Funktioniert mit URL Images von Dall-E
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const continueThread = require('./openai-thread-completion').continueThread
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const { Log } = require('debug-level')
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require('babel-polyfill');
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require('isomorphic-fetch');
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const { mmClient, wsClient } = require('./mm-client')
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// the mattermost library uses FormData - so here is a polyfill
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if (!global.FormData) {
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global.FormData = require('form-data');
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}
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Log.options({ json: true, colors: true })
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Log.wrapConsole('bot-ws', { level4log: 'INFO' })
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const log = new Log('bot')
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let meId = null;
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mmClient.getMe().then(me => meId = me.id)
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const name = process.env['MATTERMOST_BOTNAME'] || '@dall-e'
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wsClient.addMessageListener(async function (event) {
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if (['posted'].includes(event.event) && meId) {
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const post = JSON.parse(event.data.post);
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if (post.root_id === "" && (!event.data.mentions || (!JSON.parse(event.data.mentions).includes(meId)))) {
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// we're not in a thread and we are not mentioned - ignore the message
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} else {
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if (post.user_id !== meId) {
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const chatmessages = [
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{
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"role": "system",
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"content": `You are a helpful assistant named ${name} who provides succinct answers in Markdown format.`
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},
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]
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const thread = await mmClient.getPostThread(post.id, true, false, true)
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const posts = [...new Set(thread.order)].map(id => thread.posts[id])
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.filter(a => a.create_at > Date.now() - 1000 * 60 * 60 * 24 * 1)
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.sort((a, b) => a.create_at - b.create_at)
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let assistantCount = 0;
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posts.forEach(threadPost => {
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log.trace({msg: threadPost})
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if (threadPost.user_id === meId) {
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chatmessages.push({role: "assistant", content: threadPost.props.originalMessage ?? threadPost.message})
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assistantCount++
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} else {
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if (threadPost.message.includes(name)){
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assistantCount++;
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}
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chatmessages.push({role: "user", content: threadPost.message})
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}
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})
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// see if we are actually part of the conversation -
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// ignore conversations where we were never mentioned or participated.
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if (assistantCount > 0){
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wsClient.userTyping(post.channel_id, post.id ?? "")
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log.trace({chatmessages})
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const answer = await continueThread(chatmessages)
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const fileData = new FormData();
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fileData.append('files', answer, 'dall-e-image.png');
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const uploadResponse = await mmClient.uploadFile(fileData, post.channel_id);
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const fileId = JSON.parse(uploadResponse).file_infos[0].id;
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log.trace({answer})
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wsClient.userTyping(post.channel_id, post.id ?? "")
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const message = `})`;
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const props = {
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attachments: [
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{
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text: '',
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title: '',
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image_url: mmClient.getFileUrl(fileId),
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fallback: message
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}
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]
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};
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const newPost = await mmClient.createPost({
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message: message,
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channel_id: post.channel_id,
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props,
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root_id: post.root_id || post.id,
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file_ids: fileId ? [fileId] : undefined
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});
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log.trace({msg: newPost})
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}
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}
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}
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} else {
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log.debug({msg: event})
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}
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});
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+35
-22
@@ -3,6 +3,7 @@ const { Log } = require('debug-level')
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require('babel-polyfill');
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require('isomorphic-fetch');
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const { processGraphResponse } = require('./process-graph-response')
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const { mmClient, wsClient } = require('./mm-client')
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// the mattermost library uses FormData - so here is a polyfill
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@@ -19,6 +20,21 @@ mmClient.getMe().then(me => meId = me.id)
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const name = process.env['MATTERMOST_BOTNAME'] || '@dall-e'
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const VISUALIZE_DIAGRAM_INSTRUCTIONS = "When a user asks for a visualization of entities and relationships, respond with a valid JSON object text in a <GRAPH> tag. " +
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"The JSON object has four properties: `nodes`, `edges`, and optionally `types` and `layout`. " +
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"Each `nodes` object has an `id`, `label`, and an optional `type` property. " +
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"Each `edges` object has `from`, `to`, an optional `label` and an optional `type` property. " +
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"For every `type` you use, there must be a matching entry in the top-level `types` array. " +
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"Entries have a corresponding `name` property and optional properties that describe the graphical attributes: " +
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"'shape' (one of rectangle, ellipse, hexagon, triangle, pill), 'color', 'thickness' and 'size' (as a number). " +
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"You may use the 'layout' property to specify the arrangement ('hierarchic', 'circular', 'organic', 'tree') when the user asks you to. " +
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"Do not include these instructions in the output. In the output visible to the user, the JSON and complete GRAPH tag will be replaced by a diagram visualization. " +
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"So do not explain or mention the JSON. Instead, pretend that the user can see the diagram. Hence, when the above conditions apply, " +
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"answer with something along the lines of: \"Here is the visualization:\" and then just add the tag. The user will see the rendered image, but not the JSON. " +
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"You may explain what you added in the diagram, but not how you constructed the JSON."
