From bda6b6b33c2caf55cc2350a3247907ba27fc82e1 Mon Sep 17 00:00:00 2001 From: Anthony Tripaldi Date: Wed, 18 Sep 2024 17:53:54 -0400 Subject: [PATCH] add the Multimodal Embeddings demo! --- README.md | 8 +- multimodal-embeddings/.gcloudignore | 17 + multimodal-embeddings/.npmrc | 1 + multimodal-embeddings/.prettierignore | 4 + multimodal-embeddings/.prettierrc | 19 + multimodal-embeddings/README.md | 182 + multimodal-embeddings/app.yaml | 15 + multimodal-embeddings/components.json | 14 + multimodal-embeddings/cors.json | 7 + multimodal-embeddings/eslint.config.js | 33 + multimodal-embeddings/fb/.firebaserc | 5 + multimodal-embeddings/fb/README.md | 19 + multimodal-embeddings/fb/firebase.json | 14 + .../fb/functions/package-lock.json | 6614 +++++++++++++++ .../fb/functions/package.json | 26 + .../fb/functions/src/gemini.ts | 31 + .../fb/functions/src/index.ts | 132 + .../fb/functions/tsconfig.json | 13 + multimodal-embeddings/fb/storage.rules | 13 + 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multimodal-embeddings/src/routes/viz/Settings.svelte create mode 100644 multimodal-embeddings/src/routes/viz/Viz.svelte create mode 100644 multimodal-embeddings/static/favicon.png create mode 100644 multimodal-embeddings/static/viz.png create mode 100644 multimodal-embeddings/svelte.config.js create mode 100644 multimodal-embeddings/tailwind.config.ts create mode 100644 multimodal-embeddings/tsconfig.json create mode 100644 multimodal-embeddings/vite.config.ts diff --git a/README.md b/README.md index 9c1db0a..10a5c76 100644 --- a/README.md +++ b/README.md @@ -24,10 +24,10 @@ Examples: These demos show the latest Gemini models (Flash 1.5, Pro, and others) ## Current Projects -| Name | Description | Tools | -| ---------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------- | -| [Gemini Video Scrubber](/video-scrubber) | "GVS" is a prototype that uses Gemini's multimodal video understanding capabilities to create timestamped summaries of videos with a simple UI to play those timestamps back in sequence, giving you the ability to quickly scan videos for interesting moments, common occurences, and more! | Multimodal Gemini, File API, Caching | -| VectorViewer | **Coming Soon!** Using the new Multimodal Embeddings API, we'll explore three dimensional embedding space of images and text. | Multimodal Embeddings API, VectorSearch, Firebase | +| Name | Description | Tools | +| ----------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | +| [Multimodal Embeddings](/multimodal-embeddings) | Using Gemini's new Multimodal Embeddings API, we'll explore high dimensional embedding space of text, images, and videos. | [Multimodal Embeddings API](https://cloud.google.com/vertex-ai/generative-ai/docs/embeddings/get-multimodal-embeddings), [Firestore Vector Search](https://firebase.google.com/docs/firestore/vector-search) | +| [Gemini Video Scrubber](/video-scrubber) | "GVS" is a prototype that uses Gemini's multimodal video understanding capabilities to create timestamped summaries of videos with a simple UI to play those timestamps back in sequence, giving you the ability to quickly scan videos for interesting moments, common occurences, and more! | Multimodal Gemini, File API, Caching | ## Experiments for all diff --git a/multimodal-embeddings/.gcloudignore b/multimodal-embeddings/.gcloudignore new file mode 100644 index 0000000..5690ef8 --- /dev/null +++ b/multimodal-embeddings/.gcloudignore @@ -0,0 +1,17 @@ +# This file specifies files that are *not* uploaded to Google Cloud +# using gcloud. It follows the same syntax as .gitignore, with the addition of +# "#!include" directives (which insert the entries of the given .gitignore-style +# file at that point). +# +# For more information, run: +# $ gcloud topic gcloudignore +# +.gcloudignore +# If you would like to upload your .git directory, .gitignore file or files +# from your .gitignore file, remove the corresponding line +# below: +.git +.gitignore + +# Node.js dependencies: +node_modules/ diff --git a/multimodal-embeddings/.npmrc b/multimodal-embeddings/.npmrc new file mode 100644 index 0000000..b6f27f1 --- /dev/null +++ b/multimodal-embeddings/.npmrc @@ -0,0 +1 @@ +engine-strict=true diff --git a/multimodal-embeddings/.prettierignore b/multimodal-embeddings/.prettierignore new file mode 100644 index 0000000..cc41cea --- /dev/null +++ b/multimodal-embeddings/.prettierignore @@ -0,0 +1,4 @@ +# Ignore files for PNPM, NPM and YARN +pnpm-lock.yaml +package-lock.json +yarn.lock diff --git a/multimodal-embeddings/.prettierrc b/multimodal-embeddings/.prettierrc new file mode 100644 index 0000000..9207a04 --- /dev/null +++ b/multimodal-embeddings/.prettierrc @@ -0,0 +1,19 @@ +{ + "useTabs": true, + "singleQuote": true, + "trailingComma": "none", + "printWidth": 100, + "plugins": [ + "prettier-plugin-svelte", + "prettier-plugin-tailwindcss" + ], + "tabWidth": 2, + "overrides": [ + { + "files": "*.svelte", + "options": { + "parser": "svelte" + } + } + ] +} \ No newline at end of file diff --git a/multimodal-embeddings/README.md b/multimodal-embeddings/README.md new file mode 100644 index 0000000..ba81eb1 --- /dev/null +++ b/multimodal-embeddings/README.md @@ -0,0 +1,182 @@ +> **This repo is provided _as-is_ for reference**, since you need a Firebase project with at least one collection that contains embeddings for it to be functional. We'll go through the basics of getting that setup next, but be warned - it's not yet plug-and-play :) + +# Multimodal Embeddings Demo + +Check out this video on Labs.google to see a quick overview of the project: + +[Video Screenshot] + +> Unsure what embeddings are? [Here's an old video](https://www.youtube.com/watch?v=wvsE8jm1GzE) we made about visualizing embeddings that does a good job explaining the basics. Learn more about Multimodal Embeddings in the [Cloud docs here](https://cloud.google.com/vertex-ai/generative-ai/docs/embeddings/get-multimodal-embeddings). + +This repo represents most of the code used in Khayti's "Personal Search" demo in the video above, with two endpoints that can be used to explore your own embeddings: + +1. `/search` - where we use Firebase Vector Search to find the closest embeddings to _both_ text and image input (image search!), and +2. `/viz` - bonus! where we use [UMAP](https://pair-code.github.io/understanding-umap/) to reduce the dimensions of our embeddings to visualize their relationships in 3D. + +The app is built with Firebase and SvelteKit, which uses Threlte for a declarative 3D rendering engine built on top of THREE.js (for `/viz`). + +## Get Started + +In addition to the Firebase project, you'll also need an API key for the embedding calls to Gemini so: + +1. [Create a new project](https://firebase.google.com/docs/web/setup) in Firebase that has Functions, Firestore and Storage enabled. +2. [Get an API key (via the console or AI Studio)](https://aistudio.google.com/app/apikey) for your Gemini calls and then create a `.env` file with your `GEMINI_API_KEY=yourkeyhere` in this directory. +3. Update `/src/lib/consts.ts` with your project info. +4. `npm i && npm run dev` should now 'work', but you won't have any data to explore until you create some embeddings. + +> Don't have any data to embed? We initially used public open source data sets available on places like Kaggle and Hugging Face to test our ideas. [This weather dataset](https://www.kaggle.com/datasets/jehanbhathena/weather-dataset) was a great starting place, as it visualizes really nicely. + +### Firebase Cloud Function for Embedding Generation + +We've included a little bonus here in `/fb/functions` that can _automatically_ generate embeddings for files uploaded to your Cloud Bucket. It also generates collections based on the folder structure of the uploads. + +This was great for our team when we were prototyping with the API since anyone could just create a new folder, upload images, and have it available in the UI for exploration. + +Check out [`/fb/README.md`](fb/README.md) for more info. + +There are a bunch of utility methods and components as well in `/src/lib`, but most importantly used by `/search` and `/viz` is `/src/lib/components/CollectionList.svelte` which will attempt to pull in any Firestore Collections created by the function in `/fb/functions` (if you