Llama Nemotron Embed VL 1B v2
Llama Nemotron Embed VL v2 creates vectors for semantic document search. Ailoot's current catalogue presents text input for queries and material.
- Ailoot · Embeddings
POST /v1/embeddingsnvidia/llama-nemotron-embed-vl-1b-v2 - Ailoot · Embeddings
POST /v1/embeddingsnemotron-embed-vl - Ailoot · Embeddings
POST /v1/embeddingsnemotron-embed-vl-latest
About the model
This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below.
Finding the relevant page from a question
Llama Nemotron Embed VL 1B v2 was developed by NVIDIA to retrieve material that helps answer a question. The query is text. Ailoot's current catalogue presents text input for the model: the question and material are encoded as text. The manufacturer also describes a document as a page image or an image together with extracted text. These visual variants require a separate method supporting images. With the appropriate input, the model represents tables, charts and infographics in a shared vector space.
This retrieval is suited to question-answering assistants over large document collections, internal reference material and research material. Encoding produces a numerical vector. The retrieved page or passage can then be supplied to a model that forms an answer: Embed VL itself performs the context-retrieval stage.
The manufacturer's document options
- A text document: an individual section, passage or already recognized page text.
- An image: a complete page with its text, tables and graphic elements.
- An image with text: a page's visual appearance and textual content combined in one representation.
Query and document are encoded independently. A collection can therefore be processed in advance, with vectors stored alongside source pages, and only the query vector computed for a new question. Vector similarity helps select material by meaning. The text roles of queries and documents differ, which needs to be preserved when preparing data.
Representation size and input length
Each input becomes one vector of up to 2048 numbers. Text mode uses a budget of 8192 tokens. The manufacturer's visual method combining an image and text describes a shared budget of 10,240 tokens including both parts; this is not the text limit of Ailoot's method. The image is split into patches: the described configuration permits up to six patches and a downscaled copy of the whole page, totaling around 1792 visual tokens. Long text needs to be divided or shortened.
The language component is based on Llama 3.2 1B and the visual component on SigLIP2. They are combined into an encoder that averages input-token representations and returns a single vector. Contrastive training moves a question closer to the appropriate page and farther from irrelevant material. Retrieval remains probabilistic: vector proximity does not guarantee that the selected passage contains the correct answer.
Ailoot · Embeddings POST /v1/embeddings nvidia/llama-nemotron-embed-vl-1b-v2
nvidia/llama-nemotron-embed-vl-1b-v2
nvidia/llama-nemotron-embed-vl-1b-v2nvidia/llama-nemotron-embed-vl-1b-v2nemotron-embed-vlnemotron-embed-vl-latest
Overview
Llama Nemotron Embed VL v2 creates vectors for semantic document search. Ailoot's current catalogue presents text input for queries and material.
- Accepted inputs
- Text
- Results
- embedding
Available in Ailoot · inputs & methods
Ailoot · Embeddings POST /v1/embeddings
- Input types
text- Output types
embedding- Context
- 4,096 tokens
- Output format
- json
| Field | Type | Requirement | Values & limits | Default |
|---|---|---|---|---|
Fieldmodel | Typestring | RequirementRequired | Values & limits nvidia/llama-nemotron-embed-vl-1b-v2 | Default— |
Fieldinput | Typestring|string[] | RequirementRequired | Values & limits Minimum items: 1 Conditions: Every string nonempty after trim; data URIs are rejected; accepted budget measures relevant UTF8 bytes. | Default— |
Fieldinput_type | Typestring | RequirementOptional | Values & limits query, passage | Defaultquery |
Fieldencoding_format | Typestring | RequirementOptional | Values & limits float, base64 | Defaultfloat |
Fieldtruncate | Typestring | RequirementOptional | Values & limits NONE, START, END | Default— |
Fielddimensions | Typeinteger | RequirementOptional | Values & limits Minimum: 1 | Default— |
Fielduser | Typestring | RequirementOptional | Values & limits | Default— |
Input material combinations
text_to_embedding
Method parameters and lifecycle
| Parameter | Contract |
|---|---|
Parametermaterial_types | Contracttext |
Parameterrequired | Contractmodel, input |
Parameteroptional_controls | Contractnull |
Parametercontent_type | Contractapplication/json |
Lifecycle: exact retained published method
Method shapes and IDs are exact; supplier capability is separate from admission.
