Source: https://ailoot.ai/models/en/openai-text-embedding-ada-002/
Canonical: https://ailoot.ai/models/en/openai-text-embedding-ada-002/
Language: en

# OpenAI: Text Embedding Ada 002

[Markdown](https://ailoot.ai/models/en/openai-text-embedding-ada-002/index.md) · [JSON](https://ailoot.ai/models/en/openai-text-embedding-ada-002/facts.json)

## [OpenAI: Text Embedding Ada 002](https://ailoot.ai/models/en/openai-text-embedding-ada-002/)

An OpenAI text-embedding model that returns a 1,536-dimensional numerical vector. Use those representations for semantic search, related-content recommendations, clustering, classification or detecting unusual text records.

- [**Ailoot Ultra · Embeddings** `POST /v1/embeddings` `openai/text-embedding-ada-002`](https://ailoot.ai/models/en/openai-text-embedding-ada-002/#method-0)
- [**Ailoot Ultra · Embeddings · text-embedding-ada-002** `POST /v1/embeddings` `text-embedding-ada-002`](https://ailoot.ai/models/en/openai-text-embedding-ada-002/#method-1)

About the model

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

text-embedding-ada-002 is OpenAI’s text-embedding model for representing written material as a numerical vector. Its output contains 1,536 components. The maker combines text similarity, document/query search and code-search roles in one representation. The result is data for comparison or another algorithm, rather than a generated answer to the text being embedded.

For semantic search, prepare meaningful document passages and retain their source identifiers. An illustrative workflow embeds each passage and the user’s question with the same model, compares the vectors and returns the relevant source passages. The application determines the retrieval rule, filters and presentation. A subsequent text-generation step can use the retrieved material to develop an answer, while the embedding model supplies the numerical representation.

Other documented uses include grouping similar records, recommending related content, classifying text and identifying outliers. For a recommendation, compare descriptions of the objects whose relationship matters. For classification, the guide describes comparison with category representations or using vectors as features in a classifier. For anomaly detection, the relevant result is a record that differs from the surrounding collection, interpreted according to the product’s task.

OpenAI lists an 8,192-token maximum input for this model. The producer’s embedding API accepts text strings or tokenized input, permits multiple inputs in one request and rejects empty strings. Its aggregate limit is 300,000 tokens across a single request, in addition to the per-input limit. Split longer documents into useful passages while preserving titles, definitions and the reference back to the original material.

The output is a vector of floating-point values; the producer also documents base64 as an alternative transport encoding. OpenAI’s dimension-shortening parameter is for third-generation embedding models and later, so the Ada model’s documented 1,536-dimensional result is kept distinct from that control. Store the model identity and vector with the source record so later comparisons use a consistent representation.

The embedding guide recommends cosine similarity and states that OpenAI embeddings are normalized to unit length. For those vectors, a dot product can compute cosine similarity and Euclidean distance gives the same ranking. A similarity score is part of the application’s retrieval or classification logic, not a categorical decision supplied automatically by the model.

Define which source fields become the input text and how passages should be divided. An illustrative result is a ranked set of document passages or related records, each connected to its original source and the rule used for selection. Keep the preparation consistent and evaluate whether the returned material addresses the actual question. This makes the numerical output useful to the next search, recommendation or analysis step.

**Ailoot Ultra · Embeddings** `POST /v1/embeddings` `openai/text-embedding-ada-002`

[`openai/text-embedding-ada-002`](https://ailoot.ai/models/en/#m=openai%2Ftext-embedding-ada-002&route=Ultra&caller=openai%2Ftext-embedding-ada-002&fmt=embeddings)

OpenAI /`openai/text-embedding-ada-002`

Model ID

- `openai/text-embedding-ada-002`
- `text-embedding-ada-002`

### Overview

An OpenAI text-embedding model that returns a 1,536-dimensional numerical vector. Use those representations for semantic search, related-content recommendations, clustering, classification or detecting unusual text records.

