NVIDIA NIM connection actions and objects

• •The Invoke Healthcare Model action invokes the Healthcare API models, such as deepmind/alphafold2 and nvidia/genmol. It invokes NVIDIA healthcare models to generate predictions or analysis based on the input data.
The connector is tested with the following models:
• - Deepmind/alphafold2
• - Nvidia/esmfold
• - Proteinmpnn/predict
• - Nvidia/genmol
• - Nvidia/molmim
You must provide values to the input fields, such as payload, version, and NVCF_POLL_SECONDS.
For example, consider the nvidia/genmol model with the following endpoint URL:
https://health.api.nvidia.com/v1/biology/nvidia/genmol/generate
In this URL, v1 is the API version and biology, nvidia, genmol, and generate are the individual URL path segments.
NVCF_POLL_SECONDS - interval in seconds between status checks for a pending request to an NVIDIA Cloud Function (NVCF). The maximum supported value is 59 seconds.
The following snippet is a payload example:
<root xmlns:m="urn:informatica:ae:xquery:json2xml:meta-data">
<smiles>C124CN3C1.S3(=O)(=O)CC.C4C#N.[*{20-20}]</smiles>
<num_molecules m:type="xs:int">30</num_molecules>
<temperature>0.01</temperature>
<noise>1</noise>
<step_size m:type="xs:int">1</step_size>
<scoring>QED</scoring>
<unique m:type="xs:boolean">false</unique>
</root>
The following snippet is a sample response:
200 OK
<root>
<molecules>
<score>0.851</score>
<smiles>CCS(=O)(=O)N1CC(CC#N)(c2ccc(C(F)(F)F)cc2)C1</smiles>
</molecules>
<molecules>
<score>0.851</score>
<smiles>CCS(=O)(=O)N1CC(CC#N)(c2ccc(C(F)(F)F)cc2)C1</smiles>
</molecules>
...
...
<molecules>
<score>0.851</score>
<smiles>CCS(=O)(=O)N1CC(CC#N)(c2ccc(C(F)(F)F)cc2)C1</smiles>
</molecules>
<molecules>
<score>0.851</score>
<smiles>CCS(=O)(=O)N1CC(CC#N)(c2ccc(C(F)(F)F)cc2)C1</smiles>
</molecules>
<molecules>
<score>0.805</score>
<smiles>CCS(=O)(=O)N1CC(CC#N)(NC(=O)C2(C)CCC2)C1</smiles>
</molecules>
<molecules>
<score>0.802</score>
<smiles>CCS(=O)(=O)N1CC(CC#N)(OCc2ccc(Cl)cc2)C1</smiles>
</molecules>
<molecules>
<score>0.798</score>
<smiles>CCS(=O)(=O)N1CC(CC#N)(NC(=O)CCC(C)(C)C)C1</smiles>
</molecules>
<molecules>
<score>0.785</score>
<smiles>CCS(=O)(=O)N1CC(CC#N)(OCC2CCCC(O)C2)C1</smiles>
</molecules>
<molecules>
<score>0.78</score>
<smiles>CCS(=O)(=O)N1CC(CC#N)(NC(=O)CCC(F)(F)F)C1</smiles>
</molecules>
<molecules>
<score>0.78</score>
<smiles>CCS(=O)(=O)N1CC(CC#N)(NC(=O)CCC(F)(F)F)C1</smiles>
</molecules>
...
...
<molecules>
<score>0.78</score>
<smiles>CCS(=O)(=O)N1CC(CC#N)(NC(=O)CCC(F)(F)F)C1</smiles>
</molecules>
<molecules>
<score>0.773</score>
<smiles>CCS(=O)(=O)N1CC(CC#N)(NC(=O)C2CCCCCC2)C1</smiles>
</molecules>
<molecules>
<score>0.752</score>
<smiles>CCS(=O)(=O)N1CC(CC#N)(NC(=O)CCC(C)(F)F)C1</smiles>
</molecules>
<molecules>
<score>0.75</score>
<smiles>CCS(=O)(=O)N1CC(CC#N)(C(=O)CCC2(O)CCC2)C1</smiles>
</molecules>
<molecules>
<score>0.668</score>
<smiles>CCS(=O)(=O)N1CC(CC#N)(C(=O)CCC2CCCCCC2)C1</smiles>
</molecules>
<molecules>
<score>0.655</score>
<smiles>CCS(=O)(=O)N1CC(CC#N)(OC(=O)CCC2CC2)C1</smiles>
</molecules>
<molecules>
<score>0.627</score>
<smiles>CCS(=O)(=O)N1CC(CC#N)(C(=O)CCCN2CCOCC2)C1</smiles>
</molecules>
<molecules>
<score>0.562</score>
<smiles>CCS(=O)(=O)N1CC(CC#N)(OC(=O)C2CC=CCC2)C1</smiles>
</molecules>
<molecules>
<score>0.481</score>
<smiles>CCS(=O)(=O)N1CC(CC#N)(OC(=O)CCCCC(=O)O)C1</smiles>
</molecules>
<status>success</status>
</root>
• •The Get Polling Status action gets the result of an earlier function invocation request that returned a 202 status. It retrieves the result of a previous function invocation that returned a 202 status code.
