599 lines
23 KiB
TypeScript
599 lines
23 KiB
TypeScript
import { uniq } from 'lodash'
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import { DataSource } from 'typeorm'
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import { z } from 'zod'
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import { BaseMessagePromptTemplateLike, ChatPromptTemplate, MessagesPlaceholder } from '@langchain/core/prompts'
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import { RunnableSequence, RunnablePassthrough, RunnableConfig } from '@langchain/core/runnables'
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import { BaseMessage } from '@langchain/core/messages'
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import { BaseChatModel } from '@langchain/core/language_models/chat_models'
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import {
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ConversationHistorySelection,
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ICommonObject,
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IDatabaseEntity,
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INode,
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INodeData,
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INodeOutputsValue,
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INodeParams,
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ISeqAgentNode,
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ISeqAgentsState
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} from '../../../src/Interface'
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import {
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getInputVariables,
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getVars,
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handleEscapeCharacters,
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prepareSandboxVars,
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transformBracesWithColon,
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executeJavaScriptCode,
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createCodeExecutionSandbox
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} from '../../../src/utils'
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import {
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checkCondition,
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convertStructuredSchemaToZod,
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customGet,
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transformObjectPropertyToFunction,
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filterConversationHistory,
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restructureMessages
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} from '../commonUtils'
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interface IConditionGridItem {
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variable: string
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operation: string
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value: string
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output: string
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}
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const examplePrompt = `You are an expert customer support routing system.
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Your job is to detect whether a customer support representative is routing a user to the technical support team, or just responding conversationally.`
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const exampleHumanPrompt = `The previous conversation is an interaction between a customer support representative and a user.
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Extract whether the representative is routing the user to the technical support team, or just responding conversationally.
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If representative want to route the user to the technical support team, respond only with the word "TECHNICAL".
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Otherwise, respond only with the word "CONVERSATION".
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Remember, only respond with one of the above words.`
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const howToUseCode = `
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1. Must return a string value at the end of function. For example:
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\`\`\`js
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if ("X" === "X") {
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return "Agent"; // connect to next agent node
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} else {
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return "End"; // connect to end node
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}
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\`\`\`
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2. In most cases, you would probably get the last message to do some comparison. You can get all current messages from the state: \`$flow.state.messages\`:
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\`\`\`json
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[
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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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\`\`\`
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For example, to get the last message content:
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\`\`\`js
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const messages = $flow.state.messages;
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const lastMessage = messages[messages.length - 1];
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// Proceed to do something with the last message content
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\`\`\`
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3. If you want to use the Condition Agent's output for conditional checks, it is available as \`$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, we can check if the agent's output contains specific keyword:
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\`\`\`js
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const result = $flow.output.content;
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if (result.includes("some-keyword")) {
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return "Agent"; // connect to next agent node
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} else {
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return "End"; // connect to end node
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}
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\`\`\`
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If Structured Output is enabled, \`$flow.output\` will be in the JSON format as defined in the Structured Output configuration:
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\`\`\`json
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{
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"foo": 'var'
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}
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\`\`\`
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4. 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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- \`$flow.state\`
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5. You can get custom variables: \`$vars.<variable-name>\`
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`
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const defaultFunc = `const result = $flow.output.content;
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if (result.includes("some-keyword")) {
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return "Agent";
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}
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return "End";
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`
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const TAB_IDENTIFIER = 'selectedConditionFunctionTab'
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class ConditionAgent_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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credential: INodeParams
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inputs: INodeParams[]
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documentation?: string
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outputs: INodeOutputsValue[]
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constructor() {
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this.label = 'Condition Agent'
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this.name = 'seqConditionAgent'
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this.version = 3.1
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this.type = 'ConditionAgent'
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this.icon = 'condition.svg'
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this.category = 'Sequential Agents'
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this.description = 'Uses an agent to determine which route to take next'
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this.baseClasses = [this.type]
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this.documentation = 'https://docs.flowiseai.com/using-flowise/agentflows/sequential-agents#id-8.-conditional-agent-node'
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this.inputs = [
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{
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label: 'Name',
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name: 'conditionAgentName',
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type: 'string',
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placeholder: 'Condition Agent'
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},
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{
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label: 'Sequential Node',
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name: 'sequentialNode',
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type: 'Start | Agent | LLMNode | ToolNode | CustomFunction | ExecuteFlow',
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description:
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'Can be connected to one of the following nodes: Start, Agent, LLM Node, Tool Node, Custom Function, Execute Flow',
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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 agent`
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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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default: examplePrompt,
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additionalParams: true,
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optional: true
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},
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{
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label: 'Conversation History',
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name: 'conversationHistorySelection',
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type: 'options',
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options: [
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{
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label: 'User Question',
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name: 'user_question',
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description: 'Use the user question from the historical conversation messages as input.'
