Source: https://ailoot.ai/models/en/thenlper-gte-base/
Canonical: https://ailoot.ai/models/en/thenlper-gte-base/
Language: en

# Thenlper: GTE-Base

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## [Thenlper: GTE-Base](https://ailoot.ai/models/en/thenlper-gte-base/)

Alibaba DAMO Academy GTE-base embeds English text into 768-dimensional vectors for retrieval and semantic similarity. The exact model uses average pooling, supports sequences up to 512 tokens and documents cosine-style comparison with optional normalization.

- [**Ailoot Ultra · Embeddings** `POST /v1/embeddings` `thenlper/gte-base`](https://ailoot.ai/models/en/thenlper-gte-base/#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.

GTE-base is the base-sized General Text Embeddings model trained by Alibaba DAMO Academy. This English text encoder is mainly based on the BERT framework and produces 768-dimensional dense vectors. Its training uses a large corpus of relevance pairs across domains, with information retrieval, semantic textual similarity and embedding-based text reranking among the stated applications.

#### Prepare English queries and passages

Use readable English text for both the information need and the material to be searched. For example, a query about replacing a worn component can be compared with short maintenance sections describing replacement criteria, inspection steps and ordering instructions. Retain the component name and the conditions that distinguish those sections.

The published example encodes questions and candidate text directly, without an added task instruction. Keep that preparation consistent across the collection and later queries. Store the original passage and its identifier alongside the vector so a ranked result can show the information that was represented.

#### Average pooling and comparison

The reference recipe averages the model’s token representations using the attention mask, preventing padding positions from contributing to the result. It then shows optional L2 normalization and compares the normalized vectors through their dot product. A separate example uses cosine similarity.

Choose and retain a consistent normalization and comparison setup for indexing and retrieval. The output contains 768 numerical components; it does not contain a generated answer. For a candidate-reranking stage, encode the candidate passages with the same model and order them by their similarity to the query. This remains embedding-based comparison, rather than a claim that the model jointly reads every query–passage pair as a cross-encoder.

#### English scope and 512-token inputs

The exact card limits this model to English text and states that long inputs are truncated to a maximum of 512 tokens. A long manual should therefore become meaningful sections before indexing. Keep a warning, exception or qualification with the rule it modifies whenever those details are needed to interpret the passage.

For a query about maintenance intervals, a section containing the interval and the operating condition is more useful than a fragment containing only a number. Preserve the heading or equipment identity when it disambiguates the instruction. Check tokenizer length rather than relying on the visible number of words.

#### Use source passages behind the score

Retrieval returns candidates from the indexed collection. Inspect the leading passages for the exact task, and retain their source locations when passing them into a RAG answer-writing stage. A related passage may discuss inspection without authorizing replacement; that distinction must remain visible in the source material.

GTE-base is distinct from GTE-large, which produces 1024-dimensional vectors, and from later multilingual or extended-context GTE editions. Its published model identity defines the English scope, 768-component representation and 512-token boundary. Use this exact profile with representative queries and source records when assessing a retrieval collection.

**Ailoot Ultra · Embeddings** `POST /v1/embeddings` `thenlper/gte-base`

[`thenlper/gte-base`](https://ailoot.ai/models/en/#m=thenlper%2Fgte-base&route=Ultra&caller=thenlper%2Fgte-base&fmt=embeddings)

Thenlper /`thenlper/gte-base`

### Overview

Alibaba DAMO Academy GTE-base embeds English text into 768-dimensional vectors for retrieval and semantic similarity. The exact model uses average pooling, supports sequences up to 512 tokens and documents cosine-style comparison with optional normalization.

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`

Output format

json

| Field | Type | Requirement | Values & limits | Default |
| --- | --- | --- | --- | --- |
| Field`model` | Type`string` | RequirementRequired | Values & limits thenlper/gte-base | 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: 512 · 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` | Contract512 |
| 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

Not specified When fast mode is selected

Flex mode

Not specified When flex mode is selected

Maximum reasoning effort

Not specified 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 **$0.00001** / 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": "thenlper/gte-base", "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='thenlper/gte-base',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** [`thenlper/gte-base`](https://ailoot.ai/models/en/#m=thenlper%2Fgte-base&route=Ultra&caller=thenlper%2Fgte-base&fmt=embeddings)

#### Method path aliases

Use the method paths shown above.

#### Related caller IDs

[`thenlper/gte-base`](https://ailoot.ai/models/en/#m=thenlper%2Fgte-base&route=Ultra&caller=thenlper%2Fgte-base&fmt=embeddings)

FAQ · Thenlper: GTE-Base

What inputs and results are available?

`POST /v1/embeddings` · `thenlper/gte-base`: `text`, `token_ids` → `embedding`

Which fields does the request use?

`POST /v1/embeddings` · `thenlper/gte-base`: `model`, `input`, `encoding_format`, `provider`

What limits apply?

`POST /v1/embeddings` · `thenlper/gte-base`: `input.budget.maximum`: `512` (UTF8 bytes); `input.min_items`: `1`

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