diff --git a/docs/components/data-sources/overview.mdx b/docs/components/data-sources/overview.mdx index 3c63ecd3..3a253423 100644 --- a/docs/components/data-sources/overview.mdx +++ b/docs/components/data-sources/overview.mdx @@ -17,6 +17,7 @@ We handle the complexity of loading unstructured data from these data sources, a + diff --git a/docs/components/data-sources/twelvelabs-video.mdx b/docs/components/data-sources/twelvelabs-video.mdx new file mode 100644 index 00000000..962d706d --- /dev/null +++ b/docs/components/data-sources/twelvelabs-video.mdx @@ -0,0 +1,47 @@ +--- +title: '🎬 TwelveLabs Video' +--- + +To add any video to your app, use the `TwelveLabsVideoLoader`. It analyses the video with the [TwelveLabs](https://twelvelabs.io) Pegasus video understanding model — which watches the visuals, motion and audio — and loads the resulting description into your RAG application. This lets you run RAG over video content directly from a public URL, without a separate transcription step. + +- Sign up for an account with TwelveLabs and grab an API key. There's a generous free tier at [twelvelabs.io](https://twelvelabs.io). + +- Set the key in the environment variable `TWELVELABS_API_KEY` (or pass it directly to the loader). + +```bash +TWELVELABS_API_KEY="" +``` + +## Install TwelveLabs addon + +```bash +npm install @llm-tools/embedjs-twelvelabs +``` + +## Usage + +```ts +import { RAGApplicationBuilder } from '@llm-tools/embedjs'; +import { OpenAiEmbeddings } from '@llm-tools/embedjs-openai'; +import { HNSWDb } from '@llm-tools/embedjs-hnswlib'; +import { TwelveLabsVideoLoader } from '@llm-tools/embedjs-twelvelabs'; + +const app = await new RAGApplicationBuilder() +.setModel(SIMPLE_MODELS.OPENAI_GPT4_O) +.setEmbeddingModel(new OpenAiEmbeddings()) +.setVectorDatabase(new HNSWDb()) +.build(); + +app.addLoader(new TwelveLabsVideoLoader({ url: 'https://example.com/video.mp4' })) +``` + +You can customise the analysis with an optional `prompt`, `model` (`pegasus1.2` or `pegasus1.5`) and `maxTokens`: + +```ts +app.addLoader(new TwelveLabsVideoLoader({ + url: 'https://example.com/video.mp4', + prompt: 'Summarize the key moments in this video.', + model: 'pegasus1.2', + maxTokens: 2048, +})) +``` diff --git a/docs/components/embeddings/overview.mdx b/docs/components/embeddings/overview.mdx index 293d1065..b455946a 100644 --- a/docs/components/embeddings/overview.mdx +++ b/docs/components/embeddings/overview.mdx @@ -12,6 +12,7 @@ EmbedJs supports several embedding models from the following providers: +
diff --git a/docs/components/embeddings/twelvelabs.mdx b/docs/components/embeddings/twelvelabs.mdx new file mode 100644 index 00000000..809ee9f4 --- /dev/null +++ b/docs/components/embeddings/twelvelabs.mdx @@ -0,0 +1,38 @@ +--- +title: 'TwelveLabs (Marengo)' +--- + +The library supports usage of the [TwelveLabs](https://twelvelabs.io) `marengo3.0` multimodal embedding model out of the box. Marengo embeds text, image, audio and video into a single shared latent space, which makes it a great fit for retrieval over video libraries. This model returns vectors with dimension 512. + +Here's what you have to do to use it - + +- Sign up for an account with TwelveLabs and grab an API key. There's a generous free tier at [twelvelabs.io](https://twelvelabs.io). + +- Set the key in the environment variable `TWELVELABS_API_KEY` (or pass it directly to the constructor). + +```bash +TWELVELABS_API_KEY="" +``` + +## Install TwelveLabs addon + +```bash +npm install @llm-tools/embedjs-twelvelabs +``` + +## Usage + +```ts +import { RAGApplicationBuilder } from '@llm-tools/embedjs'; +import { MarengoEmbeddings } from '@llm-tools/embedjs-twelvelabs'; +import { HNSWDb } from '@llm-tools/embedjs-hnswlib'; + +const