OpenAI: Text Embedding 3 Small
OpenAI’s small third-generation text-embedding model returns 1,536-dimensional vectors by default and supports shorter representations. Use the numerical output for semantic search, code retrieval, grouping, recommendations and classification.
- Ailoot Ultra · Embeddings
POST /v1/embeddingsopenai/text-embedding-3-small - Ailoot Ultra · Embeddings · text-embedding-3-small
POST /v1/embeddingstext-embedding-3-small
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-3-small is OpenAI’s smaller third-generation text-embedding model. It converts written material into numerical vectors with 1,536 components by default. Those vectors measure relationships between texts for another search or analysis step; the model does not generate a conversational answer to the input.
Like the larger third-generation model, it supports the producer’s dimensions control for shorter embeddings. The training is designed to retain concept-representing information in a shortened vector, with the choice of size affecting the application’s quality and resource requirements. Keep the model and selected dimensions consistent across the records and queries being compared.
A practical application is code search. The maker’s guide describes representing source functions as text and embedding them, then embedding a natural-language query with the same model and comparing the resulting vectors. An illustrative result is a ranked set of relevant functions linked to their source locations. For document search, the equivalent artifact is a set of passages whose text and original references remain available to the reader or next model.
Other documented uses include clustering, related-content recommendations, classification, outlier detection and measuring diversity. A focused message workflow can group similar requests or compare them with descriptions of categories. The application defines how a similarity result becomes a category, recommendation or review item; the embedding itself remains numerical data.
OpenAI specifies a maximum input of 8,192 tokens per text. Its embedding API accepts strings or tokenized input and multiple inputs in one request, rejects empty strings and applies a 300,000-token aggregate limit. Prepare long material as meaningful passages and retain the titles or definitions required to interpret them. Use a consistent set of source fields rather than embedding different kinds of records with unrelated preparation rules.
The producer returns float vectors by default and also documents base64 encoding. The guide states that OpenAI embeddings are normalized to unit length and recommends cosine similarity; a dot product can compute it and Euclidean distance gives the same ranking. If a third-generation embedding is shortened manually after it is returned, normalize that shortened vector again as the guide describes. The dimensions control avoids making manual truncation the ordinary preparation step.
Record the model identity, dimensions and source link with each vector. A useful downstream artifact is a ranked list, grouped collection or set of classification features connected to the text that produced them. Check whether the selected material actually supports the task and keep missing facts separate from semantic relatedness. The next step can then use the representation for a specific retrieval or decision workflow.
Ailoot Ultra · Embeddings POST /v1/embeddings openai/text-embedding-3-small
openai/text-embedding-3-smallopenai/text-embedding-3-smalltext-embedding-3-small
Overview
OpenAI’s small third-generation text-embedding model returns 1,536-dimensional vectors by default and supports shorter representations. Use the numerical output for semantic search, code retrieval, grouping, recommendations and classification.
- 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 |
|---|---|---|---|---|
Fieldmodel | Typestring | RequirementRequired | Values & limits openai/text-embedding-3-small | Default— |
Fieldinput | Typestring|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— |
Fieldencoding_format | Typestring | RequirementOptional | Values & limits float | Defaultfloat |
Fielddimensions | Typeinteger | RequirementNot supported | Values & limits Minimum: 1 | Default— |
Fielduser | Typejson_value | RequirementOptional | Values & limits Conditions: Source allows this JSON metadata field; exact upstream semantics are model-specific. | Default— |
Fieldsession_id | Typejson_value | RequirementOptional | Values & limits Conditions: Source allows this JSON metadata field; exact upstream semantics are model-specific. | Default— |
Fieldtrace | Typejson_value | RequirementOptional | Values & limits Conditions: Source allows this JSON metadata field; exact upstream semantics are model-specific. | Default— |
Fieldprovider | Typeobject | RequirementOptional | Values & limits Conditions: provider.options rejected | Default— |
Input material combinations
text_to_embedding
Method parameters and lifecycle
| Parameter | Contract |
|---|---|
Parametercontent_type | Contractapplication/json |
Parameterrequired | Contractmodel, input |
Parameterinput_shapes | Contractstring, string[], nonnegative_integer_token_array, nonnegative_integer_token_array[] |
Parameterencoding_format | Contractfloat |
Parameterdimensions_override | Contractfalse |
Parametermax_input_budget_units | Contract8192 |
Parametermultimodal_objects | Contractfalse |
Parametermaterial_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 $0.00004 / 1K tokens Output · Output tokens $0 / 1K tokens Input · Cache read — / 1K tokens Input · Cache write — / 1K tokens Input · Cache write, 1 hour — / 1K tokens |
Ailoot reserves an estimated amount before a request. After the result, it settles the final charge from measured usage. Streaming settlement uses the final usage record. How reservation and settlement work →
Example requests
Ailoot Ultra · Embeddings
set -eu
: "${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-3-small", "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
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-3-small',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 |
|---|---|
Fieldobject | Typestring |
Fieldmodel | Typestring |
Fielddata[].index | Typeinteger |
Fielddata[].embedding | Typefinite float[] |
Fieldusage | 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-3-small, text-embedding-3-small
Method path aliases
Use the method paths shown above.