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},
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{
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label: 'Last Conversation Message',
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name: 'last_message',
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description: 'Use the last conversation message from the historical conversation messages as input.'
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},
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{
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label: 'All Conversation Messages',
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name: 'all_messages',
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description: 'Use all conversation messages from the historical conversation messages as input.'
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},
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{
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label: 'Empty',
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name: 'empty',
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description:
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'Do not use any messages from the conversation history. ' +
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'Ensure to use either System Prompt, Human Prompt, or Messages History.'
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}
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],
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default: 'all_messages',
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optional: true,
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description:
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'Select which messages from the conversation history to include in the prompt. ' +
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'The selected messages will be inserted between the System Prompt (if defined) and ' +
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'Human Prompt.',
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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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default: exampleHumanPrompt,
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additionalParams: true,
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optional: true
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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: 'conditionAgentStructuredOutput',
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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: 'Condition',
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name: 'condition',
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type: 'conditionFunction', // This is a custom type to show as button on the UI and render anchor points when saved
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tabIdentifier: TAB_IDENTIFIER,
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tabs: [
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{
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label: 'Condition (Table)',
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name: 'conditionUI',
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type: 'datagrid',
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description: 'If a condition is met, the node connected to the respective output will be executed',
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optional: true,
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datagrid: [
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{
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field: 'variable',
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headerName: 'Variable',
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type: 'freeSolo',
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editable: true,
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loadMethod: ['getPreviousMessages', 'loadStateKeys'],
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valueOptions: [
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{
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label: 'Agent Output (string)',
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value: '$flow.output.content'
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},
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{
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label: `Agent's JSON Key Output (string)`,
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value: '$flow.output.<replace-with-key>'
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},
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{
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label: 'Total Messages (number)',
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value: '$flow.state.messages.length'
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},
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{
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label: 'First Message Content (string)',
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value: '$flow.state.messages[0].content'
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},
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{
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label: 'Last Message Content (string)',
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value: '$flow.state.messages[-1].content'
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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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flex: 0.5,
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minWidth: 200
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},
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{
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field: 'operation',
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headerName: 'Operation',
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type: 'singleSelect',
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valueOptions: [
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'Contains',
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'Not Contains',
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'Start With',
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'End With',
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'Is',
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'Is Not',
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'Is Empty',
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'Is Not Empty',
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'Greater Than',
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'Less Than',
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'Equal To',
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'Not Equal To',
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'Greater Than or Equal To',
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'Less Than or Equal To'
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],
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editable: true,
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flex: 0.4,
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minWidth: 150
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},
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{
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field: 'value',
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headerName: 'Value',
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flex: 1,
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editable: true
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},
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{
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field: 'output',
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headerName: 'Output Name',
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editable: true,
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flex: 0.3,
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minWidth: 150
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}
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]
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},
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{
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label: 'Condition (Code)',
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name: 'conditionFunction',
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type: 'code',
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description: 'Function to evaluate the condition',
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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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hideCodeExecute: true,
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codeExample: defaultFunc,
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optional: true
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}
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]
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}
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]
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this.outputs = [
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{
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label: 'Next',
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name: 'next',
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baseClasses: ['Condition'],
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isAnchor: true
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},
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{
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label: 'End',
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name: 'end',
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baseClasses: ['Condition'],
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isAnchor: true
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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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const conditionLabel = nodeData.inputs?.conditionAgentName as string
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const conditionName = conditionLabel.toLowerCase().replace(/\s/g, '_').trim()
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const output = nodeData.outputs?.output as string
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const sequentialNodes = nodeData.inputs?.sequentialNode as ISeqAgentNode[]
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let agentPrompt = nodeData.inputs?.systemMessagePrompt as string
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agentPrompt = transformBracesWithColon(agentPrompt)
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let humanPrompt = nodeData.inputs?.humanMessagePrompt as string
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humanPrompt = transformBracesWithColon(humanPrompt)
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const promptValuesStr = nodeData.inputs?.promptValues
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const conditionAgentStructuredOutput = nodeData.inputs?.conditionAgentStructuredOutput
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const model = nodeData.inputs?.model as BaseChatModel
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if (!sequentialNodes || !sequentialNodes.length) throw new Error('Condition Agent must have a predecessor!')