choose to use that). + +### Create embeddings yourself + +Read through the [Multimodal Embeddings documentation](https://cloud.google.com/vertex-ai/generative-ai/docs/embeddings/get-multimodal-embeddings) as our code in `/src/lib/embedder.ts` implements this almost exactly. You'll need to add your project name to this file as well for it to work. You send text, images, or video, and receive back a vector that needs to be store in Firebase. + +> `embedder.ts` is also called directly from the Function in `/fb/functions/index.ts` so we didn't have to have two copies of the code in each sub module. + +### I have embeddings, now what? + +Next, read through the [Firebase Vector Search docs](https://firebase.google.com/docs/firestore/vector-search). + +You'll store each image embedding in [a Firestore document](https://firebase.google.com/docs/firestore/vector-search#write_operation_with_a_vector_embedding) via `FieldValue.vector()`, and once you've done this for all your embeddings, you'll need to [create an index of that collection](https://firebase.google.com/docs/firestore/vector-search#create_and_manage_vector_indexes) so the Vector Search can work. + +If you have _not_ created an index, but go to `/search` and try to search your collection, you'll get a handy error that gives you the exact code to run in order to start that, something like this: + +```bash +gcloud firestore indexes composite create \ +--collection-group=collection-group \ +--query-scope=COLLECTION \ +--field-config field-path=vector-field,vector-config='vector-configuration' \ +--database=your-database-id +``` + +Once indexed, your collection can be searched! + +### Searching + +Again, everything in this repo follows the [Firebase Vector Search docs](https://firebase.google.com/docs/firestore/vector-search) closely, and for searching, we're [making a nearest-neighbor query](https://firebase.google.com/docs/firestore/vector-search#make_a_nearest-neighbor_query). + +Conceptually though, you're doing two things: + +1. Embedding _the query_ in order to place it within the same space as your collections embeddings, then +2. Doing a nearest-neighbor lookup to find any results that are nearby to your query. + +And since we're using the Multimodal Embeddings API, your query can be text, an image, or a video. + +**Important Note** - you'll notice that we also have a file `/src/lib/cloud-firebase.ts`. At time of creating this demo, the actual Search APIs only resided in `@google-cloud/firestore` on NPM, which is separate from the normal Firebase web APIs in npm's `firebase` that are used elsewhere in the app. + +### Visualizing + +`/viz` takes your Firestore collections and attempts to plot them in 3D using UMAP, an API similar to T-SNE but much faster (and just as non-deterministic). + +
+ +
/viz using the public weather dataset mentioned above
+
+ +It was a WIP that was never fully completed but should get you 90% of the way there. What's important to note is lowering dimensions on embeddings inherently loses information, so while its a really nice way to visualize things it shouldn't be considered an exact representation of the embeddings (which are 1408 dimensions). + +## Experiments for all + +This is an experiment, not an official Google product. We’ll do our best to support and maintain this experiment but your mileage may vary. + +We encourage open sourcing projects as a way of learning from each other. Please respect our and other creators’ rights, including copyright and trademark rights when present, when sharing these works and creating derivative work. If you want more info on Google's policy, you can find that [here](https://www.google.com/permissions/). + +> **This repo is provided _as-is_ for reference**, since you need a Firebase project with at least one collection that contains embeddings for it to be functional. We'll go through the basics of getting that setup next, but be warned - it's not yet plug-and-play :) + +# Multimodal Embeddings Demo + +Check out this video on Labs.google to see a quick overview of the project: + +[Video Screenshot] + +> Unsure what embeddings are? [Here's an old video](https://www.youtube.com/watch?v=wvsE8jm1GzE) we made about visualizing embeddings that does a good job explaining the basics. Learn more about Multimodal Embeddings in the [Cloud docs here](https://cloud.google.com/vertex-ai/generative-ai/docs/embeddings/get-multimodal-embeddings). + +This repo represents most of the code used in Khayti's "Personal Search" demo in the video above, with two endpoints that can be used to explore your own embeddings: + +1. `/search` - where we use Firebase Vector Search to find the closest embeddings to _both_ text and image input (image search!), and +2. `/viz` - bonus! where we use [UMAP](https://pair-code.github.io/understanding-umap/) to reduce the dimensions of our embeddings to visualize their relationships in 3D. + +The app is built with Firebase and SvelteKit, which uses Threlte for a declarative 3D rendering engine built on top of THREE.js (for `/viz`). + +## Get Started + +In addition to the Firebase project, you'll also need an API key for the embedding calls to Gemini so: + +1. [Create a new project](https://firebase.google.com/docs/web/setup) in Firebase that has Functions, Firestore and Storage enabled. +2. [Get an API key (via the console or AI Studio)](https://aistudio.google.com/app/apikey) for your Gemini calls and then create a `.env` file with your `GEMINI_API_KEY=yourkeyhere` in this directory. +3. Update `/src/lib/consts.ts` with your project info. +4. `npm i && npm run dev` should now 'work', but you won't have any data to explore until you create some embeddings. + +> Don't have any data to embed? We initially used public open source data sets available on places like Kaggle and Hugging Face to test our ideas. [This weather dataset](https://www.kaggle.com/datasets/jehanbhathena/weather-dataset) was a great starting place, as it visualizes really nicely. + +### Firebase Cloud Function for Embedding Generation + +We've included a little bonus here in `/fb/functions` that can _automatically_ generate embeddings for files uploaded to your Cloud Bucket. It also generates collections based on the folder structure of the uploads. + +This was great for our team when we were prototyping with the API since anyone could just create a new folder, upload images, and have it available in the UI for exploration. + +Check out [`/fb/README.md`](/fb/README.md) for more info. + +There are a bunch of utility methods and components as well in `/src/lib`, but most importantly used by `/search` and `/viz` is `/src/lib/components/CollectionList.svelte` which will attempt to pull in any Firestore Collections created by the function in `/fb/functions` (if you choose to use that). + +### Create embeddings yourself + +Read through the [Multimodal Embeddings documentation](https://cloud.google.com/vertex-ai/generative-ai/docs/embeddings/get-multimodal-embeddings) as our code in `/src/lib/embedder.ts` implements this almost exactly. You'll need to add your project name to this file as well for it to work. You send text, images, or video, and receive back a vector that needs to be store in Firebase. + +> `embedder.ts` is also called directly from the Function in `/fb/functions/index.ts` so we didn't have to have two copies of the code in each sub module. + +### I have embeddings, now what? + +Next, read through the [Firebase Vector Search docs](https://firebase.google.com/docs/firestore/vector-search). + +You'll store each image embedding in [a Firestore document](https://firebase.google.com/docs/firestore/vector-search#write_operation_with_a_vector_embedding) via `FieldValue.vector()`, and once you've done this for all your embeddings, you'll need to [create an index of that collection](https://firebase.google.com/docs/firestore/vector-search#create_and_manage_vector_indexes) so the Vector Search can work. + +If you have _not_ created an index, but go to `/search` and try to search your collection, you'll get a handy error that gives you the exact code to run in order to start that, something like this: + +```bash +gcloud firestore indexes composite create \ +--collection-group=collection-group \ +--query-scope=COLLECTION \ +--field-config