Pricing
Ailoot method rates
POST /v1/embeddings- Input
- $0.0001 / 1K
Ailoot reserves an estimated amount before a request. After the result, it settles the final charge from measured usage. Streaming settlement uses the final usage record. How reservation and settlement work →
Example requests
Ailoot · Embeddings
set -eu
: "${AILOOT_API_KEY:?Set your Ailoot API key}"
: "${AILOOT_TEXT_FILE:?Set your actual UTF-8 document}"
export AILOOT_TEXT_FILE
python3 - <<'PY_REQUEST'
import json,os
from pathlib import Path
p=json.loads('{"model": "nvidia/llama-nemotron-embed-vl-1b-v2", "input": []}')
f=Path(os.environ["AILOOT_TEXT_FILE"])
if not f.is_file():raise SystemExit("Choose your actual UTF-8 source material")
text=f.read_text(encoding="utf-8")
if not text.strip():raise SystemExit("Source material must contain text")
p["input"]=[text]
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
Receive and save results
Ailoot · Embeddings
json
Keep all completed results
Keep every complete returned response, file and content part.
Decode base64 vectors only when that format was requested; retain every vector and its original index.
If the original response needs checking
No embedding job endpoint is introduced.
Response fields
| Field | Type |
|---|---|
Fielddata[].index | Typeinteger |
Fielddata[].embedding | Typefinite float[]|base64 float32 vector |
Fieldmodel | Typestring |
Fieldusage | 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 nvidia/llama-nemotron-embed-vl-1b-v2, nemotron-embed-vl, nemotron-embed-vl-latest
Method path aliases
Use the method paths shown above.
Related caller IDs
nvidia/llama-nemotron-embed-vl-1b-v2, nemotron-embed-vl, nemotron-embed-vl-latest
Ailoot · Embeddings POST /v1/embeddings nemotron-embed-vl
nemotron-embed-vlnvidia/llama-nemotron-embed-vl-1b-v2nemotron-embed-vlnemotron-embed-vl-latest
Overview
Llama Nemotron Embed VL v2 creates vectors for semantic document search. Ailoot's current catalogue presents text input for queries and material.
- Accepted inputs
- Text
- Results
- embedding
Available in Ailoot · inputs & methods
Ailoot · Embeddings POST /v1/embeddings
- Input types
text- Output types
embedding- Context
- 4,096 tokens
- Output format
- json
| Field | Type | Requirement | Values & limits | Default |
|---|---|---|---|---|
Fieldmodel | Typestring | RequirementRequired | Values & limits nemotron-embed-vl | Default— |
Fieldinput | Typestring|string[] | RequirementRequired | Values & limits Minimum items: 1 Conditions: Every string nonempty after trim; data URIs are rejected; accepted budget measures relevant UTF8 bytes. | Default— |
Fieldinput_type | Typestring | RequirementOptional | Values & limits query, passage | Defaultquery |
Fieldencoding_format | Typestring | RequirementOptional | Values & limits float, base64 | Defaultfloat |
Fieldtruncate | Typestring | RequirementOptional | Values & limits NONE, START, END | Default— |
Fielddimensions | Typeinteger | RequirementOptional | Values & limits Minimum: 1 | Default— |
Fielduser | Typestring | RequirementOptional | Values & limits | Default— |
Input material combinations
text_to_embedding
Method parameters and lifecycle
| Parameter | Contract |
|---|---|
Parametermaterial_types | Contracttext |
Parameterrequired | Contractmodel, input |
Parameteroptional_controls | Contractnull |
Parametercontent_type | Contractapplication/json |
Lifecycle: exact retained published method
Method shapes and IDs are exact; supplier capability is separate from admission.
Pricing
Ailoot method rates
POST /v1/embeddings- Input
- $0.0001 / 1K
Ailoot reserves an estimated amount before a request. After the result, it settles the final charge from measured usage. Streaming settlement uses the final usage record. How reservation and settlement work →
Example requests
Ailoot · Embeddings
set -eu
: "${AILOOT_API_KEY:?Set your Ailoot API key}"
: "${AILOOT_TEXT_FILE:?Set your actual UTF-8 document}"
export AILOOT_TEXT_FILE
python3 - <<'PY_REQUEST'
import json,os
from pathlib import Path
p=json.loads('{"model": "nemotron-embed-vl", "input": []}')
f=Path(os.environ["AILOOT_TEXT_FILE"])
if not f.is_file():raise SystemExit("Choose your actual UTF-8 source material")
text=f.read_text(encoding="utf-8")
if not text.strip():raise SystemExit("Source material must contain text")
p["input"]=[text]
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
Receive and save results
Ailoot · Embeddings
json
Keep all completed results
Keep every complete returned response, file and content part.
Decode base64 vectors only when that format was requested; retain every vector and its original index.
If the original response needs checking
No embedding job endpoint is introduced.
Response fields
| Field | Type |
|---|---|
Fielddata[].index | Typeinteger |
Fielddata[].embedding | Typefinite float[]|base64 float32 vector |
Fieldmodel | Typestring |
Fieldusage | 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 nvidia/llama-nemotron-embed-vl-1b-v2, nemotron-embed-vl, nemotron-embed-vl-latest
Method path aliases
Use the method paths shown above.