For example, when invoking a model, if the response takes longer than the configured response timeout, the server returns a status header as in-progress and a status code 202. It also provides a request ID in the response header, which can be used to poll for the final result.
You must provide values to the input fields, such as host, version, and NVCF_POLL_SECONDS.
By default, the status polling is performed using the following URL:
https://optimize.api.nvidia.com/v1/status/{requestId}
In this URL, optimize.api.nvidia.com is the host, v1 is the API version, and status is a URL path segment.
Note: Some models might use different status polling URLs. For more information, see the NVIDIA documentation for model-specific details.
The following snippets are some additional payload examples for other models:
• - Deepmind/alphafold2
<root xmlns:m="urn:informatica:ae:xquery:json2xml:meta-data">
<sequence>MVPSAGQLALFALGIVLAACQALENSTSPLSADPPVAAAVVSHFNDCPDSHTQFCFHGTCRFLVQEDKPACVCHSGYVGARCEHADLLAVVAASQKKQAITALVVVSIVALAVLIITCVLIHCCQVRKHCEWCRALICRHEKPSALLKGRTACCHSETVV</sequence>
<algorithm>mmseqs2</algorithm>
<databases m:isArray="true">uniref90</databases>
<databases m:isArray="true">small_bfd</databases>
<e_value m:type="xs:double">0.0001</e_value>
<iterations m:type="xs:int">1</iterations>
<relax_prediction m:type="xs:boolean">false</relax_prediction>
<skip_template_search m:type="xs:boolean">true</skip_template_search>
</root>
• - Nvidia/esmfold
<root xmlns:m="urn:informatica:ae:xquery:json2xml:meta-data">
<sequence>MVPSAGQLALFALGIVLAACQALENSTSPLSADPPVAAAVVSHFNDCPDSHTQFCFHGTCRFLVQEDKPACVCHSGYVGARCEHADLLAVVAASQKKQAITALVVVSIVALAVLIITCVLIHCCQVRKHCEWCRALICRHEKPSALLKGRTACCHSETVV</sequence>
<algorithm>mmseqs2</algorithm>
<databases m:isArray="true">uniref90</databases>
<databases m:isArray="true">small_bfd</databases>
<e_value m:type="xs:double">0.0001</e_value>
<iterations m:type="xs:int">1</iterations>
<relax_prediction m:type="xs:boolean">false</relax_prediction>
<skip_template_search m:type="xs:boolean">true</skip_template_search>
</root>
• - Proteinmpnn/predict
<root xmlns:m="urn:informatica:ae:xquery:json2xml:meta-data">
<input_pdb> ATOM 1 N GLY A 1 11.104 13.207 10.678 1.00 20.00 N
ATOM 2 CA GLY A 1 12.560 13.207 10.678 1.00 20.00 C
ATOM 3 C GLY A 1 13.104 14.207 11.678 1.00 20.00 C
ATOM 4 O GLY A 1 12.560 15.207 11.678 1.00 20.00 O
TER
END </input_pdb>
<input_pdb_chains m:isArray="true">A</input_pdb_chains>
<ca_only m:type="xs:boolean">false</ca_only>
<use_soluble_model m:type="xs:boolean">true</use_soluble_model>
<random_seed m:type="xs:int">42</random_seed>
<num_seq_per_target m:type="xs:int">1</num_seq_per_target>
<sampling_temp m:isArray="true" m:type="xs:double">0.1</sampling_temp>
</root>
• - Nvidia/molmim
<root xmlns:m="urn:informatica:ae:xquery:json2xml:meta-data">
<algorithm>CMA-ES</algorithm>
<smi>[H][C@@]12Cc3c[nH]c4cccc(C1=C[C@H](NC(=O)N(CC)CC)CN2C)c34</smi>
<num_molecules m:type="xs:int">30</num_molecules>
<iterations m:type="xs:int">10</iterations>
<property_name>QED</property_name>
<particles m:type="xs:int">30</particles>
<minimize m:type="xs:boolean">false</minimize>
<min_similarity m:type="xs:double">0.3</min_similarity>
<scaled_radius m:type="xs:double">1</scaled_radius>
</root>
• - nvidia/maisi
<root xmlns:m="urn:informatica:ae:xquery:json2xml:meta-data">
<num_output_samples m:type="xs:int">1</num_output_samples>
<body_region m:isArray="true">abdomen</body_region>
<anatomy_list m:isArray="true">liver</anatomy_list>
