Answers from the documentation; checking which model is on duty
Hi - I answer from the OpenSmartRoute documentation: routing, the API, plans and quotas, self-hosting. Ask away, or open a support ticket if you need a person.
Grounded in the docs - follow a source before acting on it.
upstash-vector-js - Skill - OpenSmartRoute
Skillv1.0.0
upstash-vector-js
Work with the @upstash/vector TypeScript/JavaScript SDK, a serverless vector database for embeddings, similarity search, semantic search, and RAG (retrieval-augmented generation).
Imported from upstash/skills (skills/upstash-vector-js/SKILL.md) via skills.sh. Install upstream with npx skills add upstash/skills --skill upstash-vector-js. Copyright stays with the author (MIT).
Vector Documentation Skill
Quick Start
Vector is a high‑performance vector database for storing, querying, and managing vector embeddings.
Basic workflow:
Install the Vector TS SDK.
Connect to a Vector instance.
Upsert vectors, query them, and manage namespaces.
Example (TypeScript):
import { Index } from "@upstash/vector";const index = new Index({ url: process.env.UPSTASH_VECTOR_REST_URL!, token: process.env.UPSTASH_VECTOR_REST_TOKEN!,});await index.upsert([{ id: "1", vector: [0.1, 0.2], metadata: { tag: "example" } }]);const results = await index.query({ vector: [0.1, 0.2], topK: 5,});
For full usage, refer to the linked skill files below.
features/namespaces: Explains namespaces and dataset organization.
features/index-structure: Covers hybrid and sparse index structures.
features/filtering-and-metadata: Details metadata storage and server-side filtering.
Use these files for deeper guidance on SDK usage, advanced configurations, algorithms, and integrations.
Installed into a catalogue, chosen by a router
Install upstash-vector-js and it becomes one more candidate the router can pick - when it fits.
A listing is a routing target with a manifest: what it does, which domains it covers, what it costs and who publishes it. Once installed it sits beside your own models and tools, is scored like any other candidate for each request, and shows up in the trace when it wins. Ratings come from workspaces that installed it, one per account.
When a provider is down or over quota the request moves on; the trace says it did.
Use it
Copy one of these into your project. Installing also returns the manifest and these snippets.
# after Install: the listing is in your workspace's routing pool - a plan picks it for its slotcurl-s-X POST https://osr-api.kindrock-caaab1a2.centralindia.azurecontainerapps.io/api/v1/route -H 'Authorization: Bearer $OSR_API_KEY' -H 'Content-Type: application/json' -d '{"text": "...", "plan": true}'
Manifest
An Open Capability Manifest: the router reads it to know what this does, what it costs and when to pick it.
upstash-skills-upstash-vector-js.ocm.jsonjson
{
"ocm": "1",
"id": "upstash-skills-upstash-vector-js",
"kind": "skill",
"name": "upstash-vector-js",
"description": "Work with the @upstash/vector TypeScript/JavaScript SDK, a serverless vector database for embeddings, similarity search, semantic search, and RAG (retrieval-augmented generation). Use when upserting, querying, fetching, ranging, or deleting vectors, upserting raw text against an index with a built-in embedding model, choosing dense, sparse, or hybrid indexes, filtering by metadata, organizing data with namespaces, running resumable queries, or connecting Upstash Vector to an AI or LLM application. Also use when the user asks for a vector store, vector search, nearest-neighbor or kNN search, embeddings storage, semantic cache, recommendations or similarity features, or a hosted vector index that needs no infrastructure.",
"publisher": "upstash",
"version": "1.0.0",
"capabilities": {
"domains": [
"data_analysis",
"coding"
],
"tags": [
"skill-md",
"skills-sh"
],
"languages": [
"en"
]
},
"quality_prior": 0.6,
"examples": [
"Work with the @upstash/vector TypeScript/JavaScript SDK, a serverless vector database for embeddings, similarity search, semantic search, and RAG (retrieval-augmented generation). Use when upserting, querying, fetching, ranging, or deleting vectors, upserting raw text against an index with a built-in embedding model, choosing dense, sparse, or hybrid indexes, filtering by metadata, organizing data with namespaces, running resumable queries, or connecting Upstash Vector to an AI or LLM application. Also use when the user asks for a vector store, vector search, nearest-neighbor or kNN search, embeddings storage, semantic cache, recommendations or similarity features, or a hosted vector index that needs no infrastructure."
],
"primary": false,
"metadata": {
"source": {
"provider": "skills.sh",
"repository": "https://github.com/upstash/skills",
"path": "skills/upstash-vector-js/SKILL.md",
"ref": "HEAD",
"url": "https://www.skills.sh/upstash/skills/upstash-vector-js",
"key": "upstash/skills/skills/upstash-vector-js/SKILL.md"
},
"license": "MIT"
},
"instructions": "# Vector Documentation Skill\n\n## Quick Start\n\nVector is a high‑performance vector database for storing, querying, and managing vector embeddings.\n\nBasic workflow:\n\n- Install the Vector TS SDK.\n- Connect to a Vector instance.\n- Upsert vectors, query them, and manage namespaces.\n\nExample (TypeScript):\n\n```ts\nimport { Index } from \"@upstash/vector\";\nconst index = new Index({\n url: process.env.UPSTASH_VECTOR_REST_URL!,\n token: process.env.UPSTASH_VECTOR_REST_TOKEN!,\n});\n\nawait index.upsert([{ id: \"1\", vector: [0.1, 0.2], metadata: { tag: \"example\" } }]);\n\nconst results = await index.query({\n ve",
"cost": {
"context_tokens": 299
}
}
Fetch it by URL: GET /api/v1/registry/upstash-skills-upstash-vector-js/manifest?version=1.0.0
Reviews
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