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official Node.js / Typescript library for EyePop.ai's inference API","maintainers":[{"name":"andy_eyepop.ai","email":"andy@eyepop.ai"},{"name":"tschulz_ep","email":"torsten@eyepop.ai"}],"readme":"# EyePop.ai Node SDK\n\nThe EyePop.ai Node SDK provides JavaScript and TypeScript access to EyePop worker sessions for image and video inference.\n\n## Installation\n\n### Node\n\nRequires Node.js 18 or newer. CI validates Node.js 22.\n\n```shell\nnpm install --save @eyepop.ai/eyepop\n```\n\nSee the [Node examples](https://github.com/eyepop-ai/eyepop-sdk-node/blob/main/examples/node) for runnable scripts.\n\n### Browser\n\nBrowser applications can use the bundled SDK directly:\n\n```html\n<script src=\"https://cdn.jsdelivr.net/npm/@eyepop.ai/eyepop/dist/eyepop.min.js\"></script>\n```\n\nAPI keys must not be used in browser code. Browser apps should connect with sessions created by your trusted backend.\n\n### React Native\n\nReact Native applications should use the React Native package:\n\n```shell\nnpm install --save @eyepop.ai/react-native-eyepop\n```\n\nSee the [React Native example](https://github.com/eyepop-ai/eyepop-sdk-node/tree/main/examples/react_native/upload-example).\n\n## Authentication\n\nThe SDK supports these authentication patterns:\n\n- API key authentication for trusted server-side code.\n- Persistent worker session selection for provisioned deployments.\n- Browser OAuth for dashboard users building local demos.\n\n### Server-Side API Key\n\nSet `EYEPOP_API_KEY` in your server environment. API keys are secrets and must not be exposed in browser bundles, mobile app bundles, or public repositories.\n\n```shell\nexport EYEPOP_API_KEY=<your_api_key>\n```\n\nCreate transient sessions with a pop when the worker endpoint is constructed. This lets EyePop schedule the right compute before media is processed.\n\n```typescript\nimport { EyePop } from '@eyepop.ai/eyepop'\n;(async () => {\n    const endpoint = await EyePop.workerEndpoint({\n        pop: {\n            components: [\n                {\n                    type: 'inference',\n                    ability: 'eyepop.person:latest',\n                    categoryName: 'person',\n                },\n            ],\n        },\n    }).connect()\n    try {\n        const results = await endpoint.process({ source: { path: 'examples/example.jpg' } })\n        for await (const result of results) {\n            console.log(result)\n        }\n    } finally {\n        await endpoint.disconnect()\n    }\n})()\n```\n\nFor transient sessions, pass `pop` when creating the endpoint whenever possible. Use `endpoint.changePop(pop)` only when an already connected transient worker needs to switch Pops.\n\nWhen `sessionUuid` is not provided, the SDK creates compute sessions with `POST /v1/sessions?wait=true`.\n\nModelLess CPU pops use the same constructor-pop path:\n\n```typescript\nconst endpoint = await EyePop.workerEndpoint({\n    pop: {\n        components: [\n            {\n                type: 'inference',\n                ability: 'eyepop.localize-objects:latest',\n                categoryName: 'objects',\n                params: {\n                    prompts: [{ prompt: 'person' }],\n                },\n            },\n        ],\n    },\n}).connect()\n```\n\nYou can also pass the key explicitly from server-side configuration:\n\n```typescript\nconst endpoint = await EyePop.workerEndpoint({\n    apiKey: process.env.EYEPOP_API_KEY,\n}).connect()\n```\n\n`accessToken` is accepted as the same bearer credential shape:\n\n```typescript\nconst endpoint = await EyePop.workerEndpoint({\n    accessToken: process.env.EYEPOP_ACCESS_TOKEN,\n}).connect()\n```\n\nThe nested `auth` option is still supported but deprecated. Pass `apiKey`, `accessToken`, `session`, or `oAuth2` at the top level for new code.\n\n### Persistent Sessions\n\nIf EyePop has provisioned a persistent worker session for your deployment, set `EYEPOP_SESSION_UUID` in the trusted server environment or pass `sessionUuid` explicitly.\n\n```shell\nexport EYEPOP_API_KEY=<your_api_key>\nexport EYEPOP_SESSION_UUID=<your_session_uuid>\n```\n\n```typescript\nimport { EyePop } from '@eyepop.ai/eyepop'\n;(async () => {\n    const endpoint = await EyePop.workerEndpoint({\n        sessionUuid: process.env.EYEPOP_SESSION_UUID,\n    }).connect()\n\n    try {\n        const results = await endpoint.process({ source: { path: 'sample.mp4' } })\n        for await (const result of results) {\n            console.log(result)\n        }\n    } finally {\n        await endpoint.disconnect()\n    }\n})()\n```\n\nPersistent sessions are normally preconfigured. In that case, process media directly and do not call `changePop()` unless your deployment is meant to accept runtime Pop changes.