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POST
/
v1
/
agent
/
create
!pip install vlmrun

from pydantic import BaseModel, Field
import datetime

from vlmrun.client import VLMRun
from vlmrun.client.types import AgentCreationResponse, AgentCreationConfig, AgentExecutionResponse
from vlmrun.types import MessageContent, FileUrl

# Define a Pydantic model for the execution inputs
class ExecutionInputs(BaseModel):
  file: MessageContent = Field(..., description="The file to extract data from")

# Define a Pydantic model for the response
class Invoice(BaseModel):
  invoice_id: str = Field(..., description="The ID of the invoice")
  invoice_date: datetime.date = Field(..., description="The date of the invoice")
  total_amount: float = Field(..., description="The total amount of the invoice")

# Initialize the client
client = VLMRun(api_key="<VLMRUN_API_KEY>")

# Create the agent using a prompt
response: AgentCreationResponse = client.agent.create(
  name="invoice-extractor",  # Optional name
  inputs=ExecutionInputs(file=MessageContent(type="file_url", file_url=FileUrl(url="https://example.com/invoice.pdf")))  # Optional test inputs
  config=AgentCreationConfig(prompt="Extract the invoice_id, date and amount from the invoice.", response_model=Invoice),
)

{
  "id": "<string>",
  "name": "<string>",
  "created_at": "2023-11-07T05:31:56Z",
  "updated_at": "2023-11-07T05:31:56Z",
  "status": "pending"
}

Documentation Index

Fetch the complete documentation index at: https://docs.vlm.run/llms.txt

Use this file to discover all available pages before exploring further.

!pip install vlmrun

from pydantic import BaseModel, Field
import datetime

from vlmrun.client import VLMRun
from vlmrun.client.types import AgentCreationResponse, AgentCreationConfig, AgentExecutionResponse
from vlmrun.types import MessageContent, FileUrl

# Define a Pydantic model for the execution inputs
class ExecutionInputs(BaseModel):
  file: MessageContent = Field(..., description="The file to extract data from")

# Define a Pydantic model for the response
class Invoice(BaseModel):
  invoice_id: str = Field(..., description="The ID of the invoice")
  invoice_date: datetime.date = Field(..., description="The date of the invoice")
  total_amount: float = Field(..., description="The total amount of the invoice")

# Initialize the client
client = VLMRun(api_key="<VLMRUN_API_KEY>")

# Create the agent using a prompt
response: AgentCreationResponse = client.agent.create(
  name="invoice-extractor",  # Optional name
  inputs=ExecutionInputs(file=MessageContent(type="file_url", file_url=FileUrl(url="https://example.com/invoice.pdf")))  # Optional test inputs
  config=AgentCreationConfig(prompt="Extract the invoice_id, date and amount from the invoice.", response_model=Invoice),
)

Authorizations

​
Authorization
string
header
required

Bearer authentication header of the form Bearer <token>, where <token> is your auth token.

Body

application/json

Request to create an agent.

​
config
AgentCreationConfig · object

The configuration for the agent creation request.

​
id
string

Unique identifier of the request.

​
created_at
string<date-time>

Date and time when the request was created (in UTC timezone)

​
callback_url
string<uri> | null

The URL to call when the request is completed.

Minimum string length: 1
​
model
enum<string>
default:vlmrun-orion-1:auto

VLM Run Agent model to use for agent creation

Available options:
vlmrun-orion-1,
vlmrun-orion-1:lite,
vlmrun-orion-1:auto,
vlmrun-orion-1:fast,
vlmrun-orion-1:pro,
vlmrun-orion-1.5,
vlmrun-orion-1.5:lite,
vlmrun-orion-1.5:auto,
vlmrun-orion-1.5:fast,
vlmrun-orion-1.5:pro
​
name
string | null

Name of the agent. If not provided, a pretty-name will be generated.

​
inputs
AgentExecutionInputs · object

The inputs to the agent.

Response

Successful Response

Response to the agent creation request.

​
id
string
required

ID of the agent

​
name
string
required

Name of the agent

​
created_at
string<date-time>
required

Date and time when the agent was created (in UTC timezone)

​
updated_at
string<date-time>
required

Date and time when the agent was updated (in UTC timezone)

​
status
enum<string>
default:pending

The status of the job.

Available options:
pending,
enqueued,
running,
completed,
failed,
paused