mirror of
https://github.com/farcasclaudiu/Flowise.git
synced 2026-06-28 15:00:57 +03:00
add claude 3 and xml agent
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import { flatten } from 'lodash'
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import { ChainValues } from '@langchain/core/utils/types'
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import { AgentStep } from '@langchain/core/agents'
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import { RunnableSequence } from '@langchain/core/runnables'
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import { ChatOpenAI } from '@langchain/openai'
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import { Tool } from '@langchain/core/tools'
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import { ChatPromptTemplate, HumanMessagePromptTemplate, MessagesPlaceholder } from '@langchain/core/prompts'
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import { XMLAgentOutputParser } from 'langchain/agents/xml/output_parser'
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import { formatLogToMessage } from 'langchain/agents/format_scratchpad/log_to_message'
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import { getBaseClasses } from '../../../src/utils'
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import { FlowiseMemory, ICommonObject, IMessage, INode, INodeData, INodeParams } from '../../../src/Interface'
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import { ConsoleCallbackHandler, CustomChainHandler, additionalCallbacks } from '../../../src/handler'
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import { AgentExecutor } from '../../../src/agents'
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//import { AgentExecutor } from "langchain/agents";
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const defaultSystemMessage = `You are a helpful assistant. Help the user answer any questions.
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You have access to the following tools:
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{tools}
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In order to use a tool, you can use <tool></tool> and <tool_input></tool_input> tags. You will then get back a response in the form <observation></observation>
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For example, if you have a tool called 'search' that could run a google search, in order to search for the weather in SF you would respond:
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<tool>search</tool><tool_input>weather in SF</tool_input>
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<observation>64 degrees</observation>
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When you are done, respond with a final answer between <final_answer></final_answer>. For example:
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<final_answer>The weather in SF is 64 degrees</final_answer>
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Begin!
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Previous Conversation:
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{chat_history}
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Question: {input}
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{agent_scratchpad}`
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class XMLAgent_Agents 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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sessionId?: string
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constructor(fields?: { sessionId?: string }) {
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this.label = 'XML Agent'
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this.name = 'xmlAgent'
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this.version = 1.0
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this.type = 'XMLAgent'
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this.category = 'Agents'
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this.icon = 'xmlagent.svg'
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this.description = `Agent that is designed for LLMs that are good for reasoning/writing XML (e.g: Anthropic Claude)`
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this.baseClasses = [this.type, ...getBaseClasses(AgentExecutor)]
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this.inputs = [
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{
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label: 'Tools',
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name: 'tools',
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type: 'Tool',
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list: true
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},
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{
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label: 'Memory',
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name: 'memory',
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type: 'BaseChatMemory'
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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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},
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{
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label: 'System Message',
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name: 'systemMessage',
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type: 'string',
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warning: 'Prompt must include input variables: {tools}, {chat_history}, {input} and {agent_scratchpad}',
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rows: 4,
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default: defaultSystemMessage,
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additionalParams: true
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}
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]
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this.sessionId = fields?.sessionId
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}
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async init(): Promise<any> {
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return null
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}
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async run(nodeData: INodeData, input: string, options: ICommonObject): Promise<string | ICommonObject> {
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const memory = nodeData.inputs?.memory as FlowiseMemory
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const executor = await prepareAgent(nodeData, { sessionId: this.sessionId, chatId: options.chatId, input }, options.chatHistory)
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const loggerHandler = new ConsoleCallbackHandler(options.logger)
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const callbacks = await additionalCallbacks(nodeData, options)
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let res: ChainValues = {}
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let sourceDocuments: ICommonObject[] = []
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if (options.socketIO && options.socketIOClientId) {
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const handler = new CustomChainHandler(options.socketIO, options.socketIOClientId)
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res = await executor.invoke({ input }, { callbacks: [loggerHandler, handler, ...callbacks] })
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if (res.sourceDocuments) {
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options.socketIO.to(options.socketIOClientId).emit('sourceDocuments', flatten(res.sourceDocuments))
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sourceDocuments = res.sourceDocuments
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}
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} else {
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res = await executor.invoke({ input }, { callbacks: [loggerHandler, ...callbacks] })
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if (res.sourceDocuments) {
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sourceDocuments = res.sourceDocuments
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}
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}
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await memory.addChatMessages(
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[
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{
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text: input,
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type: 'userMessage'
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},
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{