<output_size m:isArray="true" m:type="xs:int">256</output_size>
<output_size m:isArray="true" m:type="xs:int">256</output_size>
<output_size m:isArray="true" m:type="xs:int">256</output_size>
<controllable_anatomy_size m:isArray="true">
<anonymous_array m:isArray="true">hepatic tumor</anonymous_array>
<anonymous_array m:isArray="true" m:type="xs:double">0.3</anonymous_array>
</controllable_anatomy_size>
<controllable_anatomy_size m:isArray="true">
<anonymous_array m:isArray="true">liver</anonymous_array>
<anonymous_array m:isArray="true" m:type="xs:double">0.5</anonymous_array>
</controllable_anatomy_size>
<image_output_ext>.nii.gz</image_output_ext>
<label_output_ext>.nii.gz</label_output_ext>
<pre_signed_url></pre_signed_url>
<spacing m:type="xs:double">1</spacing>
<spacing m:type="xs:double">1</spacing>
<spacing m:type="xs:double">1</spacing>
</root>
• - adept/fuyu-8b
<root xmlns:m="urn:informatica:ae:xquery:json2xml:meta-data">
<messages m:isArray="true">
<content>Hi! What is in this image?
&lt;img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAgAAAAIAQMAAAD+wSzIAAAABlBMVEX///+/v7+jQ3Y5AAAADklEQVQI12P4AIX8EAgALgAD/aNpbtEAAAAASUVORK5CYII==" /&gt;
</content>
<role>user</role>
</messages>
<temperature m:type="xs:double">0.2</temperature>
<top_p m:type="xs:double">0.7</top_p>
<max_tokens m:type="xs:int">1024</max_tokens>
<seed m:type="xs:int">42</seed>
<stream m:type="xs:boolean">false</stream>
</root>
• - Google/deplot
<root xmlns:m="urn:informatica:ae:xquery:json2xml:meta-data">
<messages m:isArray="true">
<content>Generate underlying data table of the figure below:
&lt;img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAgAAAAIAQMAAAD+wSzIAAAABlBMVEX///+/v7+jQ3Y5AAAADklEQVQI12P4AIX8EAgALgAD/aNpbtEAAAAASUVORK5CYII=="/&gt;
</content>
<role>user</role>
</messages>
<temperature m:type="xs:double">0.2</temperature>
<top_p m:type="xs:double">0.7</top_p>
<max_tokens m:type="xs:int">1024</max_tokens>
<stream m:type="xs:boolean">false</stream>
</root>
• - Google/paligemma
<root xmlns:m="urn:informatica:ae:xquery:json2xml:meta-data">
<messages m:isArray="true">
<content>Hi! What is in this image? &lt;img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAgAAAAIAQMAAAD+wSzIAAAABlBMVEX///+/v7+jQ3Y5AAAADklEQVQI12P4AIX8EAgALgAD/aNpbtEAAAAASUVORK5CYII=="/&gt;
</content>
<role>user</role>
</messages>
<temperature m:type="xs:double">0.2</temperature>
<top_p m:type="xs:double">0.7</top_p>
<max_tokens m:type="xs:int">1024</max_tokens>
<stream m:type="xs:boolean">false</stream>
</root>
• - microsoft/kosmos-2
<root xmlns:m="urn:informatica:ae:xquery:json2xml:meta-data">
<messages m:isArray="true">
<content>Where is the coach in this image? &lt;img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAgAAAAIAQMAAAD+wSzIAAAABlBMVEX///+/v7+jQ3Y5AAAADklEQVQI12P4AIX8EAgALgAD/aNpbtEAAAAASUVORK5CYII=="/&gt;
</content>
<role>user</role>
</messages>
<grounded_response m:type="xs:boolean">true</grounded_response>
<temperature m:type="xs:double">0.2</temperature>
<top_p m:type="xs:double">0.7</top_p>
<max_tokens m:type="xs:int">1024</max_tokens>
<response_mode>brief</response_mode>
<task>VQA</task>
</root>
• - microsoft/phi-3-vision-128k-instruct
<root xmlns:m="urn:informatica:ae:xquery:json2xml:meta-data">
<messages m:isArray="true">
<content>Hi! What is in this image? &lt;img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAgAAAAIAQMAAAD+wSzIAAAABlBMVEX///+/v7+jQ3Y5AAAADklEQVQI12P4AIX8EAgALgAD/aNpbtEAAAAASUVORK5CYII=="/&gt;
</content>
<role>user</role>
</messages>
