all-MiniLM-L6-v2 is a Sentence Transformers sentence and short-paragraph encoder. It maps each text to a 384-dimensional dense vector for retrieval, clustering and sentence similarity. The model was fine-tuned from the MiniLM-L6-H384-uncased backbone using contrastive learning on a billion-pair sentence collection.
Choose the sentence or paragraph that should appear in a search result. For an FAQ collection, retain the question and the short explanation that distinguishes its topic. A passage about resetting a forgotten password should keep the reset procedure, rather than being reduced to an isolated word such as “Account.”
For a long article, index coherent short sections and keep their source locations. Retrieve candidate sections by comparing the query’s vector with the stored representations, then show the original passages so the reader can inspect the actual instructions.
The maker’s direct encoding recipe averages token representations with the attention mask, excluding padding positions from the mean. It then applies L2 normalization. Those steps produce the sentence embedding used for comparison; individual token representations from the underlying transformer serve a different role.
Each output has 384 components. Use the same checkpoint, pooling and normalization when building the collection and encoding later queries. Cosine similarity can compare the resulting vectors. A similarity value is useful for ranking candidates under that setup, while the original text determines whether a candidate answers the question.
The published default truncates inputs longer than 256 word pieces. This tokenizer unit does not correspond directly to a normal word or character. Keep important conditions inside the indexed unit, and split a lengthy procedure at meaningful boundaries before encoding it.
Training sequences were limited to 128 tokens. That training setting is separate from the 256-word-piece default used for encoding. For unusually long or specialised passages, use representative query-and-passage examples to assess the retrieval behavior rather than assuming that fitting the input boundary guarantees equal treatment of every detail.
The exact L6-v2 edition is an English embedding profile. The L12-v2 and paraphrase-MiniLM editions have separate model identities and configurations. Their common MiniLM name or 384-dimensional output does not establish interchangeable vectors.
For duplicate-question review, retrieve similar questions and compare the actual wording before merging records. “Can I reset my password?” and “Can an administrator reset another user’s password?” concern related subjects but different permissions. Preserve that distinction in the encoded text and in the review stage.
For clustering, use the embeddings as numerical inputs to a grouping process, then inspect representative records before naming a group. This model supplies vectors rather than written cluster labels or a generated FAQ answer. Its useful result is a compact representation of a short source passage that remains linked to the original record.
", "aboutText": "all-MiniLM-L6-v2 is a Sentence Transformers sentence and short-paragraph encoder. It maps each text to a 384-dimensional dense vector for retrieval, clustering and sentence similarity. The model was fine-tuned from the MiniLM-L6-H384-uncased backbone using contrastive learning on a billion-pair sentence collection.\n\n### Prepare concise, meaningful records\n\nChoose the sentence or paragraph that should appear in a search result. For an FAQ collection, retain the question and the short explanation that distinguishes its topic. A passage about resetting a forgotten password should keep the reset procedure, rather than being reduced to an isolated word such as “Account.”\n\nFor a long article, index coherent short sections and keep their source locations. Retrieve candidate sections by comparing the query’s vector with the stored representations, then show the original passages so the reader can inspect the actual instructions.\n\n### Obtain the sentence representation\n\nThe maker’s direct encoding recipe averages token representations with the attention mask, excluding padding positions from the mean. It then applies L2 normalization. Those steps produce the sentence embedding used for comparison; individual token representations from the underlying transformer serve a different role.\n\nEach output has 384 components. Use the same checkpoint, pooling and normalization when building the collection and encoding later queries. Cosine similarity can compare the resulting vectors. A similarity value is useful for ranking candidates under that setup, while the original text determines whether a candidate answers the question.\n\n### Length and edition boundaries\n\nThe published default truncates inputs longer than 256 word pieces. This tokenizer unit does not correspond directly to a normal word or character. Keep important conditions inside the indexed unit, and split a lengthy procedure at meaningful boundaries before encoding it.\n\nTraining sequences were limited to 128 tokens. That training setting is separate from the 256-word-piece default used for encoding. For unusually long or specialised passages, use representative query-and-passage examples to assess the retrieval behavior rather than assuming that fitting the input boundary guarantees equal treatment of every detail.\n\nThe exact L6-v2 edition is an English embedding profile. The L12-v2 and paraphrase-MiniLM editions have separate model identities and configurations. Their common MiniLM name or 384-dimensional output does not establish interchangeable vectors.\n\n### Search, similarity and grouping\n\nFor duplicate-question review, retrieve similar questions and compare the actual wording before merging records. “Can I reset my password?” and “Can an administrator reset another user’s password?” concern related subjects but different permissions. Preserve that distinction in the encoded text and in the review stage.\n\nFor clustering, use the embeddings as numerical inputs to a grouping process, then inspect representative records before naming a group. This model supplies vectors rather than written cluster labels or a generated FAQ answer. Its useful result is a compact representation of a short source passage that remains linked to the original record.", "editorialOverlay": { "copy_fields": null, "copy_sha256": null, "copy_source": null, "html_sha256": "724ec1ed5f34c3bbdd12aff44e9b9c13bb5c15896a8444b960cb0904ed910ffd", "module_sha256": "ee9dc1c50b1e6a8fb7fa062030fcb4646b9aaaeb08be513b723a5a92a7290f06", "raw_model_unchanged": true, "schema": "ailoot-native-model-editorial-v1" }, "locale": "en", "methods": [ { "html": "sentence-transformers/all-minilm-l6-v2Sentence Transformers all-MiniLM-L6-v2 represents sentences and short paragraphs as 384-dimensional vectors for semantic search, clustering and similarity. Its published recipe combines mean pooling with L2 normalization and a 256-word-piece default.
