#langextract — Public Fediverse posts
Live and recent posts from across the Fediverse tagged #langextract, aggregated by home.social.
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*Google acaba de cambiar cómo se sacan datos de documentos.
Ha lanzado #LangExtract: una herramienta que convierte textos largos y desordenados en datos claros y verificables.
Es gratis y open-source 👇
https://bsky.app/profile/jesusgallent.com/post/3meja22duo22v -
🧠 #LangExtract è una libreria Python open source di #Google pensata per estrarre informazioni strutturate da testi non strutturati usando i #LLM.
👉 I dettagli: https://www.linkedin.com/posts/alessiopomaro_langextract-google-llm-activity-7426884315931668480-ROEI___
✉️ 𝗦𝗲 𝘃𝘂𝗼𝗶 𝗿𝗶𝗺𝗮𝗻𝗲𝗿𝗲 𝗮𝗴𝗴𝗶𝗼𝗿𝗻𝗮𝘁𝗼/𝗮 𝘀𝘂 𝗾𝘂𝗲𝘀𝘁𝗲 𝘁𝗲𝗺𝗮𝘁𝗶𝗰𝗵𝗲, 𝗶𝘀𝗰𝗿𝗶𝘃𝗶𝘁𝗶 𝗮𝗹𝗹𝗮 𝗺𝗶𝗮 𝗻𝗲𝘄𝘀𝗹𝗲𝘁𝘁𝗲𝗿: https://bit.ly/newsletter-alessiopomaro -
🧠 #LangExtract è una libreria Python open source di #Google pensata per estrarre informazioni strutturate da testi non strutturati usando i #LLM.
👉 I dettagli: https://www.linkedin.com/posts/alessiopomaro_langextract-google-llm-activity-7426884315931668480-ROEI___
✉️ 𝗦𝗲 𝘃𝘂𝗼𝗶 𝗿𝗶𝗺𝗮𝗻𝗲𝗿𝗲 𝗮𝗴𝗴𝗶𝗼𝗿𝗻𝗮𝘁𝗼/𝗮 𝘀𝘂 𝗾𝘂𝗲𝘀𝘁𝗲 𝘁𝗲𝗺𝗮𝘁𝗶𝗰𝗵𝗲, 𝗶𝘀𝗰𝗿𝗶𝘃𝗶𝘁𝗶 𝗮𝗹𝗹𝗮 𝗺𝗶𝗮 𝗻𝗲𝘄𝘀𝗹𝗲𝘁𝘁𝗲𝗿: https://bit.ly/newsletter-alessiopomaro -
Le site de #Korben: #LangExtract - La nouvelle pépite de #Google pour extraire des données structurées avec l' #IA
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Le site de #Korben: #LangExtract - La nouvelle pépite de #Google pour extraire des données structurées avec l' #IA
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#ITByte: #LangExtract is a new open-source Python library from Google that uses large language models (LLMs) to extract structured information from unstructured text.
Instead of requiring domain-specific training, it uses prompts and examples to instruct LLMs on how to structure the data.
https://knowledgezone.co.in/posts/Google-LangExtract-68ee737e35590fd1b52bf506
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#ITByte: #LangExtract is a new open-source Python library from Google that uses large language models (LLMs) to extract structured information from unstructured text.
Instead of requiring domain-specific training, it uses prompts and examples to instruct LLMs on how to structure the data.
https://knowledgezone.co.in/posts/Google-LangExtract-68ee737e35590fd1b52bf506
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Discover how LangExtract turns URLs and plain text lists into structured data using LLMs. From Gutenberg books to API endpoints, the open‑source toolkit shows seamless extraction with gpt‑4o, Gemini 2.5 Flash, and Ollama. See the code, benchmarks, and tips for your own projects. #LangExtract #LLM #gpt4o #Ollama
🔗 https://aidailypost.com/news/how-langextract-uses-urls-text-lists-data-extraction-llms
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Discover how LangExtract turns URLs and plain text lists into structured data using LLMs. From Gutenberg books to API endpoints, the open‑source toolkit shows seamless extraction with gpt‑4o, Gemini 2.5 Flash, and Ollama. See the code, benchmarks, and tips for your own projects. #LangExtract #LLM #gpt4o #Ollama
🔗 https://aidailypost.com/news/how-langextract-uses-urls-text-lists-data-extraction-llms
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Meet #LangExtract - an #opensource #Python library!
