multi-qa-mpnet-base-dot-v1 is a Sentence Transformers model designed to retrieve text passages for a question or query. Its training used about 215 million question–answer pairs. Each input becomes a 768-dimensional dense vector, with CLS pooling and dot-product scoring defining this exact edition.
Write the query as the information need and prepare corpus entries as readable text passages. For example, a question about changing a delivery address can be compared with separate paragraphs covering address changes, order cancellation and delivery tracking. Keep the relevant conditions in the address-change passage, including whether the order has already been dispatched.
Encode the query and passages with this same model. The published example does this directly, without a query/document prefix, and sorts the passages by their dot-product scores. Keep each vector attached to its original passage and document identifier so the ranked result can show the material that was actually indexed.
The model’s reference recipe takes the representation of the first token, known as CLS pooling. Its technical table explicitly states that the embeddings are not normalized and lists dot product as the suitable scoring function. Preserve that recipe when setting up the collection and the query side.
L2-normalizing these vectors changes their scoring geometry. The normalized all-mpnet-base-v2 profile uses a different recipe and is a separate checkpoint, despite sharing the MPNet family and a 768-component output. Keep the exact edition and comparison function with the stored collection instead of choosing them from the vector length alone.
The stated input limit is 512 word pieces; longer text is truncated. The maker also notes that training inputs reached only 250 word pieces and that longer text may work less well. The accepted tokenizer length and the training-length caveat therefore describe different practical boundaries.
For a large help article, prepare short sections that answer specific questions. Retain a heading where it identifies the topic, and keep a rule with the exception needed to read it correctly. A single full-page input may lose later material through truncation, while an overly small fragment can lose the condition that makes it useful.
The dense vectors support a first retrieval stage. Return the highest-scoring candidate passages with their source references, then inspect whether they address the actual question. In a RAG workflow, supply those passages to the answer-writing stage as source material; retrieval itself does not compose the answer.
Use representative questions with known relevant passages to choose the result count and assess the ranking. Include examples with similar vocabulary but different intent, such as modifying an address versus viewing the current address. Interpret scores relative to candidates under the same model and scoring setup, rather than as calibrated probabilities of correctness.
The useful output of this checkpoint is a question-and-passage embedding space for semantic search. Its unnormalized vectors, CLS pooling, dot-product score and published long-input caveat are the details to preserve when preparing and using results.
", "aboutText": "multi-qa-mpnet-base-dot-v1 is a Sentence Transformers model designed to retrieve text passages for a question or query. Its training used about 215 million question–answer pairs. Each input becomes a 768-dimensional dense vector, with CLS pooling and dot-product scoring defining this exact edition.\n\n### Prepare a question and candidate passages\n\nWrite the query as the information need and prepare corpus entries as readable text passages. For example, a question about changing a delivery address can be compared with separate paragraphs covering address changes, order cancellation and delivery tracking. Keep the relevant conditions in the address-change passage, including whether the order has already been dispatched.\n\nEncode the query and passages with this same model. The published example does this directly, without a query/document prefix, and sorts the passages by their dot-product scores. Keep each vector attached to its original passage and document identifier so the ranked result can show the material that was actually indexed.\n\n### CLS pooling and the dot-product edition\n\nThe model’s reference recipe takes the representation of the first token, known as CLS pooling. Its technical table explicitly states that the embeddings are not normalized and lists dot product as the suitable scoring function. Preserve that recipe when setting up the collection and the query side.\n\nL2-normalizing these vectors changes their scoring geometry. The normalized all-mpnet-base-v2 profile uses a different recipe and is a separate checkpoint, despite sharing the MPNet family and a 768-component output. Keep the exact edition and comparison function with the stored collection instead of choosing them from the vector length alone.\n\n### Length limit and longer passages\n\nThe stated input limit is 512 word pieces; longer text is truncated. The maker also notes that training inputs reached only 250 word pieces and that longer text may work less well. The accepted tokenizer length and the training-length caveat therefore describe different practical boundaries.\n\nFor a large help article, prepare short sections that answer specific questions. Retain a heading where it identifies the topic, and keep a rule with the exception needed to read it correctly. A single full-page input may lose later material through truncation, while an overly small fragment can lose the condition that makes it useful.