||||
|
||||
let chain
|
||||
|
||||
if (!llmNodeInputVariablesValues || !Object.keys(llmNodeInputVariablesValues).length) {
|
||||
chain = RunnableSequence.from([prompt, llm]).withConfig({
|
||||
metadata: { sequentialNodeName: llmNodeName }
|
||||
})
|
||||
} else {
|
||||
chain = RunnableSequence.from([
|
||||
RunnablePassthrough.assign(transformObjectPropertyToFunction(llmNodeInputVariablesValues, state)),
|
||||
prompt,
|
||||
llm
|
||||
]).withConfig({
|
||||
metadata: { sequentialNodeName: llmNodeName }
|
||||
})
|
||||
}
|
||||
|
||||
// @ts-ignore
|
||||
return chain
|
||||
}
|
||||
|
||||
async function agentNode(
|
||||
{
|
||||
state,
|
||||
llm,
|
||||
agent,
|
||||
name,
|
||||
abortControllerSignal,
|
||||
nodeData,
|
||||
input,
|
||||
options
|
||||
}: {
|
||||
state: ISeqAgentsState
|
||||
llm: BaseChatModel
|
||||
agent: AgentExecutor | RunnableSequence
|
||||
name: string
|
||||
abortControllerSignal: AbortController
|
||||
nodeData: INodeData
|
||||
input: string
|
||||
options: ICommonObject
|
||||
},
|
||||
config: RunnableConfig
|
||||
) {
|
||||
try {
|
||||
if (abortControllerSignal.signal.aborted) {
|
||||
throw new Error('Aborted!')
|
||||
}
|
||||
|
||||
// @ts-ignore
|
||||
state.messages = restructureMessages(llm, state)
|
||||
|
||||
let result: AIMessageChunk | ICommonObject = await agent.invoke({ ...state, signal: abortControllerSignal.signal }, config)
|
||||
|
||||
const llmStructuredOutput = nodeData.inputs?.llmStructuredOutput
|
||||
if (llmStructuredOutput && llmStructuredOutput !== '[]' && result.tool_calls && result.tool_calls.length) {
|
||||
let jsonResult = {}
|
||||
for (const toolCall of result.tool_calls) {
|
||||
jsonResult = { ...jsonResult, ...toolCall.args }
|
||||
}
|
||||
result = { ...jsonResult, additional_kwargs: { nodeId: nodeData.id } }
|
||||
}
|
||||
|
||||
if (nodeData.inputs?.updateStateMemoryUI || nodeData.inputs?.updateStateMemoryCode) {
|
||||
const returnedOutput = await getReturnOutput(nodeData, input, options, result, state)
|
||||
|
||||
if (nodeData.inputs?.llmStructuredOutput && nodeData.inputs.llmStructuredOutput !== '[]') {
|
||||
const messages = [
|
||||
new AIMessage({
|
||||
content: typeof result === 'object' ? JSON.stringify(result) : result,
|
||||
name,
|
||||
additional_kwargs: { nodeId: nodeData.id }
|
||||
})
|
||||
]
|
||||
return {
|
||||
...returnedOutput,
|
||||
messages
|
||||
}
|
||||
} else {
|
||||
result.name = name
|
||||
result.additional_kwargs = { ...result.additional_kwargs, nodeId: nodeData.id }
|
||||
return {
|
||||
...returnedOutput,
|
||||
messages: [result]
|
||||
}
|
||||
}
|
||||
} else {
|
||||
if (nodeData.inputs?.llmStructuredOutput && nodeData.inputs.llmStructuredOutput !== '[]') {
|
||||
const messages = [
|
||||
new AIMessage({
|
||||
content: typeof result === 'object' ? JSON.stringify(result) : result,
|
||||
name,
|
||||
additional_kwargs: { nodeId: nodeData.id }
|
||||
})
|
||||
]
|
||||
return {
|
||||
messages
|
||||
}
|
||||
} else {
|
||||
result.name = name
|
||||
result.additional_kwargs = { ...result.additional_kwargs, nodeId: nodeData.id }
|
||||
return {
|
||||
messages: [result]
|
||||
}
|
||||
}
|
||||
}
|
||||
} catch (error) {
|
||||
throw new Error(error)
|
||||
}
|
||||
}
|
||||
|
||||
const getReturnOutput = async (nodeData: INodeData, input: string, options: ICommonObject, output: any, state: ISeqAgentsState) => {
|
||||
const appDataSource = options.appDataSource as DataSource
|
||||
const databaseEntities = options.databaseEntities as IDatabaseEntity
|
||||
const tabIdentifier = nodeData.inputs?.[`${TAB_IDENTIFIER}_${nodeData.id}`] as string
|
||||
const updateStateMemoryUI = nodeData.inputs?.updateStateMemoryUI as string
|
||||
const updateStateMemoryCode = nodeData.inputs?.updateStateMemoryCode as string
|
||||
|
||||
const selectedTab = tabIdentifier ? tabIdentifier.split(`_${nodeData.id}`)[0] : 'updateStateMemoryUI'
|
||||
const variables = await getVars(appDataSource, databaseEntities, nodeData)
|
||||
|
||||
const flow = {
|
||||
chatflowId: options.chatflowid,
|
||||
sessionId: options.sessionId,
|
||||
chatId: options.chatId,
|
||||
input,
|
||||
output,
|
||||
state,
|
||||
vars: prepareSandboxVars(variables)
|
||||
}
|
||||
|
||||
if (selectedTab === 'updateStateMemoryUI' && updateStateMemoryUI) {
|
||||
try {
|
||||
const parsedSchema = typeof updateStateMemoryUI === 'string' ? JSON.parse(updateStateMemoryUI) : updateStateMemoryUI
|
||||
const obj: ICommonObject = {}
|
||||
for (const sch of parsedSchema) {
|
||||
const key = sch.key
|
||||
if (!key) throw new Error(`Key is required`)
|
||||
let value = sch.value as string
|
||||
if (value.startsWith('$flow')) {
|
||||
value = customGet(flow, sch.value.replace('$flow.', ''))
|
||||
} else if (value.startsWith('$vars')) {
|
||||
value = customGet(flow, sch.value.replace('$', ''))
|
||||
}
|
||||
obj[key] = value
|
||||
}
|
||||
return obj
|
||||
} catch (e) {
|
||||
throw new Error(e)
|
||||
}
|
||||
} else if (selectedTab === 'updateStateMemoryCode' && updateStateMemoryCode) {
|
||||
const vm = await getVM(appDataSource, databaseEntities, nodeData, flow)
|
||||
try {
|
||||
const response = await vm.run(`module.exports = async function() {${updateStateMemoryCode}}()`, __dirname)
|
||||
if (typeof response !== 'object') throw new Error('Return output must be an object')
|
||||
return response
|
||||
} catch (e) {
|
||||
throw new Error(e)
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
module.exports = { nodeClass: LLMNode_SeqAgents }
|
||||
Reference in New Issue
Block a user