Extracting structured data from PDFs using Gemini 2.0 and Genkit in Firebase: Web Architecture & Implementation Guide
Original: https://codingcat.dev/post/firebase-2025-02-gemini-genkit-pdf-structured-data Building AI features in TypeScript or Go quickly turns into spaghetti when you try to bolt structured output validation, tool calling, and OpenTelemetry tracing onto raw model API calls. Following the announcement of Extracting structured data from PDFs using Gemini 2.0 and Genkit by Peter Friese on the…
Google released Gemini 2.0, and after reading a blog post about parsing PDFs using Gemini 2.0, the author decided to rebuild the sample using Genkit. Genkit is an AI integration framework that simplifies implementing AI features for web applications. It provides strongly typed flows, dotprompt templates, local developer UI trace inspection, and production observability. The author focuses on Genkit for Node.js, but the framework supports Go (alpha) and Python (under development) runtimes.
To use Gemini 2.0 Flash in Genkit, install Genkit and the Google AI SDK plugin. Instantiate Genkit and define a default model, which will be used for all subsequent API calls. The author provides an example of Extracting structured data from PDFs using Gemini 2.0 and Genkit in a simple command-line utility written in TypeScript. The utility processes an invoice PDF and extracts various details such as invoice number, date, line items, and summary.
To enforce a specific data structure, the author utilizes Zod schemas in Genkit. By defining a schema, the generated output will conform to the specified structure, providing a consistent format for further processing.
Written by urgent.news from Dev.to's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
