deeprails 1.16.0

The official Python library for the deeprails API

Deeprails Python API library

The Deeprails Python library provides convenient access to the Deeprails REST API from any Python 3.9+ application. The library includes type definitions for all request params and response fields, and offers both synchronous and asynchronous clients powered by httpx.

Documentation

The REST API documentation can be found on docs.deeprails.ai. The full API of this library can be found in api.md.

Installation

# install from PyPI
pip install deeprails

Usage

The full API of this library can be found in api.md.

import os
from deeprails import Deeprails

client = Deeprails(
    api_key=os.environ.get("DEEPRAILS_API_KEY"),  # This is the default and can be omitted
)

defend_create_response = client.defend.create_workflow(
    improvement_action="fixit",
    name="Push Alert Workflow",
    threshold_type="custom",
    custom_hallucination_threshold_values={
        "completeness": 0.7,
        "instruction_adherence": 0.75,
    },
    web_search=True,
)
print(defend_create_response.workflow_id)

While you can provide an api_key keyword argument, we recommend using python-dotenv to add DEEPRAILS_API_KEY="My API Key" to your .env file so that your API Key is not stored in source control.

Async usage

Simply import AsyncDeeprails instead of Deeprails and use await with each API call:

import os
import asyncio
from deeprails import AsyncDeeprails

client = AsyncDeeprails(
    api_key=os.environ.get("DEEPRAILS_API_KEY"),  # This is the default and can be omitted
)


async def main() -> None:
    defend_create_response = await client.defend.create_workflow(
        improvement_action="fixit",
        name="Push Alert Workflow",
        threshold_type="custom",
        custom_hallucination_threshold_values={
            "completeness": 0.7,
            "instruction_adherence": 0.75,
        },
        web_search=True,
    )
    print(defend_create_response.workflow_id)


asyncio.run(main())

Functionality between the synchronous and asynchronous clients is otherwise identical.

With aiohttp

By default, the async client uses httpx for HTTP requests. However, for improved concurrency performance you may also use aiohttp as the HTTP backend.

You can enable this by installing aiohttp:

# install from PyPI
pip install deeprails[aiohttp]

Then you can enable it by instantiating the client with http_client=DefaultAioHttpClient():

import asyncio
from deeprails import DefaultAioHttpClient
from deeprails import AsyncDeeprails


async def main() -> None:
    async with AsyncDeeprails(
        api_key="My API Key",
        http_client=DefaultAioHttpClient(),
    ) as client:
        defend_create_response = await client.defend.create_workflow(
            improvement_action="fixit",
            name="Push Alert Workflow",
            threshold_type="custom",
            custom_hallucination_threshold_values={
                "completeness": 0.7,
                "instruction_adherence": 0.75,
            },
            web_search=True,
        )
        print(defend_create_response.workflow_id)


asyncio.run(main())

Using types

Nested request parameters are TypedDicts. Responses are Pydantic models which also provide helper methods for things like:

• Serializing back into JSON, model.to_json()
• Converting to a dictionary, model.to_dict()

Typed requests and responses provide autocomplete and documentation within your editor. If you would like to see type errors in VS Code to help catch bugs earlier, set python.analysis.typeCheckingMode to basic.

Nested params

Nested parameters are dictionaries, typed using TypedDict, for example:

from deeprails import Deeprails

client = Deeprails()

workflow_event_response = client.defend.submit_event(
    workflow_id="workflow_id",
    model_input={"user_prompt": "user_prompt"},
    model_output="model_output",
    model_used="model_used",
    run_mode="precision_plus",
)
print(workflow_event_response.model_input)

Handling errors

When the library is unable to connect to the API (for example, due to network connection problems or a timeout), a subclass of deeprails.APIConnectionError is raised.

When the API returns a non-success status code (that is, 4xx or 5xx response), a subclass of deeprails.APIStatusError is raised, containing status_code and response properties.