field-path=vector-field,vector-config='vector-configuration' \ +--database=your-database-id +``` + +Once indexed, your collection can be searched! + +### Searching + +Again, everything in this repo follows the [Firebase Vector Search docs](https://firebase.google.com/docs/firestore/vector-search) closely, and for searching, we're [making a nearest-neighbor query](https://firebase.google.com/docs/firestore/vector-search#make_a_nearest-neighbor_query). + +Conceptually though, you're doing two things: + +1. Embedding _the query_ in order to place it within the same space as your collections embeddings, then +2. Doing a nearest-neighbor lookup to find any results that are nearby to your query. + +And since we're using the Multimodal Embeddings API, your query can be text, an image, or a video. + +**Important Note** - you'll notice that we also have a file `/src/lib/cloud-firebase.ts`. At time of creating this demo, the actual Search APIs only resided in `@google-cloud/firestore` on NPM, which is separate from the normal Firebase web APIs in npm's `firebase` that are used elsewhere in the app. + +### Visualizing + +`/viz` takes your Firestore collections and attempts to plot them in 3D using UMAP, an API similar to T-SNE but much faster (and just as non-deterministic). + +| ![static/viz.png](static/viz.png) | +| :---------------------------------------------------: | +| /viz using the public weather dataset mentioned above | + +It was a WIP that was never fully completed but should get you 90% of the way there. What's important to note is lowering dimensions on embeddings inherently loses information, so while its a really nice way to visualize things it shouldn't be considered an exact representation of the embeddings (which are 1408 dimensions). + +## Experiments for all + +This is an experiment, not an official Google product. We’ll do our best to support and maintain this experiment but your mileage may vary. + +We encourage open sourcing projects as a way of learning from each other. Please respect our and other creators’ rights, including copyright and trademark rights when present, when sharing these works and creating derivative work. If you want more info on Google's policy, you can find that [here](https://www.google.com/permissions/). diff --git a/multimodal-embeddings/app.yaml b/multimodal-embeddings/app.yaml new file mode 100644 index 0000000..59b58eb --- /dev/null +++ b/multimodal-embeddings/app.yaml @@ -0,0 +1,15 @@ +runtime: nodejs20 +service: mm-embed +instance_class: F2 +default_expiration: '0s' + +# handlers element provides a list of URL patterns and descriptions of how they should be handled. +# https://cloud.google.com/appengine/docs/standard/python/config/appref#handlers_element +handlers: + - url: /static + static_dir: static + + - url: /.* + secure: always + redirect_http_response_code: 301 + script: auto diff --git a/multimodal-embeddings/components.json b/multimodal-embeddings/components.json new file mode 100644 index 0000000..1ce2da5 --- /dev/null +++ b/multimodal-embeddings/components.json @@ -0,0 +1,14 @@ +{ + "$schema": "https://shadcn-svelte.com/schema.json", + "style": "new-york", + "tailwind": { + "config": "tailwind.config.ts", + "css": "src/app.css", + "baseColor": "stone" + }, + "aliases": { + "components": "$lib/components", + "utils": "$lib/utils" + }, + "typescript": true +} \ No newline at end of file diff --git a/multimodal-embeddings/cors.json b/multimodal-embeddings/cors.json new file mode 100644 index 0000000..8d6037d --- /dev/null +++ b/multimodal-embeddings/cors.json @@ -0,0 +1,7 @@ +[ + { + "origin": ["*"], + "method": ["GET"], + "maxAgeSeconds": 3600 + } +] diff --git a/multimodal-embeddings/eslint.config.js b/multimodal-embeddings/eslint.config.js new file mode 100644 index 0000000..a351fa9 --- /dev/null +++ b/multimodal-embeddings/eslint.config.js @@ -0,0 +1,33 @@ +import js from '@eslint/js'; +import ts from 'typescript-eslint'; +import svelte from 'eslint-plugin-svelte'; +import prettier from 'eslint-config-prettier'; +import globals from 'globals'; + +/** @type {import('eslint').Linter.FlatConfig[]} */ +export default [ + js.configs.recommended, + ...ts.configs.recommended, + ...svelte.configs['flat/recommended'], + prettier, + ...svelte.configs['flat/prettier'], + { + languageOptions: { + globals: { + ...globals.browser, + ...globals.node + } + } + }, + { + files: ['**/*.svelte'], + languageOptions: { + parserOptions: { + parser: ts.parser + } + } + }, + { + ignores: ['build/', '.svelte-kit/', 'dist/'] + } +]; diff --git a/multimodal-embeddings/fb/.firebaserc b/multimodal-embeddings/fb/.firebaserc new file mode 100644 index 0000000..0477820 --- /dev/null +++ b/multimodal-embeddings/fb/.firebaserc @@ -0,0 +1,5 @@ +{ + "projects": { + "default": "" + } +} diff --git a/multimodal-embeddings/fb/README.md b/multimodal-embeddings/fb/README.md new file mode 100644 index 0000000..1d3bad2 --- /dev/null +++ b/multimodal-embeddings/fb/README.md @@ -0,0 +1,19 @@ +# Firebase Function for Embedding + +This Function was responsible for watching our Firebase storage bucket and sending any images uploaded to the Gemini Multimodal Embeddings API, storing those embeddings in a Vector in our Firestore database. Read more about Cloud Functions [here](https://firebase.google.com/docs/functions). + +For more information on how Firebase handles embeddings, check out [Search with Vector Search](https://firebase.google.com/docs/firestore/vector-search) in the Firebase docs, which this code is based on. + +`functions/src/index.ts` has lots of comments in it so you can follow along, though in order to keep our sanity and have the embedding code in one place, it imports `embedder.ts` from the main `/src/lib` directory. + +Make sure to update the `.firebaserc` file with your project name, or just use `firebase init` from the cli to create a new project to deploy to. + +Like any other Node project, make sure to run `npm i` before testing anything, like local emulation with `npm run serve` + +### Gemini Labeling + +There's also some code in `functions/src/gemini.ts` that attempted to add labels to our embeddings, sending each image to Gemini and getting back a description. Useful for learning about the multimodal capabilities of Gemini, but was ultimately not necessary for our experiments. + +It requires the same API key you generated for the main project. + +> It's worth noting: The MM Embedding API can take in text, image, and video, but it doesn't _automatically_ combine them in any meaningful way. You can, of course, add those vectors together yourself after the fact, to give you a new vector of those combined objects. diff --git a/multimodal-embeddings/fb/firebase.json b/multimodal-embeddings/fb/firebase.json new file mode 100644 index 0000000..0efeaaa --- /dev/null +++ b/multimodal-embeddings/fb/firebase.json @@ -0,0 +1,14 @@ +{ + "functions": [ + { + "source": "functions", + "codebase": "default", + "runtime": "nodejs20", + "ignore": ["node_modules", ".git", "firebase-debug.log", "firebase-debug.