# client\_notebook

## Edison platform client usage example

```python
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.

```python
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.

```python
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.

```python
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.

```python
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}")
```

```python
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}")
```


---

# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://docs.edisonscientific.com/edison-client/docs/client_notebook.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.