Related caller IDs
Ailoot Ultra · Embeddings · text-embedding-3-small POST /v1/embeddings text-embedding-3-small
text-embedding-3-smallopenai/text-embedding-3-smalltext-embedding-3-small
Overview
OpenAI’s small third-generation text-embedding model returns 1,536-dimensional vectors by default and supports shorter representations. Use the numerical output for semantic search, code retrieval, grouping, recommendations and classification.
- Accepted inputs
- Text
- Results
- embedding
Available in Ailoot · inputs & methods
Ailoot Ultra · Embeddings · text-embedding-3-small POST /v1/embeddings
- Input types
text- Output types
embedding- Context
- 8,192 tokens
- Output format
- json
| Field | Type | Requirement | Values & limits | Default |
|---|---|---|---|---|
Fieldmodel | Typestring | RequirementRequired | Values & limits text-embedding-3-small | Default— |
Fieldinput | Typestring|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— |
Fieldencoding_format | Typestring | RequirementOptional | Values & limits float | Defaultfloat |
Fielddimensions | Typeinteger | RequirementNot supported | Values & limits Minimum: 1 | Default— |
Fielduser | Typejson_value | RequirementOptional | Values & limits Conditions: Optional JSON metadata; exact upstream semantics depend on the selected model. | Default— |
Fieldsession_id | Typejson_value | RequirementOptional | Values & limits Conditions: Optional JSON metadata; exact upstream semantics depend on the selected model. | Default— |
Fieldtrace | Typejson_value | RequirementOptional | Values & limits Conditions: Optional JSON metadata; exact upstream semantics depend on the selected model. | Default— |
Fieldprovider | Typeobject | RequirementOptional | Values & limits Conditions: provider.options rejected | Default— |
Input material combinations
text_to_embedding
Method parameters and lifecycle
| Parameter | Contract |
|---|---|
Parametercontent_type | Contractapplication/json |
Parameterrequired | Contractmodel, input |
Parameterinput_shapes | Contractstring, string[], nonnegative_integer_token_array, nonnegative_integer_token_array[] |
Parameterencoding_format | Contractfloat |
Parameterdimensions_override | Contractfalse |
Parametermax_input_budget_units | Contract8192 |
Parametermultimodal_objects | Contractfalse |
Parametermaterial_types | Contracttext |
Parametertext_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 $0.00004 / 1K tokens Output · Output tokens $0 / 1K tokens Input · Cache read — / 1K tokens Input · Cache write — / 1K tokens Input · Cache write, 1 hour — / 1K tokens |
Ailoot reserves an estimated amount before a request. After the result, it settles the final charge from measured usage. Streaming settlement uses the final usage record. How reservation and settlement work →
Example requests
Ailoot Ultra · Embeddings · text-embedding-3-small
{
"model": "text-embedding-3-small",
"input": [
"First document",
"Second document"
],
"encoding_format": "float"
}set -eu
: "${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-3-small', '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
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-3-small', '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-3-small
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 |
|---|---|
Fieldobject | Typestring |
Fieldmodel | Typestring |
Fielddata[].index | Typeinteger |
Fielddata[].embedding | Typefinite float[] |
Fieldusage | 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-3-small, text-embedding-3-small
Method path aliases
Use the method paths shown above.
Related caller IDs
FAQ · OpenAI: Text Embedding 3 Small
- What inputs and results are available?
POST /v1/embeddings·openai/text-embedding-3-small:text,token_ids→embeddingPOST /v1/embeddings·text-embedding-3-small:text→embedding- Which fields does the request use?
POST /v1/embeddings·openai/text-embedding-3-small:model,input,encoding_format,providerPOST /v1/embeddings·text-embedding-3-small:model,input,encoding_format,provider- What limits apply?
POST /v1/embeddings·openai/text-embedding-3-small:ctx:8192;input.budget.maximum:8192(UTF8 bytes);input.min_items:1POST /v1/embeddings·text-embedding-3-small:ctx:8192;input.budget.maximum:8192(UTF8 bytes);input.min_items:1