✨ (openai) Add tools and functions support (#1167)
Closes #863 Got helped from #1162 for the implementation. Closing it in favor of this PR. <!-- This is an auto-generated comment: release notes by coderabbit.ai --> ## Summary by CodeRabbit - **New Features** - Enhanced `CodeEditor` with additional properties for better form control and validation. - Introduced tools and functions in OpenAI integrations documentation for custom JavaScript execution. - Added capability to define and use custom JavaScript functions with the OpenAI assistant. - Expanded layout metadata options to include various input types and languages. - **Improvements** - Updated the OpenAI actions to support new function execution features. - **Documentation** - Added new sections for tools and functions in the OpenAI integrations guide. - **Refactor** - Refactored components and actions to integrate new features and improve existing functionalities. <!-- end of auto-generated comment: release notes by coderabbit.ai -->
This commit is contained in:
Baptiste Arnaud
GitHub
@@ -1,11 +1,13 @@
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import { option, createAction } from '@typebot.io/forge'
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import OpenAI, { ClientOptions } from 'openai'
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import { defaultOpenAIOptions } from '../constants'
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import { OpenAIStream } from 'ai'
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import { OpenAIStream, ToolCallPayload } from 'ai'
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import { parseChatCompletionMessages } from '../helpers/parseChatCompletionMessages'
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import { isDefined } from '@typebot.io/lib'
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import { auth } from '../auth'
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import { baseOptions } from '../baseOptions'
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import { ChatCompletionTool } from 'openai/resources/chat/completions'
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import { parseToolParameters } from '../helpers/parseToolParameters'
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const nativeMessageContentSchema = {
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content: option.string.layout({
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@@ -32,6 +34,48 @@ const assistantMessageItemSchema = option
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})
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.extend(nativeMessageContentSchema)
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export const parameterSchema = option.object({
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name: option.string.layout({
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label: 'Name',
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placeholder: 'myVariable',
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withVariableButton: false,
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}),
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type: option.enum(['string', 'number', 'boolean']).layout({
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label: 'Type:',
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direction: 'row',
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}),
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description: option.string.layout({
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label: 'Description',
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withVariableButton: false,
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}),
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required: option.boolean.layout({
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label: 'Is required?',
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}),
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})
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const functionToolItemSchema = option.object({
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type: option.literal('function'),
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name: option.string.layout({
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label: 'Name',
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placeholder: 'myFunctionName',
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withVariableButton: false,
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}),
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description: option.string.layout({
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label: 'Description',
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placeholder: 'A brief description of what this function does.',
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withVariableButton: false,
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}),
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parameters: option.array(parameterSchema).layout({ accordion: 'Parameters' }),
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code: option.string.layout({
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inputType: 'code',
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label: 'Code',
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lang: 'javascript',
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moreInfoTooltip:
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'A javascript code snippet that can use the defined parameters. It should return a value.',
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withVariableButton: false,
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}),
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})
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const dialogueMessageItemSchema = option.object({
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role: option.literal('Dialogue'),
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dialogueVariableId: option.string.layout({
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@@ -61,6 +105,9 @@ export const options = option.object({
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])
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)
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.layout({ accordion: 'Messages', itemLabel: 'message', isOrdered: true }),
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tools: option
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.array(option.discriminatedUnion('type', [functionToolItemSchema]))
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.layout({ accordion: 'Tools', itemLabel: 'tool' }),
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temperature: option.number.layout({
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accordion: 'Advanced settings',
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label: 'Temperature',
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@@ -131,20 +178,74 @@ export const createChatCompletion = createAction({
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const openai = new OpenAI(config)
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const tools = options.tools
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? (options.tools
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.filter((t) => t.name && t.parameters)
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.map((t) => ({
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type: 'function',
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function: {
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name: t.name as string,
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description: t.description,
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parameters: parseToolParameters(t.parameters!),
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},
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})) satisfies ChatCompletionTool[])
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: undefined
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const messages = parseChatCompletionMessages({ options, variables })
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const response = await openai.chat.completions.create({
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model: options.model ?? defaultOpenAIOptions.model,
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temperature: options.temperature
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? Number(options.temperature)
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: undefined,
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messages: parseChatCompletionMessages({ options, variables }),
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messages,
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tools,
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})
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let message = response.choices[0].message
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let totalTokens = response.usage?.total_tokens
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if (message.tool_calls) {
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messages.push(message)
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for (const toolCall of message.tool_calls) {
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const name = toolCall.function?.name
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if (!name) continue
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const toolDefinition = options.tools?.find((t) => t.name === name)
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if (!toolDefinition?.code || !toolDefinition.parameters) continue
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const func = AsyncFunction(
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...toolDefinition.parameters?.map((p) => p.name),
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toolDefinition.code
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)
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const content = await func(
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...Object.values(JSON.parse(toolCall.function.arguments))
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)
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messages.push({
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tool_call_id: toolCall.id,
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role: 'tool',
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content,
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})
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}
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const secondResponse = await openai.chat.completions.create({
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model: options.model ?? defaultOpenAIOptions.model,
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temperature: options.temperature
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? Number(options.temperature)
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: undefined,
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messages,
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tools,
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})
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message = secondResponse.choices[0].message
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totalTokens = secondResponse.usage?.total_tokens
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}
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options.responseMapping?.forEach((mapping) => {
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if (!mapping.variableId) return
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if (!mapping.item || mapping.item === 'Message content')
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variables.set(mapping.variableId, response.choices[0].message.content)
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variables.set(mapping.variableId, message.content)
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if (mapping.item === 'Total tokens')
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variables.set(mapping.variableId, response.usage?.total_tokens)
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variables.set(mapping.variableId, totalTokens)
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})
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},
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stream: {
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@@ -168,17 +269,70 @@ export const createChatCompletion = createAction({
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const openai = new OpenAI(config)
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const tools = options.tools
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? (options.tools
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.filter((t) => t.name && t.parameters)
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.map((t) => ({
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type: 'function',
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function: {
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name: t.name as string,
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description: t.description,
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parameters: parseToolParameters(t.parameters!),
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},
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})) satisfies ChatCompletionTool[])
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: undefined
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const messages = parseChatCompletionMessages({ options, variables })
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const response = await openai.chat.completions.create({
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model: options.model ?? defaultOpenAIOptions.model,
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temperature: options.temperature
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? Number(options.temperature)
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: undefined,
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stream: true,
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messages: parseChatCompletionMessages({ options, variables }),
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messages,
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tools,
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})
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return OpenAIStream(response)
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return OpenAIStream(response, {
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experimental_onToolCall: async (
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call: ToolCallPayload,
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appendToolCallMessage
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) => {
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for (const toolCall of call.tools) {
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const name = toolCall.func?.name
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if (!name) continue
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const toolDefinition = options.tools?.find((t) => t.name === name)
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if (!toolDefinition?.code || !toolDefinition.parameters) continue
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const func = AsyncFunction(
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...toolDefinition.parameters?.map((p) => p.name),
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toolDefinition.code
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)
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const content = await func(
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...Object.values(JSON.parse(toolCall.func.arguments as any))
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)
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const newMessages = appendToolCallMessage({
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tool_call_id: toolCall.id,
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function_name: toolCall.func.name,
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tool_call_result: content,
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})
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return openai.chat.completions.create({
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messages: [
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...messages,
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...newMessages,
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] as OpenAI.Chat.Completions.ChatCompletionMessageParam[],
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model: options.model ?? defaultOpenAIOptions.model,
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stream: true,
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tools,
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})
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}
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},
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})
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},
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},
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},
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})
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const AsyncFunction = Object.getPrototypeOf(async function () {}).constructor
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