Generative AI for Beginners - A 12-Lesson Course
Are you fascinated by the world of Artificial Intelligence and its endless possibilities? Are you a beginner eager to dive into the realm of Generative AI? If so, you're in the right place! In this blog post, we're excited to introduce you to a comprehensive 12-lesson course designed to teach you everything you need to know to start building Generative AI applicationsUse generative AI to extract structured data out of emails
One thing we regularly hear from clients is that they receive information that are key to their business such as order requests via email in an unstructured format and sometimes there are structured information within the body of those emails in a variety of table formats. In today’s fast-paced digital world, businesses need a way to automatically extract, structure, and integrate this information into their existing applications. Whether it’s leveraging AI-powered document processing, natural language processing (NLP), or intelligent automation, the right approach can transform email-based orders into structured, actionable data. In this blog, we’ll explore one such scenario where AI can be leveraged to extract information in tabular format that has been provided within an email. The emails contextually belong to a specific domain, but the tables are not with consistent headers or shapes. Sometimes in the body of one email there could be multiple tables. The problem Statement Extract tabular information with varying table formats from emails The typical approach to this problem involves rule-based processing, where individual tables are extracted and merged based on predefined logic. However, given the variety of email formats from hundreds or even thousands of different senders, maintaining such rule-based logic becomes increasingly complex and difficult to manage. A more optimal solution is leveraging the cognitive capabilities of generative AI, which can dynamically adapt to different table structures, column names, and formatting variations—eliminating the need for constant rule updates while improving accuracy and scalability. To create this sample code, I used below email with test data, with two tables with inconsistent column names. It is going to provide some upcoming trainings information. Please note the difference between the column headers: Hi there, Regarding the upcoming trainings, this is the list: Event Date Description of Event Length Grade 2025-01-21 Digital environments 20 hours 5 2025-03-01 AI for Industry A 10 hours 3 and some further events in below list Date Subject Duration Grade 2025-01-21 Digital environments 2 2 days 1 2025-03-01 AI for Industry B 2 weeks 4 These sessions are designed to be interactive and informative, so your timely participation is crucial. Please make sure to log in or arrive on time to avoid missing key insights. If you have any questions or need assistance, feel free to reach out. Looking forward to seeing you there! Thanks, Azadeh These are the two tables within the email, and we need to extract one consistent table format with all the rows from these two tables. Table 1 Event Date Description of Event Length Grade 2025-01-21 Digital environments 20 hours 5 2025-03-01 AI for Industry A 10 hours 3 Table 2 Date Subject Duration Grade 2025-01-21 Digital environments 2 2 days 1 2025-03-01 AI for Industry B 2 weeks 4 To extract the tabular data into one single table in json format, I am using python with below libraries installed in my environment: pandas beautifulsoup4 openai lxml The Code I use azure OpenAI service with a gpt 4o deployment. Below code is just one way of solving this type of problem and can be customized or improved to fit to other similar problems. I have provided some guidelines about merging the tables and column names similarity in the user prompt. This sample code is using an email message that is saved in 'eml' format in a local path, but the email library has other capabilities to help you connect to a mailbox and get the emails. import email import pandas as pd from bs4 import BeautifulSoup import os from openai import AzureOpenAI endpoint = os.getenv("ENDPOINT_URL", "https://....myendpointurl....openai.azure.com/") deployment = os.getenv("DEPLOYMENT_NAME", "gpt-4o") subscription_key = os.getenv("AZURE_OPENAI_API_KEY", "myapikey) # Initialize Azure OpenAI Service client with key-based authentication client = AzureOpenAI( azure_endpoint=endpoint, api_key=subscription_key, api_version="2024-05-01-preview", ) # Process email content with GPT-4 def extract_information(email_body, client): soup = BeautifulSoup(email_body, "html.parser") body = soup.get_text() print(body) #Prepare the chat prompt chat_prompt = [ { "role": "system", "content": [ { "type": "text", "text": "You are an AI assistant that is expert in extracting structured data from emails." } ] }, { "role": "user", "content": [ { "type": "text", "text": f"Extract the required information from the following email and format it as JSON and consolidate the tables using the common column names. For example the columns length and duration are the same and the columns Event and Subject are the same:\n\n{body}" } ] } ] messages = chat_prompt # Generate the completion completion = client.chat.completions.create( model=deployment, messages=messages, max_tokens=800, temperature=0.1, top_p=0.95, frequency_penalty=0, presence_penalty=0, stop=None, stream=False ) return completion.choices[0].message.content email_file_name = r'...path to your file....\Test Email with Tables.eml' with open(email_file_name, "r") as f: msg = email.message_from_file(f) email_body = "" for part in msg.walk(): if part.get_content_type() == "text/plain": email_body = part.get_payload(decode=True).decode() elif part.get_content_type() == "text/html": email_body = part.get_payload(decode=True).decode() extracted_info = extract_information(email_body, client) print(extracted_info) The output is: ``` [ { "Event": "Digital environments", "Date": "2025-01-21", "Length": "20 hours", "Grade": 5 }, { "Event": "AI for Industry A", "Date": "2025-03-01", "Length": "10 hours", "Grade": 3 }, { "Event": "Digital environments 2", "Date": "2025-01-21", "Length": "2 days", "Grade": 1 }, { "Event": "AI for Industry B", "Date": "2025-03-01", "Length": "2 weeks", "Grade": 4 } ] ``` Key points in the code: Read an email and extract the body Use a gen AI model with the right instructions prompt to complete the task Gen AI will follow the instructions and create a combined consistent table Get the output in the right format, e.g. 'json' I hope you find this blog post helpful, and you can apply it to your use case/domain. Or you can simply get the idea of how to use generative AI to solve a problem, instead of building layers of custom logic.Laying the Groundwork: Key Elements for Effective AI Deployment
This post explores the essential components required to build production-ready AI solutions, including the importance of solid architectural foundations, robust data management practices, and responsible AI development. We discuss the complexities of integrating AI into existing systems, the need for continuous evaluation to ensure optimal performance, and the ethical considerations vital for deploying AI responsibly. Whether you're starting your AI journey or looking to refine your approach, this post provides valuable insights into creating scalable, reliable, and ethical AI solutions.Microsoft Semantic Kernel and AutoGen: Open Source Frameworks for AI Solutions
Explore Microsoft’s open-source frameworks, Semantic Kernel and AutoGen. Semantic Kernel enables developers to create AI solutions across various domains using a single Large Language Model (LLM). AutoGen, on the other hand, uses AI Agents to perform smart tasks through agent dialogues. Discover how these technologies serve different scenarios and can be used to build powerful AI applications.The Future of AI: Harnessing AI for E-commerce - personalized shopping agents
Explore the development of personalized shopping agents that enhance user experience by providing tailored product recommendations based on uploaded images. Leveraging Azure AI Foundry, these agents analyze images for apparel recognition and generate intelligent product recommendations, creating a seamless and intuitive shopping experience for retail customers.Microsoft Learn Launches a New Series on Generative AI for Innovators
Do you have a great idea for a startup, but don’t know how to turn it into reality? Do you want to learn how to use the latest AI technologies to create innovative products and services? If yes, then you should check out the new series on Microsoft Learn: Generative AI for Innovators. This series will teach you how to use powerful AI tools such as GPT-4 and DALL-E, to generate novel and creative solutions for various domains and challenges. You will also learn how to use AI to quickly prototype and test your product, and how to build a sustainable and profitable business model for your startup. Whether you are a beginner or an expert, this series will help you unleash your entrepreneurial potential and make your startup dreams come true. Don’t miss this opportunity to learn from the best and join the generative AI revolution!