\n\n### Use the ranking in retrieval and RAG\n\nThe dense vectors support a first retrieval stage. Return the highest-scoring candidate passages with their source references, then inspect whether they address the actual question. In a RAG workflow, supply those passages to the answer-writing stage as source material; retrieval itself does not compose the answer.\n\nUse representative questions with known relevant passages to choose the result count and assess the ranking. Include examples with similar vocabulary but different intent, such as modifying an address versus viewing the current address. Interpret scores relative to candidates under the same model and scoring setup, rather than as calibrated probabilities of correctness.\n\nThe useful output of this checkpoint is a question-and-passage embedding space for semantic search. Its unnormalized vectors, CLS pooling, dot-product score and published long-input caveat are the details to preserve when preparing and using results.", "editorialOverlay": { "copy_fields": null, "copy_sha256": null, "copy_source": null, "html_sha256": "65353364a31c9741fe49e9658e6b920222c84c6202951938b11e12b20b2cac17", "module_sha256": "ee9dc1c50b1e6a8fb7fa062030fcb4646b9aaaeb08be513b723a5a92a7290f06", "raw_model_unchanged": true, "schema": "ailoot-native-model-editorial-v1" }, "locale": "en", "methods": [ { "html": "sentence-transformers/multi-qa-mpnet-base-dot-v1Sentence Transformers multi-qa-mpnet-base-dot-v1 encodes questions and passages into 768-dimensional vectors for semantic search. It uses CLS pooling and unnormalized embeddings designed for dot-product scoring, with a 512-word-piece input limit.
This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below.
multi-qa-mpnet-base-dot-v1 is a Sentence Transformers model designed to retrieve text passages for a question or query. Its training used about 215 million question–answer pairs. Each input becomes a 768-dimensional dense vector, with CLS pooling and dot-product scoring defining this exact edition.
Write the query as the information need and prepare corpus entries as readable text passages. For example, a question about changing a delivery address can be compared with separate paragraphs covering address changes, order cancellation and delivery tracking. Keep the relevant conditions in the address-change passage, including whether the order has already been dispatched.
Encode the query and passages with this same model. The published example does this directly, without a query/document prefix, and sorts the passages by their dot-product scores. Keep each vector attached to its original passage and document identifier so the ranked result can show the material that was actually indexed.
The model’s reference recipe takes the representation of the first token, known as CLS pooling. Its technical table explicitly states that the embeddings are not normalized and lists dot product as the suitable scoring function. Preserve that recipe when setting up the collection and the query side.
L2-normalizing these vectors changes their scoring geometry. The normalized all-mpnet-base-v2 profile uses a different recipe and is a separate checkpoint, despite sharing the MPNet family and a 768-component output. Keep the exact edition and comparison function with the stored collection instead of choosing them from the vector length alone.
The stated input limit is 512 word pieces; longer text is truncated. The maker also notes that training inputs reached only 250 word pieces and that longer text may work less well. The accepted tokenizer length and the training-length caveat therefore describe different practical boundaries.
For a large help article, prepare short sections that answer specific questions. Retain a heading where it identifies the topic, and keep a rule with the exception needed to read it correctly. A single full-page input may lose later material through truncation, while an overly small fragment can lose the condition that makes it useful.
The dense vectors support a first retrieval stage. Return the highest-scoring candidate passages with their source references, then inspect whether they address the actual question. In a RAG workflow, supply those passages to the answer-writing stage as source material; retrieval itself does not compose the answer.
Use representative questions with known relevant passages to choose the result count and assess the ranking. Include examples with similar vocabulary but different intent, such as modifying an address versus viewing the current address. Interpret scores relative to candidates under the same model and scoring setup, rather than as calibrated probabilities of correctness.
The useful output of this checkpoint is a question-and-passage embedding space for semantic search. Its unnormalized vectors, CLS pooling, dot-product score and published long-input caveat are the details to preserve when preparing and using results.
POST /v1/embeddingstext, token_idsembedding| Field | Type | Requirement | Values & limits | Default |
|---|---|---|---|---|
Fieldmodel | Typestring | RequirementRequired | Values & limits sentence-transformers/multi-qa-mpnet-base-dot-v1 | 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/multi-qa-mpnet-base-dot-v1", "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/multi-qa-mpnet-base-dot-v1',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/multi-qa-mpnet-base-dot-v1
Use the method paths shown above.