Google Releases LangExtract for LLM Data Extraction

✅Google has open‑sourced LangExtract. What’s that ? A Python library that turns long, messy PDFs and unstructured text into clean, grounded JSON in a few lines of code. ✅ No brittle regex, support for Gemini/OpenAI/Ollama, and every extracted field links back to its exact spot in the original document—perfect for invoices, contracts, and compliance workflows. 🔗 https://lnkd.in/g47XsbqE #ai #langextract #messypdf #google #openai #ollama Google

https://lnkd.in/gS24MGvb LangExtract is a new open-source Python library for extracting structured data from unstructured text using LLMs like Gemini, with traceability back to exact source offsets + built-in visualization for fast QA. #gemini #AI #AIXYZ

Looking at this list, one thing becomes very clear. Python is not just a language anymore. It’s an ecosystem. From data analysis (NumPy, Pandas), to visualization (Matplotlib, Plotly), to machine learning (Scikit‑learn, PyTorch, TensorFlow), to web development (Flask, Django, FastAPI), to big data (PySpark), to computer vision (OpenCV) and NLP (SpaCy, NLTK) Python quietly powers almost every layer of modern tech. As a data professional, I’ve realized something important: It’s not about knowing all these libraries. It’s about knowing: • When to use which one   • How they connect together   • And how to move from experimentation to production  Beginners often try to learn everything at once. Experienced professionals focus on building depth, then expanding strategically. Because tools change. But the ability to think clearly with data, design clean workflows, and choose the right stack that’s what truly compounds over time. Python didn’t become dominant because it’s “EASY.” It became dominant because it reduces friction between idea and execution. Curious to hear from others Which Python library changed the way you work? If you’re looking for structured guidance, practical roadmaps, or mentorship in Data Analytics / Data Science, you can explore here: https://lnkd.in/gasgBQ6k #Python

Google quietly shipped something most people will underestimate. It’s called LangExtract. A Python library that turns complex, unstructured text into clean, structured data using LLMs with precise source grounding. Here’s what makes it interesting: You define what you want with a few examples. LangExtract handles the rest. • It chunks long documents intelligently • Processes chunks in parallel across multiple passes • Links every extracted entity back to its exact source location • Generates an interactive HTML view so you can verify everything This is not just extraction. It’s extraction with traceability. It already has 17K+ GitHub stars. Supports Gemini, GPT-4o, and local models through Ollama. Fully open source under Apache 2.0. For teams building document intelligence systems, compliance tools, research pipelines, or agent workflows, this is worth exploring. Structured data from raw text is no longer the hard part. Link is in the comments #ai #llms #python #opensource #mlengineering #generativeai

I built my first AI agent in 2 hours… and it completely changed how I understand LLMs. 🤯 Here are 5 things no tutorial told me. 1️⃣ The LLM doesn’t run code. It just suggests what to run. This was the biggest mindset shift for me. The model doesn’t execute Python. It simply generates text like: Action: compute_stats(revenue) My program reads that text, figures out which function is being called, runs the Python code, and then sends the result back to the LLM. That back-and-forth loop is basically how tool use in AI agents works. Once this clicked, everything made much more sense. 💡 2️⃣ The agent is only as good as the tools you give it. I ended up giving my agent a small toolkit: • load_data() • describe_data() • compute_stats() • find_correlations() • detect_outliers() • plot_histogram() • value_counts() What I realized quickly: If the tools are vague or poorly designed, the agent gets confused. But when the tools are clear and focused, the agent suddenly feels way smarter than expected. 🤖 3️⃣ Output structure matters a lot. If the model responds in random formats, parsing becomes messy. So I forced a very simple structure: Thought: what the agent plans to do Action: tool_name(argument) A tiny regex parser reads it every time. Simple… but surprisingly reliable. ⚙️ 4️⃣ Always add a step limit. Without something like max_steps = 15, the agent can keep looping forever. And trust me — Your API bill doesn’t care that you’re “just experimenting.” 😅💸 5️⃣ I ended up using the LLM twice. One inside the loop for reasoning and choosing tools. And another at the end to write the final analysis report. Separating those roles made the output much cleaner. ✨ The build: ⏱ Time: ~2 hours 💰 Cost: $0 (Gemini free tier) Stack: Python · pandas · matplotlib · Gemini 2.0 Flash I’m still learning this space, but building things like this makes the concepts click way faster. Next post, I’ll share the full project and demo. 🚀 If you’re also exploring AI agents or data + LLM workflows, follow along! #AgenticAI #Python #DataScience #LLM #BuildInPublic #LearningInPublic