app = await new RAGApplicationBuilder() +.setEmbeddingModel(new MarengoEmbeddings()) +``` + +You can also pass the API key explicitly: + +```ts +new MarengoEmbeddings({ apiKey: 'YOUR_KEY' }) +``` diff --git a/docs/mint.json b/docs/mint.json index 07e00dba..ba1f792e 100644 --- a/docs/mint.json +++ b/docs/mint.json @@ -85,6 +85,7 @@ "components/data-sources/xml", "components/data-sources/directory", "components/data-sources/image", + "components/data-sources/twelvelabs-video", "components/data-sources/custom" ] } @@ -139,7 +140,8 @@ "components/embeddings/cohere", "components/embeddings/ollama", "components/embeddings/huggingface", - "components/embeddings/vertexai" + "components/embeddings/vertexai", + "components/embeddings/twelvelabs" ] } ] diff --git a/models/embedjs-twelvelabs/README.md b/models/embedjs-twelvelabs/README.md new file mode 100644 index 00000000..efb410f9 --- /dev/null +++ b/models/embedjs-twelvelabs/README.md @@ -0,0 +1,13 @@ +# embedjs-twelvelabs + +

+NPM Version +License +

+ +This package extends [embedJs](https://www.npmjs.com/package/@llm-tools/embedjs) with [TwelveLabs](https://twelvelabs.io) video understanding: + +- **`MarengoEmbeddings`** — multimodal embeddings from the Marengo model (512 dimensions), usable as a drop in embedding model. +- **`TwelveLabsVideoLoader`** — a data source that analyses a video with the Pegasus model and loads the resulting description into your RAG application. + +Refer to the [embedJs documentation](https://llm-tools.mintlify.app) for more details. diff --git a/models/embedjs-twelvelabs/eslint.config.js b/models/embedjs-twelvelabs/eslint.config.js new file mode 100644 index 00000000..4c3c47f6 --- /dev/null +++ b/models/embedjs-twelvelabs/eslint.config.js @@ -0,0 +1,20 @@ +import baseConfig from '../../eslint.config.js'; +import parser from '@nx/eslint-plugin'; + +export default [ + ...baseConfig, + { + files: ['**/*.json'], + rules: { + '@nx/dependency-checks': [ + 'error', + { + ignoredFiles: ['{projectRoot}/eslint.config.{js,cjs,mjs}'], + }, + ], + }, + languageOptions: { + parser, + }, + }, +]; diff --git a/models/embedjs-twelvelabs/package.json b/models/embedjs-twelvelabs/package.json new file mode 100644 index 00000000..a4ef8ad1 --- /dev/null +++ b/models/embedjs-twelvelabs/package.json @@ -0,0 +1,40 @@ +{ + "name": "@llm-tools/embedjs-twelvelabs", + "version": "0.1.31", + "description": "Enable usage of TwelveLabs Marengo embeddings and Pegasus video understanding with embedjs", + "dependencies": { + "@langchain/textsplitters": "^1.0.0", + "@llm-tools/embedjs-interfaces": "0.1.31", + "@llm-tools/embedjs-utils": "0.1.31", + "debug": "^4.4.3", + "md5": "^2.3.0", + "twelvelabs-js": "^1.2.8" + }, + "type": "module", + "main": "./src/index.js", + "license": "Apache-2.0", + "publishConfig": { + "access": "public" + }, + "keywords": [ + "llm", + "ai", + "twelvelabs", + "marengo", + "pegasus", + "video", + "multimodal", + "embeddings", + "vectorstores", + "rag" + ], + "author": "K V Adhityan", + "bugs": { + "url": "https://github.com/llm-tools/embedjs/issues" + }, + "homepage": "https://github.com/llm-tools/embedjs#readme", + "repository": { + "type": "git", + "url": "git+https://github.com/llm-tools/embedjs.git" + } +} diff --git a/models/embedjs-twelvelabs/project.json b/models/embedjs-twelvelabs/project.json new file mode 100644 index 00000000..05c77c2e --- /dev/null +++ b/models/embedjs-twelvelabs/project.json @@ -0,0 +1,19 @@ +{ + "name": "embedjs-twelvelabs", + "$schema": "../../node_modules/nx/schemas/project-schema.json", + "sourceRoot": "models/embedjs-twelvelabs/src", + "projectType": "library", + "tags": [], + "targets": { + "build": { + "executor": "@nx/js:tsc", + "outputs": ["{options.outputPath}"], + "options": { + "outputPath": "dist/embedjs-twelvelabs", + "main": "models/embedjs-twelvelabs/src/index.ts", + "tsConfig": "models/embedjs-twelvelabs/tsconfig.json", + "assets": ["models/embedjs-twelvelabs/*.md"] + } + } + } +} diff --git a/models/embedjs-twelvelabs/src/index.ts b/models/embedjs-twelvelabs/src/index.ts new file mode 100644 index 00000000..51811d12 --- /dev/null +++ b/models/embedjs-twelvelabs/src/index.ts @@ -0,0 +1,2 @@ +export * from './marengo-embeddings.js'; +export * from './twelvelabs-video-loader.js'; diff --git a/models/embedjs-twelvelabs/src/marengo-embeddings.ts b/models/embedjs-twelvelabs/src/marengo-embeddings.ts new file mode 100644 index 00000000..2a61b01c --- /dev/null +++ b/models/embedjs-twelvelabs/src/marengo-embeddings.ts @@ -0,0 +1,63 @@ +import { TwelveLabs } from 'twelvelabs-js'; +import { BaseEmbeddings } from '@llm-tools/embedjs-interfaces'; + +/** + * Multimodal embeddings backed by TwelveLabs' Marengo model. Marengo embeds text, + * image, audio and video into a single shared latent space, which makes it a good + * fit for RAG over video libraries. This class exposes the text side of that space + * so it can be used as a drop in embedding model for embedJs. + * + * The Marengo `marengo3.0` model returns 512 dimensional vectors. + */ +export class MarengoEmbeddings extends BaseEmbeddings { + private readonly client: TwelveLabs; + private readonly model: string; + private readonly dimensions: number; + + constructor({ + apiKey, + model = 'marengo3.0', + dimensions = 512, + }: { + /** TwelveLabs API key. Falls back to the `TWELVELABS_API_KEY` environment variable. */ + apiKey?: string; + /** Marengo model name. Defaults to `marengo3.0`. */ + model?: string; + /** Embedding dimensions returned by the model. Defaults to `512` (marengo3.0). */ + dimensions?: number; + } = {}) { + super(); + + const key = apiKey ?? process.env.TWELVELABS_API_KEY; + if (!key) { + throw new Error( + 'TwelveLabs API key is required. Pass it via the `apiKey` option or set the TWELVELABS_API_KEY environment variable.', + ); + } + + this.client = new TwelveLabs({ apiKey: key }); + this.model = model; + this.dimensions = dimensions; + } + + override async getDimensions(): Promise { + return this.dimensions; + } + + override async embedDocuments(texts: string[]): Promise { + // The Marengo embed endpoint accepts a single text per request, so we fan out. + return Promise.all(texts.map((text) => this.embedQuery(text))); + } + + override async embedQuery(text: string): Promise { + const response = await this.client.embed.create({ modelName: this.model, text }); + const float = response.textEmbedding?.segments?.[0]?.float; + + if (!float) { + const reason = response.textEmbedding?.errorMessage ?? 'no embedding was returned'; + throw new Error(`TwelveLabs Marengo did not return a text embedding: ${reason}`); + } + + return float; + } +} diff --git a/models/embedjs-twelvelabs/src/twelvelabs-video-loader.ts b/models/embedjs-twelvelabs/src/twelvelabs-video-loader.ts new file mode 100644 index 00000000..9b22aec3 --- /dev/null +++ b/models/embedjs-twelvelabs/src/twelvelabs-video-loader.ts @@ -0,0 +1,102 @@ +import { RecursiveCharacterTextSplitter } from '@langchain/textsplitters'; +import { TwelveLabs } from 'twelvelabs-js'; +import createDebugMessages from 'debug'; +import md5 from 'md5'; + +import { BaseLoader } from '@llm-tools/embedjs-interfaces'; +import { cleanString } from '@llm-tools/embedjs-utils'; + +/** + * Loads a video into embedJs by analysing it with TwelveLabs' Pegasus video + * understanding model. Pegasus watches the video (visuals, motion and audio) + * and returns a natural language description, which is then