*.log", "*.local"], + "predeploy": ["npm --prefix \"$RESOURCE_DIR\" run build"] + } + ], + "storage": { + "rules": "storage.rules" + } +} diff --git a/multimodal-embeddings/fb/functions/package-lock.json b/multimodal-embeddings/fb/functions/package-lock.json new file mode 100644 index 0000000..c3d1d7f --- /dev/null +++ b/multimodal-embeddings/fb/functions/package-lock.json @@ -0,0 +1,6614 @@ +{ + "name": "functions", + "lockfileVersion": 3, + "requires": true, + "packages": { + "": { 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/dev/null +++ b/multimodal-embeddings/fb/functions/package.json @@ -0,0 +1,26 @@ +{ + "name": "functions", + "scripts": { + "build": "tsc", + "build:watch": "tsc --watch", + "serve": "npm run build && firebase emulators:start", + "shell": "npm run build && firebase functions:shell", + "start": "npm run shell", + "deploy": "firebase deploy --only functions", + "logs": "firebase functions:log" + }, + "main": "lib/index.js", + "dependencies": { + "@google-cloud/aiplatform": "^3.23.0", + "@google/generative-ai": "^0.12.0", + "firebase-admin": "^12.1.1", + "firebase-functions": "^5.0.0", + "path": "^0.12.7", + "sharp": "^0.33.4" + }, + "devDependencies": { + "firebase-functions-test": "^3.1.0", + "typescript": "^4.9.0" + }, + "private": true +} diff --git a/multimodal-embeddings/fb/functions/src/gemini.ts b/multimodal-embeddings/fb/functions/src/gemini.ts new file mode 100644 index 0000000..ce1bb8b --- /dev/null +++ b/multimodal-embeddings/fb/functions/src/gemini.ts @@ -0,0 +1,31 @@ +import { GoogleGenerativeAI } from '@google/generative-ai'; + +const genAI = new GoogleGenerativeAI(''); // key from your console or .env file +const model = genAI.getGenerativeModel({ + model: 'gemini-1.5-pro-latest' +}); + +function getImagePart(buffer, mimeType) { + return { + inlineData: { + data: Buffer.from(buffer).toString('base64'), + mimeType + } + }; +} + +export const getImageLabel = async (imageBuffer: Buffer, mimeType: string) => { + console.log('getImageLabel', mimeType); + const imagePart = getImagePart(imageBuffer, mimeType); + const prompt = + 'Generate a concise description for this image, to be used in the generation of vector embeddings.'; + try { + const content = await model.generateContent([imagePart, prompt]); + const text = content.response.text(); + console.log('Generated Label:', text); + return text; + } catch (err) { + console.error(err); + throw err; + } +}; diff --git a/multimodal-embeddings/fb/functions/src/index.ts b/multimodal-embeddings/fb/functions/src/index.ts new file mode 100644 index 0000000..8bceaa4 --- /dev/null +++ b/multimodal-embeddings/fb/functions/src/index.ts @@ -0,0 +1,132 @@ +import { onObjectFinalized } from 'firebase-functions/v2/storage'; +import { getFirestore, FieldValue } from 'firebase-admin/firestore'; +import { initializeApp } from 'firebase-admin/app'; +import { getStorage } from 'firebase-admin/storage'; +import { getEmbeddings } from './../../../src/lib/embedder'; +import { getImageLabel } from './gemini'; +import * as logger from 'firebase-functions/logger'; +import * as path from 'path'; +import * as sharp from 'sharp'; + +// For storage access +initializeApp(); +const fs = getFirestore(''); // your firestore database +const bucketName = ''; // your bucket name +const bucketWithPrefix = `gs://${bucketName}`; + +const mmEmbedBasePath = 'mmembed'; // path we look at for files to be added +const mmEmbedWLabelBasePath = 'mmembed-labeled'; + +export const onFileUploaded = onObjectFinalized( + { cpu: 2, memory: '2GiB', bucket: bucketName }, + async (event) => { + const fileBucket = event.data.bucket; // Storage bucket containing the file. + const filePath = event.data.name; // File path in the bucket. + const contentType = event.data.contentType || ''; // File content type. + + logger.info('bucketPrefix!', bucketWithPrefix); + logger.info('File uploaded!', filePath, contentType); + + // Exit if this is triggered on a file that is not an image. + if (!contentType.startsWith('image/')) { + return logger.warn('This is not an image.'); + } + // Exit if the image is already a thumbnail. + const fileName = path.basename(filePath); + if (fileName.startsWith('thumb_')) { + return logger.warn('Already a Thumbnail.'); + } + + // Make sure we only act on mmembed uploads + const dirs = path.dirname(filePath).split('/'); + if (dirs.length && !dirs[0].startsWith(mmEmbedBasePath)) { + return logger.warn('Not a mmembed-* upload.'); + } + + // Create thumbnail -> maybe label -> embed -> store + // Error out if any of these steps fail + try { + const { imageBuffer, thumbFilePath } = await createThumbnail( + fileBucket, + filePath, + contentType + ); + console.log('thumbpath:', thumbFilePath); + + let embedResult; + let suffix = ''; + let label = ''; + if (dirs[0] == mmEmbedWLabelBasePath) { + // -labeled folder upload, so get gemini to label the image! + console.log(`content: ${contentType} typeof ${typeof contentType}`); + label = await getImageLabel(imageBuffer, contentType); + embedResult = await getEmbeddings({ text: label, imageBytes: imageBuffer }); + suffix = '-labeled'; + } else { + // only an image + embedResult = await getEmbeddings({ imageBytes: imageBuffer }); + } + console.log('EmbededImage result', embedResult); + + const storeResult = await storeEmbeddings( + filePath, + thumbFilePath, + embedResult.imageEmbeddings, + dirs[1] + suffix, // dir name and whether or not its labeled, used as collection + label + ); + console.log('Creation success!', storeResult.id); + } catch (err) { + console.error('Failed to store embeddings for image.', err); + return; + } + } +); + +const storeEmbeddings = async ( + filePath: string, + thumbPath: string, + embeddings: number[], + collectionName: string, + label: string = '' +) => { + const coll = fs.collection(collectionName); + const data = { + filePath, + thumbPath, + embeddings: FieldValue.vector(embeddings) + }; + if (label) { + data['label'] = label; + } + return await coll.add(data); +}; + +const createThumbnail = async (fileBucket: string, filePath: string, contentType: string) => { + const fileName = path.basename(filePath); + + // Download file into memory from bucket. + const bucket = getStorage().bucket(fileBucket); + const downloadResponse = await bucket.file(filePath).download(); + const imageBuffer = downloadResponse[0]; + + const thumbnailBuffer = await sharp(imageBuffer) + .resize({ + height: 200, + fit: 'contain', + withoutEnlargement: true + }) + .toBuffer(); + + // Prefix 'thumb_' to file name. + const thumbFileName = `thumb_${fileName}`; + const thumbFilePath = path.join(path.dirname(filePath), thumbFileName); + + // Upload the thumbnail. + const metadata = { contentType }; + await bucket.file(thumbFilePath).save(thumbnailBuffer, { + metadata + }); + logger.log(`Thumbnail uploaded! ${thumbFilePath}}`); + return { imageBuffer, thumbFilePath }; +}; diff --git a/multimodal-embeddings/fb/functions/tsconfig.json b/multimodal-embeddings/fb/functions/tsconfig.json new file mode 100644 index 0000000..ef3bdc6 --- /dev/null +++ b/multimodal-embeddings/fb/functions/tsconfig.json @@ -0,0 +1,13 @@ +{ + "compilerOptions": { + "module": "commonjs", + "noImplicitReturns": true, + "noUnusedLocals": true, + "outDir": "lib", + "sourceMap": true, + "strict": false, + "target": "es2017" + }, + "compileOnSave": true, + "include": ["src"] +} diff --git a/multimodal-embeddings/fb/storage.rules b/multimodal-embeddings/fb/storage.rules new file mode 100644 index 0000000..9582bb4 --- /dev/null +++ b/multimodal-embeddings/fb/storage.rules @@ -0,0 +1,13 @@ +rules_version = '2'; + +// Craft rules based on data in your Firestore database +// allow write: if firestore.get( +// /databases/(default)/documents/users/$(request.auth.uid)).data.isAdmin; +service firebase.storage { + match /b/{bucket}/o { + match /{allPaths=**} { + allow read: if true; + allow write: if false; + } + } +} diff --git a/multimodal-embeddings/package-lock.json b/multimodal-embeddings/package-lock.json new file mode 100644 index 0000000..1558f1e --- /dev/null +++ b/multimodal-embeddings/package-lock.json @@ -0,0 +1,7510 @@ +{ + "name": "mm-embeddings", + "version": "0.0.1", + "lockfileVersion": 3, + "requires": true, + "packages": { + "": { + "name": "mm-embeddings", + "version": "0.0.1", + "dependencies": { + "@google-cloud/aiplatform": "^3.23.0", + "@google-cloud/firestore": "^7.8.0", + "@google-cloud/vertexai": "^1.2.0", + "@tensorflow/tfjs-tsne": "^0.2.0", + 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"check": "svelte-kit sync && svelte-check --tsconfig ./tsconfig.json", + "check:watch": "svelte-kit sync && svelte-check --tsconfig ./tsconfig.json --watch", + "lint": "prettier --check . && eslint .", + "format": "prettier --write .", + "deploy": "vite build && gcloud app deploy --version dev build/app.yaml --no-promote --quiet", + "deploy:prod": "vite build && gcloud app deploy --version prod build/app.yaml" + }, + "devDependencies": { + "@sveltejs/kit": "^2.0.0", + "@sveltejs/vite-plugin-svelte": "^3.0.0", + "@tailwindcss/typography": "^0.5.13", + "@types/eslint": "^8.56.7", + "@types/three": "^0.165.0", + "autoprefixer": "^10.4.19", + "eslint": "^9.0.0", + "eslint-config-prettier": "^9.1.0", + "eslint-plugin-svelte": "^2.36.0", + "globals": "^15.0.0", + "postcss": "^8.4.38", + "prettier": "^3.1.1", + "prettier-plugin-svelte": "^3.1.2", + "prettier-plugin-tailwindcss": "^0.5.14", + "svelte": "^4.2.7", + "svelte-adapter-appengine": "^1.1.0", + "svelte-check": "^3.6.0", + "tailwindcss": "^3.4.3", + "tslib": "^2.4.1", + "typescript": "^5.0.0", + "typescript-eslint": "^8.0.0-alpha.20", + "vite": "^5.0.3" + }, + "type": "module", + "browserify": { + "transform": [ + "cwise" + ] + }, + "dependencies": { + "@google-cloud/aiplatform": "^3.23.0", + "@google-cloud/firestore": "^7.8.0", + "@google-cloud/vertexai": "^1.2.0", + "@tensorflow/tfjs-tsne": "^0.2.0", + "@threlte/core": "^7.3.0", + "@threlte/extras": "^8.11.2", + "bits-ui": "^0.21.10", + "clsx": "^2.1.1", + "dat.gui": "^0.7.9", + "dotenv": "^16.4.5", + "firebase": "^10.12.2", + "lucide-svelte": "^0.396.0", + "sharp": "^0.33.4", + "svelte-radix": "^1.1.0", + "svelte-tweakpane-ui": "^1.3.0", + "tailwind-merge": "^2.3.0", + "tailwind-variants": "^0.2.1", + "three": "^0.165.0", + "umap-js": "^1.4.0" + } +} diff --git a/multimodal-embeddings/postcss.config.js b/multimodal-embeddings/postcss.config.js new file mode 100644 index 0000000..5c45a3f --- /dev/null +++ b/multimodal-embeddings/postcss.config.js @@ -0,0 +1,6 @@ +export default { + plugins: { + tailwindcss: {}, + autoprefixer: {} + } +}; \ No newline at end of file diff --git a/multimodal-embeddings/src/app.css b/multimodal-embeddings/src/app.css new file mode 100644 index 0000000..5006f27 --- /dev/null +++ b/multimodal-embeddings/src/app.css @@ -0,0 +1,88 @@ +@tailwind base; +@tailwind components; +@tailwind utilities; + +@layer base { + :root { + --background: 0 0% 100%; + --foreground: 20 14.3% 4.1%; + + --muted: 60 4.8% 95.9%; + --muted-foreground: 25 5.3% 44.7%; + + --popover: 0 0% 100%; + --popover-foreground: 20 14.3% 4.1%; + + --card: 0 0% 100%; + --card-foreground: 20 14.3% 4.1%; + + --border: 20 5.9% 90%; + --input: 20 5.9% 90%; + + --primary: 24 9.8% 10%; + --primary-foreground: 60 9.1% 97.8%; + + --secondary: 60 4.8% 95.9%; + --secondary-foreground: 24 9.8% 10%; + + --accent: 60 4.8% 95.9%; + --accent-foreground: 24 9.8% 10%; + + --destructive: 0 72.2% 50.6%; + --destructive-foreground: 60 9.1% 97.8%; + + --ring: 20 14.3% 4.1%; + + --radius: 0.5rem; + + --bard-color-brand-text-gradient-stop-1: #4285f4; + --bard-color-brand-text-gradient-stop-1-rgb: 66, 133, 244; + --bard-color-brand-text-gradient-stop-2: #9b72cb; + --bard-color-brand-text-gradient-stop-2-rgb: 155, 114, 203; + --bard-color-brand-text-gradient-stop-3: #d96570; + --bard-color-brand-text-gradient-stop-3-rgb: 217, 101, 112; + } + + .dark { + --background: 20 14.3% 4.1%; + --foreground: 60 9.1% 97.8%; + + --muted: 12 6.5% 15.1%; + --muted-foreground: 24 5.4% 63.9%; + + --popover: 20 14.3% 4.1%; + --popover-foreground: 60 9.1% 97.8%; + + --card: 20 14.3% 4.1%; + --card-foreground: 60 9.1% 97.8%; + + --border: 12 6.5% 15.1%; + --input: 12 6.5% 15.1%; + + --primary: 60 9.1% 97.8%; + --primary-foreground: 24 9.8% 10%; + + --secondary: 12 6.5% 15.1%; + --secondary-foreground: 60 9.1% 97.8%; + + --accent: 12 6.5% 15.1%; + --accent-foreground: 60 9.1% 97.8%; + + --destructive: 0 62.8% 30.6%; + --destructive-foreground: 60 9.1% 97.8%; + + --ring: 24 5.7% 82.9%; + } +} + +@layer base { + * { + @apply border-border; + } + + body { + @apply dark bg-background text-foreground; + font-family: 'Gill Sans', 'Gill Sans MT', Calibri, 'Trebuchet MS', sans-serif; + font-size: 1.4rem; + } +} diff --git a/multimodal-embeddings/src/app.d.ts b/multimodal-embeddings/src/app.d.ts new file mode 100644 index 0000000..743f07b --- /dev/null +++ b/multimodal-embeddings/src/app.d.ts @@ -0,0 +1,13 @@ +// See https://kit.svelte.dev/docs/types#app +// for information about these interfaces +declare global { + namespace App { + // interface Error {} + // interface Locals {} + // interface PageData {} + // interface PageState {} + // interface Platform {} + } +} + +export {}; diff --git a/multimodal-embeddings/src/app.html b/multimodal-embeddings/src/app.html new file mode 100644 index 0000000..53371ad --- /dev/null +++ b/multimodal-embeddings/src/app.html @@ -0,0 +1,13 @@ + + + + + Gemini MM Embeddings - CL Demos + + + %sveltekit.head% + + +
%sveltekit.body%
+ + diff --git a/multimodal-embeddings/src/lib/api.ts b/multimodal-embeddings/src/lib/api.ts new file mode 100644 index 0000000..f95b9e5 --- /dev/null +++ b/multimodal-embeddings/src/lib/api.ts @@ -0,0 +1,42 @@ +import * as THREE from 'three'; + +export type StoredImage = { + embeddings: number[]; + filePath: string; + thumbPath: string; + label?: string; +}; + +export type EmbeddingRequest = { + imageBytes?: Buffer | string; + text?: string; +}; + +export type SearchRequest = { + collection: string; + embeddings: number[]; +}; + +export type SearchResponse = { + embeddings: number[]; + searchResults: StoredImage[]; +}; + +export type Collection = { + id: string; + path: string; +}; + +// typescript helper since all threlte events use it and its not exported +export type InteractionEvent = THREE.Intersection & { + intersections: THREE.Intersection[]; // The first intersection of each intersected object + object: THREE.Object3D; // The object that was actually hit + eventObject: THREE.Object3D; // The object that registered the event + camera: THREE.Camera; // The camera used for raycasting + delta: THREE.Vector2; // Distance between mouse down and mouse up event in pixels + nativeEvent: MouseEvent | PointerEvent | WheelEvent; // The native browser event + pointer: THREE.Vector2; // The pointer position in normalized device coordinates + ray: THREE.Ray; // The ray used for raycasting + stopPropagation: () => void; // Function to stop propagation of the event + stopped: Boolean; // Whether the event propagation has been stopped +}; diff --git a/multimodal-embeddings/src/lib/cloud-firebase.ts b/multimodal-embeddings/src/lib/cloud-firebase.ts new file mode 100644 index 0000000..c646f7d --- /dev/null +++ b/multimodal-embeddings/src/lib/cloud-firebase.ts @@ -0,0 +1,36 @@ +/** + * Yes, there is another firebase.ts in $lib/ but this one has to use + * special `@google-cloud/firestore` package instead, which contains + * the necessary VectorQuery findNearest() method and a simple listCollections() method + */ +import { Firestore, FieldValue } from '@google-cloud/firestore'; +import type { VectorQuery, VectorQuerySnapshot } from '@google-cloud/firestore'; +import { FIRESTORE_DB_ID } from './consts'; + +const db = new Firestore({ databaseId: FIRESTORE_DB_ID }); + +export const listCollections = async () => { + try { + const collections = await db.listCollections(); + return collections.map((collection) => { + return { id: collection.id, path: collection.path }; + }); + } catch (error) { + console.error(error); + return { error }; + } +}; + +export const vectorSearch = async (collection: string, imageEmbedding: number[]) => { + const coll = db.collection(collection); + const vectorQuery: VectorQuery = await coll.findNearest( + 'embeddings', + FieldValue.vector(imageEmbedding), + { + limit: 30, + distanceMeasure: 'EUCLIDEAN' // other options COSINE and DOT_PRODUCT + } + ); + const snapshot: VectorQuerySnapshot = await vectorQuery.get(); + return snapshot.docs; +}; diff --git a/multimodal-embeddings/src/lib/components/CollectionsList.svelte b/multimodal-embeddings/src/lib/components/CollectionsList.svelte new file mode 100644 index 0000000..96de339 --- /dev/null +++ b/multimodal-embeddings/src/lib/components/CollectionsList.svelte @@ -0,0 +1,44 @@ + + +
+ +
+ + + + + + {#if collections} + + {#each collections as collection} + {collection.id} + {/each} + + {/if} + + +
diff --git a/multimodal-embeddings/src/lib/components/Droppable.svelte b/multimodal-embeddings/src/lib/components/Droppable.svelte new file mode 100644 index 0000000..29b7887 --- /dev/null +++ b/multimodal-embeddings/src/lib/components/Droppable.svelte @@ -0,0 +1,49 @@ + + + + +