Related caller IDs
nvidia/llama-nemotron-embed-vl-1b-v2, nemotron-embed-vl, nemotron-embed-vl-latest
Ailoot · Embeddings POST /v1/embeddings nemotron-embed-vl-latest
nemotron-embed-vl-latestnvidia/llama-nemotron-embed-vl-1b-v2nemotron-embed-vlnemotron-embed-vl-latest
Overview
Llama Nemotron Embed VL v2 creates vectors for semantic document search. Ailoot's current catalogue presents text input for queries and material.
- Accepted inputs
- Text
- Results
- embedding
Available in Ailoot · inputs & methods
Ailoot · Embeddings POST /v1/embeddings
- Input types
text- Output types
embedding- Context
- 4,096 tokens
- Output format
- json
| Field | Type | Requirement | Values & limits | Default |
|---|---|---|---|---|
Fieldmodel | Typestring | RequirementRequired | Values & limits nemotron-embed-vl-latest | Default— |
Fieldinput | Typestring|string[] | RequirementRequired | Values & limits Minimum items: 1 Conditions: Every string nonempty after trim; data URIs are rejected; accepted budget measures relevant UTF8 bytes. | Default— |
Fieldinput_type | Typestring | RequirementOptional | Values & limits query, passage | Defaultquery |
Fieldencoding_format | Typestring | RequirementOptional | Values & limits float, base64 | Defaultfloat |
Fieldtruncate | Typestring | RequirementOptional | Values & limits NONE, START, END | Default— |
Fielddimensions | Typeinteger | RequirementOptional | Values & limits Minimum: 1 | Default— |
Fielduser | Typestring | RequirementOptional | Values & limits | Default— |
Input material combinations
text_to_embedding
Method parameters and lifecycle
| Parameter | Contract |
|---|---|
Parametermaterial_types | Contracttext |
Parameterrequired | Contractmodel, input |
Parameteroptional_controls | Contractnull |
Parametercontent_type | Contractapplication/json |
Lifecycle: exact retained published method
Method shapes and IDs are exact; supplier capability is separate from admission.
Pricing
Ailoot method rates
POST /v1/embeddings- Input
- $0.0001 / 1K
Ailoot reserves an estimated amount before a request. After the result, it settles the final charge from measured usage. Streaming settlement uses the final usage record. How reservation and settlement work →
Example requests
Ailoot · Embeddings
set -eu
: "${AILOOT_API_KEY:?Set your Ailoot API key}"
: "${AILOOT_TEXT_FILE:?Set your actual UTF-8 document}"
export AILOOT_TEXT_FILE
python3 - <<'PY_REQUEST'
import json,os
from pathlib import Path
p=json.loads('{"model": "nemotron-embed-vl-latest", "input": []}')
f=Path(os.environ["AILOOT_TEXT_FILE"])
if not f.is_file():raise SystemExit("Choose your actual UTF-8 source material")
text=f.read_text(encoding="utf-8")
if not text.strip():raise SystemExit("Source material must contain text")
p["input"]=[text]
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
Receive and save results
Ailoot · Embeddings
json
Keep all completed results
Keep every complete returned response, file and content part.
Decode base64 vectors only when that format was requested; retain every vector and its original index.
If the original response needs checking
No embedding job endpoint is introduced.
Response fields
| Field | Type |
|---|---|
Fielddata[].index | Typeinteger |
Fielddata[].embedding | Typefinite float[]|base64 float32 vector |
Fieldmodel | Typestring |
Fieldusage | 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 nvidia/llama-nemotron-embed-vl-1b-v2, nemotron-embed-vl, nemotron-embed-vl-latest
Method path aliases
Use the method paths shown above.
Related caller IDs
nvidia/llama-nemotron-embed-vl-1b-v2, nemotron-embed-vl, nemotron-embed-vl-latest
FAQ · nvidia/llama-nemotron-embed-vl-1b-v2
- What inputs and results are available?
POST /v1/embeddings·nvidia/llama-nemotron-embed-vl-1b-v2:text→embeddingPOST /v1/embeddings·nemotron-embed-vl:text→embeddingPOST /v1/embeddings·nemotron-embed-vl-latest:text→embedding- Which fields does the request use?
POST /v1/embeddings·nvidia/llama-nemotron-embed-vl-1b-v2:model,input,input_type,encoding_format,truncate,dimensions,userPOST /v1/embeddings·nemotron-embed-vl:model,input,input_type,encoding_format,truncate,dimensions,userPOST /v1/embeddings·nemotron-embed-vl-latest:model,input,input_type,encoding_format,truncate,dimensions,user- What limits apply?
POST /v1/embeddings·nvidia/llama-nemotron-embed-vl-1b-v2:ctx:4096;input.min_items:1;dimensions.minimum:1POST /v1/embeddings·nemotron-embed-vl:ctx:4096;input.min_items:1;dimensions.minimum:1POST /v1/embeddings·nemotron-embed-vl-latest:ctx:4096;input.min_items:1;dimensions.minimum:1