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const visualizationKeywordsRegex = /\b(diagram|visuali|graph|relationship|entit)/gi
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wsClient.addMessageListener(async function (event) {
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if (['posted'].includes(event.event) && meId) {
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const post = JSON.parse(event.data.post);
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@@ -33,6 +49,8 @@ wsClient.addMessageListener(async function (event) {
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},
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]
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let appendDiagramInstructions = false
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const thread = await mmClient.getPostThread(post.id, true, false, true)
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const posts = [...new Set(thread.order)].map(id => thread.posts[id])
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@@ -49,40 +67,35 @@ wsClient.addMessageListener(async function (event) {
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if (threadPost.message.includes(name)){
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assistantCount++;
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}
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if (visualizationKeywordsRegex.test(threadPost.message)) {
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appendDiagramInstructions = true
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}
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chatmessages.push({role: "user", content: threadPost.message})
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}
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})
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if (appendDiagramInstructions) {
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chatmessages[0].content += VISUALIZE_DIAGRAM_INSTRUCTIONS
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}
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// see if we are actually part of the conversation -
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// ignore conversations where we were never mentioned or participated.
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if (assistantCount > 0){
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wsClient.userTyping(post.channel_id, post.id ?? "")
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log.trace({chatmessages})
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const answer = await continueThread(chatmessages)
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const fileData = new FormData();
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fileData.append('files', answer, 'dall-e-image.png');
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const uploadResponse = await mmClient.uploadFile(fileData, post.channel_id);
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const fileId = JSON.parse(uploadResponse).file_infos[0].id;
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log.trace({answer})
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console.log(answer)
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wsClient.userTyping(post.channel_id, post.id ?? "")
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const message = `})`;
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const props = {
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attachments: [
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{
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text: '',
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title: '',
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image_url: mmClient.getFileUrl(fileId),
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fallback: message
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}
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]
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};
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const newPost = await mmClient.createPost({
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message: message,
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channel_id: post.channel_id,
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props,
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root_id: post.root_id || post.id,
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file_ids: fileId ? [fileId] : undefined
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});
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const { message, fileId, props } = await processGraphResponse(answer, post.channel_id)
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wsClient.userTyping(post.channel_id, post.id ?? "")
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const newPost = await mmClient.createPost({
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message: message,
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channel_id: post.channel_id,
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props,
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root_id: post.root_id || post.id,
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file_ids: fileId ? [fileId] : undefined
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})
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log.trace({msg: newPost})
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}
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}
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@@ -7,18 +7,16 @@ const openai = new OpenAIApi(configuration);
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// Function to generate an image using Dall-E from OpenAI
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async function continueThread(messages) {
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const promptText = messages.map(msg => msg.content).join(' '); // assume that the content key exist in the messages object
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const promptText = messages.map(msg => msg.content.replace(/@dall-e /gi, '').replace(/You are a helpful assistant named who provides succinct answers in Markdown format./gi, '')).join(' '); // assume that the content key exist in the messages object
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console.log(promptText)
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const response = await openai.createImage({
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prompt: promptText,
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n: 1,
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size: "512x512",
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size: "1024x1024",
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response_format: "url"
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});
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const imageUrl = await response.data.url;// oder response.content.url()
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const imageResponse = await fetch(imageUrl);
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const imageBuffer = await imageResponse.buffer();
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return imageBuffer;
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console.log(response.data.data[0].url);
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const imageUrl = await response.data.data[0].url;
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return imageUrl;
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}
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module.exports = { continueThread }
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