pplx-embed-v1-0.6B is Perplexity’s smaller independent embedding model in the v1 family. It turns query or document text into a 1024-dimensional representation for retrieval and semantic search. The maker describes a Qwen3-based family adapted through diffusion continued pretraining, with mean pooling and a 32K context. The model encodes the supplied text directly, without a required instruction prefix.
A useful collection can contain product explanations, knowledge-base sections or short research notes. Give each text enough source context to be understood when it is returned by search: a heading, the statement being sought and the condition which changes its meaning. Keep the original text and record identifier alongside the resulting embedding.
For a note-search task, a question about what changed in a process should retrieve the note which describes that change. Embed the actual question and compare it with the prepared corpus records. Do not add another model family’s query/passage instruction by default; direct text is the maker’s documented preparation for this model.
The native representation is an unnormalised INT8 embedding. Perplexity explicitly recommends cosine similarity for comparison. A raw dot score on unnormalised output changes the comparison because vector magnitude contributes to it. Select a search index and scoring path that can interpret the chosen representation and metric together.
Binary representation is also documented, and the model table identifies Matryoshka support. Those representation options can be relevant to index storage, but their components and packed bytes must be interpreted correctly. The 1024-dimensional full output is distinct from the 2560-dimensional 4B model. A change in model size or output representation should be checked on the query and corpus sides rather than treated as an automatic common space.
The maker’s context specification is 32K. A long note or document section can fit more surrounding explanation than a short-window encoder, while the retrieval unit still needs a clear purpose. Choose whether a hit should return a whole section or a focused answer-bearing passage. Keep definitions and exceptions with the statement they qualify.
For larger source material, retain the parent-document identifier and version outside the vector. This lets the search result lead back to the source and lets changed text replace its corresponding representation. Inspect candidate results for real questions in the intended language mix and verify that the passage supplies the requested information rather than merely discussing a similar topic.
The model card distinguishes this independent encoder from pplx-embed-context-v1-0.6B. The contextual model uses document surroundings for chunk representations; the ordinary v1 model represents each supplied text on its own. A list of independent strings does not establish that contextual chunk processing occurred.
The useful output is a corpus representation or query vector connected to the actual source text, with the selected INT8 or binary format and cosine comparison recorded. A later ranking or RAG answer stage uses the retrieved text as a separate artifact. This profile belongs to the exact 0.6B independent edition, not to the 4B or context-v1 models.
", "aboutText": "pplx-embed-v1-0.6B is Perplexity’s smaller independent embedding model in the v1 family. It turns query or document text into a 1024-dimensional representation for retrieval and semantic search. The maker describes a Qwen3-based family adapted through diffusion continued pretraining, with mean pooling and a 32K context. The model encodes the supplied text directly, without a required instruction prefix.\n\n### Prepare an independent record\n\nA useful collection can contain product explanations, knowledge-base sections or short research notes. Give each text enough source context to be understood when it is returned by search: a heading, the statement being sought and the condition which changes its meaning. Keep the original text and record identifier alongside the resulting embedding.\n\nFor a note-search task, a question about what changed in a process should retrieve the note which describes that change. Embed the actual question and compare it with the prepared corpus records. Do not add another model family’s query/passage instruction by default; direct text is the maker’s documented preparation for this model.\n\n### Quantised vectors and cosine comparison\n\nThe native representation is an unnormalised INT8 embedding. Perplexity explicitly recommends cosine similarity for comparison. A raw dot score on unnormalised output changes the comparison because vector magnitude contributes to it. Select a search index and scoring path that can interpret the chosen representation and metric together.\n\nBinary representation is also documented, and the model table identifies Matryoshka support. Those representation options can be relevant to index storage, but their components and packed bytes must be interpreted correctly. The 1024-dimensional full output is distinct from the 2560-dimensional 4B model. A change in model size or output representation should be checked on the query and corpus sides rather than treated as an automatic common space.\n\n### Use the context deliberately\n\nThe maker’s context specification is 32K. A long note or document section can fit more surrounding explanation than a short-window encoder, while the retrieval unit still needs a clear purpose. Choose whether a hit should return a whole section or a focused answer-bearing passage. Keep definitions and exceptions with the statement they qualify.\n\nFor larger source material, retain the parent-document identifier and version outside the vector. This lets the search result lead back to the source and lets changed text replace its corresponding representation. Inspect candidate results for real questions in the intended language mix and verify that the passage supplies the requested information rather than merely discussing a similar topic.\n\n### Separate independent and contextual processing\n\nThe model card distinguishes this independent encoder from pplx-embed-context-v1-0.6B. The contextual model uses document surroundings for chunk representations; the ordinary v1 model represents each supplied text on its own. A list of independent strings does not establish that contextual chunk processing occurred.\n\nThe useful output is a corpus representation or query vector connected to the actual source text, with the selected INT8 or binary format and cosine comparison recorded. A later ranking or RAG answer stage uses the retrieved text as a separate artifact. This profile belongs to the exact 0.6B independent edition, not to the 4B or context-v1 models.", "editorialOverlay": { "copy_fields": null, "copy_sha256": null, "copy_source": null, "html_sha256": "1435c3401683311dc5c8a5446d22636446c28e4e8593c82af3d88ef76e3b0421", "module_sha256": "ee9dc1c50b1e6a8fb7fa062030fcb4646b9aaaeb08be513b723a5a92a7290f06", "raw_model_unchanged": true, "schema": "ailoot-native-model-editorial-v1" }, "locale": "en", "methods": [ { "html": "perplexity/pplx-embed-v1-0.6bPerplexity pplx-embed-v1-0.6B produces 1024-dimensional embeddings for independent multilingual text retrieval. Its 32K context, direct text preparation and native INT8 or binary representations define a smaller v1-family index profile.
