ai_workflows Python Package Documentation

The ai_workflows package is a toolkit for supporting AI workflows (i.e., workflows that are pre-scripted and repeatable, but utilize LLMs for various tasks).

The goal is to lower the bar for social scientists and others to leverage LLMs in repeatable, reliable, and transparent ways. See this blog post for a discussion, here for the full documentation, and here for a custom GPT that can help you use this package. If you learn best by example, see these:

example-doc-conversion.ipynb: loading different file formats and converting them into a Markdown syntax that LLMs can understand.
example-doc-extraction.ipynb: extracting structured data from unstructured documents (edit notebook to customize).
example-doc-extraction-templated.ipynb: extracting structured data from unstructured documents (supply an Excel template to customize).
example-qual-analysis-1.ipynb: a more realistic workflow example that performs a simple qualitative analysis on a set of interview transcripts.
example-surveyeval-lite.ipynb: another workflow example that critically evaluates surveys question-by-question.

Tip: if you’re not completely comfortable working in Python, use GitHub Copilot in VS Code or Gemini as a copilot in Google Colab. Also do make use of this custom GPT coding assistant.

Installation

Install the latest version with pip:

pip install py-ai-workflows[docs]

If you don’t need anything in the document_utilities module (relating to reading, parsing, and converting documents), you can install a slimmed-down version with:

pip install py-ai-workflows

Additional document-parsing dependencies

If you installed the full version with document-processing capabilities (py-ai-workflows[docs]), you’ll also need to install several other dependencies, which you can do by running the initial-setup.ipynb Jupyter notebook — or by installing them manually as follows.

First, download NTLK data for natural language text processing:

# download NLTK data
import nltk
nltk.download('punkt', force=True)

Then install libreoffice for converting Office documents to PDF.

On Linux:

# install LibreOffice for document processing
!apt-get install -y libreoffice

On MacOS:

# install LibreOffice for document processing
brew install libreoffice

On Windows:

# install LibreOffice for document processing
choco install -y libreoffice

AWS Bedrock support

Finally, if you’re accessing models via AWS Bedrock, the AWS CLI needs to be installed and configured for AWS access.

Jupyter notebooks with Google Colab support

You can use the colab-or-not package to initialize a Jupyter notebook for Google Colab or other environments:

%pip install colab-or-not py-ai-workflows

# download NLTK data
import nltk
nltk.download('punkt', force=True)

# set up our notebook environment (including LibreOffice)
from colab_or_not import NotebookBridge
notebook_env = NotebookBridge(
    system_packages=["libreoffice"],
    config_path="~/.hbai/ai-workflows.env",
    config_template={
        "openai_api_key": "",
        "openai_model": "",
        "azure_api_key": "",
        "azure_api_base": "",
        "azure_api_engine": "",
        "azure_api_version": "",
        "anthropic_api_key": "",
        "anthropic_model": "",
        "langsmith_api_key": "",
    }
)
notebook_env.setup_environment()

Overview

Here are the basics:

The llm_utilities module provides a simple interface for interacting with a large language model (LLM). It includes the LLMInterface class that can be used for executing individual workflow steps.
The document_utilities module provides an interface for extracting Markdown-formatted text and structured data from various file formats. It includes functions for reading Word, PDF, Excel, CSV, HTML, and other file formats, and then converting them into Markdown or structured data for use in LLM interactions.
The example-doc-conversion.ipynb notebook provides a simple example of how to use the document_utilities module to convert files to Markdown format, in either Google Colab or a local environment.
The example-doc-extraction.ipynb notebook provides an example of how to extract structured data from unstructured documents.
The example-doc-extraction-templated.ipynb notebook provides an easier-to-customize version of the above: you supply an Excel template with your data extraction needs.
The example-qual-analysis-1.ipynb notebook provides a more realistic workflow example that uses both the document_utilities and the llm_utilities modules to perform a simple qualitative analysis on a set of interview transcripts. It also works in either Google Colab or a local environment.
The example-surveyeval-lite.ipynb notebook provides another workflow example that uses the document_utilities module to convert a survey file to Markdown format and then to JSON format, and then uses the llm_utilities module to evaluate survey questions using an LLM. It also works in either Google Colab or a local environment.
The example-testing.ipynb notebook provides a basic set-up for testing Markdown conversion methods (LLM-assisted vs. not-LLM-assisted). At the moment, this notebook only works in a local environment.