<temperature m:type="xs:double">0.2</temperature>
<top_p m:type="xs:double">0.7</top_p>
<max_tokens m:type="xs:int">1024</max_tokens>
<stream m:type="xs:boolean">false</stream>
</root>

• - briaai/bria-2.3
<root xmlns:m="urn:informatica:ae:xquery:json2xml:meta-data">
<prompt>Give me image of an orange cat</prompt>
<aspect_ratio>1:1</aspect_ratio>
<mode>text-to-image</mode>
<negative_prompt></negative_prompt>
<model>bria-2.3</model>
<seed m:type="xs:int">0</seed>
<output_format>jpeg</output_format>
<cfg_scale m:type="xs:double">5</cfg_scale>
<steps m:type="xs:int">30</steps>
</root>
• - nvidia/nv-dinov2
<root xmlns:m="urn:informatica:ae:xquery:json2xml:meta-data">
<messages m:isArray="true">
<content>
<type>image_url</type>
<image_url>
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</image_url>
</content>
</messages>
</root>
• - nvidia/nv-grounding-dino
<root xmlns:m="urn:informatica:ae:xquery:json2xml:meta-data">
<model>Grounding Dino</model>
<messages m:isArray="true">
<role>user</role>
<content m:isArray="true">
<type>text</type>
<text>person, cars, bus, backpack</text>
</content>
<content m:isArray="true">
<type>media_url</type>
<media_url>
<url>data:image/jpeg;base64,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</url>
</media_url>
</content>
</messages>
<threshold m:type="xs:double">0.7</threshold>
</root>
• - stabilityai/stable-diffusion-3-medium
<root xmlns:m="urn:informatica:ae:xquery:json2xml:meta-data">
<aspect_ratio>1:1</aspect_ratio>
<cfg_scale m:type="xs:double">5</cfg_scale>
<mode>text-to-image</mode>
<model>sd3</model>
<negative_prompt></negative_prompt>
<output_format>jpeg</output_format>
<prompt>An orange cat</prompt>
<seed m:type="xs:int">0</seed>
<steps m:type="xs:int">50</steps>
</root>
• - stabilityai/sdxl-turbo
<root xmlns:m="urn:informatica:ae:xquery:json2xml:meta-data">
<height m:type="xs:int">512</height>
<width m:type="xs:int">512</width>
<text_prompts m:isArray="true">
<text>A photo of a Shiba Inu dog with a backpack riding a bike</text>
<weight m:type="xs:double">1</weight>
</text_prompts>
<cfg_scale m:type="xs:double">0</cfg_scale>
<clip_guidance_preset>NONE</clip_guidance_preset>
<sampler>K_EULER_ANCESTRAL</sampler>
<samples m:type="xs:int">1</samples>
<seed m:type="xs:int">0</seed>
<steps m:type="xs:int">4</steps>
<style_preset>none</style_preset>
</root>
• - stabilityai/stable-diffusion-xl
<root xmlns:m="urn:informatica:ae:xquery:json2xml:meta-data">
<height m:type="xs:int">1024</height>
<width m:type="xs:int">1024</width>
<text_prompts m:isArray="true">
<text>A photo of a Shiba Inu dog with a backpack riding a bike</text>
<weight m:type="xs:double">1</weight>
</text_prompts>
<cfg_scale m:type="xs:double">5</cfg_scale>
<clip_guidance_preset>NONE</clip_guidance_preset>
<sampler>K_DPM_2_ANCESTRAL</sampler>
<samples m:type="xs:int">1</samples>
<seed m:type="xs:int">0</seed>
<steps m:type="xs:int">25</steps>
<style_preset>none</style_preset>
</root>
• - stabilityai/stable-video-diffusion
<root xmlns:m="urn:informatica:ae:xquery:json2xml:meta-data">
<image>data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAEAAAABCAIAAACQd1PeAAAAEElEQVR4nGK6HcwNCAAA//8DTgE8HuxwEQAAAABJRU5ErkJggg==</image>
<seed m:type="xs:int">0</seed>
<cfg_scale m:type="xs:double">1.8</cfg_scale>
<motion_bucket_id m:type="xs:int">127</motion_bucket_id>
</root>
• •The Invoke Climate Simulation Model action invokes the Climate Simulation API models, such as nvidia/fourcastnet. It runs models for climate prediction and environmental simulation.