All errors inherit from deeprails.APIError.

import deeprails
from deeprails import Deeprails

client = Deeprails()

try:
    client.defend.create_workflow(
        improvement_action="fixit",
        name="Push Alert Workflow",
        threshold_type="custom",
        custom_hallucination_threshold_values={
            "completeness": 0.7,
            "instruction_adherence": 0.75,
        },
        web_search=True,
    )
except deeprails.APIConnectionError as e:
    print("The server could not be reached")
    print(e.__cause__)  # an underlying Exception, likely raised within httpx.
except deeprails.RateLimitError as e:
    print("A 429 status code was received; we should back off a bit.")
except deeprails.APIStatusError as e:
    print("Another non-200-range status code was received")
    print(e.status_code)
    print(e.response)

Error codes are as follows:

Status Code	Error Type
400	BadRequestError
401	AuthenticationError
403	PermissionDeniedError
404	NotFoundError
422	UnprocessableEntityError
429	RateLimitError
>=500	InternalServerError
N/A	APIConnectionError

Retries

Certain errors are automatically retried 2 times by default, with a short exponential backoff. Connection errors (for example, due to a network connectivity problem), 408 Request Timeout, 409 Conflict, 429 Rate Limit, and >=500 Internal errors are all retried by default.

You can use the max_retries option to configure or disable retry settings:

from deeprails import Deeprails

# Configure the default for all requests:
client = Deeprails(
    # default is 2
    max_retries=0,
)

# Or, configure per-request:
client.with_options(max_retries=5).defend.create_workflow(
    improvement_action="fixit",
    name="Push Alert Workflow",
    threshold_type="custom",
    custom_hallucination_threshold_values={
        "completeness": 0.7,
        "instruction_adherence": 0.75,
    },
    web_search=True,
)

Timeouts

By default requests time out after 1 minute. You can configure this with a timeout option, which accepts a float or an httpx.Timeout object:

from deeprails import Deeprails

# Configure the default for all requests:
client = Deeprails(
    # 20 seconds (default is 1 minute)
    timeout=20.0,
)

# More granular control:
client = Deeprails(
    timeout=httpx.Timeout(60.0, read=5.0, write=10.0, connect=2.0),
)

# Override per-request:
client.with_options(timeout=5.0).defend.create_workflow(
    improvement_action="fixit",
    name="Push Alert Workflow",
    threshold_type="custom",
    custom_hallucination_threshold_values={
        "completeness": 0.7,
        "instruction_adherence": 0.75,
    },
    web_search=True,
)

On timeout, an APITimeoutError is thrown.

Note that requests that time out are retried twice by default.

Advanced

Logging

We use the standard library logging module.

You can enable logging by setting the environment variable DEEPRAILS_LOG to info.

$ export DEEPRAILS_LOG=info

Or to debug for more verbose logging.

How to tell whether None means null or missing

In an API response, a field may be explicitly null, or missing entirely; in either case, its value is None in this library. You can differentiate the two cases with .model_fields_set:

if response.my_field is None:
  if 'my_field' not in response.model_fields_set:
    print('Got json like {}, without a "my_field" key present at all.')
  else:
    print('Got json like {"my_field": null}.')

Accessing raw response data (e.g. headers)

The "raw" Response object can be accessed by prefixing .with_raw_response. to any HTTP method call, e.g.,

from deeprails import Deeprails

client = Deeprails()
response = client.defend.with_raw_response.create_workflow(
    improvement_action="fixit",
    name="Push Alert Workflow",
    threshold_type="custom",
    custom_hallucination_threshold_values={
        "completeness": 0.7,
        "instruction_adherence": 0.75,
    },
    web_search=True,
)
print(response.headers.get('X-My-Header'))

defend = response.parse()  # get the object that `defend.create_workflow()` would have returned
print(defend.workflow_id)

These methods return an APIResponse object.

The async client returns an AsyncAPIResponse with the same structure, the only difference being awaitable methods for reading the response content.

.with_streaming_response

The above interface eagerly reads the full response body when you make the request, which may not always be what you want.

To stream the response body, use .with_streaming_response instead, which requires a context manager and only reads the response body once you call .read(), .text(), .json(), .iter_bytes(), .iter_text(), .iter_lines() or .parse(). In the async client, these are async methods.

with client.defend.with_streaming_response.create_workflow(
    improvement_action="fixit",
    name="Push Alert Workflow",
    threshold_type="custom",
    custom_hallucination_threshold_values={
        "completeness": 0.7,
        "instruction_adherence": 0.75,
    },
    web_search=True,
) as response:
    print(response.headers.get("X-My-Header"))

    for line in response.iter_lines():
        print(line)

The context manager is required so that the response will reliably be closed.

Requirements

Python 3.9 or higher.