unstructured-client 0.46.2

Python SDK for the Unstructured API

This is a HTTP client for the Unstructured Platform API. You can sign up here and process 1000 free pages per day for 14 days.

Please refer to the our documentation for a full guide on integrating the Workflow Endpoint and Partition Endpoint into your Python code.

Summary

Table of Contents

• SDK Installation
• Retries
• Error Handling
• Custom HTTP Client
• IDE Support
• SDK Example Usage
• Configuration
• File uploads
• Resource Management
• Debugging

SDK Installation

The SDK can be installed with uv, pip, or poetry package managers.

uv

uv is a fast Python package installer and resolver, designed as a drop-in replacement for pip and pip-tools. It's recommended for its speed and modern Python tooling capabilities.

uv add unstructured-client

PIP

PIP is the default package installer for Python, enabling easy installation and management of packages from PyPI via the command line.

pip install unstructured-client

Poetry

Poetry is a modern tool that simplifies dependency management and package publishing by using a single pyproject.toml file to handle project metadata and dependencies.

poetry add unstructured-client

Shell and script usage with uv

You can use this SDK in a Python shell with uv and the uvx command that comes with it like so:

uvx --from unstructured-client python

It's also possible to write a standalone Python script without needing to set up a whole project like so:

#!/usr/bin/env -S uv run --script
# /// script
# requires-python = ">=3.9"
# dependencies = [
#     "unstructured-client",
# ]
# ///

from unstructured_client import UnstructuredClient

sdk = UnstructuredClient(
  # SDK arguments
)

# Rest of script here...

Once that is saved to a file, you can run it with uv run script.py where script.py can be replaced with the actual file name.

Retries

Some of the endpoints in this SDK support retries. If you use the SDK without any configuration, it will fall back to the default retry strategy provided by the API. However, the default retry strategy can be overridden on a per-operation basis, or across the entire SDK.

To change the default retry strategy for a single API call, simply provide a RetryConfig object to the call:

from unstructured_client import UnstructuredClient
from unstructured_client.utils import BackoffStrategy, RetryConfig


with UnstructuredClient() as uc_client:

    res = uc_client.destinations.create_connection_check_destinations(request={
        "destination_id": "cb9e35c1-0b04-4d98-83fa-fa6241323f96",
    },
        RetryConfig("backoff", BackoffStrategy(1, 50, 1.1, 100), False))

    assert res.dag_node_connection_check is not None

    # Handle response
    print(res.dag_node_connection_check)

If you'd like to override the default retry strategy for all operations that support retries, you can use the retry_config optional parameter when initializing the SDK:

from unstructured_client import UnstructuredClient
from unstructured_client.utils import BackoffStrategy, RetryConfig


with UnstructuredClient(
    retry_config=RetryConfig("backoff", BackoffStrategy(1, 50, 1.1, 100), False),
) as uc_client:

    res = uc_client.destinations.create_connection_check_destinations(request={
        "destination_id": "cb9e35c1-0b04-4d98-83fa-fa6241323f96",
    })

    assert res.dag_node_connection_check is not None

    # Handle response
    print(res.dag_node_connection_check)

Error Handling

UnstructuredClientError is the base class for all HTTP error responses. It has the following properties:

Example

from unstructured_client import UnstructuredClient
from unstructured_client.models import errors


with UnstructuredClient() as uc_client:
    res = None
    try:

        res = uc_client.destinations.create_connection_check_destinations(request={
            "destination_id": "cb9e35c1-0b04-4d98-83fa-fa6241323f96",
        })

        assert res.dag_node_connection_check is not None

        # Handle response
        print(res.dag_node_connection_check)


    except errors.UnstructuredClientError as e:
        # The base class for HTTP error responses
        print(e.message)
        print(e.status_code)
        print(e.body)
        print(e.headers)
        print(e.raw_response)

        # Depending on the method different errors may be thrown
        if isinstance(e, errors.HTTPValidationError):
            print(e.data.detail)  # Optional[errors.Detail]

Error Classes

Primary errors:

• UnstructuredClientError: The base class for HTTP error responses.
  • HTTPValidationError: Validation Error. Status code 422.

* Check the method documentation to see if the error is applicable.

Custom HTTP Client

The Python SDK makes API calls using the httpx HTTP library. In order to provide a convenient way to configure timeouts, cookies, proxies, custom headers, and other low-level configuration, you can initialize the SDK client with your own HTTP client instance. Depending on whether you are using the sync or async version of the SDK, you can pass an instance of HttpClient or AsyncHttpClient respectively, which are Protocol's ensuring that the client has the necessary methods to make API calls. This allows you to wrap the client with your own custom logic, such as adding custom headers, logging, or error handling, or you can just pass an instance of httpx.Client or httpx.AsyncClient directly.

