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Our extraction agent in LlamaExtract has gotten a huge upgrade in capabilities 🔥- it can read a complex, multimodal document and extract into a Pydantic schema with 3+ layers of nesting. A sneak peek on what’s possible: I fed in an equity research report which is completely… https://t.co/QSs1YOq2EU
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"text": "Our extraction agent in LlamaExtract has gotten a huge upgrade in capabilities 🔥- it can read a complex, multimodal document and extract into a Pydantic schema with 3+ layers of nesting.\n\nA sneak peek on what’s possible: I fed in an equity research report which is completely tiled over with text, table scans, and charts, and extracted out a fully structured research summary with the following nested fields:\n\n1. Report metadata - high-level company info like name, ticker, price\n2. Company overview - describe business models, segments, geographic breakdown\n3. Financial metrics - key ratios like EBITDA + margins/revenue growth over time\n4. Investment thesis\n5. Valuation\n6. Risk factors\n\nHuge shoutout to @NeerajPrad for all the continued work on this. \n\nLlamaExtract: https://t.co/XYZmx5TFz8\nExample notebooks here: https://t.co/sjjs27lcVL",
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