client_notebook

Edison platform client usage example

import os

from edison_client import (
    EdisonClient,
    JobNames,
    TaskRequest,
)
from edison_client.models import RuntimeConfig
from ldp.agent import AgentConfig

Client instantiation

Here we instantiate an Edison client and authenticate our access to the platform. By default, the client will use the EDISON_PLATFORM_API_KEY environment variable to authenticate. The option api_key can be used to pass your API key.

Please log in to the platform and go to your user settings to get your API key.

client = EdisonClient(
    api_key=os.getenv("EDISON_PLATFORM_API_KEY", "your-api-key"),
)

Submit a task to an available Edison job

In the edison platform, we refer to the deployed combination of agent and environment as a job. Submitting a task to an edison job is done by calling the create_task method, which receives a TaskRequest object.

For convenience, one can use the run_tasks_until_done method, which submits the task, waits for the task to be completed, and returns a list of TaskResponse objects.

task_data = TaskRequest(
    name=JobNames.from_string("literature"),
    query="What is the molecule known to have the greatest solubility in water?",
)
responses = client.run_tasks_until_done(task_data)
task_response = responses[0]

print(f"Job status: {task_response.status}")
print(f"Job answer: \n{task_response.formatted_answer}")

You can also pass a runtime_config to the run_tasks_until_done method, which will be used to configure the agent on runtime. Here, we will define a agent configuration and include it in the TaskRequest. This agent is used to decide the next action to take. We will also use the max_steps parameter to limit the number of steps the agent will take.

agent = AgentConfig(
    agent_type="SimpleAgent",
    agent_kwargs={
        "model": "gpt-4o",
        "temperature": 0.0,
    },
)
task_data = TaskRequest(
    name=JobNames.LITERATURE,
    query="How many moons does earth have?",
    runtime_config=RuntimeConfig(agent=agent, max_steps=10),
)
responses = client.run_tasks_until_done(task_data)
task_response = responses[0]

print(f"Job status: {task_response.status}")
print(f"Job answer: \n{task_response.formatted_answer}")

Continue a job

The platform allows to ask follow-up questions to the previous job. To accomplish that, we can use the runtime_config to pass the task_id of the previous task.

Notice that run_tasks_until_done accepts both a TaskRequest object and a dictionary with keywords arguments.

task_data = TaskRequest(
    name=JobNames.LITERATURE, query="How many species of birds are there?"
)

responses = client.run_tasks_until_done(task_data)
task_response = responses[0]

print(f"First job status: {task_response.status}")
print(f"First job answer: \n{task_response.formatted_answer}")

continued_job_data = {
    "name": JobNames.LITERATURE,
    "query": (
        "From the previous answer, specifically, how many species of crows are there?"
    ),
    "runtime_config": {"continued_job_id": task_response.task_id},
}

responses = client.run_tasks_until_done(continued_job_data)
continued_task_response = responses[0]


print(f"Continued job status: {continued_task_response.status}")
print(f"Continued job answer: \n{continued_task_response.formatted_answer}")