Source: https://ailoot.ai/models/en/google-gemini-embedding-001/
Canonical: https://ailoot.ai/models/en/google-gemini-embedding-001/
Language: en

# Google: Gemini Embedding 001

[Markdown](https://ailoot.ai/models/en/google-gemini-embedding-001/index.md) · [JSON](https://ailoot.ai/models/en/google-gemini-embedding-001/facts.json)

## [Google: Gemini Embedding 001](https://ailoot.ai/models/en/google-gemini-embedding-001/)

Text or token-ID input to vectors, with float encoding and a listed 20,000-token hosted context.

- [**Ailoot Ultra · Embeddings** `POST /v1/embeddings` `google/gemini-embedding-001`](https://ailoot.ai/models/en/google-gemini-embedding-001/#method-0)

About the model

This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below.

google/gemini-embedding-001 represents text as vectors for retrieval. A practical use is finding the relevant technical passage when a question uses different wording from the document. The Ailoot route lists a 20,000-token hosted context and accepts text strings, string arrays or token-ID arrays, using one consistent input kind per request.

The result contains indexed finite-float vectors. The accepted contract does not publish a default vector width and explicitly excludes a dimensions override; establish your index width from the actual returned vector length and keep it fixed for that index. encoding\_format supports float. No task-type or query/document selector is exposed on this route. Use a consistent preparation convention for questions and documents, and keep the exact model and vector width together when building or updating the index.

**Ailoot Ultra · Embeddings** `POST /v1/embeddings` `google/gemini-embedding-001`

[`google/gemini-embedding-001`](https://ailoot.ai/models/en/#m=google%2Fgemini-embedding-001&route=Ultra&caller=google%2Fgemini-embedding-001&fmt=embeddings)

Google /`google/gemini-embedding-001`

### Overview

Text or token-ID input to vectors, with float encoding and a listed 20,000-token hosted context.

Accepted inputs

Text · token\_ids

Results

embedding

### Available in Ailoot · inputs & methods

**Ailoot Ultra · Embeddings** `POST /v1/embeddings`

Input types

`text`, `token_ids`

Output types

`embedding`

Context

20,000 tokens

Output format

json

| Field | Type | Requirement | Values & limits | Default |
| --- | --- | --- | --- | --- |
| Field`model` | Type`string` | RequirementRequired | Values & limits google/gemini-embedding-001 | Default— |
| Field`input` | Type`string\|string[]\|integer[]\|integer[][]` | RequirementRequired | Values & limits Minimum items: 1 Conditions: Nonempty homogeneous batch. Token IDs are nonnegative integers. Input budget: Maximum: 20,000 · Text unit: UTF8 bytes · Token unit: token-array elements | Default— |
| Field`encoding_format` | Type`string` | RequirementOptional | Values & limits float | Defaultfloat |
| Field`dimensions` | Type`integer` | RequirementNot supported | Values & limits Minimum: 1 | Default— |
| Field`user` | Type`json_value` | RequirementOptional | Values & limits Conditions: Source allows this JSON metadata field; exact upstream semantics are model-specific. | Default— |
| Field`session_id` | Type`json_value` | RequirementOptional | Values & limits Conditions: Source allows this JSON metadata field; exact upstream semantics are model-specific. | Default— |
| Field`trace` | Type`json_value` | RequirementOptional | Values & limits Conditions: Source allows this JSON metadata field; exact upstream semantics are model-specific. | Default— |
| Field`provider` | Type`object` | RequirementOptional | Values & limits Conditions: provider.options rejected | Default— |

Input material combinations

**text\_to\_embedding**

Method parameters and lifecycle

| Parameter | Contract |
| --- | --- |
| Parameter`content_type` | Contractapplication/json |
| Parameter`required` | Contractmodel, input |
| Parameter`input_shapes` | Contractstring, string\[\], nonnegative\_integer\_token\_array, nonnegative\_integer\_token\_array\[\] |
| Parameter`encoding_format` | Contractfloat |
| Parameter`dimensions_override` | Contractfalse |
| Parameter`max_input_budget_units` | Contract20000 |
| Parameter`multimodal_objects` | Contractfalse |
| Parameter`material_types` | Contracttext, token\_ids |

Lifecycle: `synchronous_json`

Keep every vector index and dimension.

