Introduction Convert text to vector embeddings using Gemini native interface. The model is specified via URL path (e.g., gemini-embedding-001), suitable for scenarios requiring Google embedding models or alignment with Gemini API.
Complements the OpenAI format in Text Embedding : this document covers the Gemini native path; the same capability is also available via POST /v1/embeddings.
Authentication Bearer Token, e.g., Bearer sk-xxxxxxxxxx
Path Parameters Embedding model name, e.g., gemini-embedding-001.
Request Parameters Content to embed. Must contain a parts array, each item as { "text": "text content" }.
Output vector dimensions (only supported by some models, e.g., gemini-embedding-001, text-embedding-004).
Task type, e.g., RETRIEVAL_DOCUMENT, RETRIEVAL_QUERY (optional).
Code Examples cURL
cURL (with dimensions)
Python
curl -X POST "https://console.mixroute.io/v1/models/gemini-embedding-001:embedContent" \ -H "Content-Type: application/json" \ -H "Authorization: Bearer sk-xxxxxxxxxx" \ -d '{ "content": { "parts": [ { "text": "Text content to embed" } ] } }'
curl -X POST "https://console.mixroute.io/v1/models/gemini-embedding-001:embedContent" \ -H "Content-Type: application/json" \ -H "Authorization: Bearer sk-xxxxxxxxxx" \ -d '{ "content": { "parts": [ { "text": "Text content to embed" } ] }, "outputDimensionality": 768 }'
import requests url = "https://console.mixroute.io/v1/models/gemini-embedding-001:embedContent" headers = { "Content-Type" : "application/json" , "Authorization" : "Bearer sk-xxxxxxxxxx" } payload = { "content" : { "parts" : [ { "text" : "Text content to embed" } ] } } response = requests.post(url, json = payload, headers = headers) data = response.json() embedding = data[ "embedding" ][ "values" ] print ( f "Vector dimensions: { len (embedding) } " )
Response Example { "embedding" : { "values" : [ 0.0023064255 , -0.009327292 , 0.015797347 , ... ] }, "metadata" : { "usage" : { "prompt_tokens" : 6 , "total_tokens" : 6 } } }
Batch Interface (batchEmbedContents) For batch embedding, use: POST /v1/models/{model}:batchEmbedContents. The request body is a requests array, each item with the same structure as single requests (including content.parts), and do not include the model field in each item.
curl -X POST "https://console.mixroute.io/v1/models/gemini-embedding-001:batchEmbedContents" \ -H "Content-Type: application/json" \ -H "Authorization: Bearer sk-xxxxxxxxxx" \ -d '{ "requests": [ { "content": { "parts": [{ "text": "First text" }] } }, { "content": { "parts": [{ "text": "Second text" }] } } ] }'
Supported Models Model Description gemini-embedding-001 General-purpose embedding model, supports outputDimensionality text-embedding-004 High-precision embedding model
Notes
content.parts is required, at least one text must be non-emptyModel is specified via URL path, do not include model field in request body
Usage information is in metadata.usage in the response (prompt_tokens, total_tokens)
If your application already uses the OpenAI SDK, consider using the /v1/embeddings compatible interface to minimize code changes.
cURL
Python
JavaScript
PHP
Go
Java
Ruby
curl --request POST \ --url https://console.mixroute.io/v1/models/gemini-embedding-001:embedContent \ --header 'Authorization: Bearer sk-xxxxxxxxxx' \ --header 'Content-Type: application/json' \ --data '{ "content": { "parts": [{"text": "This is a test text"}] } }'
{ "embedding" : { "values" : [ 0.0023064255 , -0.009327292 , 0.015797347 ] }, "metadata" : { "usage" : { "prompt_tokens" : 6 , "total_tokens" : 6 } } }
