@DataScienceDojo: Google's 𝐥𝐚𝐧𝐠𝐞𝐱𝐭𝐫𝐚𝐜𝐭 has crossed 37k stars on GitHub. The core idea: point an LLM at unstructured text and g…

Google's 𝐥𝐚𝐧𝐠𝐞𝐱𝐭𝐫𝐚𝐜𝐭 has crossed 37k stars on GitHub. The core idea: point an LLM at unstructured text and get back structured extractions that are grounded in the source, not hallucinated. What makes it hold up: • Every extraction maps to its exact character position in the original text, so you can trace a claim back to the sentence it came from instead of trusting the model's word for it • Long documents get chunked, processed in parallel, and passed through multiple extraction rounds to improve recall on the needle-in-a-haystack problem • Define a task with a handful of examples and it adapts to any domain without fine-tuning • Works with Gemini and OpenAI out of the box, plus local models through Ollama if you want to skip API keys entirely If you're building pipelines that pull structured data out of clinical notes, reports, or any messy text corpus, this is worth testing against whatever regex or prompt-and-pray setup you're running now. video credits: oliviscusAI/x #LangExtract #GeminiAPI #LLMEngineering #DataExtraction #AIEngineering #OpenSource