> ## Documentation Index
> Fetch the complete documentation index at: https://api-tools.memories.ai/llms.txt
> Use this file to discover all available pages before exploring further.
# Generate Text Embedding
> Generate vector embeddings for text using language models
**Product**: Visual Intelligence — Embeddings
**Use case**: Generate vector embeddings for image, video, or text inputs for semantic search and similarity tasks
**Host**: `https://mavi-backend.memories.ai/serve/api/v2`
**Auth**: `Authorization: sk-mavi-...` (no `Bearer` prefix)
This endpoint generates vector embeddings for text strings. You can embed single or multiple text inputs in one request, and optionally specify the output dimensionality.
**Pricing:**
* \$0.0000002 per token (2e-7/token)
### Code Examples
```javascript fetch theme={null}
const BASE_URL = "https://mavi-backend.memories.ai/serve/api/v2";
const API_KEY = "sk-mavi-...";
const response = await fetch(`${BASE_URL}/embeddings/text`, {
method: 'POST',
headers: {
'Content-Type': 'application/json',
'Authorization': API_KEY
},
body: JSON.stringify({
input: ['Sample text to embed', 'Another text string'],
model: 'gemini-embedding-001',
dimensionality: 512
})
});
const data = await response.json();
console.log(data);
```
```javascript axios theme={null}
import axios from 'axios';
const BASE_URL = "https://mavi-backend.memories.ai/serve/api/v2";
const API_KEY = "sk-mavi-...";
const response = await axios.post(`${BASE_URL}/embeddings/text`, {
input: ['Sample text to embed', 'Another text string'],
model: 'gemini-embedding-001',
dimensionality: 512
}, {
headers: {
'Authorization': API_KEY
}
});
console.log(response.data);
```
```python Python theme={null}
import requests
BASE_URL = "https://mavi-backend.memories.ai/serve/api/v2"
API_KEY = "sk-mavi-..."
HEADERS = {
"Authorization": f"{API_KEY}"
}
def text_embedding(input_list, model="gemini-embedding-001", dimensionality=512):
url = f"{BASE_URL}/embeddings/text"
data = {
"input": input_list,
"model": model,
"dimensionality": dimensionality
}
response = requests.post(url, headers=HEADERS, json=data)
return response.json()
# Usage example
result = text_embedding(
["Sample text to embed", "Another text string"],
model="gemini-embedding-001",
dimensionality=512
)
print(result)
```
### Request Body
| Field | Type | Required | Description |
| -------------- | -------------- | -------- | -------------------------------------------------------------------------- |
| input | array\[string] | Yes | List of text strings to embed (can be a single string or multiple strings) |
| model | string | Yes | Embedding model name |
| dimensionality | integer | No | Output embedding dimension size (e.g., 256, 512, 768). Model-dependent. |
**Supported Models:**
* `gemini-embedding-001` - Google's Gemini text embedding model
### Response
Returns embedding vectors for each input text.
**The live response shape does not match the previously documented "singular vs plural" split.** Live response is always `data: [ {embedding: [...]} ]` — an **array of objects**, each carrying its own `embedding` (singular) key — regardless of whether you sent one input or many. There is no top-level `data.embedding` / `data.embeddings` field. Verified live with `gemini-embedding-001`.
```json Response theme={null}
{
"code": 200,
"msg": "success",
"data": [
{
"embedding": [
-0.031649195,
0.00069497403,
0.011988669,
"... (continues for `dimensionality` length)"
]
},
{
"embedding": [
0.012345,
-0.067890,
"... (one entry per input string)"
]
}
],
"success": true,
"failed": false
}
```
### Response Parameters
| Parameter | Type | Description |
| ----------------- | -------------- | ----------------------------------------------------------------------------- |
| code | integer | Response code (`200` on success) |
| msg | string | Response message describing the operation result |
| data | array\[object] | One entry per `input` string, in the same order. |
| data\[].embedding | array\[number] | Vector for that input. Length equals `dimensionality` (or the model default). |
| success | boolean | Indicates whether the operation was successful |
| failed | boolean | Indicates whether the operation failed |
### Notes
* Text embeddings are returned synchronously in the response.
* **The response is always an array** — index into `data[i].embedding` to get the vector for `input[i]`. There is no singular-vs-plural variant.
* The `dimensionality` parameter allows you to control the output vector size
* Supported dimensionality depends on the model (common values: 256, 512, 768, 1024)
* Lower dimensionality results in faster processing and reduced storage, but may have lower accuracy
* Use text embeddings for:
* Semantic search and similarity matching
* Text classification and clustering
* Question answering and information retrieval
* Recommendation systems
* Duplicate detection
## OpenAPI
````yaml POST /embeddings/text
openapi: 3.1.0
info:
title: Embeddings API Reference
description: REST APIs for generating vector embeddings from images, videos, and text
version: v1.0.0
servers:
- url: https://mavi-backend.memories.ai/serve/api/v2
security:
- ApiKeyAuth: []
paths:
/embeddings/text:
post:
summary: Generate Text Embedding
operationId: text_embedding
requestBody:
required: true
content:
application/json:
schema:
type: object
properties:
input:
type: array
items:
type: string
description: List of text strings to embed
example:
- Sample text to embed
- Another text string
model:
type: string
description: Embedding model name
example: gemini-embedding-001
enum:
- gemini-embedding-001
dimensionality:
type: integer
description: Output embedding dimension size
example: 512
required:
- input
- model
responses:
'200':
description: Embeddings generated successfully
content:
application/json:
schema:
oneOf:
- type: object
description: Response for single input
properties:
code:
type: string
example: 200
description: Response code indicating the result status
msg:
type: string
example: success
description: Response message describing the operation result
data:
type: object
properties:
embedding:
type: array
items:
type: number
description: Vector embedding array for the input text
example:
- 0.0234375
- -0.015625
- 0.0390625
- 0.0078125
description: Response data object containing the embedding
success:
type: boolean
example: true
description: Indicates whether the operation was successful
failed:
type: boolean
example: false
description: Indicates whether the operation failed
- type: object
description: Response for multiple inputs
properties:
code:
type: string
example: 200
description: Response code indicating the result status
msg:
type: string
example: success
description: Response message describing the operation result
data:
type: object
properties:
embeddings:
type: array
items:
type: array
items:
type: number
description: >-
Array of embedding vectors, one for each input
text
example:
- - 0.0234375
- -0.015625
- 0.0390625
- - 0.03125
- -0.0234375
- 0.046875
description: Response data object containing the embeddings
success:
type: boolean
example: true
description: Indicates whether the operation was successful
failed:
type: boolean
example: false
description: Indicates whether the operation failed
components:
securitySchemes:
ApiKeyAuth:
type: apiKey
in: header
name: Authorization
````