Metadata-Version: 2.4
Name: llmwhisperer-client
Version: 2.7.0
Summary: Client library for LLM Whisperer
Project-URL: Homepage, https://unstract.com/llmwhisperer/
Project-URL: Source, https://github.com/Zipstack/llm-whisperer-python-client
Author-email: Zipstack Inc <devsupport@zipstack.com>
License: AGPL v3
License-File: LICENSE
Keywords: llmwhisperer tools-development-kit apps development-kit sdk
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: GNU Affero General Public License v3
Classifier: Operating System :: MacOS :: MacOS X
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: POSIX :: Linux
Classifier: Programming Language :: Python
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Requires-Python: >=3.12
Requires-Dist: requests>=2
Requires-Dist: tenacity>=8.0
Description-Content-Type: text/markdown

# Unstract LLMWhisperer Python Client

[![PyPI - Downloads](https://img.shields.io/pypi/dm/llmwhisperer-client)](https://pypi.org/project/llmwhisperer-client/)
[![Python Version from PEP 621 TOML](https://img.shields.io/python/required-version-toml?tomlFilePath=https%3A%2F%2Fraw.githubusercontent.com%2FZipstack%2Fllm-whisperer-python-client%2Fmain%2Fpyproject.toml)
](https://pypi.org/project/llmwhisperer-client/)
[![PyPI - Version](https://img.shields.io/pypi/v/llmwhisperer-client)](https://pypi.org/project/llmwhisperer-client/)

LLMs are powerful, but their output is as good as the input you provide. LLMWhisperer is a technology that presents data from complex documents (different designs and formats) to LLMs in a way that they can best understand. LLMWhisperer features include Layout Preserving Mode, Auto-switching between native text and OCR modes, proper representation of radio buttons and checkboxes in PDF forms as raw text, among other features. You can now extract raw text from complex PDF documents or images without having to worry about whether the document is a native text document, a scanned image or just a picture clicked on a smartphone. Extraction of raw text from invoices, purchase orders, bank statements, etc works easily for structured data extraction with LLMs powered by LLMWhisperer's Layout Preserving mode. 

Refer to the client documentation for more information: [LLMWhisperer Client Documentation](https://docs.unstract.com/llmwhisperer/llm_whisperer/python_client/llm_whisperer_python_client_intro/)

## Client

This package provides **LLMWhispererClientV2**, the client for LLMWhisperer API v2. It is required for all users on API version 2.0.0 and above.

Documentation is available [here](https://docs.unstract.com/llmwhisperer/).

## Running Tests

Install test dependencies and run all tests:

```bash
uv run --group test pytest
```

To run only unit tests (skipping integration tests):

```bash
uv run --group test pytest tests/unit tests/utils_test.py
```

To run only integration tests:

```bash
uv run --group test pytest tests/integration
```

Integration tests require a valid API key. Copy `sample.env` to `.env` and fill in your credentials before running them.

## Questions and Feedback

On Slack, [join great conversations](https://join-slack.unstract.com/) around LLMs, their ecosystem and leveraging them to automate the previously unautomatable!

[LLMWhisperer Playground](https://pg.llmwhisperer.unstract.com/): Test drive LLMWhisperer with your own documents. No sign up needed!

[LLMWhisperer developer documentation and playground](https://dev-pg.llmwhisperer.unstract.com/): Learn more about LLMWhisperer and its API.
