> For the complete documentation index, see [llms.txt](https://docs.fastrouter.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.fastrouter.ai/api-reference/embeddings.md).

# Embeddings

## Create Embeddings

> Creates vector embeddings for text input. Supports models from OpenAI, Google, and DeepInfra. Compatible with OpenAI SDK.

```json
{"openapi":"3.1.0","info":{"title":"FastRouter API Reference","version":"1.0.0"},"tags":[{"name":"Embeddings","description":"Create vector embeddings for text using various embedding models."}],"servers":[{"url":"https://api.fastrouter.ai","description":"Production API"}],"security":[{"bearerAuth":[]}],"components":{"securitySchemes":{"bearerAuth":{"type":"http","scheme":"bearer","bearerFormat":"API Key","description":"FastRouter API Key. Get yours at https://fastrouter.ai\n\nFormat: `Authorization: Bearer YOUR_API_KEY`"}},"responses":{"UnauthorizedError":{"description":"Invalid Credentials - Your API key is invalid, missing, or disabled. Check your credentials.\n\nNote: the 401 error body uses `code`, `message`, `param`, and `type` (there is no `status` field), and `type` is `invalid_request_error`.","content":{"application/json":{"schema":{"type":"object","properties":{"error":{"type":"object","properties":{"message":{"type":"string"},"type":{"type":"string"},"param":{"type":"string","nullable":true},"code":{"type":"string"}}}}}}}},"RateLimitError":{"description":"Rate Limited - You have exceeded your request limits (TPM/RPM). Slow down or increase your limits.","content":{"application/json":{"schema":{"type":"object","properties":{"error":{"type":"object","properties":{"message":{"type":"string"},"type":{"type":"string"},"code":{"type":"string"},"status":{"type":"integer"}}}}}}}}}},"paths":{"/api/v1/embeddings":{"post":{"operationId":"createEmbedding","tags":["Embeddings"],"summary":"Create Embeddings","description":"Creates vector embeddings for text input. Supports models from OpenAI, Google, and DeepInfra. Compatible with OpenAI SDK.","requestBody":{"required":true,"content":{"application/json":{"schema":{"type":"object","required":["model","input"],"properties":{"model":{"type":"string","description":"The fully qualified model slug. Choose from supported models (openai/text-embedding-3-small, openai/text-embedding-3-large, openai/text-embedding-ada-002, google/gemini-embedding-001, deepinfra/intfloat-e5-base-v2)","enum":["openai/text-embedding-3-small","openai/text-embedding-3-large","openai/text-embedding-ada-002","google/gemini-embedding-001","deepinfra/intfloat-e5-base-v2"]},"input":{"oneOf":[{"type":"string","description":"Single text string to embed"},{"type":"array","items":{"type":"string"},"description":"Array of text strings to embed (batch processing)"}],"description":"The input text or list of text items to embed. Can be a single string or array of strings for batch processing."},"dimensions":{"type":"integer","minimum":1,"description":"Optional: Requested embedding size/dimensions. Supported only in text-embedding-3 series and newer models. Allows you to reduce embedding size for efficiency."},"encoding_format":{"type":"string","enum":["float","base64"],"default":"float","description":"Format for the embedding vector. 'float' returns array of numbers, 'base64' returns base64-encoded string."},"user":{"type":"string","description":"Optional: A unique identifier representing your end-user, for monitoring and abuse detection."}}}}}},"responses":{"200":{"description":"Embeddings created successfully","content":{"application/json":{"schema":{"type":"object","properties":{"object":{"type":"string","description":"Object type, always 'list'"},"data":{"type":"array","description":"Array of embedding objects","items":{"type":"object","properties":{"object":{"type":"string","description":"Object type, always 'embedding'"},"index":{"type":"integer","description":"Index of the embedding in the input array"},"embedding":{"type":"array","items":{"type":"number"},"description":"Vector representation of the input text"}}}},"model":{"type":"string","description":"Model used for embedding generation"},"usage":{"type":"object","description":"Token usage statistics","properties":{"prompt_tokens":{"type":"integer","description":"Number of tokens in the input"},"total_tokens":{"type":"integer","description":"Total tokens used (same as prompt_tokens for embeddings)"}}}}}}}},"400":{"description":"Bad Request - Invalid parameters or model"},"401":{"$ref":"#/components/responses/UnauthorizedError"},"429":{"$ref":"#/components/responses/RateLimitError"},"500":{"description":"Internal Server Error"}}}}}}
```


---

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