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

# BAAI: bge-large-en-v1.5

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

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

BAAI bge-large-en-v1.5 produces 1024-dimensional English text vectors for retrieval and semantic comparison. Its v1.5 preparation supports an optional query instruction, with a documented 512-token sequence length.

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

bge-large-en-v1.5 is BAAI’s large English dense embedding edition. It maps a text to a 1024-dimensional vector for passage retrieval and semantic comparison. Version 1.5 is the maker’s update aimed at a more useful similarity distribution and improved retrieval without an instruction. Large remains a distinct checkpoint from Base, Small, Chinese editions and the multilingual BGE-M3 model.

#### Set up passage retrieval

For a short search query against longer documents, the maker recommends the query instruction “Represent this sentence for searching relevant passages:”. Add it to the query side; corpus passages keep their own source text without that instruction. In other uses, direct text is sufficient. BAAI also documents retrieval without the prefix in v1.5 and recommends choosing the preparation that works best on the actual task.

A practical knowledge collection can index the sections of an English technical manual. A question about which setting changes the output format should return the section explaining that setting and its conditions. Retain the heading, relevant explanation and source location with each passage. The model provides the numerical representation; the returned source text is what a reader or a later RAG stage uses.

#### Representation and matching

The manufacturer’s direct encoding example uses the final hidden state of the first CLS token as the pooled embedding and then normalises the vector. Comparing normalised vectors with a dot product gives cosine similarity. Each passage can therefore be prepared once and stored for comparison with incoming query vectors, while changed source material receives an updated representation.

The 1024 dimensions specify the output vector length. They differ from the 768-dimensional Base representation and do not establish a compatible shared space with another checkpoint. Keep the model and preparation convention associated with the stored index. A change in the embedding edition calls for checking the corpus and query representations together.

#### Fit the source into the window

The documented sequence length is 512 tokens. Prepare a long manual as coherent sections rather than one oversized input. Keep a condition or exception beside the rule it qualifies, and include enough context for a retrieved section to make sense on its own. An added query instruction also occupies input tokens, so the model’s window concerns the full prepared input.

A useful passage boundary can include a heading and the paragraphs answering one user question. Preserve the parent-document identifier and version outside the vector so a result remains traceable to the correct source. For a comparison task, use the actual texts to compare rather than replacing them with unrelated summaries that remove the distinction you need to find.

#### Interpret the score

The maker warns against treating absolute similarity as a universal rule for semantic equivalence. Relative score order is central to retrieval, and a threshold should be calibrated on the task’s own text distribution. Review candidate passages for questions with known answers and for questions whose answer is absent. That reveals whether a returned section provides the required information rather than merely resembling the topic.

bge-large-en-v1.5 supplies English dense vectors. A separate reranker can refine candidate order, and a separate language model can compose an answer from the selected passages. The useful output from this edition is a searchable representation tied to its source text, with the exact Large v1.5 model choice and input preparation retained.

**Ailoot Ultra · Embeddings** `POST /v1/embeddings` `baai/bge-large-en-v1.5`

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

BAAI /`baai/bge-large-en-v1.5`

### Overview

BAAI bge-large-en-v1.5 produces 1024-dimensional English text vectors for retrieval and semantic comparison. Its v1.5 preparation supports an optional query instruction, with a documented 512-token sequence length.

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 baai/bge-large-en-v1.5 | 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 **$0.00002** / 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": "baai/bge-large-en-v1.5", "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='baai/bge-large-en-v1.5',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** [`baai/bge-large-en-v1.5`](https://ailoot.ai/models/en/#m=baai%2Fbge-large-en-v1.5&route=Ultra&caller=baai%2Fbge-large-en-v1.5&fmt=embeddings)

#### Method path aliases

Use the method paths shown above.

#### Related caller IDs

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

FAQ · BAAI: bge-large-en-v1.5

What inputs and results are available?

`POST /v1/embeddings` · `baai/bge-large-en-v1.5`: `text`, `token_ids` → `embedding`

Which fields does the request use?

`POST /v1/embeddings` · `baai/bge-large-en-v1.5`: `model`, `input`, `encoding_format`, `provider`

What limits apply?

`POST /v1/embeddings` · `baai/bge-large-en-v1.5`: `input.budget.maximum`: `512` (UTF8 bytes); `input.min_items`: `1`

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