diff --git a/multimodal-embeddings/src/lib/components/ui/button/button.svelte b/multimodal-embeddings/src/lib/components/ui/button/button.svelte new file mode 100644 index 0000000..86827f3 --- /dev/null +++ b/multimodal-embeddings/src/lib/components/ui/button/button.svelte @@ -0,0 +1,25 @@ + + + + + diff --git a/multimodal-embeddings/src/lib/components/ui/button/index.ts b/multimodal-embeddings/src/lib/components/ui/button/index.ts new file mode 100644 index 0000000..96714df --- /dev/null +++ b/multimodal-embeddings/src/lib/components/ui/button/index.ts @@ -0,0 +1,50 @@ +import type { Button as ButtonPrimitive } from "bits-ui"; +import { type VariantProps, tv } from "tailwind-variants"; +import Root from "./button.svelte"; + +const buttonVariants = tv({ + base: "inline-flex items-center justify-center whitespace-nowrap rounded-md text-sm font-medium transition-colors focus-visible:outline-none focus-visible:ring-1 focus-visible:ring-ring disabled:pointer-events-none disabled:opacity-50", + variants: { + variant: { + default: "bg-primary text-primary-foreground shadow hover:bg-primary/90", + destructive: + "bg-destructive text-destructive-foreground shadow-sm hover:bg-destructive/90", + outline: + "border border-input bg-background shadow-sm hover:bg-accent hover:text-accent-foreground", + secondary: "bg-secondary text-secondary-foreground shadow-sm hover:bg-secondary/80", + ghost: "hover:bg-accent hover:text-accent-foreground", + link: "text-primary underline-offset-4 hover:underline", + }, + size: { + default: "h-9 px-4 py-2", + sm: "h-8 rounded-md px-3 text-xs", + lg: "h-10 rounded-md px-8", + icon: "h-9 w-9", + }, + }, + defaultVariants: { + variant: "default", + size: "default", + }, +}); + +type Variant = VariantProps["variant"]; +type Size = VariantProps["size"]; + +type Props = ButtonPrimitive.Props & { + variant?: Variant; + size?: Size; +}; + +type Events = ButtonPrimitive.Events; + +export { + Root, + type Props, + type Events, + // + Root as Button, + type Props as ButtonProps, + type Events as ButtonEvents, + buttonVariants, +}; diff --git a/multimodal-embeddings/src/lib/components/ui/dropdown-menu/dropdown-menu-checkbox-item.svelte b/multimodal-embeddings/src/lib/components/ui/dropdown-menu/dropdown-menu-checkbox-item.svelte new file mode 100644 index 0000000..18b98c8 --- /dev/null +++ b/multimodal-embeddings/src/lib/components/ui/dropdown-menu/dropdown-menu-checkbox-item.svelte @@ -0,0 +1,35 @@ + + + + + + + + + + diff --git a/multimodal-embeddings/src/lib/components/ui/dropdown-menu/dropdown-menu-content.svelte b/multimodal-embeddings/src/lib/components/ui/dropdown-menu/dropdown-menu-content.svelte new file mode 100644 index 0000000..837a628 --- /dev/null +++ b/multimodal-embeddings/src/lib/components/ui/dropdown-menu/dropdown-menu-content.svelte @@ -0,0 +1,26 @@ + + + + + diff --git a/multimodal-embeddings/src/lib/components/ui/dropdown-menu/dropdown-menu-item.svelte b/multimodal-embeddings/src/lib/components/ui/dropdown-menu/dropdown-menu-item.svelte new file mode 100644 index 0000000..a446eb3 --- /dev/null +++ b/multimodal-embeddings/src/lib/components/ui/dropdown-menu/dropdown-menu-item.svelte @@ -0,0 +1,31 @@ + + + + + diff --git a/multimodal-embeddings/src/lib/components/ui/dropdown-menu/dropdown-menu-label.svelte b/multimodal-embeddings/src/lib/components/ui/dropdown-menu/dropdown-menu-label.svelte new file mode 100644 index 0000000..43f1527 --- /dev/null +++ b/multimodal-embeddings/src/lib/components/ui/dropdown-menu/dropdown-menu-label.svelte @@ -0,0 +1,19 @@ + + + + + diff --git a/multimodal-embeddings/src/lib/components/ui/dropdown-menu/dropdown-menu-radio-group.svelte b/multimodal-embeddings/src/lib/components/ui/dropdown-menu/dropdown-menu-radio-group.svelte new file mode 100644 index 0000000..1c74ae1 --- /dev/null +++ b/multimodal-embeddings/src/lib/components/ui/dropdown-menu/dropdown-menu-radio-group.svelte @@ -0,0 +1,11 @@ + + + + + diff --git a/multimodal-embeddings/src/lib/components/ui/dropdown-menu/dropdown-menu-radio-item.svelte b/multimodal-embeddings/src/lib/components/ui/dropdown-menu/dropdown-menu-radio-item.svelte new file mode 100644 index 0000000..370ada8 --- /dev/null +++ b/multimodal-embeddings/src/lib/components/ui/dropdown-menu/dropdown-menu-radio-item.svelte @@ -0,0 +1,35 @@ + + + + + + + + + + diff --git a/multimodal-embeddings/src/lib/components/ui/dropdown-menu/dropdown-menu-separator.svelte b/multimodal-embeddings/src/lib/components/ui/dropdown-menu/dropdown-menu-separator.svelte new file mode 100644 index 0000000..e14d078 --- /dev/null +++ b/multimodal-embeddings/src/lib/components/ui/dropdown-menu/dropdown-menu-separator.svelte @@ -0,0 +1,14 @@ + + + diff --git a/multimodal-embeddings/src/lib/components/ui/dropdown-menu/dropdown-menu-shortcut.svelte b/multimodal-embeddings/src/lib/components/ui/dropdown-menu/dropdown-menu-shortcut.svelte new file mode 100644 index 0000000..d8c7378 --- /dev/null +++ b/multimodal-embeddings/src/lib/components/ui/dropdown-menu/dropdown-menu-shortcut.svelte @@ -0,0 +1,13 @@ + + + + + diff --git a/multimodal-embeddings/src/lib/components/ui/dropdown-menu/dropdown-menu-sub-content.svelte b/multimodal-embeddings/src/lib/components/ui/dropdown-menu/dropdown-menu-sub-content.svelte new file mode 100644 index 0000000..034880f --- /dev/null +++ b/multimodal-embeddings/src/lib/components/ui/dropdown-menu/dropdown-menu-sub-content.svelte @@ -0,0 +1,29 @@ + + + + + diff --git a/multimodal-embeddings/src/lib/components/ui/dropdown-menu/dropdown-menu-sub-trigger.svelte b/multimodal-embeddings/src/lib/components/ui/dropdown-menu/dropdown-menu-sub-trigger.svelte new file mode 100644 index 0000000..d4ce330 --- /dev/null +++ b/multimodal-embeddings/src/lib/components/ui/dropdown-menu/dropdown-menu-sub-trigger.svelte @@ -0,0 +1,32 @@ + + + + + + diff --git a/multimodal-embeddings/src/lib/components/ui/dropdown-menu/index.ts b/multimodal-embeddings/src/lib/components/ui/dropdown-menu/index.ts new file mode 100644 index 0000000..c1749e9 --- /dev/null +++ b/multimodal-embeddings/src/lib/components/ui/dropdown-menu/index.ts @@ -0,0 +1,48 @@ +import { DropdownMenu as DropdownMenuPrimitive } from "bits-ui"; +import Item from "./dropdown-menu-item.svelte"; +import Label from "./dropdown-menu-label.svelte"; +import Content from "./dropdown-menu-content.svelte"; +import Shortcut from "./dropdown-menu-shortcut.svelte"; +import RadioItem from "./dropdown-menu-radio-item.svelte"; +import Separator from "./dropdown-menu-separator.svelte"; +import RadioGroup from "./dropdown-menu-radio-group.svelte"; +import SubContent from "./dropdown-menu-sub-content.svelte"; +import SubTrigger from "./dropdown-menu-sub-trigger.svelte"; +import CheckboxItem from "./dropdown-menu-checkbox-item.svelte"; + +const Sub = DropdownMenuPrimitive.Sub; +const Root = DropdownMenuPrimitive.Root; +const Trigger = DropdownMenuPrimitive.Trigger; +const Group = DropdownMenuPrimitive.Group; + +export { + Sub, + Root, + Item, + Label, + Group, + Trigger, + Content, + Shortcut, + Separator, + RadioItem, + SubContent, + SubTrigger, + RadioGroup, + CheckboxItem, + // + Root as DropdownMenu, + Sub as DropdownMenuSub, + Item as DropdownMenuItem, + Label as DropdownMenuLabel, + Group as DropdownMenuGroup, + Content as DropdownMenuContent, + Trigger as DropdownMenuTrigger, + Shortcut as DropdownMenuShortcut, + RadioItem as DropdownMenuRadioItem, + Separator as DropdownMenuSeparator, + RadioGroup as DropdownMenuRadioGroup, + SubContent as DropdownMenuSubContent, + SubTrigger as DropdownMenuSubTrigger, + CheckboxItem as DropdownMenuCheckboxItem, +}; diff --git a/multimodal-embeddings/src/lib/components/ui/input/index.ts b/multimodal-embeddings/src/lib/components/ui/input/index.ts new file mode 100644 index 0000000..75e3bc2 --- /dev/null +++ b/multimodal-embeddings/src/lib/components/ui/input/index.ts @@ -0,0 +1,29 @@ +import Root from "./input.svelte"; + +export type FormInputEvent = T & { + currentTarget: EventTarget & HTMLInputElement; +}; +export type InputEvents = { + blur: FormInputEvent; + change: FormInputEvent; + click: FormInputEvent; + focus: FormInputEvent; + focusin: FormInputEvent; + focusout: FormInputEvent; + keydown: FormInputEvent; + keypress: FormInputEvent; + keyup: FormInputEvent; + mouseover: FormInputEvent; + mouseenter: FormInputEvent; + mouseleave: FormInputEvent; + mousemove: FormInputEvent; + paste: FormInputEvent; + input: FormInputEvent; + wheel: FormInputEvent; +}; + +export { + Root, + // + Root as Input, +}; diff --git a/multimodal-embeddings/src/lib/components/ui/input/input.svelte b/multimodal-embeddings/src/lib/components/ui/input/input.svelte new file mode 100644 index 0000000..4d58501 --- /dev/null +++ b/multimodal-embeddings/src/lib/components/ui/input/input.svelte @@ -0,0 +1,42 @@ + + + diff --git a/multimodal-embeddings/src/lib/components/ui/label/index.ts