Developers can now extract structured information from unstructured text using large language models such as the Gemini models.
Learn moreon #InfoQ 👉 https://bit.ly/45a1krY
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Meet #LangExtract - an #opensource #Python library!
Developers can now extract structured information from unstructured text using large language models such as the Gemini models.
Learn moreon #InfoQ 👉 https://bit.ly/45a1krY
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🧠 #Google ha rilasciato #LangExtract, una libreria Python open-source che trasforma testo non strutturato in dati strutturati.
👉 I dettagli: https://www.linkedin.com/posts/alessiopomaro_google-langextract-llm-activity-7359097710513111040-oTij
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✉️ 𝗦𝗲 𝘃𝘂𝗼𝗶 𝗿𝗶𝗺𝗮𝗻𝗲𝗿𝗲 𝗮𝗴𝗴𝗶𝗼𝗿𝗻𝗮𝘁𝗼/𝗮 𝘀𝘂 𝗾𝘂𝗲𝘀𝘁𝗲 𝘁𝗲𝗺𝗮𝘁𝗶𝗰𝗵𝗲, 𝗶𝘀𝗰𝗿𝗶𝘃𝗶𝘁𝗶 𝗮𝗹𝗹𝗮 𝗺𝗶𝗮 𝗻𝗲𝘄𝘀𝗹𝗲𝘁𝘁𝗲𝗿: https://bit.ly/newsletter-alessiopomaro -
🧠 #Google ha rilasciato #LangExtract, una libreria Python open-source che trasforma testo non strutturato in dati strutturati.
👉 I dettagli: https://www.linkedin.com/posts/alessiopomaro_google-langextract-llm-activity-7359097710513111040-oTij
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✉️ 𝗦𝗲 𝘃𝘂𝗼𝗶 𝗿𝗶𝗺𝗮𝗻𝗲𝗿𝗲 𝗮𝗴𝗴𝗶𝗼𝗿𝗻𝗮𝘁𝗼/𝗮 𝘀𝘂 𝗾𝘂𝗲𝘀𝘁𝗲 𝘁𝗲𝗺𝗮𝘁𝗶𝗰𝗵𝗲, 𝗶𝘀𝗰𝗿𝗶𝘃𝗶𝘁𝗶 𝗮𝗹𝗹𝗮 𝗺𝗶𝗮 𝗻𝗲𝘄𝘀𝗹𝗲𝘁𝘁𝗲𝗿: https://bit.ly/newsletter-alessiopomaro -
[LangExtract](https://developers.googleblog.com/en/introducing-langextract-a-gemini-powered-information-extraction-library/) has got me curious, but I don't get what makes it different from a [spacy-llm/prodigy](https://prodi.gy/docs/large-language-models) setup. Is it just that I am spared the effort of chunking long input and/or constructing output JSON from entities and offsets by writing the corresponding python code myself?...
Ah, one more difference is that langextract is #OpenSource whereas prodigy is not (?). (On the other hand, prodigy has a better integration with a correction+training workflow.)
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[LangExtract](https://developers.googleblog.com/en/introducing-langextract-a-gemini-powered-information-extraction-library/) has got me curious, but I don't get what makes it different from a [spacy-llm/prodigy](https://prodi.gy/docs/large-language-models) setup. Is it just that I am spared the effort of chunking long input and/or constructing output JSON from entities and offsets by writing the corresponding python code myself?...
Ah, one more difference is that langextract is #OpenSource whereas prodigy is not (?). (On the other hand, prodigy has a better integration with a correction+training workflow.)