Extract named entities from text
Identify and extract people, organizations, locations, dates, and other entities from text using LLMs.
Automatically translate content into multiple languages using LLMs.
Text · Notebook · Updated
Colab runs it in your browser. If it calls a model provider, it asks for that provider's API key in a cell.
You add computed columns for each target language. Translations are generated automatically when you insert new content and cached for future queries.
pip install pixeltable openai
OPENAI_API_KEYopenai.chat_completions()pxt.create_table()add_computed_column()insert()# Add Spanish translation columnspanish_prompt = ('Translate the following text to Spanish. Return only the translation, no explanations:\n\n'+ content.text_en)content.add_computed_column(response_es=chat_completions(messages=[{'role': 'user', 'content': spanish_prompt}],model='gpt-4o-mini',))content.add_computed_column(text_es=content.response_es.choices[0].message.content)
One cell of the notebook, copied as it is. Open it in Colab for the rest.
Identify and extract people, organizations, locations, dates, and other entities from text using LLMs.
Insert a URL as a document, then compare chunking strategies on it
Break PDFs and documents into searchable chunks for retrieval-augmented generation (RAG) pipelines.
Transform office documents into searchable, analyzable text data.