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* WIP: initial logging changes add several transports in ~/config/winston omit messages in logs, truncate long strings add short blurb in dotenv for debug logging GoogleClient: using logger OpenAIClient: using logger, handleOpenAIErrors Adding typedef for payload message bumped winston and using winston-daily-rotate-file moved config for server paths to ~/config dir Added `DEBUG_LOGGING=true` to .env.example * WIP: Refactor logging statements in code * WIP: Refactor logging statements and import configurations * WIP: Refactor logging statements and import configurations * refactor: broadcast Redis initialization message with `info` not `debug` * refactor: complete Refactor logging statements and import configurations * chore: delete unused tools * fix: circular dependencies due to accessing logger * refactor(handleText): handle booleans and write tests * refactor: redact sensitive values, better formatting * chore: improve log formatting, avoid passing strings to 2nd arg * fix(ci): fix jest tests due to logger changes * refactor(getAvailablePluginsController): cache plugins as they are static and avoids async addOpenAPISpecs call every time * chore: update docs * chore: update docs * chore: create separate meiliSync logger, clean up logs to avoid being unnecessarily verbose * chore: spread objects where they are commonly logged to allow string truncation * chore: improve error log formatting
135 lines
4.7 KiB
JavaScript
135 lines
4.7 KiB
JavaScript
// From https://platform.openai.com/docs/api-reference/images/create
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// To use this tool, you must pass in a configured OpenAIApi object.
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const fs = require('fs');
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const path = require('path');
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const OpenAI = require('openai');
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// const { genAzureEndpoint } = require('~/utils/genAzureEndpoints');
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const { Tool } = require('langchain/tools');
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const { HttpsProxyAgent } = require('https-proxy-agent');
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const extractBaseURL = require('~/utils/extractBaseURL');
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const saveImageFromUrl = require('./saveImageFromUrl');
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const { logger } = require('~/config');
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const { DALLE_REVERSE_PROXY, PROXY } = process.env;
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class OpenAICreateImage extends Tool {
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constructor(fields = {}) {
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super();
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let apiKey = fields.DALLE_API_KEY || this.getApiKey();
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const config = { apiKey };
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if (DALLE_REVERSE_PROXY) {
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config.baseURL = extractBaseURL(DALLE_REVERSE_PROXY);
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}
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if (PROXY) {
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config.httpAgent = new HttpsProxyAgent(PROXY);
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}
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// let azureKey = fields.AZURE_API_KEY || process.env.AZURE_API_KEY;
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// if (azureKey) {
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// apiKey = azureKey;
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// const azureConfig = {
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// apiKey,
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// azureOpenAIApiInstanceName: process.env.AZURE_OPENAI_API_INSTANCE_NAME || fields.azureOpenAIApiInstanceName,
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// azureOpenAIApiDeploymentName: process.env.AZURE_OPENAI_API_DEPLOYMENT_NAME || fields.azureOpenAIApiDeploymentName,
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// azureOpenAIApiVersion: process.env.AZURE_OPENAI_API_VERSION || fields.azureOpenAIApiVersion
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// };
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// config = {
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// apiKey,
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// basePath: genAzureEndpoint({
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// ...azureConfig,
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// }),
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// baseOptions: {
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// headers: { 'api-key': apiKey },
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// params: {
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// 'api-version': azureConfig.azureOpenAIApiVersion // this might change. I got the current value from the sample code at https://oai.azure.com/portal/chat
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// }
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// }
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// };
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// }
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this.openai = new OpenAI(config);
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this.name = 'dall-e';
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this.description = `You can generate images with 'dall-e'. This tool is exclusively for visual content.
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Guidelines:
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- Visually describe the moods, details, structures, styles, and/or proportions of the image. Remember, the focus is on visual attributes.
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- Craft your input by "showing" and not "telling" the imagery. Think in terms of what you'd want to see in a photograph or a painting.
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- It's best to follow this format for image creation. Come up with the optional inputs yourself if none are given:
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"Subject: [subject], Style: [style], Color: [color], Details: [details], Emotion: [emotion]"
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- Generate images only once per human query unless explicitly requested by the user`;
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}
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getApiKey() {
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const apiKey = process.env.DALLE_API_KEY || '';
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if (!apiKey) {
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throw new Error('Missing DALLE_API_KEY environment variable.');
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}
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return apiKey;
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}
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replaceUnwantedChars(inputString) {
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return inputString
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.replace(/\r\n|\r|\n/g, ' ')
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.replace(/"/g, '')
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.trim();
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}
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getMarkdownImageUrl(imageName) {
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const imageUrl = path
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.join(this.relativeImageUrl, imageName)
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.replace(/\\/g, '/')
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.replace('public/', '');
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return ``;
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}
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async _call(input) {
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const resp = await this.openai.images.generate({
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prompt: this.replaceUnwantedChars(input),
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// TODO: Future idea -- could we ask an LLM to extract these arguments from an input that might contain them?
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n: 1,
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// size: '1024x1024'
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size: '512x512',
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});
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const theImageUrl = resp.data[0].url;
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if (!theImageUrl) {
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throw new Error('No image URL returned from OpenAI API.');
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}
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const regex = /img-[\w\d]+.png/;
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const match = theImageUrl.match(regex);
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let imageName = '1.png';
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if (match) {
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imageName = match[0];
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logger.debug('[DALL-E]', { imageName }); // Output: img-lgCf7ppcbhqQrz6a5ear6FOb.png
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} else {
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logger.debug('[DALL-E] No image name found in the string.', {
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theImageUrl,
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data: resp.data[0],
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});
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}
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this.outputPath = path.resolve(__dirname, '..', '..', '..', '..', 'client', 'public', 'images');
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const appRoot = path.resolve(__dirname, '..', '..', '..', '..', 'client');
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this.relativeImageUrl = path.relative(appRoot, this.outputPath);
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// Check if directory exists, if not create it
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if (!fs.existsSync(this.outputPath)) {
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fs.mkdirSync(this.outputPath, { recursive: true });
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}
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try {
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await saveImageFromUrl(theImageUrl, this.outputPath, imageName);
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this.result = this.getMarkdownImageUrl(imageName);
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} catch (error) {
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logger.error('Error while saving the DALL-E image:', error);
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this.result = theImageUrl;
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}
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return this.result;
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}
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}
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module.exports = OpenAICreateImage;
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