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.
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.
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.
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.
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.
", "aboutText": "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.\n\n### Set up passage retrieval\n\nFor 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.\n\nA 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.\n\n### Representation and matching\n\nThe 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.\n\nThe 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.\n\n### Fit the source into the window\n\nThe 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.\n\nA 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.\n\n### Interpret the score\n\nThe 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.\n\nbge-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.", "editorialOverlay": { "copy_fields": null, "copy_sha256": null, "copy_source": null, "html_sha256": "4b40e3103c74637b5c7d8de3ddc42dba918ae6596e92db93671c35f0ed7ea2c6", "module_sha256": "ee9dc1c50b1e6a8fb7fa062030fcb4646b9aaaeb08be513b723a5a92a7290f06", "raw_model_unchanged": true, "schema": "ailoot-native-model-editorial-v1" }, "locale": "en", "methods": [ { "html": "baai/bge-large-en-v1.5BAAI 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.
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.
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.
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.
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.
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.
POST /v1/embeddingstext, token_idsembedding| Field | Type | Requirement | Values & limits | Default |
|---|---|---|---|---|
Fieldmodel | Typestring | RequirementRequired | Values & limits baai/bge-large-en-v1.5 | Default— |
Fieldinput | Typestring|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— |
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— |
text_to_embedding
| 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 | Contract512 |
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.
Tariff references do not add request options. Supported parameters are listed for the selected method.
| Configuration | Rate schedule | Charge |
|---|---|---|
| ConfigurationBase token rates | Rate scheduleBase rate | Charge Input · Input tokens $0.00002 / 1K tokens Output · Output tokens $0 / 1K tokens Input · Cache read — / 1K tokens Input · Cache write — / 1K tokens Input · Cache write, 1 hour — / 1K tokens |
Ailoot reserves an estimated amount before a request. After the result, it settles the final charge from measured usage. Streaming settlement uses the final usage record. How reservation and settlement work →
set -eu\n: "${AILOOT_API_KEY:?Set your Ailoot API key}"\npython3 - <<'PY_REQUEST'\nimport json,os\nfrom pathlib import Path\np=json.loads('{"model": "baai/bge-large-en-v1.5", "input": ["First document", "Second document"]}')\n\nPath("request.json").write_text(json.dumps(p,ensure_ascii=False))\nPY_REQUEST\ncurl --fail-with-body --silent --show-error https://api.ailoot.ai/v1/embeddings \\\n -H "Authorization: Bearer $AILOOT_API_KEY" -H 'Content-Type: application/json' \\\n --data-binary @request.json -D response-headers.txt -o result.json\npython3 - <<'PY_RESULT'\nimport json\nfrom pathlib import Path\nr=json.loads(Path("result.json").read_text())\nfor x in r.get("data",[]): print(json.dumps({"index":x.get("index"),"embedding":x.get("embedding")}))\nPY_RESULT\nfrom openai import OpenAI\nimport os\n\nclient=OpenAI(base_url="https://api.ailoot.ai/v1",api_key=os.environ["AILOOT_API_KEY"],max_retries=0)\nresult=client.embeddings.create(model='baai/bge-large-en-v1.5',input=["First document","Second document"])\nprint(result)\nfor item in result.data:print(item.index,item.embedding)\njson
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.
No embedding job endpoint is introduced.
| Field | Type |
|---|---|
Fieldobject | Typestring |
Fieldmodel | Typestring |
Fielddata[].index | Typeinteger |
Fielddata[].embedding | Typefinite float[] |
Fieldusage | Typeobject |
| 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. |
Ailoot Ultra baai/bge-large-en-v1.5
Use the method paths shown above.