𝐏𝐲𝐭𝐡𝐨𝐧 𝐟𝐨𝐫 𝐄𝐯𝐞𝐫𝐲𝐭𝐡𝐢𝐧𝐠 🐍 — 𝐖𝐡𝐲 𝐈𝐭’𝐬 𝐌𝐨𝐫𝐞 𝐓𝐡𝐚𝐧 𝐉𝐮𝐬𝐭 𝐚 𝐏𝐫𝐨𝐠𝐫𝐚𝐦𝐦𝐢𝐧𝐠 𝐋𝐚𝐧𝐠𝐮𝐚𝐠𝐞 One of the biggest reasons Python dominates the tech world isn’t just its simple syntax — it’s the ecosystem. Whatever you want to build, Python already has a powerful library waiting for you. Python Certification Course :- https://lnkd.in/dZT8h2vp Here’s how Python fits into almost every domain of technology: 🔹 Data Manipulation → Pandas 🔹 Numerical Computing → NumPy 🔹 Data Visualization → Matplotlib & Seaborn 🔹 Machine Learning → Scikit-learn 🔹 Deep Learning → TensorFlow & PyTorch 🔹 Database Interaction → SQLAlchemy 🔹 Web Development → Flask & Django 🔹 Web Scraping → BeautifulSoup & Scrapy 🔹 Computer Vision → OpenCV 🔹 Natural Language Processing → NLTK & spaCy 🔹 Big Data Processing → PySpark 🔹 API Development → FastAPI 🔹 Exploratory Data Analysis → Jupyter Notebooks 🔹 Neural Networks → Keras 🔹 Image Processing → PIL / Pillow 📌 The real power of Python: You don’t need to switch languages when your career grows. You can start with basic scripting → move to data analysis → then machine learning → and even deploy production APIs — all in one language.

🚀 Python is powering more of your daily tech than you realize. From AI assistants to data-driven apps and cloud automation, Python sits quietly behind the scenes making modern systems smarter and faster. Why does Python keep dominating? 👇 🔹 Data manipulation → Pandas, NumPy 🔹 Deep learning & neural networks → TensorFlow, PyTorch, Keras 🔹 Data visualization → Matplotlib, Seaborn, Plotly 🔹 Web scraping & automation → BeautifulSoup, Scrapy, Selenium 🔹 Machine learning → Scikit-learn 🔹 Web development → Flask, Django 🔹 Image processing & computer vision → OpenCV 🔹 Database access → SQLAlchemy 🔹 HTTP requests & API handling → Requests 🔹 Interactive apps & dashboards → Streamlit 🔹 Testing & debugging → Pytest 🔹 Cloud & automation → Boto3 (AWS), Twilio 💡 One language. Unlimited real-world impact. That’s why Python remains one of the most future-proof and in-demand skills in tech today. 👇 Let’s make this interactive: Which Python library do you use most in real projects? #Python #Programming #DataScience #WebDevelopment #DeepLearning #Automation #MachineLearning #SoftwareDevelopment #CloudComputing #AI #TechSkills #CodeAndFork

AI-Powered Address Completion Tool I developed a Python solution that processes partial address data and uses AI to identify and fill in missing details, such as city and ZIP code. It validates and completes records automatically, ensuring accurate location data. The solution improves data accuracy and consistency, reduces manual verification, and ensures reliable records for operations and reporting. Built with Python, OpenAI API, and Excel integration, this project demonstrates how AI-powered data enrichment can transform incomplete address information into structured, actionable datasets for logistics, customer databases, and business operations. Portfolio: https://lnkd.in/dpiy69BF #Python #AI #OpenAI #DataProcessing #ExcelAutomation #DataEnrichment #Portfolio

🧠 What if your data could answer: "What did we know about this — and when?" Most data tools tell you what the current state is. But what about reconstructing knowledge at an arbitrary point in the past? My co-authors Jeremiah Lowhorn, Seth Thor, and Matthew Morais and I just published "Building a Temporal Knowledge Graph with Python and NetworkX" in Towards AI — and it tackles exactly that. We built a graph that ingests scientific publications, extracts typed entities (drugs, targets, indications, trials, orgs), and supports time-travel queries — letting you ask what was structurally known at any historical date. From ~20 documents, the system produced:  🔹 66 nodes, 230 edges across 8 node types and 10 relationship types 🔹 Chronological publication chains via temporal edges 🔹 Time-constrained path finding between entities 🔹 Full serialization to GraphML, JSON, and Pickle Relational databases store facts. Knowledge graphs store relationships. Add temporal awareness — and you have a fundamentally different analytical primitive. This is part of the research infrastructure we're quietly building over at NumenAI — but the patterns transfer to any domain where relationships matter more than raw records. https://lnkd.in/eM3QKA_g #KnowledgeGraph #Python #NetworkX #GraphML #TemporalAI #MachineLearning #DataScience

I’ve been working on Mimir‑AIP, a toolkit for ontology‑backed data processing and reasoning in Go and Python. In my first proper write‑up about the project, I focus on something that’s come up often during its development; scope creep. As AI speeds up how we build and prototype, it’s easy to keep layering features onto an early design, often with mixed results. Sometimes, the better move is to stop, step back, and rebuild. I’ve done that twice with Mimir‑AIP, and each time it’s led to a system that makes more sense and gets closer to the project vision(even when that vision is changing over-time). Blog Post: https://lnkd.in/e64GBV8D #AI #ScopeCreep #TechnicalDebt #SoftwareArchitecture