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https://github.com/danny-avila/LibreChat.git
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* WIP: gemini-1.5 support * feat: extended vertex ai support * fix: handle possibly undefined modelName * fix: gpt-4-turbo-preview invalid vision model * feat: specify `fileConfig.imageOutputType` and make PNG default image conversion type * feat: better truncation for errors including base64 strings * fix: gemini inlineData formatting * feat: RAG augmented prompt for gemini-1.5 * feat: gemini-1.5 rates and token window * chore: adjust tokens, update docs, update vision Models * chore: add back `ChatGoogleVertexAI` for chat models via vertex ai * refactor: ask/edit controllers to not use `unfinished` field for google endpoint * chore: remove comment * chore(ci): fix AppService test * chore: remove comment * refactor(GoogleSearch): use `GOOGLE_SEARCH_API_KEY` instead, issue warning for old variable * chore: bump data-provider to 0.5.4 * chore: update docs * fix: condition for gemini-1.5 using generative ai lib * chore: update docs * ci: add additional AppService test for `imageOutputType` * refactor: optimize new config value `imageOutputType` * chore: bump CONFIG_VERSION * fix(assistants): avatar upload
136 lines
3.9 KiB
JavaScript
136 lines
3.9 KiB
JavaScript
const axios = require('axios');
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const { EModelEndpoint, FileSources, VisionModes } = require('librechat-data-provider');
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const { getStrategyFunctions } = require('../strategies');
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const { logger } = require('~/config');
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/**
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* Fetches an image from a URL and returns its base64 representation.
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*
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* @async
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* @param {string} url The URL of the image.
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* @returns {Promise<string>} The base64-encoded string of the image.
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* @throws {Error} If there's an issue fetching the image or encoding it.
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*/
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async function fetchImageToBase64(url) {
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try {
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const response = await axios.get(url, {
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responseType: 'arraybuffer',
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});
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return Buffer.from(response.data).toString('base64');
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} catch (error) {
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logger.error('Error fetching image to convert to base64', error);
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throw error;
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}
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}
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const base64Only = new Set([EModelEndpoint.google, EModelEndpoint.anthropic]);
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/**
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* Encodes and formats the given files.
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* @param {Express.Request} req - The request object.
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* @param {Array<MongoFile>} files - The array of files to encode and format.
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* @param {EModelEndpoint} [endpoint] - Optional: The endpoint for the image.
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* @param {string} [mode] - Optional: The endpoint mode for the image.
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* @returns {Promise<Object>} - A promise that resolves to the result object containing the encoded images and file details.
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*/
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async function encodeAndFormat(req, files, endpoint, mode) {
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const promises = [];
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const encodingMethods = {};
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const result = {
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files: [],
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image_urls: [],
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};
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if (!files || !files.length) {
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return result;
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}
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for (let file of files) {
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const source = file.source ?? FileSources.local;
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if (!file.height) {
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promises.push([file, null]);
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continue;
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}
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if (!encodingMethods[source]) {
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const { prepareImagePayload } = getStrategyFunctions(source);
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if (!prepareImagePayload) {
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throw new Error(`Encoding function not implemented for ${source}`);
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}
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encodingMethods[source] = prepareImagePayload;
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}
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const preparePayload = encodingMethods[source];
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/* Google & Anthropic don't support passing URLs to payload */
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if (source !== FileSources.local && base64Only.has(endpoint)) {
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const [_file, imageURL] = await preparePayload(req, file);
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promises.push([_file, await fetchImageToBase64(imageURL)]);
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continue;
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}
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promises.push(preparePayload(req, file));
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}
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const detail = req.body.imageDetail ?? 'auto';
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/** @type {Array<[MongoFile, string]>} */
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const formattedImages = await Promise.all(promises);
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for (const [file, imageContent] of formattedImages) {
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const fileMetadata = {
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type: file.type,
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file_id: file.file_id,
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filepath: file.filepath,
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filename: file.filename,
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embedded: !!file.embedded,
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};
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if (file.height && file.width) {
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fileMetadata.height = file.height;
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fileMetadata.width = file.width;
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}
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if (!imageContent) {
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result.files.push(fileMetadata);
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continue;
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}
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const imagePart = {
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type: 'image_url',
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image_url: {
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url: imageContent.startsWith('http')
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? imageContent
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: `data:${file.type};base64,${imageContent}`,
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detail,
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},
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};
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if (endpoint && endpoint === EModelEndpoint.google && mode === VisionModes.generative) {
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delete imagePart.image_url;
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imagePart.inlineData = {
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mimeType: file.type,
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data: imageContent,
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};
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} else if (endpoint && endpoint === EModelEndpoint.google) {
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imagePart.image_url = imagePart.image_url.url;
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} else if (endpoint && endpoint === EModelEndpoint.anthropic) {
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imagePart.type = 'image';
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imagePart.source = {
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type: 'base64',
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media_type: file.type,
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data: imageContent,
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};
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delete imagePart.image_url;
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}
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result.image_urls.push(imagePart);
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result.files.push(fileMetadata);
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}
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return result;
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}
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module.exports = {
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encodeAndFormat,
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};
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