\n\nPersistent deployments are created through compute-api or EyePop tooling. For example, a trusted server can create a persistent deployment with an inline pop and then pass the returned `session_uuid` to the SDK:\n\n```shell\ncurl -sS -X POST \"https://compute.eyepop.ai/v1/deployments?wait=true\" \\\n  -H \"Authorization: Bearer $EYEPOP_API_KEY\" \\\n  -H \"Content-Type: application/json\" \\\n  -d '{\n    \"display_name\": \"node-modeless-deployment\",\n    \"pop\": {\n      \"components\": [\n        {\n          \"type\": \"inference\",\n          \"ability\": \"eyepop.localize-objects:latest\",\n          \"categoryName\": \"objects\",\n          \"params\": {\n            \"prompts\": [{ \"prompt\": \"person\" }]\n          }\n        }\n      ]\n    }\n  }'\n```\n\nThe response includes `session_uuid`, `session_status`, `status_url`, and worker access metadata. Poll `status_url` until the deployment reaches `pipeline_ok`, then connect with `sessionUuid`.\n\n### Browser and Mobile Clients\n\nDo not put `EYEPOP_API_KEY` in browser or mobile application code. Create EyePop sessions from a trusted backend, then pass only the session JSON to the client.\n\n### Browser OAuth for Local Dashboard Demos\n\nDashboard users can run local browser demos with the current browser session:\n\n```html\n<script>\n    document.addEventListener('DOMContentLoaded', async () => {\n        const endpoint = await EyePop.workerEndpoint({ oAuth2: true }).connect()\n        try {\n            await endpoint.changePop({\n                components: [\n                    {\n                        type: 'inference',\n                        ability: 'eyepop.person:latest',\n                        categoryName: 'person',\n                    },\n                ],\n            })\n        } finally {\n            await endpoint.disconnect()\n        }\n    })\n</script>\n```\n\n## Processing Media\n\n### Local Files\n\n```typescript\nconst results = await endpoint.process({ source: { path: 'examples/example.jpg' } })\nfor await (const result of results) {\n    console.log(result)\n}\n```\n\n`process()` returns an `AsyncIterable` of predictions. Images normally produce one prediction. Videos and animated image containers produce one prediction per frame.\n\n### Readable Streams\n\nStreams require an explicit MIME type:\n\n```typescript\nimport fs from 'node:fs'\n\nconst stream = fs.createReadStream('examples/example.jpg')\nconst results = await endpoint.process({\n    source: { stream, mimeType: 'image/jpeg' },\n})\n```\n\n### Public URLs\n\nPublic HTTP(S), RTSP, and RTMP URLs can be processed without uploading the file from your application.\n\n```typescript\nconst results = await endpoint.process({\n    source: { url: 'https://demo-eyepop-videos.s3.amazonaws.com/test1_vlog.mp4' },\n})\n```\n\n### Image Groups (multiple images, one result)\n\nSend several images as a **single** source that the pop processes **together** as one inference unit — for example a multi-image VLM prompt. The group yields **one** prediction for the whole set, unlike batching below, where each image is an independent inference.\n\n```typescript\n// Local files\nconst results = await endpoint.uploadGroup(['a.jpg', 'b.jpg', 'c.jpg'])\nfor await (const result of results) {\n    console.log(result)\n}\n\n// In-memory streams (optional parallel MIME types)\nimport fs from 'node:fs'\nimport { Readable } from 'node:stream'\nconst stream1 = Readable.toWeb(fs.createReadStream('a.jpg'))\nconst stream2 = Readable.toWeb(fs.createReadStream('b.jpg'))\nconst results = await endpoint.uploadStreamGroup([stream1, stream2], ['image/jpeg', 'image/jpeg'])\n\n// Remote URLs (the server fetches each)\nconst results = await endpoint.loadFromGroup(['https://example.com/a.jpg', 'https://example.com/b.jpg'])\n```\n\nImage order is preserved end-to-end. A group may contain **up to 16 images** (enforced server-side). The pop's ability must be multi-image-capable; a single-image ability handed a group returns an error.\n\n### Canceling Jobs\n\nQueued and in-progress jobs can be cancelled from the result iterator:\n\n```typescript\nconst results = await endpoint.process({\n    source: { url: 'https://demo-eyepop-videos.s3.amazonaws.com/test1_vlog.mp4' },\n})\n\nfor await (const result of results) {\n    console.log(result)\n    if ((result.seconds ?? 0) >= 10) {\n        results.cancel()\n    }\n}\n```\n\n## Endpoint Options\n\nBy default, `EyePop.workerEndpoint({ pop }).connect()` starts a transient worker when needed and cancels queued jobs on that worker when it connects.\n\nDisable worker startup:\n\n```typescript\nconst endpoint = await EyePop.workerEndpoint({ pop, autoStart: false }).connect()\n```\n\nKeep pending jobs:\n\n```typescript\nconst endpoint = await EyePop.workerEndpoint({ pop, stopJobs: false }).connect()\n```\n\n## Visualization\n\nRendering helpers are provided by [@eyepop.ai/eyepop-render-2d](https://www.npmjs.com/package/@eyepop.ai/eyepop-render-2d).\n\n## Data Endpoint Preview\n\n`EyePop.dataEndpoint()` provides preview support for dataset management and model optimization workflows. This API is experimental and subject to change.\n\n## Composable Pops\n\nSee [Composable Pops](composable-pops.md) for client-side Pop composition.\n","readmeFilename":"README.md"}