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

8,192 tokens

Output format

json

| Field | Type | Requirement | Values & limits | Default |
| --- | --- | --- | --- | --- |
| Field`model` | Type`string` | RequirementRequired | Values & limits openai/text-embedding-ada-002 | 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: 8,192 · 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` | Contract8192 |
| 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 **$0.0002** / 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": "openai/text-embedding-ada-002", "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='openai/text-embedding-ada-002',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** [`openai/text-embedding-ada-002`](https://ailoot.ai/models/en/#m=openai%2Ftext-embedding-ada-002&route=Ultra&caller=openai%2Ftext-embedding-ada-002&fmt=embeddings), [`text-embedding-ada-002`](https://ailoot.ai/models/en/#m=openai%2Ftext-embedding-ada-002&route=Ultra&caller=text-embedding-ada-002&fmt=embeddings)

#### Method path aliases

Use the method paths shown above.

#### Related caller IDs

[`openai/text-embedding-ada-002`](https://ailoot.ai/models/en/#m=openai%2Ftext-embedding-ada-002&route=Ultra&caller=openai%2Ftext-embedding-ada-002&fmt=embeddings), [`text-embedding-ada-002`](https://ailoot.ai/models/en/#m=openai%2Ftext-embedding-ada-002&route=Ultra&caller=text-embedding-ada-002&fmt=embeddings)

**Ailoot Ultra · Embeddings · text-embedding-ada-002** `POST /v1/embeddings` `text-embedding-ada-002`

[`text-embedding-ada-002`](https://ailoot.ai/models/en/#m=openai%2Ftext-embedding-ada-002&route=Ultra&caller=text-embedding-ada-002&fmt=embeddings)

OpenAI /`text-embedding-ada-002`

Model ID

- `openai/text-embedding-ada-002`
- `text-embedding-ada-002`

### Overview

An OpenAI text-embedding model that returns a 1,536-dimensional numerical vector. Use those representations for semantic search, related-content recommendations, clustering, classification or detecting unusual text records.

Accepted inputs

Text

Results

embedding

### Available in Ailoot · inputs & methods

**Ailoot Ultra · Embeddings · text-embedding-ada-002** `POST /v1/embeddings`

Input types

`text`

Output types

`embedding`

Context

8,192 tokens

Output format

json

| Field | Type | Requirement | Values & limits | Default |
| --- | --- | --- | --- | --- |
| Field`model` | Type`string` | RequirementRequired | Values & limits text-embedding-ada-002 | 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: 8,192 · 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: Optional JSON metadata; exact upstream semantics depend on the selected model. | Default— |
| Field`session_id` | Type`json_value` | RequirementOptional | Values & limits Conditions: Optional JSON metadata; exact upstream semantics depend on the selected model. | Default— |
| Field`trace` | Type`json_value` | RequirementOptional | Values & limits Conditions: Optional JSON metadata; exact upstream semantics depend on the selected model. | 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` | Contract8192 |
| Parameter`multimodal_objects` | Contractfalse |
| Parameter`material_types` | Contracttext |
| Parameter`text_file_material` | ContractRead the actual UTF-8 document and put its text in the indicated input or message content string. |

Lifecycle: `synchronous_json`

Send the exact model ID and required fields.

Keep every returned item and its original identifiers.

### 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 **$0.0002** / 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 · text-embedding-ada-002