chunked and embedded + * like any other text source. This lets you run RAG over video content directly + * from a public URL, without a separate transcription step. + */ +export class TwelveLabsVideoLoader extends BaseLoader<{ type: 'TwelveLabsVideoLoader' }> { + private readonly debug = createDebugMessages('embedjs:loader:TwelveLabsVideoLoader'); + private readonly client: TwelveLabs; + private readonly url: string; + private readonly model: 'pegasus1.2' | 'pegasus1.5'; + private readonly prompt: string; + private readonly maxTokens: number; + + constructor({ + url, + apiKey, + model = 'pegasus1.2', + prompt = 'Describe everything that happens in this video in detail, including the visuals, actions, spoken words and on-screen text.', + maxTokens = 2048, + chunkSize, + chunkOverlap, + }: { + /** Publicly accessible URL of the video file (direct link to raw media). */ + url: string; + /** TwelveLabs API key. Falls back to the `TWELVELABS_API_KEY` environment variable. */ + apiKey?: string; + /** Pegasus model name. Defaults to `pegasus1.2`. */ + model?: 'pegasus1.2' | 'pegasus1.5'; + /** Prompt that guides the analysis. Defaults to a detailed description prompt. */ + prompt?: string; + /** Maximum response length in tokens. Defaults to `2048`. */ + maxTokens?: number; + chunkSize?: number; + chunkOverlap?: number; + }) { + super( + `TwelveLabsVideoLoader_${md5(`${url}_${model}_${prompt}`)}`, + { url }, + chunkSize ?? 2000, + chunkOverlap ?? 0, + ); + + const key = apiKey ?? process.env.TWELVELABS_API_KEY; + if (!key) { + throw new Error( + 'TwelveLabs API key is required. Pass it via the `apiKey` option or set the TWELVELABS_API_KEY environment variable.', + ); + } + + this.client = new TwelveLabs({ apiKey: key }); + this.url = url; + this.model = model; + this.prompt = prompt; + this.maxTokens = maxTokens; + } + + override async *getUnfilteredChunks() { + const chunker = new RecursiveCharacterTextSplitter({ + chunkSize: this.chunkSize, + chunkOverlap: this.chunkOverlap, + }); + + try { + const response = await this.client.analyze({ + modelName: this.model, + video: { type: 'url', url: this.url }, + prompt: this.prompt, + maxTokens: this.maxTokens, + }); + + const text = response.data; + if (!text) { + this.debug('Pegasus returned no analysis for video', this.url); + return; + } + + this.debug(`Pegasus analysis (length ${text.length}) obtained for video`, this.url); + + for (const chunk of await chunker.splitText(cleanString(text))) { + yield { + pageContent: chunk, + metadata: { + type: 'TwelveLabsVideoLoader' as const, + source: this.url, + }, + }; + } + } catch (e) { + this.debug('Could not analyze video', this.url, e); + } + } +} diff --git a/models/embedjs-twelvelabs/tsconfig.json b/models/embedjs-twelvelabs/tsconfig.json new file mode 100644 index 00000000..e24c463d --- /dev/null +++ b/models/embedjs-twelvelabs/tsconfig.json @@ -0,0 +1,23 @@ +{ + "extends": "../../tsconfig.base.json", + "compilerOptions": { + "target": "ES2022", + "lib": ["ES2022", "ES2022.Object"], + "module": "NodeNext", + "moduleResolution": "nodenext", + "esModuleInterop": true, + "declaration": true, + "noImplicitReturns": true, + "noFallthroughCasesInSwitch": true, + "noUnusedLocals": true, + "noUnusedParameters": true, + "useDefineForClassFields": true, + "strictPropertyInitialization": false, + "allowJs": false, + "strict": false, + "outDir": "../../dist/out-tsc", + "types": ["node"] + }, + "include": ["src/**/*.ts"], + "exclude": ["src/**/*.spec.ts", "src/**/*.test.ts"] +} diff --git a/package-lock.json b/package-lock.json index edded963..bfaeaa20 100644 --- a/package-lock.json +++ b/package-lock.json @@ -900,110 +900,6 @@ } } }, - 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