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const startLLM = sequentialNodes[0].startLLM
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const llm = model || startLLM
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if (nodeData.inputs) nodeData.inputs.model = llm
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let conditionAgentInputVariablesValues: ICommonObject = {}
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if (promptValuesStr) {
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try {
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conditionAgentInputVariablesValues = typeof promptValuesStr === 'object' ? promptValuesStr : JSON.parse(promptValuesStr)
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} catch (exception) {
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throw new Error("Invalid JSON in the Condition Agent's Prompt Input Values: " + exception)
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}
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}
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conditionAgentInputVariablesValues = handleEscapeCharacters(conditionAgentInputVariablesValues, true)
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const conditionAgentInputVariables = uniq([...getInputVariables(agentPrompt), ...getInputVariables(humanPrompt)])
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if (!conditionAgentInputVariables.every((element) => Object.keys(conditionAgentInputVariablesValues).includes(element))) {
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throw new Error('Condition Agent input variables values are not provided!')
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}
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const abortControllerSignal = options.signal as AbortController
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const conditionalEdge = async (state: ISeqAgentsState, config: RunnableConfig) =>
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await runCondition(
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conditionName,
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nodeData,
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input,
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options,
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state,
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config,
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llm,
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agentPrompt,
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humanPrompt,
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conditionAgentInputVariablesValues,
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conditionAgentStructuredOutput,
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abortControllerSignal
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)
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const returnOutput: ISeqAgentNode = {
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id: nodeData.id,
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node: conditionalEdge,
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name: conditionName,
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label: conditionLabel,
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type: 'condition',
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output,
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llm,
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startLLM,
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multiModalMessageContent: sequentialNodes[0]?.multiModalMessageContent,
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predecessorAgents: sequentialNodes
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}
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return returnOutput
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}
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}
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const runCondition = async (
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conditionName: string,
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nodeData: INodeData,
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input: string,
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options: ICommonObject,
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state: ISeqAgentsState,
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config: RunnableConfig,
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llm: BaseChatModel,
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agentPrompt: string,
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humanPrompt: string,
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conditionAgentInputVariablesValues: ICommonObject,
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conditionAgentStructuredOutput: string,
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abortControllerSignal: AbortController
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) => {
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const appDataSource = options.appDataSource as DataSource
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const databaseEntities = options.databaseEntities as IDatabaseEntity
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const tabIdentifier = nodeData.inputs?.[`${TAB_IDENTIFIER}_${nodeData.id}`] as string
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const conditionUI = nodeData.inputs?.conditionUI as string
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const conditionFunction = nodeData.inputs?.conditionFunction as string
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const selectedTab = tabIdentifier ? tabIdentifier.split(`_${nodeData.id}`)[0] : 'conditionUI'
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const promptArrays = [new MessagesPlaceholder('messages')] as BaseMessagePromptTemplateLike[]
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if (agentPrompt) promptArrays.unshift(['system', agentPrompt])
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if (humanPrompt) promptArrays.push(['human', humanPrompt])
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const prompt = ChatPromptTemplate.fromMessages(promptArrays)
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let model
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if (conditionAgentStructuredOutput && conditionAgentStructuredOutput !== '[]') {
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try {
