omniroute-embeddings

Solid

Embeddings via OmniRoute using OpenAI /v1/embeddings format with auto-fallback across text-embedding-3-large, Voyage, Cohere, Gemini embeddings, Jina. Use when the user needs vector embeddings for RAG, similarity search, or clustering.

AI & Automation 5,612 stars 967 forks Updated today MIT

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Skill Content

# OmniRoute — Embeddings Requires `OMNIROUTE_URL` and `OMNIROUTE_KEY`. See [entry-point SKILL](https://raw.githubusercontent.com/diegosouzapw/OmniRoute/main/skills/omniroute/SKILL.md) for setup. ## Endpoint - `POST $OMNIROUTE_URL/v1/embeddings` ## Discover ```bash curl $OMNIROUTE_URL/v1/models/embedding | jq '.data[]' ``` Each entry: `{ id, owned_by, dimensions, max_input_tokens }`. ## Example ```bash curl -X POST $OMNIROUTE_URL/v1/embeddings \ -H "Authorization: Bearer $OMNIROUTE_KEY" \ -H "Content-Type: application/json" \ -d '{ "model": "text-embedding-3-large", "input": ["first text", "second text"], "encoding_format": "float" }' ``` Response: `{ data:[{ embedding:[...], index }], usage:{ prompt_tokens, total_tokens } }` ## Batch input `input` accepts a string or array of strings (up to provider batch limit, typically 2048 items). ## Errors - `400 input_too_long` → input exceeds `max_input_tokens` for this model - `400 invalid_encoding_format` → use `float` or `base64` - `503` → provider unavailable; try another model in `/v1/models/embedding`

Details

Author
diegosouzapw
Repository
diegosouzapw/OmniRoute
Created
3 months ago
Last Updated
today
Language
TypeScript
License
MIT

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