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

# Embeddings

Create vector embeddings for input text.

## Create embeddings

> Get a vector representation of a given input that can be easily consumed by machine learning models and algorithms.

```json
{"openapi":"3.0.0","info":{"title":"Nexos AI Public API Production","version":"1.0.0"},"tags":[{"name":"Embeddings","description":"Create vector embeddings for input text."}],"servers":[{"url":"https://api.nexos.ai"}],"security":[{"bearerAuth":[]},{"apiKeyHeader":[]}],"components":{"securitySchemes":{"bearerAuth":{"type":"http","scheme":"bearer"},"apiKeyHeader":{"type":"apiKey","in":"header","name":"X-Api-Key","description":"Authenticate by sending your nexos API key in the `X-Api-Key` header (e.g. `X-Api-Key: user-...` for a user key or `X-Api-Key: team-...` for a team key). This is an alternative to the `Authorization: Bearer` scheme. If both `X-Nexos-Key` and `X-Api-Key` are sent, `X-Nexos-Key` takes precedence."}},"schemas":{"EmbeddingsRequest":{"type":"object","properties":{"model":{"type":"string"},"input":{"description":"Input text to get embeddings for.","oneOf":[{"type":"string"},{"type":"array","items":{"type":"string"}},{"type":"array","items":{"type":"integer"}},{"type":"array","items":{"type":"array","items":{"type":"integer"}}}]},"encoding_format":{"type":"string","description":"The format to return the embeddings in. Can be either float or base64.","enum":["float","base64"],"default":"float"},"dimensions":{"type":"integer","description":"The number of dimensions the resulting output embeddings should have. Only supported in text-embedding-3 and later models.","minimum":1}},"required":["model","prompt"]}},"responses":{"EmbeddingsResponse":{"description":"Represents an embedding response returned by model, based on the provided input.","content":{"application/json":{"schema":{"type":"object","properties":{"object":{"type":"string","description":"The object type, which is always \"list\""},"data":{"type":"array","items":{"type":"object","properties":{"object":{"type":"string","description":"The object type, which is always \"embedding\""},"embedding":{"type":"array","items":{"type":"number","format":"float"},"description":"The embedding vector"},"index":{"type":"integer","description":"The index of the embedding in the list"}}}},"model":{"type":"string","description":"The model used for generating embeddings"},"usage":{"type":"object","properties":{"prompt_tokens":{"type":"integer","description":"The number of tokens used in the prompt"},"total_tokens":{"type":"integer","description":"The total number of tokens used"},"nexos_credits_cost":{"type":"number","format":"double","description":"Cost of the request in nexos credits."}}}}}}}},"OutOfCredits":{"description":"The company has run out of AI credits. Returned when the company's budget limit has been reached.\n","content":{"application/json":{"schema":{"type":"object","required":["error"],"properties":{"error":{"type":"object","required":["message"],"properties":{"message":{"type":"string"}}}}}}}}}},"paths":{"/v1/embeddings":{"post":{"operationId":"post-embeddings-v1","summary":"Create embeddings","tags":["Embeddings"],"description":"Get a vector representation of a given input that can be easily consumed by machine learning models and algorithms.","requestBody":{"required":true,"content":{"application/json":{"schema":{"$ref":"#/components/schemas/EmbeddingsRequest"}}}},"responses":{"200":{"$ref":"#/components/responses/EmbeddingsResponse"},"400":{"description":"Invalid request data."},"402":{"$ref":"#/components/responses/OutOfCredits"},"500":{"description":"Server error."}}}}}}
```


---

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