Prompt Deployment Node

Example Usage

1 from vellum.workflows.nodes import PromptDeploymentNode
2 from vellum.client import PromptDeploymentExpandMetaRequest, RawPromptExecutionOverridesRequest
3 
4 class MyPromptDeploymentNode(PromptDeploymentNode):
5     deployment = "my_prompt_deployment"
6     prompt_inputs = {
7         "question": "What is the meaning of life?",
8         "chat_history": [
9             ChatMessage(role="USER", text="Hello!"),
10             ChatMessage(role="ASSISTANT", text="Hi there!"),
11         ],
12         "context": {
13             "source": "philosophy_book",
14             "chapter": 42
15         }
16     }
17     release_tag = "production"
18     external_id = "unique-execution-id"
19     expand_meta = PromptDeploymentExpandMetaRequest(
20         model_name=True,
21         usage=True,
22         cost=True,
23         finish_reason=True,
24         latency=True,
25         deployment_release_tag=True,
26         prompt_version_id=True
27     )
28     metadata = {
29         "user_id": "123",
30         "session_id": "abc"
31     }

1 MyPromptDeploymentNode.Outputs(
2     text="happily",
3     results=[
4         StringVellumValue(value="h"),
5         StringVellumValue(value="app"),
6         StringVellumValue(value="ily"),
7     ]
8 )

from vellum.workflows.nodes import PromptDeploymentNode
from vellum.client import PromptDeploymentExpandMetaRequest, RawPromptExecutionOverridesRequest
class MyPromptDeploymentNode(PromptDeploymentNode):
    deployment = "my_prompt_deployment"
    prompt_inputs = {
        "question": "What is the meaning of life?",
        "chat_history": [
            ChatMessage(role="USER", text="Hello!"),
            ChatMessage(role="ASSISTANT", text="Hi there!"),
        ],
        "context": {
            "source": "philosophy_book",
            "chapter": 42
        }
    }
    release_tag = "production"
    external_id = "unique-execution-id"
    expand_meta = PromptDeploymentExpandMetaRequest(
        model_name=True,
        usage=True,
        cost=True,
        finish_reason=True,
        latency=True,
        deployment_release_tag=True,
        prompt_version_id=True
    )
    metadata = {
        "user_id": "123",
        "session_id": "abc"
    }

MyPromptDeploymentNode.Outputs(
    text="happily",
    results=[
        StringVellumValue(value="h"),
        StringVellumValue(value="app"),
        StringVellumValue(value="ily"),
    ]
)

vellum.workflows.nodes.PromptDeploymentNode

Used to execute a Prompt Deployment and surface a string output for convenience.

Attributes

deployment

Union[UUID, str]Required

Either the Prompt Deployment’s UUID or its name

prompt_inputs

EntityInputsInterfaceRequired

The inputs for the Prompt

release_tag

strDefaults to LATEST

The release tag to use for the Prompt Execution

external_id

Optional[str]

Optionally include a unique identifier for tracking purposes. Must be unique within a given Prompt Deployment.

expand_meta

Optional[PromptDeploymentExpandMetaRequest]

Expandable execution fields to include in the response. See more here.

raw_overrides

Optional[RawPromptExecutionOverridesRequest]

The raw overrides to use for the Prompt Execution

expand_raw

Optional[Sequence[str]]

Expandable raw fields to include in the response

metadata

Optional[Dict[str, Optional[Any]]]

The metadata to use for the Prompt Execution

request_options

Optional[RequestOptions]

The request options to use for the Prompt Execution

Outputs

text

str

The generated text output from the prompt execution

results

List[PromptOutput]

The array of results from the prompt execution. PromptOutput is a union of the following types:

StringVellumValue
FunctionCallVellumValue

1	from vellum.workflows.nodes import PromptDeploymentNode
2	from vellum.client import PromptDeploymentExpandMetaRequest, RawPromptExecutionOverridesRequest
3
4	class MyPromptDeploymentNode(PromptDeploymentNode):
5	deployment = "my_prompt_deployment"
6	prompt_inputs = {
7	"question": "What is the meaning of life?",
8	"chat_history": [
9	ChatMessage(role="USER", text="Hello!"),
10	ChatMessage(role="ASSISTANT", text="Hi there!"),
11	],
12	"context": {
13	"source": "philosophy_book",
14	"chapter": 42
15	}
16	}
17	release_tag = "production"
18	external_id = "unique-execution-id"
19	expand_meta = PromptDeploymentExpandMetaRequest(
20	model_name=True,
21	usage=True,
22	cost=True,
23	finish_reason=True,
24	latency=True,
25	deployment_release_tag=True,
26	prompt_version_id=True
27	)
28	metadata = {
29	"user_id": "123",
30	"session_id": "abc"
31	}

1	MyPromptDeploymentNode.Outputs(
2	text="happily",
3	results=[
4	StringVellumValue(value="h"),
5	StringVellumValue(value="app"),
6	StringVellumValue(value="ily"),
7	]
8	)