Pydoxtools is a Python library that allows for mixing LLMs and other AI models in large, composable, and customizable pipelines. The library can be used to create documents, extract tables, and perform question-answering using AI models such as GPT, Alpaca, and Huggingface.

What are LLMs and how do they relate to AI models?

LLMs, or Language Model Libraries, are a type of AI model that are designed to understand and generate human language. They are trained on large datasets of text and use statistical methods to predict the likelihood of different words and phrases appearing in a given context. LLMs are a key component of many natural language processing (NLP) tasks, such as language translation, sentiment analysis, and text generation.

Pydoxtools allows for the integration of LLMs into larger AI pipelines, making it easier for developers to build complex NLP applications. By combining LLMs with other AI models, such as those provided by GPT, Alpaca, and Huggingface, developers can create powerful applications that can understand and generate human language with a high degree of accuracy.

The main features of Pydoxtools include:

  • Large pipelines: The Pipeline class can be used to build complex, custom pipelines which have several out-of-the-box features which makes them easy to use in modern pipelines involving the use of a lot of AI tools. They can be mixed, extended, (partially) overwritten with other pipelines, exported/imported (yaml, json, python-dict), configured & optimized, and can convert their data into pydoxtools.Document and pydoxtools.DocumentSet.
  • Functionalities: Pydoxtools offers functionalities such as pipeline management, low-resource (PDF) table extraction, document analysis, support for most document formats, vector index creation, entity and address identification, list and keyword extraction, and data normalization, translation, and cleaning.

Real-world Applications of Pydoxtools

Some ideas on how Pydoxtools can be used in a variety of real-world applications are:

  • Legal document analysis: Pydoxtools can be used to analyze legal documents and extract relevant information, such as case numbers, parties involved, and key terms.
  • Product analysis: specification extraction for product search engines.
  • Financial document analysis: Pydoxtools can be used to extract financial data from documents, such as balance sheets and income statements.
  • Healthcare document analysis: Pydoxtools can be used to analyze healthcare documents, such as medical records, and extract relevant information, such as patient demographics and diagnoses.

Some examples of AI and LLMs that can be built using this library include:

  • Creating documents, extracting tables, and performing question-answering using AI models such as GPT, Alpaca, and Huggingface.
  • Configuring pipelines to use specific AI models, such as setting the qam_model_id to any model from Huggingface for question answering.

Tutorials and Documentation

Pydoxtools provides extensive documentation at https://pydoxtools.xyntopia.com. Additionally, the sourcecode is available at https://www.github.com/xyntopia/pydoxtools. The documentation includes detailed explanations of the library's features and unctionalities, as well as examples of how to use the library to build complex AI pipelines.

Tutorials to help developers get started with the library are in development and we will post them on a regular basis on this blog.