The connector is tested with the Nvidia/fourcastnet model.
You must provide values to the input fields, such as payload, version, and NVCF_POLL_SECONDS.
For example, consider the nvidia/fourcastnet model with the following endpoint URL:
https://climate.api.nvidia.com/v1/nvidia/fourcastnet
In this URL, v1 is the API version and nvidia, and fourcastnet are individual URL path segments.
The following snippet is a payload example:
<root xmlns:m="urn:informatica:ae:xquery:json2xml:meta-data">
<input_id m:type="xs:int">0</input_id>
<variables>w10m,z500</variables>
<simulation_length m:type="xs:int">4</simulation_length>
<ensemble_size m:type="xs:int">1</ensemble_size>
<noise_amplitude m:type="xs:double">0</noise_amplitude>
</root>
The following snippet is a sample response:
200 OK
fourcastnet.zip
• •The Invoke Medical Imaging Model action invokes the Healthcare Medical Imaging API models, such as nvidia/vista-3d and nvidia/maisi. It invokes models for analyzing medical images, such as scans or X-rays.
The connector is tested with the following models:
• - nvidia/vista-3d
• - nvidia/maisi
You must provide values to the input fields, such as payload, version, and NVCF_POLL_SECONDS.
For example, consider the nvidia/vista-3d model with the following endpoint URL:
https://health.api.nvidia.com/v1/medicalimaging/nvidia/vista-3d
In this URL, v1 is the API version and medicalimaging, nvidia, and vista-3d are individual URL path segments.
The following snippet is a payload example:
<root xmlns:m="urn:informatica:ae:xquery:json2xml:meta-data">
<image>https://assets.ngc.nvidia.com/products/api-catalog/vista3d/example-1.nii.gz</image>
<prompts>
<classes m:isArray="true">spleen</classes>
<points>
<spleen m:isArray="true">
<anonymous_array m:isArray="true" m:type="xs:int">97</anonymous_array>
<anonymous_array m:isArray="true" m:type="xs:int">249</anonymous_array>
<anonymous_array m:isArray="true" m:type="xs:int">87</anonymous_array>
</spleen>
</points>
</prompts>
</root>
This XML structure uses metadata attributes such as m:isArray and m:type to represent JSON-like arrays and types. It also leverages custom fields named anonymous_array to support anonymous arrays within the payload structure.
The following snippet is an equivalent JSON representation:
{
"image": "https://assets.ngc.nvidia.com/products/api-catalog/vista3d/example-1.nii.gz",
"prompts": {
"classes": [
"spleen"
],
"points": {
"spleen": [
[
97,
249,
87
]
]
}
}
}

The following snippet is a sample response:
200 OK
vista-3d.zip
• •The Invoke Multimodal Model action invokes the Multimodal and Visual API models, such as nvidia/neva-22b and nvidia/nv-grounding-dino. It invokes models that process and combine different data types, such as text, images, or video.
