Gemini Business Card Merge: My AI Workflow

A developer built a workflow using Google's Gemini 1.5 Flash model to merge front and back images of bilingual business cards into a single JSON record, replacing fragile regex rules with a prompt that instructs the LLM to combine fields like name and company into unified strings. The system holds the first image in a user_states dictionary with a 5-minute timeout while waiting for the second side, then sends both images simultaneously to Gemini for semantic merging.

Gemini Business Card Merge: My AI Workflow Instead of writing a thousand fragile regex rules to guess if two names are the same, I just let the user tell me. I implemented a "waiting room" state. When the first side hits, the bot asks, "Is there a back side?" If the user says yes, the bot holds onto that image in a user states dictionary with a 5-minute timeout because users have the attention span of a goldfish . The real magic happens when both images are fed into Gemini /en/tags/gemini/ simultaneously. Trying to merge "Wang Daming" and "David Wang" via code is a recipe for a headache. Sending both images in one prompt and letting the LLM handle the semantic merge is the only sane way to do this. Here is the prompt logic I used to force Gemini to combine the data into a single clean JSON object: You are an expert OCR and data extraction agent. I am providing you with two images: the front and back of a single business card. Your task: 1. Extract all professional information from both images. 2. Merge the data into a single unified record. 3. If a field like Name or Company appears in both languages, combine them into a single string e.g., "Chinese Name / English Name" . 4. Ensure the output strictly follows the provided JSON schema. 5. Ignore any background noise or irrelevant text. Return only the final merged JSON. For the implementation, I used gemini-1.5-flash because it's fast enough that the user doesn't forget why they're waiting. I just wrap both image parts into the generate content call. python def generate json from two images front img: PIL.Image.Image, back img: PIL.Image.Image, prompt: str - object: model = GenerativeModel "gemini-1.5-flash", generation config={ "response mime type": "application/json", "response schema": NAMECARD SCHEMA }, front part = Part.from data data=pil to bytes front img , mime type="image/jpeg" back part = Part.from data data=pil to bytes back img , mime type="image/jpeg" response = model.generate content prompt, front part, back part , stream=False, labels={"client id": "namecard"} return response This approach turns a messy data-cleaning chore into a simple prompt engineering task. No more duplicate entries, no more manual deleting—just a clean, consolidated record. Next Understanding LLM Context and Hallucinations → /en/threads/2244/