Example snippets

Converting a file to Markdown format (without LLM assistance):

from ai_workflows.document_utilities import DocumentInterface

doc_interface = DocumentInterface()
markdown = doc_interface.convert_to_markdown(file_path)

Converting a file to Markdown format (with LLM assistance):

from ai_workflows.llm_utilities import LLMInterface
from ai_workflows.document_utilities import DocumentInterface

llm_interface = LLMInterface(openai_api_key=openai_api_key)
doc_interface = DocumentInterface(llm_interface=llm_interface)
markdown = doc_interface.convert_to_markdown(file_path)

Converting a file to JSON format:

from ai_workflows.llm_utilities import LLMInterface
from ai_workflows.document_utilities import DocumentInterface

llm_interface = LLMInterface(openai_api_key=openai_api_key)
doc_interface = DocumentInterface(llm_interface=llm_interface)
dict_list = doc_interface.convert_to_json(
    file_path,
    json_context = "The file contains a survey instrument with questions to be administered to rural Zimbabwean household heads by a trained enumerator.",
    json_job = "Your job is to extract questions and response options from the survey instrument.",
    json_output_spec = "Return correctly-formatted JSON with the following fields: ..."
)

Requesting a JSON response from an LLM:

from ai_workflows.llm_utilities import LLMInterface

llm_interface = LLMInterface(openai_api_key=openai_api_key)

json_output_spec = """Return correctly-formatted JSON with the following fields:

* `answer` (string): Your answer to the question."""

full_prompt = f"""Answer the following question:

(question)

{json_output_spec}

Your JSON response precisely following the instructions given:"""

parsed_response, raw_response, error = llm_interface.get_json_response(
    prompt = full_prompt,
    json_validation_desc = json_output_spec
)

Technical notes

Working with JSON

The ai_workflows package helps you to extract structured JSON content from documents and LLM responses. In all such cases, you have to describe the JSON format that you want with enough clarity and specificity that the system can reliably generate and validate responses (you typically supply this in a json_output_spec parameter). When describing your desired JSON, always include the field names and types, as well as detailed descriptions. For example, if you wanted a list of questions back:

json_output_spec = """Return correctly-formatted JSON with the following fields:

* `questions` (list of objects): A list of questions, each with the following fields:
    * `question` (string): The question text
    * `answer` (string): The supplied answer to the question"""

By default, the system will use this informal, human-readable description to automatically generate a formal JSON schema, which it will use to validate LLM responses (and retry if needed).

LLMInterface

The LLMInterface class provides a simple LLM interface with the following features:

Support for both OpenAI and Anthropic models, either directly or via Azure or AWS Bedrock
Support for both regular and JSON responses (using the LLM provider’s “JSON mode” when possible)
Optional support for conversation history (tracking and automatic addition to each request)
Automatic validation of JSON responses against a formal JSON schema (with automatic retry to correct invalid JSON)
Automatic (LLM-based) generation of formal JSON schemas
Automatic timeouts for long-running requests
Automatic retry for failed requests (OpenAI refusals, timeouts, and other retry-able errors)
Support for LangSmith tracing
Synchronous and async versions of all functions (async versions begin with a_)

Key methods:

get_llm_response(): Get a response from an LLM
get_json_response(): Get a JSON response from an LLM
user_message(): Get a properly-formatted user message to include in an LLM prompt
user_message_with_image(): Get a properly-formatted user message to include in an LLM prompt, including an image attachment
generate_json_schema(): Generate a JSON schema from a human-readable description (called automatically when JSON output description is supplied to get_json_response())
count_tokens(): Count the number of tokens in a string
enforce_max_tokens(): Truncate a string as necessary to fit within a maximum number of tokens

If you don’t have an API key for an AI provider yet, see here to learn what that is and how to get one.

DocumentInterface

The DocumentInterface class provides a simple interface for converting files to Markdown or JSON format.

Key methods:

convert_to_markdown(): Convert a file to Markdown format, using an LLM if available and deemed helpful (if you specify use_text=True, it will include raw text in any LLM prompt, which might improve results)
convert_to_json(): Convert a file to JSON format using an LLM (could convert the document to JSON page-by-page or convert to Markdown first and then JSON; specify markdown_first=True if you definitely don’t want to go the page-by-page route)
markdown_to_json(): Convert a Markdown string to JSON format using an LLM
markdown_to_text(): Convert a Markdown string to plain text
merge_dicts(): Merge a list of dictionaries into a single dictionary (handy for merging the results from x_to_json() methods)

Markdown conversion

The DocumentInterface.convert_to_markdown() method uses one of several methods to convert files to Markdown.

If an LLMInterface is available:

PDF files are converted to Markdown with LLM assistance: we split the PDF into pages (splitting double-page spreads as needed), convert each page to an image, and then convert to Markdown using the help of a multimodal LLM. This is the most accurate method, but it’s also the most expensive, running at about $0.015 per page as of October 2024. In the process, we try to keep narrative text that flows across pages together, drop page headers and footers, and describe images, charts, and figures as if to a blind person. We also do our best to convert tables to proper Markdown tables. If the use_text parameter is set to True, we’ll extract the raw text from each page (when possible) and provide that to the LLM to assist it with the conversion.
We use LibreOffice to convert .docx, .doc, and .pptx files to PDF and then convert the PDF to Markdown using the LLM assistance method described above.
For .xlsx files without charts or images, we use a custom parser to convert worksheets and table ranges to proper Markdown tables. If there are charts or images, we use LibreOffice to convert to PDF and, if it’s 10 pages or fewer, we convert from the PDF to Markdown using the LLM assistance method described above. If it’s more than 10 pages, we fall back to dropping charts or images and converting without LLM assistance.
For other file types, we fall back to converting without LLM assistance, as described below.