This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below.
pplx-embed-v1-0.6B is Perplexity’s smaller independent embedding model in the v1 family. It turns query or document text into a 1024-dimensional representation for retrieval and semantic search. The maker describes a Qwen3-based family adapted through diffusion continued pretraining, with mean pooling and a 32K context. The model encodes the supplied text directly, without a required instruction prefix.
A useful collection can contain product explanations, knowledge-base sections or short research notes. Give each text enough source context to be understood when it is returned by search: a heading, the statement being sought and the condition which changes its meaning. Keep the original text and record identifier alongside the resulting embedding.
For a note-search task, a question about what changed in a process should retrieve the note which describes that change. Embed the actual question and compare it with the prepared corpus records. Do not add another model family’s query/passage instruction by default; direct text is the maker’s documented preparation for this model.
The native representation is an unnormalised INT8 embedding. Perplexity explicitly recommends cosine similarity for comparison. A raw dot score on unnormalised output changes the comparison because vector magnitude contributes to it. Select a search index and scoring path that can interpret the chosen representation and metric together.
Binary representation is also documented, and the model table identifies Matryoshka support. Those representation options can be relevant to index storage, but their components and packed bytes must be interpreted correctly. The 1024-dimensional full output is distinct from the 2560-dimensional 4B model. A change in model size or output representation should be checked on the query and corpus sides rather than treated as an automatic common space.
The maker’s context specification is 32K. A long note or document section can fit more surrounding explanation than a short-window encoder, while the retrieval unit still needs a clear purpose. Choose whether a hit should return a whole section or a focused answer-bearing passage. Keep definitions and exceptions with the statement they qualify.
For larger source material, retain the parent-document identifier and version outside the vector. This lets the search result lead back to the source and lets changed text replace its corresponding representation. Inspect candidate results for real questions in the intended language mix and verify that the passage supplies the requested information rather than merely discussing a similar topic.
The model card distinguishes this independent encoder from pplx-embed-context-v1-0.6B. The contextual model uses document surroundings for chunk representations; the ordinary v1 model represents each supplied text on its own. A list of independent strings does not establish that contextual chunk processing occurred.
The useful output is a corpus representation or query vector connected to the actual source text, with the selected INT8 or binary format and cosine comparison recorded. A later ranking or RAG answer stage uses the retrieved text as a separate artifact. This profile belongs to the exact 0.6B independent edition, not to the 4B or context-v1 models.
POST /v1/embeddingstext, token_idsembedding| Field | Type | Requirement | Values & limits | Default |
|---|---|---|---|---|
Fieldmodel | Typestring | RequirementRequired | Values & limits perplexity/pplx-embed-v1-0.6b | Default— |
Fieldinput | Typestring|string[]|integer[]|integer[][] | RequirementRequired | Values & limits Minimum items: 1 Conditions: Nonempty homogeneous batch. Token IDs are nonnegative integers. Input budget: Maximum: 32,000 · 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 | Contract32000 |
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 $0.000008 / 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": "perplexity/pplx-embed-v1-0.6b", "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='perplexity/pplx-embed-v1-0.6b',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 perplexity/pplx-embed-v1-0.6b
Use the method paths shown above.