For example, you could specify a header for every request that this sdk makes as follows:

from unstructured_client import UnstructuredClient
import httpx

http_client = httpx.Client(headers={"x-custom-header": "someValue"})
s = UnstructuredClient(client=http_client)

or you could wrap the client with your own custom logic:

from unstructured_client import UnstructuredClient
from unstructured_client.httpclient import AsyncHttpClient
import httpx

class CustomClient(AsyncHttpClient):
    client: AsyncHttpClient

    def __init__(self, client: AsyncHttpClient):
        self.client = client

    async def send(
        self,
        request: httpx.Request,
        *,
        stream: bool = False,
        auth: Union[
            httpx._types.AuthTypes, httpx._client.UseClientDefault, None
        ] = httpx.USE_CLIENT_DEFAULT,
        follow_redirects: Union[
            bool, httpx._client.UseClientDefault
        ] = httpx.USE_CLIENT_DEFAULT,
    ) -> httpx.Response:
        request.headers["Client-Level-Header"] = "added by client"

        return await self.client.send(
            request, stream=stream, auth=auth, follow_redirects=follow_redirects
        )

    def build_request(
        self,
        method: str,
        url: httpx._types.URLTypes,
        *,
        content: Optional[httpx._types.RequestContent] = None,
        data: Optional[httpx._types.RequestData] = None,
        files: Optional[httpx._types.RequestFiles] = None,
        json: Optional[Any] = None,
        params: Optional[httpx._types.QueryParamTypes] = None,
        headers: Optional[httpx._types.HeaderTypes] = None,
        cookies: Optional[httpx._types.CookieTypes] = None,
        timeout: Union[
            httpx._types.TimeoutTypes, httpx._client.UseClientDefault
        ] = httpx.USE_CLIENT_DEFAULT,
        extensions: Optional[httpx._types.RequestExtensions] = None,
    ) -> httpx.Request:
        return self.client.build_request(
            method,
            url,
            content=content,
            data=data,
            files=files,
            json=json,
            params=params,
            headers=headers,
            cookies=cookies,
            timeout=timeout,
            extensions=extensions,
        )

s = UnstructuredClient(async_client=CustomClient(httpx.AsyncClient()))

IDE Support

PyCharm

Generally, the SDK will work well with most IDEs out of the box. However, when using PyCharm, you can enjoy much better integration with Pydantic by installing an additional plugin.

• PyCharm Pydantic Plugin

SDK Example Usage

Example

# Synchronous Example
from unstructured_client import UnstructuredClient


with UnstructuredClient() as uc_client:

    res = uc_client.destinations.create_connection_check_destinations(request={
        "destination_id": "cb9e35c1-0b04-4d98-83fa-fa6241323f96",
    })

    assert res.dag_node_connection_check is not None

    # Handle response
    print(res.dag_node_connection_check)

The same SDK client can also be used to make asynchronous requests by importing asyncio.

# Asynchronous Example
import asyncio
from unstructured_client import UnstructuredClient

async def main():

    async with UnstructuredClient() as uc_client:

        res = await uc_client.destinations.create_connection_check_destinations_async(request={
            "destination_id": "cb9e35c1-0b04-4d98-83fa-fa6241323f96",
        })

        assert res.dag_node_connection_check is not None

        # Handle response
        print(res.dag_node_connection_check)

asyncio.run(main())

Refer to the API parameters page for all available parameters.

Configuration

Splitting PDF by pages

See page splitting for more details.

In order to speed up processing of large PDF files, the client splits up PDFs into smaller files, sends these to the API concurrently, and recombines the results. split_pdf_page can be set to False to disable this.

The amount of workers utilized for splitting PDFs is dictated by the split_pdf_concurrency_level parameter, with a default of 5 and a maximum of 15 to keep resource usage and costs in check. The splitting process leverages asyncio to manage concurrency effectively. The size of each batch of pages (ranging from 2 to 20) is internally determined based on the concurrency level and the total number of pages in the document. Because the splitting process uses asyncio the client can encounter event loop issues if it is nested in another async runner, like running in a gevent spawned task. Instead, this is safe to run in multiprocessing workers (e.g., using multiprocessing.Pool with fork context).

Example:

req = operations.PartitionRequest(
    partition_parameters=shared.PartitionParameters(
        files=files,
        strategy="fast",
        languages=["eng"],
        split_pdf_concurrency_level=8
    )
)

Sending specific page ranges

When split_pdf_page=True (the default), you can optionally specify a page range to send only a portion of your PDF to be extracted. The parameter takes a list of two integers to specify the range, inclusive. A ValueError is thrown if the page range is invalid.