Otherwise, we convert files to Markdown using one of the following methods (in order of preference):

For .xlsx files, we use a custom parser and Markdown formatter.
For other file types, we use IBM’s Docling package for those file formats that it supports. This method drops images, charts, and figures, but it does a nice job with tables and automatically uses OCR when needed.
If Docling fails or doesn’t support a file format, we next try PyMuPDFLLM, which supports PDF files and a range of other formats. This method also drops images, charts, and figures, and it’s pretty bad at tables, but it does a good job extracting text and a better job adding Markdown formatting than most other libraries.
Finally, if we haven’t managed to convert the file using one of the higher-quality methods described above, we use the Unstructured library to parse the file into elements and then add basic Markdown formatting. This method is fast and cheap, but it’s also the least accurate.

JSON conversion

You can convert from Markdown to JSON using the DocumentInterface.markdown_to_json() method, or you can convert files directly to JSON using the DocumentInterface.convert_to_json() method. The latter method will most often convert to Markdown first and then to JSON, but it will convert straight to JSON with a page-by-page approach if:

The markdown_first parameter is explicitly provided as False and converting the file to Markdown would naturally use an LLM with a page-by-page approach (see the section above)
Or: the markdown_first parameter is left at the default (None), converting the file to Markdown would naturally use an LLM with a page-by-page approach, and the file’s Markdown content is too large to convert to JSON in a single LLM call.

The advantage of converting to JSON directly can also be a disadvantage: parsing to JSON is done page-by-page. If JSON elements don’t span page boundaries, this can be great; however, if elements do span page boundaries, it won’t work well. For longer documents, Markdown-to-JSON conversion also happens in batches due to LLM token limits, but efforts are made to split batches by natural boundaries (e.g., between sections). Thus, the doc->Markdown->JSON path can work better if page boundaries aren’t the best way to batch the conversion process.

Whether or not you convert to JSON via Markdown, JSON conversion always uses LLM assistance. The parameters you supply are:

json_context: a description of the file’s content, to help the LLM understand what it’s looking at
json_job: a description of the task you want the LLM to perform (e.g., extracting survey questions)
json_output_spec: a description of the output you expect from the LLM (see discussion further above)
json_output_schema: optionally, a formal JSON schema to validate the LLM’s output; by default, this will be automatically generated based on your json_output_spec, but you can specify your own schema or explicitly pass None if you want to disable JSON validation (if JSON validation isn’t disabled, the LLMInterface default is to retry twice if the LLM output doesn’t parse or match the schema, but you can change this behavior by specifying the json_retries parameter in the LLMInterface constructor)

The more detail you provide, the better the LLM will do at the JSON conversion. If you find that things aren’t working well, try including some few-shot examples in the json_output_spec parameter.

Note that the JSON conversion methods return a list of dict objects, one for each batch or LLM call. This is because, for all but the shortest documents, conversion will take place in multiple batches. One dict, following your requested format, is returned for each batch. You can process these returned dictionaries separately, merge them yourself, or use the handy DocumentInterface.merge_dicts() method to automatically merge them together into a single dictionary.

JSONSchemaCache

The JSONSchemaCache class provides a simple in-memory cache for JSON schemas, so that they don’t have to be regenerated repeatedly. It’s used internally by both the LLMInterface and DocumentInterface classes, to avoid repeatedly generating the same schema for the same JSON output specification.

Key methods:

get_json_schema(): Get a JSON schema from the cache (returns empty string if none found)
put_json_schema(): Put a JSON schema into the cache

Known issues

See bugs logged in GitHub issues for the most up-to-date list of known issues.

ImportError: libGL.so.1: cannot open shared object file

If you use this package in a headless environment (e.g., within a Docker container), you might encounter the following error:

ImportError: libGL.so.1: cannot open shared object file: No such file or directory

This is caused by a conflict between how the Docling and Unstructured packages depend on opencv. The fix is to install all of your requirements like normal, and then uninstall and re-install opencv:

pip uninstall -y opencv-python opencv-python-headless && pip install opencv-python-headless

In a Dockerfile (after your pip install commands):

RUN pip uninstall -y opencv-python opencv-python-headless && pip install opencv-python-headless

Roadmap

This package is a work-in-progress. See the GitHub issues page for known bugs and enhancements being considered. Feel free to react to or comment on existing issues, or to open new issues.

Credits

This toolkit was originally developed by Higher Bar AI, PBC, a public benefit corporation. To contact us, email us at info@higherbar.ai.

Many thanks also to Laterite for their contributions.

Reference

ai_workflows
- ai_workflows package
  - Submodules
    - ai_workflows.document_utilities module
    - ai_workflows.llm_utilities module
      - JSONSchemaCache
      - LLMInterface
  - Module contents

ai_workflows Python Package Documentation

Installation

Additional document-parsing dependencies

AWS Bedrock support

Jupyter notebooks with Google Colab support

Overview

Example snippets

Technical notes

Working with JSON

LLMInterface

DocumentInterface

Markdown conversion

JSON conversion

JSONSchemaCache

Known issues

ImportError: libGL.so.1: cannot open shared object file

Roadmap

Credits

Indices and tables