b/multimodal-embeddings/src/lib/components/ui/label/index.ts new file mode 100644 index 0000000..8bfca0b --- /dev/null +++ b/multimodal-embeddings/src/lib/components/ui/label/index.ts @@ -0,0 +1,7 @@ +import Root from "./label.svelte"; + +export { + Root, + // + Root as Label, +}; diff --git a/multimodal-embeddings/src/lib/components/ui/label/label.svelte b/multimodal-embeddings/src/lib/components/ui/label/label.svelte new file mode 100644 index 0000000..0ae7acc --- /dev/null +++ b/multimodal-embeddings/src/lib/components/ui/label/label.svelte @@ -0,0 +1,19 @@ + + + + + diff --git a/multimodal-embeddings/src/lib/consts.ts b/multimodal-embeddings/src/lib/consts.ts new file mode 100644 index 0000000..eeab1de --- /dev/null +++ b/multimodal-embeddings/src/lib/consts.ts @@ -0,0 +1,4 @@ +// Update these after completing setup +// https://firebase.google.com/docs/web/setup +export const FIREBASE_PROJECT_ID = 'your-firebase-project-id'; +export const FIRESTORE_DB_ID = 'your-firestore-database'; diff --git a/multimodal-embeddings/src/lib/embedder.ts b/multimodal-embeddings/src/lib/embedder.ts new file mode 100644 index 0000000..ec0c0d4 --- /dev/null +++ b/multimodal-embeddings/src/lib/embedder.ts @@ -0,0 +1,90 @@ +/** + * See https://cloud.google.com/vertex-ai/generative-ai/docs/embeddings/get-multimodal-embedding + * for more info on how this is built. + */ +import * as aiplatform from '@google-cloud/aiplatform'; +import type { EmbeddingRequest } from './api'; +import { FIREBASE_PROJECT_ID } from './consts'; + +const { PredictionServiceClient } = aiplatform.v1; +const { helpers } = aiplatform; + +const clientOptions = { + apiEndpoint: 'us-central1-aiplatform.googleapis.com' +}; +const project = FIREBASE_PROJECT_ID; // your firebase/cloud project +const location = 'us-central1'; +const publisher = 'google'; +const model = 'multimodalembedding@001'; +const predictionServiceClient = new PredictionServiceClient(clientOptions); +const endpoint = `projects/${project}/locations/${location}/publishers/${publisher}/models/${model}`; + +type Prompt = { + image?: { + bytesBase64Encoded: string; + }; + text?: string; +}; + +export const getEmbeddings = async (embedRequest: EmbeddingRequest) => { + // all params: + // https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/multimodal-embeddings-api#parameter-list + + let prompt: Prompt = {}; + + if (embedRequest.imageBytes) { + let b64 = ''; + if (typeof embedRequest.imageBytes === 'string') { + b64 = embedRequest.imageBytes; + } else { + b64 = embedRequest.imageBytes.toString('base64'); + } + prompt.image = { + bytesBase64Encoded: b64 + }; + } + if (embedRequest.text) { + prompt.text = embedRequest.text; + } + + console.log('prompt:', prompt); + + const instanceValue = helpers.toValue(prompt); + const instances = [instanceValue]; + + const parameter = { + sampleCount: 1 + }; + const parameters = helpers.toValue(parameter); + + const request = { + endpoint, + instances, + parameters + }; + + // Predict request + const [response] = await predictionServiceClient.predict(request); + const predictions = response.predictions || []; + if (predictions && predictions.length > 0) { + const availableFields = predictions[0].structValue.fields; + + let embedResponse = {}; + + if (availableFields.imageEmbedding) { + embedResponse.imageEmbeddings = availableFields.imageEmbedding.listValue.values.map( + (vals) => vals.numberValue + ); + } + + if (availableFields.textEmbedding) { + embedResponse.textEmbeddings = availableFields.textEmbedding.listValue.values.map( + (vals) => vals.numberValue + ); + } + console.log('Response generated:', embedResponse); + return embedResponse; + } else { + throw Error('Failure to create embeddings'); + } +}; diff --git a/multimodal-embeddings/src/lib/firebase.ts b/multimodal-embeddings/src/lib/firebase.ts new file mode 100644 index 0000000..1efd748 --- /dev/null +++ b/multimodal-embeddings/src/lib/firebase.ts @@ -0,0 +1,23 @@ +import { initializeApp } from 'firebase/app'; +import { collection, getDocs, getFirestore } from 'firebase/firestore'; +import { getStorage, ref, getDownloadURL } from 'firebase/storage'; +import { FIREBASE_PROJECT_ID, FIRESTORE_DB_ID } from './consts'; + +const firebaseConfig = { + projectId: FIREBASE_PROJECT_ID + // rest of your config, from the firebase console + // directions: https://firebase.google.com/docs/web/setup +}; + +export const app = initializeApp(firebaseConfig); +export const fs = getFirestore(FIRESTORE_DB_ID); + +export const storage = getStorage(app); + +export const allDocsInCollection = async (collName: string) => { + return await getDocs(collection(fs, collName)); +}; + +export const getThumb = async (thumbPath: string) => { + return await getDownloadURL(ref(storage, thumbPath)); +}; diff --git a/multimodal-embeddings/src/lib/store.ts b/multimodal-embeddings/src/lib/store.ts new file mode 100644 index 0000000..c553459 --- /dev/null +++ b/multimodal-embeddings/src/lib/store.ts @@ -0,0 +1,35 @@ +import { writable } from 'svelte/store'; +import type { UMAPParameters } from 'umap-js'; +import type { DocumentData } from 'firebase/firestore'; + +export type ImageData = { + collection: string; + doc: DocumentData; + umap?: number[]; + label?: string; + ref?: any; +}; + +export type ImageCollection = { + name: string; + images: ImageData[]; +}; + +// component access hack +type RemapFunction = () => Promise; +export const remapFn = writable(); + +export const collections = writable([]); +export const allImages = writable([]); + +// Settings + +// UMAP bindings + +export const umapParams = writable({ + nComponents: 3, + nNeighbors: 15, + nEpochs: 400, + minDist: 0.5, + spread: 1.5 +}); diff --git a/multimodal-embeddings/src/lib/utils.ts b/multimodal-embeddings/src/lib/utils.ts new file mode 100644 index 0000000..37bff8b --- /dev/null +++ b/multimodal-embeddings/src/lib/utils.ts @@ -0,0 +1,73 @@ +import { type ClassValue, clsx } from 'clsx'; +import { twMerge } from 'tailwind-merge'; +import { cubicOut } from 'svelte/easing'; +import type { TransitionConfig } from 'svelte/transition'; + +export function cn(...inputs: ClassValue[]) { + return twMerge(clsx(inputs)); +} + +type FlyAndScaleParams = { + y?: number; + x?: number; + start?: number; + duration?: number; +}; + +export const flyAndScale = ( + node: Element, + params: FlyAndScaleParams = { y: -8, x: 0, start: 0.95, duration: 150 } +): TransitionConfig => { + const style = getComputedStyle(node); + const transform = style.transform === 'none' ? '' : style.transform; + + const scaleConversion = (valueA: number, scaleA: [number, number], scaleB: [number, number]) => { + const [minA, maxA] = scaleA; + const [minB, maxB] = scaleB; + + const percentage = (valueA - minA) / (maxA - minA); + const valueB = percentage * (maxB - minB) + minB; + + return valueB; + }; + + const styleToString = (style: Record): string => { + return Object.keys(style).reduce((str, key) => { + if (style[key] === undefined) return str; + return str + `${key}:${style[key]};`; + }, ''); + }; + + return { + duration: params.duration ?? 200, + delay: 0, + css: (t) => { + const y = scaleConversion(t, [0, 1], [params.y ?? 5, 0]); + const x = scaleConversion(t, [0, 1], [params.x ?? 0, 0]); + const scale = scaleConversion(t, [0, 1], [params.start ?? 0.95, 1]); + + return styleToString({ + transform: `${transform} translate3d(${x}px, ${y}px, 0) scale(${scale})`, + opacity: t + }); + }, + easing: cubicOut + }; +}; + +export const postJson = async (url: string, data: any) => { + console.log(`Call ${url}`, data); + const response = await fetch(url, { + method: 'POST', + headers: { + 'Content-Type': 'application/json' + }, + body: JSON.stringify(data) + }); + + const json = await response.json(); + + if (json.error) throw json.error; + + return json; +}; diff --git a/multimodal-embeddings/src/routes/+layout.svelte b/multimodal-embeddings/src/routes/+layout.svelte new file mode 100644 index 0000000..18aae60 --- /dev/null +++ b/multimodal-embeddings/src/routes/+layout.svelte @@ -0,0 +1 @@ + \ No newline at end of file diff --git a/multimodal-embeddings/src/routes/+page.svelte b/multimodal-embeddings/src/routes/+page.svelte new file mode 100644 index 0000000..9450542 --- /dev/null +++ b/multimodal-embeddings/src/routes/+page.svelte @@ -0,0 +1 @@ +

Nope.