Example:

req = operations.PartitionRequest(
    partition_parameters=shared.PartitionParameters(
        files=files,
        strategy="fast",
        languages=["eng"],
        split_pdf_page_range=[10,15],
    )
)

Splitting PDF by pages - strict mode

When split_pdf_allow_failed=False (the default), any errors encountered during sending parallel request will break the process and raise an exception. When split_pdf_allow_failed=True, the process will continue even if some requests fail, and the results will be combined at the end (the output from the errored pages will not be included).

Example:

req = operations.PartitionRequest(
    partition_parameters=shared.PartitionParameters(
        files=files,
        strategy="fast",
        languages=["eng"],
        split_pdf_allow_failed=True,
    )
)

Streaming elements to a file instead of memory

For very large documents, the parsed element list can dominate the client's memory: the split-PDF path holds a list per chunk, a flattened list, a serialized blob and the SDK's re-parse of that blob. Request application/x-ndjson to skip all of it. The chunk responses are concatenated on disk and you get back a path in elements_file instead of a list in elements, which keeps peak memory at roughly one chunk.

You own the returned file and are responsible for deleting it.

Example:

import json
from pathlib import Path

from unstructured_client.general import PartitionAcceptEnum

res = client.general.partition(
    request=req,
    accept_header_override=PartitionAcceptEnum.APPLICATION_X_NDJSON,
)

try:
    with open(res.elements_file, encoding="utf-8") as f:
        for line in f:
            element = json.loads(line)
            ...
finally:
    # missing_ok so a failure to open the file isn't masked by the cleanup.
    Path(res.elements_file).unlink(missing_ok=True)

File uploads

Certain SDK methods accept file objects as part of a request body or multi-part request. It is possible and typically recommended to upload files as a stream rather than reading the entire contents into memory. This avoids excessive memory consumption and potentially crashing with out-of-memory errors when working with very large files. The following example demonstrates how to attach a file stream to a request.

from unstructured_client import UnstructuredClient


with UnstructuredClient() as uc_client:

    res = uc_client.jobs.create_job(request={
        "body_create_job": {
            "request_data": "<value>",
        },
    })

    assert res.job_information is not None

    # Handle response
    print(res.job_information)

Resource Management

The UnstructuredClient class implements the context manager protocol and registers a finalizer function to close the underlying sync and async HTTPX clients it uses under the hood. This will close HTTP connections, release memory and free up other resources held by the SDK. In short-lived Python programs and notebooks that make a few SDK method calls, resource management may not be a concern. However, in longer-lived programs, it is beneficial to create a single SDK instance via a context manager and reuse it across the application.

from unstructured_client import UnstructuredClient
def main():

    with UnstructuredClient() as uc_client:
        # Rest of application here...


# Or when using async:
async def amain():

    async with UnstructuredClient() as uc_client:
        # Rest of application here...

Debugging

You can setup your SDK to emit debug logs for SDK requests and responses.

You can pass your own logger class directly into your SDK.

from unstructured_client import UnstructuredClient
import logging

logging.basicConfig(level=logging.DEBUG)
s = UnstructuredClient(debug_logger=logging.getLogger("unstructured_client"))

Maturity

This SDK is in beta, and there may be breaking changes between versions without a major version update. Therefore, we recommend pinning usage to a specific package version. This way, you can install the same version each time without breaking changes unless you are intentionally looking for the latest version.

Installation Instructions for Local Development

The following instructions are intended to help you get up and running with unstructured-python-client locally if you are planning to contribute to the project.

• Using pyenv to manage virtualenv's is recommended but not necessary

  • Mac install instructions. See here for more detailed instructions.
    • brew install pyenv-virtualenv
    • pyenv install 3.10
  • Linux instructions are available here.

• Create a virtualenv to work in and activate it, e.g. for one named unstructured-python-client:

  pyenv virtualenv 3.10 unstructured-python-client pyenv activate unstructured-python-client

• Run make install and make test

Contributions

While we value open-source contributions to this SDK, this library is generated programmatically by Speakeasy. In order to start working with this repo, you need to:

1. Install Speakeasy client locally https://github.com/speakeasy-api/speakeasy#installation
2. Run speakeasy auth login
3. Run make client-generate. This allows to iterate development with python client.

There are two important files used by make client-generate:

1. openapi.json which is actually not stored here, but fetched from unstructured-api, represents the API that is supported on backend.
2. overlay_client.yaml is a handcrafted diff that when applied over above, produces openapi_client.json which is used to generate SDK.

Once PR with changes is merged, Github CI will autogenerate the Speakeasy client in a new PR, using the openapi.json and overlay_client.yaml You will have to manually bring back the human created lines in it.

Feel free to open a PR or a Github issue as a proof of concept and we'll do our best to include it in a future release!

SDK Created by Speakeasy