You Probably Don’t Need an Agent Framework
Most LLM applications need a clear workflow, not an autonomous agent. Here's how to build one in plain Python. The post You Probably Don’t Need an Agent Framework appeared first on Towards Data Science.
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In this tutorial, we build a workflow that uses Docling Parse to analyze PDF documents at a detailed structural level. We prepare a stable Python environment, handle common Colab dependency issues, and generate a custom multi-page PDF with text, columns, table-like content, vector shapes, and an embedded image. We then extract words, characters, and lines with page-level coordinates, render visual overlays, and save results into structured JSON and CSV. We see how low-level parsing supports layout analysis, reading-order reconstruction, and retrieval-ready document preparation. The post How to Build a Parsing Pipeline with Docling Parse for Layout-Aware Document Intelligence appeared first on MarkTechPost.
Read full articleMost LLM applications need a clear workflow, not an autonomous agent. Here's how to build one in plain Python. The post You Probably Don’t Need an Agent Framework appeared first on Towards Data Science.
We break down Google Cloud's new Open Knowledge Format (OKF), an open spec that formalizes the LLM-wiki pattern. We explain how a bundle works: a directory of markdown files with YAML frontmatter, where each concept needs only a type field. We cover the three design principles, the reference tools Google shipped, and how OKF differs from RAG. We include a working Python consumer and an interactive bundle explorer you can embed. The post Google Cloud Introduces Open Knowledge Format (OKF): A Vendor-Neutral Markdown Spec for Giving AI Agents Curated Context appeared first on MarkTechPost.
In this article, we’ll build time-series machine learning models in Python using sktime and explore its core data structures for forecasting workflows.
In this tutorial, we implement a QwenPaw workflow that provides a practical environment for building and testing an agent-powered assistant. We install and initialize QwenPaw, configure its working directory, set up authentication, connect optional model providers via Colab secrets, and create a structured workspace with custom skills and local knowledge files. We also launch the […] The post How to Build a QwenPaw Agent Workspace with Custom Skills, Model Providers, Console Access, and Streaming API Testing appeared first on MarkTechPost.
Enterprise Document Intelligence [Vol.1 #5B] - One PDF in, a relational set of DataFrames out: lines, pages, TOC, images, cross-references, captions, spans, and a parsing summary The post Stop Returning Flat Text from a PDF: The Relational Shape RAG Needs appeared first on Towards Data Science.
Enterprise Document Intelligence [Vol.1 #5A] - Document signals (metadata, native TOC, source software) and page-level content (text vs scans, tables, images, columns, page profile) The post Beyond extract_text: The Two Layers of a PDF That Drive RAG Quality appeared first on Towards Data Science.
PDFs are used everywhere, and these five Python scripts help you automate the most common PDF tasks.
Of all the reasons Python is a hit with developers, one of the biggest is its broad and ever-expanding selection of third-party packages. Convenient toolkits for everything from ingesting and formatting data to high-speed math and machine learning are just an import or pip install away. But what happens when those packages don’t play nice with each other? What do you do when different Python projects need competing or incompatible versions of the same add-ons? That’s where Python virtual environments come into play. What are Python virtual environments? A virtual environment is a way to have multiple, parallel instances of the Python interpreter, each with different sets of packages and different configurations. Each virtual environment contains a discrete copy of the Python interpreter, including copies of its support utilities (such as the package manager pip). The packages installed in each virtual environment are seen only in that virtual environment and no other. Even large, compl