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const structuredOutput = z.object(convertStructuredSchemaToZod(conditionAgentStructuredOutput))
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// @ts-ignore
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model = llm.withStructuredOutput(structuredOutput)
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} catch (exception) {
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console.error('Invalid JSON in Condition Agent Structured Output: ' + exception)
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model = llm
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}
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} else {
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model = llm
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}
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let chain
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if (!conditionAgentInputVariablesValues || !Object.keys(conditionAgentInputVariablesValues).length) {
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chain = RunnableSequence.from([prompt, model]).withConfig({
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metadata: { sequentialNodeName: conditionName }
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})
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} else {
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chain = RunnableSequence.from([
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RunnablePassthrough.assign(transformObjectPropertyToFunction(conditionAgentInputVariablesValues, state)),
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prompt,
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model
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]).withConfig({
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metadata: { sequentialNodeName: conditionName }
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})
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}
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const historySelection = (nodeData.inputs?.conversationHistorySelection || 'all_messages') as ConversationHistorySelection
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// @ts-ignore
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state.messages = filterConversationHistory(historySelection, input, state)
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// @ts-ignore
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state.messages = restructureMessages(model, state)
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let result = await chain.invoke({ ...state, signal: abortControllerSignal?.signal }, config)
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result.additional_kwargs = { ...result.additional_kwargs, nodeId: nodeData.id }
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if (conditionAgentStructuredOutput && conditionAgentStructuredOutput !== '[]' && result.tool_calls && result.tool_calls.length) {
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let jsonResult = {}
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for (const toolCall of result.tool_calls) {
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jsonResult = { ...jsonResult, ...toolCall.args }
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}
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result = { ...jsonResult, additional_kwargs: { nodeId: nodeData.id } }
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}
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const variables = await getVars(appDataSource, databaseEntities, nodeData, options)
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const flow = {
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chatflowId: options.chatflowid,
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sessionId: options.sessionId,
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chatId: options.chatId,
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input,
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state,
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output: result,
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vars: prepareSandboxVars(variables)
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}
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if (selectedTab === 'conditionFunction' && conditionFunction) {
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const sandbox = createCodeExecutionSandbox(input, variables, flow)
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try {
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const response = await executeJavaScriptCode(conditionFunction, sandbox)
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if (typeof response !== 'string') throw new Error('Condition function must return a string')
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return response
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} catch (e) {
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throw new Error(e)
|
|
}
|
|
} else if (selectedTab === 'conditionUI' && conditionUI) {
|
|
try {
|
|
const conditionItems: IConditionGridItem[] = typeof conditionUI === 'string' ? JSON.parse(conditionUI) : conditionUI
|
|
|
|
for (const item of conditionItems) {
|
|
if (!item.variable) throw new Error('Condition variable is required!')
|
|
|
|
if (item.variable.startsWith('$flow')) {
|
|
const variableValue = customGet(flow, item.variable.replace('$flow.', ''))
|
|
if (checkCondition(variableValue, item.operation, item.value)) {
|
|
return item.output
|
|
}
|
|
} else if (item.variable.startsWith('$vars')) {
|
|
const variableValue = customGet(flow, item.variable.replace('$', ''))
|
|
if (checkCondition(variableValue, item.operation, item.value)) {
|
|
return item.output
|
|
}
|
|
} else if (item.variable.startsWith('$')) {
|
|
const nodeId = item.variable.replace('$', '')
|
|
|
|
const messageOutputs = ((state.messages as unknown as BaseMessage[]) ?? []).filter(
|
|
(message) => message.additional_kwargs && message.additional_kwargs?.nodeId === nodeId
|
|
)
|
|
const messageOutput = messageOutputs[messageOutputs.length - 1]
|
|
|
|
if (messageOutput) {
|
|
if (checkCondition(messageOutput.content as string, item.operation, item.value)) {
|
|
return item.output
|
|
}
|
|
}
|
|
}
|
|
}
|
|
return 'End'
|
|
} catch (exception) {
|
|
throw new Error('Invalid Condition: ' + exception)
|
|
}
|
|
}
|
|
}
|
|
|
|
module.exports = { nodeClass: ConditionAgent_SeqAgents }
|