curl --request POST \
--url https://api.aihubmax.com/v1beta/models/{model}:embedContent \
--header 'Authorization: Bearer <token>' \
--header 'Content-Type: application/json' \
--data '
{
"content": {
"parts": [
{
"text": "What is deep learning?"
}
]
}
}
'
{
"embedding": {
"values": [
0.0123,
-0.0456,
0.0789,
0.0234,
-0.0567
]
}
}{
"error": {
"message": "Invalid request parameters",
"type": "invalid_request_error"
}
}{
"error": {
"message": "Invalid API Key",
"type": "authentication_error"
}
}{
"error": {
"message": "Invalid request parameters",
"type": "invalid_request_error"
}
}{
"error": {
"message": "Access denied",
"type": "permission_error"
}
}{
"error": {
"message": "Rate limit exceeded",
"type": "rate_limit_error"
}
}{
"error": {
"message": "Internal server error",
"type": "server_error"
}
}Gemini Format
Gemini Format - Embeddings
- Compatible with all Gemini native format embedding APIs
- Converts text into high-dimensional vector representations
- Supports multiple task types: retrieval, classification, clustering, semantic similarity, etc.
- Supports custom output dimensionality (dimensionality reduction)
POST
/
v1beta
/
models
/
{model}
:embedContent
curl --request POST \
--url https://api.aihubmax.com/v1beta/models/{model}:embedContent \
--header 'Authorization: Bearer <token>' \
--header 'Content-Type: application/json' \
--data '
{
"content": {
"parts": [
{
"text": "What is deep learning?"
}
]
}
}
'
{
"embedding": {
"values": [
0.0123,
-0.0456,
0.0789,
0.0234,
-0.0567
]
}
}{
"error": {
"message": "Invalid request parameters",
"type": "invalid_request_error"
}
}{
"error": {
"message": "Invalid API Key",
"type": "authentication_error"
}
}{
"error": {
"message": "Invalid request parameters",
"type": "invalid_request_error"
}
}{
"error": {
"message": "Access denied",
"type": "permission_error"
}
}{
"error": {
"message": "Rate limit exceeded",
"type": "rate_limit_error"
}
}{
"error": {
"message": "Internal server error",
"type": "server_error"
}
}Authorizations
All endpoints require Bearer Token authentication
Add the following to your request headers:
Authorization: Bearer YOUR_API_KEY
Path Parameters
Embedding model name, e.g. gemini-embedding-2-preview
Example:
"gemini-embedding-2-preview"
Body
application/json
The content to embed
Show child attributes
Show child attributes
Embedding task type, which affects the optimization direction of the embedding vector. Note: gemini-embedding-2-preview does not support this field; that model uses a prompt prefix approach to specify task types (e.g. task: search result | query: {content})
Available options:
RETRIEVAL_QUERY, RETRIEVAL_DOCUMENT, SEMANTIC_SIMILARITY, CLASSIFICATION, CLUSTERING, QUESTION_ANSWERING, FACT_VERIFICATION, CODE_RETRIEVAL_QUERY Document title, only effective when taskType is RETRIEVAL_DOCUMENT
Output vector dimensionality, used for dimensionality reduction. Default 3072
Response
Embedding response
Embedding result
Show child attributes
Show child attributes