Complete minimal JSON bodycurl · use your UTF-8 documentPython · save the complete result

```json
{
  "model": "text-embedding-ada-002",
  "input": [
    "First document",
    "Second document"
  ],
  "encoding_format": "float"
}
```

```bash
set -eu
: "${AILOOT_API_KEY:?Set your Ailoot API key}"
: "${AILOOT_TEXT_FILE:?Set your actual UTF-8 document}"
export AILOOT_TEXT_FILE
python3 - <<'PY_REQUEST'
import json, os
from pathlib import Path

source_file = Path(os.environ['AILOOT_TEXT_FILE'])
if not source_file.is_file():
    raise SystemExit('Set AILOOT_TEXT_FILE to your actual UTF-8 document')
text = source_file.read_text(encoding='utf-8')
if not text.strip():
    raise SystemExit('Choose a document containing text')
payload = {'model': 'text-embedding-ada-002', 'input': ['First document', 'Second document'], 'encoding_format': 'float'}
payload['input'] = [text]
Path('request.json').write_text(json.dumps(payload, ensure_ascii=False), encoding='utf-8')
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

result = json.loads(Path('result.json').read_text(encoding='utf-8'))
print(json.dumps(result, ensure_ascii=False, indent=2))
for vector in result.get('data', []):
    print('Vector', vector.get('index'), json.dumps(vector.get('embedding')))
PY_RESULT
```

```python
import json, os
from pathlib import Path

source_file = Path(os.environ['AILOOT_TEXT_FILE'])
if not source_file.is_file():
    raise SystemExit('Set AILOOT_TEXT_FILE to your actual UTF-8 document')
text = source_file.read_text(encoding='utf-8')
if not text.strip():
    raise SystemExit('Choose a document containing text')
payload = {'model': 'text-embedding-ada-002', 'input': ['First document', 'Second document'], 'encoding_format': 'float'}
payload['input'] = [text]
from urllib.request import Request, urlopen
from urllib.error import HTTPError

api_key = os.environ['AILOOT_API_KEY']
if not api_key.strip():
    raise SystemExit('Set AILOOT_API_KEY before making a request')
headers = {'Authorization': 'Bearer PLACEHOLDER_KEY', 'Content-Type': 'application/json'}
headers['Authorization'] = 'Bearer ' + api_key
request = Request('https://api.ailoot.ai/v1/embeddings', data=json.dumps(payload, ensure_ascii=False).encode('utf-8'), headers=headers, method='POST')
try:
    with urlopen(request, timeout=120) as response:
        raw = response.read()
        Path('response-headers.txt').write_text(str(response.headers), encoding='utf-8')
except HTTPError as error:
    Path('result.json').write_bytes(error.read())
    Path('response-headers.txt').write_text(str(error.headers), encoding='utf-8')
    raise
Path('result.json').write_bytes(raw)
import json
from pathlib import Path

result = json.loads(Path('result.json').read_text(encoding='utf-8'))
print(json.dumps(result, ensure_ascii=False, indent=2))
for vector in result.get('data', []):
    print('Vector', vector.get('index'), json.dumps(vector.get('embedding')))
```

### Receive and save results

#### Ailoot Ultra · Embeddings · text-embedding-ada-002

json

##### Keep all completed results

Keep every complete returned response, file and content part.

Keep every finite vector with its original index. Preserve all dimensions and the complete usage object.

##### 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** [`openai/text-embedding-ada-002`](https://ailoot.ai/models/en/#m=openai%2Ftext-embedding-ada-002&route=Ultra&caller=openai%2Ftext-embedding-ada-002&fmt=embeddings), [`text-embedding-ada-002`](https://ailoot.ai/models/en/#m=openai%2Ftext-embedding-ada-002&route=Ultra&caller=text-embedding-ada-002&fmt=embeddings)

#### Method path aliases

Use the method paths shown above.

#### Related caller IDs

[`openai/text-embedding-ada-002`](https://ailoot.ai/models/en/#m=openai%2Ftext-embedding-ada-002&route=Ultra&caller=openai%2Ftext-embedding-ada-002&fmt=embeddings), [`text-embedding-ada-002`](https://ailoot.ai/models/en/#m=openai%2Ftext-embedding-ada-002&route=Ultra&caller=text-embedding-ada-002&fmt=embeddings)

FAQ · OpenAI: Text Embedding Ada 002

What inputs and results are available?

`POST /v1/embeddings` · `openai/text-embedding-ada-002`: `text`, `token_ids` → `embedding` `POST /v1/embeddings` · `text-embedding-ada-002`: `text` → `embedding`

Which fields does the request use?

`POST /v1/embeddings` · `openai/text-embedding-ada-002`: `model`, `input`, `encoding_format`, `provider` `POST /v1/embeddings` · `text-embedding-ada-002`: `model`, `input`, `encoding_format`, `provider`

What limits apply?

`POST /v1/embeddings` · `openai/text-embedding-ada-002`: `ctx`: `8192`; `input.budget.maximum`: `8192` (UTF8 bytes); `input.min_items`: `1` `POST /v1/embeddings` · `text-embedding-ada-002`: `ctx`: `8192`; `input.budget.maximum`: `8192` (UTF8 bytes); `input.min_items`: `1`

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