The connector is tested with the following models:
• - adept/fuyu-8b
• - Google/deplot
• - google/paligemma
• - microsoft/kosmos-2
• - microsoft/phi-3-vision-128k-instruct
• - nvidia/neva-22b
• - briaai/bria-2.3
• - nvidia/nv-dinov2
• - nvidia/nv-grounding-dino
• - stabilityai/stable-diffusion-3-medium
• - stabilityai/sdxl-turbo
• - stabilityai/stable-diffusion-xl
• - stabilityai/stable-video-diffusion
You must provide values to the input fields, such as payload, version, and NVCF_POLL_SECONDS.
For example, consider the nvidia/neva-22b model with the following endpoint URL:
https://ai.api.nvidia.com/v1/vlm/nvidia/neva-22b
In this URL, v1 is the API version and vlm, nvidia, and neva-22b are individual URL path segments.
The following snippet is a payload example:
<root xmlns:m="urn:informatica:ae:xquery:json2xml:meta-data">
<messages m:isArray="true">
<content>Hi! 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</content>
<name>John</name>
<role>user</role>
</messages>
<temperature m:type="xs:double">0.2</temperature>
<top_p m:type="xs:double">0.7</top_p>
<max_tokens m:type="xs:int">1024</max_tokens>
<seed m:type="xs:int">42</seed>
<stream m:type="xs:boolean">false</stream>
</root>
The following snippet is a sample response:
<root>
<usage>
<completion_tokens>37</completion_tokens>
<prompt_tokens>754</prompt_tokens>
<total_tokens>791</total_tokens>
</usage>
<id>cd05322e-71e5-4b01-a76f-153d96b7a8c0</id>
<choices>
<finish_reason>stop</finish_reason>
<index>0</index>
<message>
<role>assistant</role>
<name/>
<content>This image shows a motorcycle on a racetrack. The motorcycle is a Kawasaki Ninja H2R. The motorcycle is a super sport motorcycle. It is a track-only motorcycle.</content>
</message>
</choices>
</root>
• •The Invoke Basic Model action invokes the basic models for chat completions, function calling, text reranking, and structured output actions. It executes actions, such as chat completions, function calling, embeddings, text reranking, and structured output actions.
You must provide values to the input fields, such as host, version, segments, and payload.
The following snippet is an example of executing Chat completion action payload:
<root xmlns:m="urn:informatica:ae:xquery:json2xml:meta-data">
<model>mistralai/mixtral-8x7b-instruct-v0.1</model>
<max_tokens m:type="xs:int">1024</max_tokens>
<stream m:type="xs:boolean">false</stream>
<temperature m:type="xs:double">1.2</temperature>
<top_p m:type="xs:double">0.8</top_p>
<frequency_penalty m:type="xs:double">0</frequency_penalty>
<presence_penalty m:type="xs:double">0</presence_penalty>
<seed m:type="xs:int">1024</seed>
<messages m:isArray="true">
<role>user</role>
<content>You are a helpful assistant.</content>
</messages>
<messages m:isArray="true">
<role>assistant</role>
<content>You are a helpful assistant because you help people with their problems and you are always there for them when they need you.</content>
</messages>
<messages m:isArray="true">
<role>user</role>
<content>Who is Leo Messi.</content>
</messages>
</root>
The following snippet is an example to execute Chat Completion Function Call action payload:
<root xmlns:m="urn:informatica:ae:xquery:json2xml:meta-data">
<model>meta/llama-3.1-70b-instruct</model>
<messages m:isArray="true">
<role>user</role>
<content>My name is John and I'm 30 years old</content>
</messages>
<tools m:isArray="true">
<type>function</type>
<function>
<name>get_name_age</name>
<description>Please provide your name and age</description>
<parameters>
<type>object</type>
<properties>
<name>
<type>string</type>
<description>The name of participant</description>
</name>
<age>
<type>integer</type>
<description>The age of participant</description>
</age>
</properties>
<required m:isArray="true">name</required>
<required m:isArray="true">age</required>
</parameters>
</function>
</tools>
<tool_choice>
<type>function</type>
<function>
<name>get_name_age</name>
</function>
</tool_choice>
</root>
The following snippet is an example to execute Embeddings action payload:
<root xmlns:m="urn:informatica:ae:xquery:json2xml:meta-data">
<input m:isArray="true">Paris is the capital of France</input>
<model>nvidia/nv-embedqa-e5-v5</model>
<input_type>query</input_type>
</root>
Note: Reranking and Structured Output actions can be executed on local environment, Docker image, or on-premise hosting eтvironment.