therizwan/flagshiprouter/flagshiprouter-embeddings

Claude Code and agent skill by therizwan/flagshiprouter

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Generate vector embeddings via FlagshipRouter /v1/embeddings using OpenAI / Gemini / Mistral / Voyage / Nvidia / GitHub embedding models for RAG, semantic search, similarity. Use when the user wants embeddings, vectors, RAG, semantic search, or to embed text.

Install

npx vskill@latest install therizwan/flagshiprouter/flagshiprouter-embeddings

SKILL.md

FlagshipRouter — Embeddings

Requires FLAGSHIPROUTER_URL (and FLAGSHIPROUTER_KEY if auth enabled). See https://raw.githubusercontent.com/theRizwan/FlagshipRouter/refs/heads/main/skills/flagshiprouter/SKILL.md for setup.

Discover

bash
curl $FLAGSHIPROUTER_URL/v1/models/embedding | jq '.data[].id'
# Per-model dimensions
curl "$FLAGSHIPROUTER_URL/v1/models/info?id=openai/text-embedding-3-small"

Endpoint

POST $FLAGSHIPROUTER_URL/v1/embeddings

FieldRequiredNotes
modelyesfrom /v1/models/embedding
inputyesstring OR array of strings
encoding_formatnofloat (default) / base64
dimensionsnoOpenAI v3 only

Examples

bash
curl -X POST $FLAGSHIPROUTER_URL/v1/embeddings \
  -H "Authorization: Bearer $FLAGSHIPROUTER_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"openai/text-embedding-3-small","input":["hello","world"]}'

JS:

js
const r = await fetch(`${process.env.FLAGSHIPROUTER_URL}/v1/embeddings`, {
  method: "POST",
  headers: { "Authorization": `Bearer ${process.env.FLAGSHIPROUTER_KEY}`, "Content-Type": "application/json" },
  body: JSON.stringify({ model: "gemini/text-embedding-004", input: "RAG chunk text" }),
});
const { data } = await r.json();
console.log(data[0].embedding.length);  // dimension

Response shape

json
{ "object": "list", "model": "openai/text-embedding-3-small",
  "data": [
    { "object": "embedding", "index": 0, "embedding": [0.0123, -0.045, ...] },
    { "object": "embedding", "index": 1, "embedding": [...] }
  ],
  "usage": { "prompt_tokens": 5, "total_tokens": 5 } }

Provider quirks

ProviderNotes
openai, openrouter, mistral, voyage-ai, fireworks, together, nebius, github, nvidia, jina-aiNative OpenAI shape — dimensions works only on OpenAI v3 (text-embedding-3-*)
gemini, google_ai_studioServer auto-converts to embedContent/batchEmbedContents — send OpenAI shape
openai-compatible-*, custom-embedding-*Custom baseUrl from credentials

Batch (input as array) is faster; some providers cap batch size.

Security

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