Ingest a web page
Insert a URL as a document, then compare chunking strategies on it
Identify and extract people, organizations, locations, dates, and other entities from text 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 use structured output to get entities in a consistent JSON format. The entities are stored as JSON columns that you can query and filter.
pip install pixeltable openai
OPENAI_API_KEYopenai.chat_completions()pxt.create_table()add_computed_column()insert()# Add entity extraction columnextraction_prompt = ('Extract all named entities from the following text:\n\n'+ articles.content)articles.add_computed_column(extraction_response=chat_completions(messages=[{'role': 'user', 'content': extraction_prompt}],model='gpt-4o-mini',model_kwargs={'response_format': entity_schema},))
One cell of the notebook, copied as it is. Open it in Colab for the rest.
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.
Generate concise summaries of long text, articles, or documents using large language models.