This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below.
all-MiniLM-L6-v2 is a Sentence Transformers sentence and short-paragraph encoder. It maps each text to a 384-dimensional dense vector for retrieval, clustering and sentence similarity. The model was fine-tuned from the MiniLM-L6-H384-uncased backbone using contrastive learning on a billion-pair sentence collection.
Choose the sentence or paragraph that should appear in a search result. For an FAQ collection, retain the question and the short explanation that distinguishes its topic. A passage about resetting a forgotten password should keep the reset procedure, rather than being reduced to an isolated word such as “Account.”
For a long article, index coherent short sections and keep their source locations. Retrieve candidate sections by comparing the query’s vector with the stored representations, then show the original passages so the reader can inspect the actual instructions.
The maker’s direct encoding recipe averages token representations with the attention mask, excluding padding positions from the mean. It then applies L2 normalization. Those steps produce the sentence embedding used for comparison; individual token representations from the underlying transformer serve a different role.
Each output has 384 components. Use the same checkpoint, pooling and normalization when building the collection and encoding later queries. Cosine similarity can compare the resulting vectors. A similarity value is useful for ranking candidates under that setup, while the original text determines whether a candidate answers the question.
The published default truncates inputs longer than 256 word pieces. This tokenizer unit does not correspond directly to a normal word or character. Keep important conditions inside the indexed unit, and split a lengthy procedure at meaningful boundaries before encoding it.
Training sequences were limited to 128 tokens. That training setting is separate from the 256-word-piece default used for encoding. For unusually long or specialised passages, use representative query-and-passage examples to assess the retrieval behavior rather than assuming that fitting the input boundary guarantees equal treatment of every detail.
The exact L6-v2 edition is an English embedding profile. The L12-v2 and paraphrase-MiniLM editions have separate model identities and configurations. Their common MiniLM name or 384-dimensional output does not establish interchangeable vectors.
For duplicate-question review, retrieve similar questions and compare the actual wording before merging records. “Can I reset my password?” and “Can an administrator reset another user’s password?” concern related subjects but different permissions. Preserve that distinction in the encoded text and in the review stage.
For clustering, use the embeddings as numerical inputs to a grouping process, then inspect representative records before naming a group. This model supplies vectors rather than written cluster labels or a generated FAQ answer. Its useful result is a compact representation of a short source passage that remains linked to the original record.
POST /v1/embeddingstext, token_idsembedding| Field | Type | Requirement | Values & limits | Default |
|---|---|---|---|---|
Fieldmodel | Typestring | RequirementRequired | Values & limits sentence-transformers/all-minilm-l6-v2 | 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 $0.00001 / 1K tokens Output · Output tokens $0 / 1K tokens Input · Cache read — / 1K tokens Input · Cache write — / 1K tokens Input · Cache write, 1 hour — / 1K tokens |
Ailoot reserves an estimated amount before a request. After the result, it settles the final charge from measured usage. Streaming settlement uses the final usage record. How reservation and settlement work →
set -eu\n: "${AILOOT_API_KEY:?Set your Ailoot API key}"\npython3 - <<'PY_REQUEST'\nimport json,os\nfrom pathlib import Path\np=json.loads('{"model": "sentence-transformers/all-minilm-l6-v2", "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='sentence-transformers/all-minilm-l6-v2',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 sentence-transformers/all-minilm-l6-v2
Use the method paths shown above.