Source: https://ailoot.ai/models/en/baai-bge-large-zh-v1-5/
Canonical: https://ailoot.ai/models/en/baai-bge-large-zh-v1-5/
Language: en

# BAAI/bge-large-zh-v1.5

[Markdown](https://ailoot.ai/models/en/baai-bge-large-zh-v1-5/index.md) · [JSON](https://ailoot.ai/models/en/baai-bge-large-zh-v1-5/facts.json)

## [BAAI/bge-large-zh-v1.5](https://ailoot.ai/models/en/baai-bge-large-zh-v1-5/)

Chinese passage-retrieval embeddings from BAAI, exposed as text-to-vector requests through Ailoot.

- [**Ailoot Ultra · Embeddings** `POST /v1/embeddings` `BAAI/bge-large-zh-v1.5`](https://ailoot.ai/models/en/baai-bge-large-zh-v1-5/#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.

BAAI/bge-large-zh-v1.5 is the Chinese edition of BAAI General Embedding v1.5. Choose it for a Chinese-language search index: for example, matching a customer question to the relevant return-policy paragraph. Its output is a numerical vector for similarity search, rather than a generated answer. The official model card identifies the Chinese language focus and describes v1.5 as improving the similarity distribution.

Ailoot accepts one text string or an array of strings at /v1/embeddings. Embed both your passages and queries with this exact edition and associate each data\[\].embedding with data\[\].index. For short-question-to-passage retrieval, BAAI recommends a retrieval instruction on the query; passages remain unprefixed. Similarity thresholds need calibration on your own material: a score is not a probability that a passage answers the question. See the [BAAI model card](https://huggingface.co/BAAI/bge-large-zh-v1.5) for the query convention.

**Ailoot Ultra · Embeddings** `POST /v1/embeddings` `BAAI/bge-large-zh-v1.5`

[`BAAI/bge-large-zh-v1.5`](https://ailoot.ai/models/en/#m=BAAI%2Fbge-large-zh-v1.5&route=Ultra&caller=BAAI%2Fbge-large-zh-v1.5&fmt=embeddings)

Other /`BAAI/bge-large-zh-v1.5`

### Overview

Chinese passage-retrieval embeddings from BAAI, exposed as text-to-vector requests through Ailoot.

Accepted inputs

Text

Results

embedding

### Available in Ailoot · inputs & methods

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

Input types

`text`

Output types

`embedding`

| Field | Type | Requirement | Values & limits | Default |
| --- | --- | --- | --- | --- |
| Field`model` | Type`string` | RequirementRequired | Values & limits BAAI/bge-large-zh-v1.5 Conditions: Use this exact Ailoot routing selector. | Default— |
| Field`input` | Type`string or array<string>` | RequirementRequired | Values & limits Conditions: | Default— |

Method parameters and lifecycle

| Parameter | Contract |
| --- | --- |
| Parameter`content_type` | Contractapplication/json |
| Parameter`required` | Contractmodel, input |
| Parameter`optional` | Contract |
| Parameter`material_types` | Contracttext |
| Parameter`conditions` | ContractUse this exact Ailoot routing selector. |

Lifecycle: `synchronous_json`

### 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 **$0.00004** / 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 →](https://ailoot.ai/docs/en/billing/reserve/)

### Example requests

#### Ailoot Ultra · Embeddings

curl · create once and retain the complete result

```bash
set -eu
: "${AILOOT_API_KEY:?Set your Ailoot API key}"
export AILOOT_API_KEY
python3 - <<'PY_REQUEST'
import json,os
from pathlib import Path
p=json.loads('{"model": "BAAI/bge-large-zh-v1.5", "input": "The quick brown fox jumps over the lazy dog"}')

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
# Submit once. Retain the complete result, original identifiers and every response header.
```

### Receive and save results

### Aliases

#### Model aliases

**Ailoot Ultra** [`BAAI/bge-large-zh-v1.5`](https://ailoot.ai/models/en/#m=BAAI%2Fbge-large-zh-v1.5&route=Ultra&caller=BAAI%2Fbge-large-zh-v1.5&fmt=embeddings)

#### Method path aliases

Use the method paths shown above.

#### Related caller IDs

[`BAAI/bge-large-zh-v1.5`](https://ailoot.ai/models/en/#m=BAAI%2Fbge-large-zh-v1.5&route=Ultra&caller=BAAI%2Fbge-large-zh-v1.5&fmt=embeddings)

FAQ · BAAI/bge-large-zh-v1.5

What inputs and results are available?

`POST /v1/embeddings` · `BAAI/bge-large-zh-v1.5`: `text` → `embedding`

Which fields does the request use?

`POST /v1/embeddings` · `BAAI/bge-large-zh-v1.5`: `model`, `input`

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The company and its project portfolio: millions of customers; monthly sales in the billions.





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