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text: res?.output,
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type: 'apiMessage'
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}
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],
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this.sessionId
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)
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return sourceDocuments.length ? { text: res?.output, sourceDocuments: flatten(sourceDocuments) } : res?.output
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}
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}
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const prepareAgent = async (
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nodeData: INodeData,
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flowObj: { sessionId?: string; chatId?: string; input?: string },
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chatHistory: IMessage[] = []
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) => {
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const model = nodeData.inputs?.model as ChatOpenAI
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const memory = nodeData.inputs?.memory as FlowiseMemory
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const systemMessage = nodeData.inputs?.systemMessage as string
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let tools = nodeData.inputs?.tools
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tools = flatten(tools)
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const inputKey = memory.inputKey ? memory.inputKey : 'input'
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const memoryKey = memory.memoryKey ? memory.memoryKey : 'chat_history'
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let promptMessage = systemMessage ? systemMessage : defaultSystemMessage
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if (memory.memoryKey) promptMessage = promptMessage.replaceAll('{chat_history}', `{${memory.memoryKey}}`)
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if (memory.inputKey) promptMessage = promptMessage.replaceAll('{input}', `{${memory.inputKey}}`)
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const prompt = ChatPromptTemplate.fromMessages([
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HumanMessagePromptTemplate.fromTemplate(promptMessage),
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new MessagesPlaceholder('agent_scratchpad')
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])
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const missingVariables = ['tools', 'agent_scratchpad'].filter((v) => !prompt.inputVariables.includes(v))
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if (missingVariables.length > 0) {
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throw new Error(`Provided prompt is missing required input variables: ${JSON.stringify(missingVariables)}`)
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}
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const llmWithStop = model.bind({ stop: ['</tool_input>', '</final_answer>'] })
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const messages = (await memory.getChatMessages(flowObj.sessionId, false, chatHistory)) as IMessage[]
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let chatHistoryMsgTxt = ''
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for (const message of messages) {
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if (message.type === 'apiMessage') {
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chatHistoryMsgTxt += `\\nAI:${message.message}`
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} else if (message.type === 'userMessage') {
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chatHistoryMsgTxt += `\\nHuman:${message.message}`
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}
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}
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const runnableAgent = RunnableSequence.from([
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{
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[inputKey]: (i: { input: string; tools: Tool[]; steps: AgentStep[] }) => i.input,
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agent_scratchpad: (i: { input: string; tools: Tool[]; steps: AgentStep[] }) => formatLogToMessage(i.steps),
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tools: (_: { input: string; tools: Tool[]; steps: AgentStep[] }) =>
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tools.map((tool: Tool) => `${tool.name}: ${tool.description}`),
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[memoryKey]: (_: { input: string; tools: Tool[]; steps: AgentStep[] }) => chatHistoryMsgTxt
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},
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prompt,
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llmWithStop,
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new XMLAgentOutputParser()
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])
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const executor = AgentExecutor.fromAgentAndTools({
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agent: runnableAgent,
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tools,
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sessionId: flowObj?.sessionId,
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chatId: flowObj?.chatId,
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input: flowObj?.input,
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isXML: true,
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verbose: process.env.DEBUG === 'true' ? true : false
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})
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return executor
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}
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module.exports = { nodeClass: XMLAgent_Agents }
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@@ -0,0 +1 @@
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<svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-file-type-xml" width="24" height="24" viewBox="0 0 24 24" stroke-width="1.5" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"/><path d="M14 3v4a1 1 0 0 0 1 1h4" /><path d="M5 12v-7a2 2 0 0 1 2 -2h7l5 5v4" /><path d="M4 15l4 6" /><path d="M4 21l4 -6" /><path d="M19 15v6h3" /><path d="M11 21v-6l2.5 3l2.5 -3v6" /></svg>
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After Width: | Height: | Size: 476 B |
@@ -95,6 +95,8 @@ class AWSChatBedrock_ChatModels implements INode {
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name: 'model',
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type: 'options',
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options: [
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{ label: 'anthropic.claude-3-sonnet', name: 'anthropic.claude-3-sonnet-20240229-v1:0' },
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{ label: 'anthropic.claude-instant-v1', name: 'anthropic.claude-instant-v1' },
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{ label: 'anthropic.claude-instant-v1', name: 'anthropic.claude-instant-v1' },
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{ label: 'anthropic.claude-v1', name: 'anthropic.claude-v1' },
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{ label: 'anthropic.claude-v2', name: 'anthropic.claude-v2' },
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@@ -43,6 +43,16 @@ class ChatAnthropic_ChatModels implements INode {
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name: 'modelName',
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type: 'options',
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options: [
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{
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label: 'claude-3-opus',
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name: 'claude-3-opus-20240229',
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description: 'Most powerful model for highly complex tasks'
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},
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{
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label: 'claude-3-sonnet',
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name: 'claude-3-sonnet-20240229',
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description: 'Ideal balance of intelligence and speed for enterprise workloads'
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},
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{
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label: 'claude-2',
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name: 'claude-2',
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