Using OpenAI's File Input Feature to Chat with PDFs in Python

Join this channel to get access to perks: https://www.youtube.com/channel/UCcA2nhdC0wzqyv9x1lk5NnA/join 👉 MCP Protocol Tutorial Playlist: https://www.youtube.com/watch?v=G9Ct5Dl4D3k&list=PLOocymQm7YWZuDHVYSvF513AxTfMs_tFC 👉 Generative AI Tutorial Playlist: https://www.youtube.com/watch?v=32Z7r8LcY8o&list=PLOocymQm7YWYODnubEmdLTfuj-T7EpOuw 🚀 Welcome to the second video in the OpenAI Built-in Tools series! In this detailed tutorial, we explore the File Input Feature — one of the most powerful and flexible ways to interact with the OpenAI API. If you’ve ever wanted to upload a PDF, extract content, and query it using AI, this video is for you. In the first video, we covered the Web Search feature — how to make your model access the internet, fetch real-time data, and generate location-based answers. Now, in this video, we’ll move one step further to learn how to make your models read and understand files such as PDFs, handle token usage efficiently, and query your uploaded documents interactively using Python. 📘 What You’ll Learn in This Video: ✅ How to use OpenAI’s File Input Feature to upload and analyze documents ✅ Step-by-step walkthrough of uploading PDF files to the OpenAI Dashboard or via code ✅ How to retrieve and use File IDs in your API calls ✅ Understanding token consumption and why PDFs can consume thousands of tokens ✅ How OpenAI models process documents — combining text extraction and page images for better accuracy ✅ How to create a chat-like loop in Python to continuously ask questions from the same document ✅ Real-world demonstration with a network automation guide PDF ✅ Tips for reducing token usage and optimizing performance ✅ Limitations: File size (32 MB), page count (100 pages), and how to handle them smartly 🧠 Key Insights from This Tutorial: When you upload a file using the OpenAI File Input API, the model automatically embeds both extracted text and image representations of each page. This ensures higher accuracy, especially for documents that include tables, charts, or diagrams — but it also significantly increases token usage. In this demo, even a 17-page PDF consumed around 12,000 tokens per query. You’ll see how to track token usage, interpret model responses, and structure your API calls for efficiency. The video also demonstrates how to: Build a Python loop to continuously query the same file Get structured JSON responses and readable text outputs Use file purpose = "user_data" for general document uploads Debug file upload issues by checking the Storage dashboard in OpenAI ⚙️ Tools and Technologies Used: OpenAI API (v4.1) Python for scripting and API integration OpenAI Dashboard (Storage tab) for managing uploaded files Example PDF file: “guide_v1.pdf” (Network Automation Lab Guide) 💡 Best Practices: Always start with small PDFs when testing (less than 5 pages). Monitor token usage to avoid unexpected costs. Prefer clean text-based PDFs over image-heavy files. Use response.model_dump_json(indent=2) for readable output in code. Keep the same file loaded in your session to avoid repeated uploads. Understand the difference between File Input, File Search, and Assistants API to use them effectively. 🔍 Coming Up Next: In the next video, we’ll explore the File Search Feature — where you can index multiple documents and perform semantic search or context-aware queries across them. This feature complements File Input and is perfect for building AI-powered document assistants, chatbots for knowledge bases, and research tools. 💬 Connect & Share: If you found this video helpful, please like, comment, and subscribe for more tutorials on OpenAI APIs, automation, and AI integrations. Have questions? Drop them in the comments — I read every one! #OpenAI #OpenAIApi #ChatGPT #FileInputFeature #OpenAITools #AIIntegration #PythonAutomation #AIinPython #OpenAIDocumentProcessing #AIDocumentReader #TechTutorial #ArtificialIntelligence #DeveloperTutorial #OpenAI4_1 #OpenAIFeatures #FileUploadAPI #OpenAISDK #LearnAI #OpenAI2025 #ChatGPTTutorial