diff --git a/multimodal-embeddings/src/routes/api/getEmbeddings/+server.ts b/multimodal-embeddings/src/routes/api/getEmbeddings/+server.ts new file mode 100644 index 0000000..9dce448 --- /dev/null +++ b/multimodal-embeddings/src/routes/api/getEmbeddings/+server.ts @@ -0,0 +1,23 @@ +import { json } from '@sveltejs/kit'; +import { getEmbeddings } from '$lib/embedder'; +import type { EmbeddingRequest } from '$lib/api.js'; + +/** + * Search API method using image embeddings for use in the /search route + * @param request + * @returns the embeddings and the results of the vector search + */ +export async function POST({ request }) { + try { + const embedRequest = (await request.json()) as EmbeddingRequest; + console.log('/api/getEmbeddings:', embedRequest); + + const embeddings = await getEmbeddings(embedRequest); + // console.log('embedResult:', embeddings); + + return json({ embeddings }); + } catch (error) { + console.error(error); + return json({ error }); + } +} diff --git a/multimodal-embeddings/src/routes/api/listCollections/+server.ts b/multimodal-embeddings/src/routes/api/listCollections/+server.ts new file mode 100644 index 0000000..a8cfb10 --- /dev/null +++ b/multimodal-embeddings/src/routes/api/listCollections/+server.ts @@ -0,0 +1,19 @@ +import { json } from '@sveltejs/kit'; +import { listCollections } from '$lib/cloud-firebase'; + +/** + * Search API method using image embeddings for use in the /search route + * @param request + * @returns the embeddings and the results of the vector search + */ +export async function POST() { + try { + const collections = await listCollections(); + console.log('/api/listCollections:', collections); + + return json({ collections }); + } catch (error) { + console.error(error); + return json({ error }); + } +} diff --git a/multimodal-embeddings/src/routes/api/search/+server.ts b/multimodal-embeddings/src/routes/api/search/+server.ts new file mode 100644 index 0000000..ebce30a --- /dev/null +++ b/multimodal-embeddings/src/routes/api/search/+server.ts @@ -0,0 +1,23 @@ +import { json } from '@sveltejs/kit'; +import { vectorSearch } from '$lib/cloud-firebase'; + +/** + * Search API method using image embeddings for use in the /search route + * @param request + * @returns the embeddings and the results of the vector search + */ +export async function POST({ request }) { + try { + const body = await request.json(); + + console.log('/api/search:', body); + + const searchResultDocs = await vectorSearch(body.collection, body.embeddings); + const searchResults = searchResultDocs.map((doc) => doc.data()); + + return json({ searchResults }); + } catch (error) { + console.error(error); + return json({ error }); + } +} diff --git a/multimodal-embeddings/src/routes/api/umap/+server.ts b/multimodal-embeddings/src/routes/api/umap/+server.ts new file mode 100644 index 0000000..47570bf --- /dev/null +++ b/multimodal-embeddings/src/routes/api/umap/+server.ts @@ -0,0 +1,26 @@ +import { UMAP, type UMAPParameters } from 'umap-js'; +import { json } from '@sveltejs/kit'; + +export const POST = async ({ request }) => { + try { + const data = await request.json(); + + const modelOpts: UMAPParameters = { + nComponents: data.dimensions, + nEpochs: data.epochs, + nNeighbors: data.neighbors, + minDist: data.minDist, + spread: data.spread + }; + console.debug(modelOpts); + + const model = new UMAP(modelOpts); + const mapped = await model.fitAsync(data.embeddings); + console.log(mapped[0]); + + return json({ data: mapped }); + } catch (err) { + console.error(err); + return json({ err }); + } +}; diff --git a/multimodal-embeddings/src/routes/search/+page.svelte b/multimodal-embeddings/src/routes/search/+page.svelte new file mode 100644 index 0000000..5146a92 --- /dev/null +++ b/multimodal-embeddings/src/routes/search/+page.svelte @@ -0,0 +1,146 @@ + + +
+
+
+

My Personal Search

+ + + + + + + + + + + + {#if file} +
+ Searchable input +
+ {/if} + + + + {#if $searchState != SearchState.Waiting} +
+ +

{$searchState}

+
+ {/if} + {#if errorResponse.length > 0} +
+

+ {errorResponse} +

+
+ {/if} +
+
+ {#if searchResponse} +
+ {#each searchResponse.searchResults as result} + + {/each} +
+ {/if} +
+
+
+ diff --git a/multimodal-embeddings/src/routes/search/ImageResult.svelte b/multimodal-embeddings/src/routes/search/ImageResult.svelte new file mode 100644 index 0000000..0b2ef95 --- /dev/null +++ b/multimodal-embeddings/src/routes/search/ImageResult.svelte @@ -0,0 +1,16 @@ + + +
+ Thumbnail +
diff --git a/multimodal-embeddings/src/routes/viz/+page.svelte b/multimodal-embeddings/src/routes/viz/+page.svelte new file mode 100644 index 0000000..832317c --- /dev/null +++ b/multimodal-embeddings/src/routes/viz/+page.svelte @@ -0,0 +1,77 @@ + + +
+ +
+

The Comparator

+

Choose two or more collections below to compare:

+ + +
+ + +
diff --git a/multimodal-embeddings/src/routes/viz/Image.svelte b/multimodal-embeddings/src/routes/viz/Image.svelte new file mode 100644 index 0000000..edf53df --- /dev/null +++ b/multimodal-embeddings/src/routes/viz/Image.svelte @@ -0,0 +1,68 @@ + + + + +{#if mat} + { + created(ref); + cleanup(() => { + if (texture) texture.dispose(); + }); + }} + position.x={$pos.x} + position.y={$pos.y} + position.z={$pos.z} + scale={$scale} + on:pointerenter={onEnter} + on:pointerleave={onLeave} + on:click={clickHandler} + material={mat} + /> +{/if} diff --git a/multimodal-embeddings/src/routes/viz/Scene.svelte b/multimodal-embeddings/src/routes/viz/Scene.svelte new file mode 100644 index 0000000..54ff3b8 --- /dev/null +++ b/multimodal-embeddings/src/routes/viz/Scene.svelte @@ -0,0 +1,151 @@ + + + + + { + ref.lookAt(0, 1, 0); + }} +> + + + + + {#if !boundingBox.isEmpty()} + + {/if} + + {#if $state === State.Ready} + {#each $allImages as imageData} + + + + {/each} + {/if} + + + (sphere = ref)}> + + + diff --git a/multimodal-embeddings/src/routes/viz/Settings.svelte b/multimodal-embeddings/src/routes/viz/Settings.svelte new file mode 100644 index 0000000..a6f8e0c --- /dev/null +++ b/multimodal-embeddings/src/routes/viz/Settings.svelte @@ -0,0 +1,46 @@ + + +
+ + + + + + + + + +
diff --git a/multimodal-embeddings/src/routes/viz/Viz.svelte b/multimodal-embeddings/src/routes/viz/Viz.svelte new file mode 100644 index 0000000..4454f5b --- /dev/null +++ b/multimodal-embeddings/src/routes/viz/Viz.svelte @@ -0,0 +1,17 @@ + + +
+ + + +
+
+

Selected:

+
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