Multimodal model name does not admit image objects on this exact path.

### Pricing

Tariff references do not add request options. Supported parameters are listed for the selected method.

Fast mode

Standard rate When fast mode is selected

Flex mode

Standard rate When flex mode is selected

Maximum reasoning effort

Standard rate When maximum reasoning effort uses the default adjustment

#### Ailoot rates

#### Prices by configuration

| Configuration | Rate schedule | Charge |
| --- | --- | --- |
| ConfigurationBase token rates | Rate scheduleBase rate | Charge Input · Input tokens **$0.0003** / 1K tokens Output · Output tokens **$0** / 1K tokens Input · Cache read **—** / 1K tokens Input · Cache write **—** / 1K tokens Input · Cache write, 1 hour **—** / 1K tokens |

Ailoot reserves an estimated amount before a request. After the result, it settles the final charge from measured usage. Streaming settlement uses the final usage record. [How reservation and settlement work →](https://ailoot.ai/docs/en/billing/reserve/)

### Example requests

#### Ailoot Ultra · Embeddings

curlPython SDK

```bash
set -eu
: "${AILOOT_API_KEY:?Set your Ailoot API key}"
python3 - <<'PY_REQUEST'
import json,os
from pathlib import Path
p=json.loads('{"model": "google/gemini-embedding-001", "input": ["First document", "Second document"]}')

Path("request.json").write_text(json.dumps(p,ensure_ascii=False))
PY_REQUEST
curl --fail-with-body --silent --show-error https://api.ailoot.ai/v1/embeddings \
  -H "Authorization: Bearer $AILOOT_API_KEY" -H 'Content-Type: application/json' \
  --data-binary @request.json -D response-headers.txt -o result.json
python3 - <<'PY_RESULT'
import json
from pathlib import Path
r=json.loads(Path("result.json").read_text())
for x in r.get("data",[]): print(json.dumps({"index":x.get("index"),"embedding":x.get("embedding")}))
PY_RESULT
```

```python
from openai import OpenAI
import os

client=OpenAI(base_url="https://api.ailoot.ai/v1",api_key=os.environ["AILOOT_API_KEY"],max_retries=0)
result=client.embeddings.create(model='google/gemini-embedding-001',input=["First document","Second document"])
print(result)
for item in result.data:print(item.index,item.embedding)
```

### Receive and save results

#### Ailoot Ultra · Embeddings

json

##### Keep all completed results

Keep every complete returned response, file and content part.

Keep every vector with its index. Vector count must equal input count; dimensions must be consistent.

##### If the original response needs checking

No embedding job endpoint is introduced.

Response fields

| Field | Type |
| --- | --- |
| Field`object` | Typestring |
| Field`model` | Typestring |
| Field`data[].index` | Typeinteger |
| Field`data[].embedding` | Typefinite float\[\] |
| Field`usage` | Typeobject |

Errors and next steps

| Response | Condition | Next step |
| --- | --- | --- |
| Response400 · `invalid_request_error` | ConditionInvalid required fields, material representation or parameter value | Next stepCorrect the indicated fields before a new creation request. |

### Aliases

#### Model aliases

**Ailoot Ultra** [`google/gemini-embedding-001`](https://ailoot.ai/models/en/#m=google%2Fgemini-embedding-001&route=Ultra&caller=google%2Fgemini-embedding-001&fmt=embeddings)

#### Method path aliases

Use the method paths shown above.

#### Related caller IDs

[`google/gemini-embedding-001`](https://ailoot.ai/models/en/#m=google%2Fgemini-embedding-001&route=Ultra&caller=google%2Fgemini-embedding-001&fmt=embeddings)

FAQ · Google: Gemini Embedding 001

What inputs and results are available?

`POST /v1/embeddings` · `google/gemini-embedding-001`: `text`, `token_ids` → `embedding`

Which fields does the request use?

`POST /v1/embeddings` · `google/gemini-embedding-001`: `model`, `input`, `encoding_format`, `provider`

What limits apply?

`POST /v1/embeddings` · `google/gemini-embedding-001`: `ctx`: `20000`; `input.budget.maximum`: `20000` (UTF8 bytes); `input.min_items`: `1`

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