FastAPI for AI Engineers - Part 8: Uploading Files with FastAPI

A developer's tutorial series on FastAPI for AI engineers covers file uploads, demonstrating how to use FastAPI's File and UploadFile classes with python-multipart. The post shows how to receive, read, and save uploaded files, which are essential for AI applications like document Q&A systems and resume analyzers. It includes code examples and explains the workflow for processing uploaded files in AI pipelines.

In the previous article, we learned how to secure our APIs using JWT Authentication and protect routes from unauthorized access. Now let's explore another feature used in almost every AI application— file uploads . If you've built applications like ChatGPT, document Q&A systems, resume analyzers, legal contract reviewers, or medical report analyzers, one thing is common across all of them: The user uploads a file. Without file uploads, there is nothing for the AI model to process. If you haven't read the previous article, check it out first to continue the series: Protecting routes with JWT Tokens https://dev.to/zeroshotanufastapi-for-ai-engineers-part-7-protecting-routes-with-jwt-tokens-273p Consider some popular AI applications: The workflow usually looks like this: User │ ▼ Upload File │ ▼ FastAPI │ ▼ Save / Read File │ ▼ Process using AI FastAPI makes uploading files extremely simple. FastAPI uses python-multipart to process uploaded files. Install it using: pip install python-multipart FastAPI provides two important classes: File UploadFile Let's import them. python from fastapi import FastAPI, File, UploadFile app = FastAPI python @app.post "/upload" def upload file file: UploadFile : return { "filename": file.filename } Run the application. Open Swagger UI. Click POST /upload . You'll notice FastAPI automatically provides a file picker. Upload a file. Response: { "filename": "resume.pdf" } Our API successfully received the uploaded file. You might wonder: Why didn't we simply use a string or bytes? FastAPI provides the UploadFile class because it contains useful information about the uploaded file. Some commonly used attributes are: file.filename Returns: resume.pdf file.content type Returns: application/pdf await file.read Reads the file contents. These attributes become extremely useful when building AI applications. Suppose we want to know how many bytes were uploaded. python @app.post "/upload" async def upload file file: UploadFile : contents = await file.read return { "filename": file.filename, "size": len contents } Example response: { "filename": "contract.pdf", "size": 254321 } Notice that we changed the function to: async def This is because file.read is an asynchronous operation. In many applications, we don't just read the file. We save it for later processing. python @app.post "/upload" async def upload file file: UploadFile : contents = await file.read with open file.filename, "wb" as f: f.write contents return { "message": "File uploaded successfully." } contents = await file.read Reads the uploaded file into memory. with open file.filename, "wb" Creates a new file. The "wb" mode means: Binary mode is important because PDFs, images, Word documents, and many other files are not plain text. f.write contents Writes the uploaded data to disk. Suppose a user uploads a legal contract. contract.pdf │ ▼ FastAPI Upload Endpoint │ ▼ Save PDF │ ▼ Extract Text │ ▼ Create Embeddings │ ▼ Store in Vector Database │ ▼ Ask Questions This is the same workflow followed by many Retrieval-Augmented Generation RAG applications. Similarly, Resume Analyzer: Resume.pdf │ ▼ Extract Text │ ▼ Skill Extraction │ ▼ ATS Score Medical Report Analyzer: Blood Report.pdf │ ▼ OCR / Text Extraction │ ▼ LLM Analysis │ ▼ Health Summary File uploads are the entry point for almost every document-based AI application. FastAPI also allows uploading files as raw bytes. python @app.post "/upload" async def upload file: bytes = File : return { "size": len file } Although this works, it is rarely used for large files. UploadFile is generally preferred because: For most production applications, UploadFile is the recommended choice. python from fastapi import FastAPI, UploadFile app = FastAPI @app.post "/upload" async def upload file file: UploadFile : contents = await file.read with open file.filename, "wb" as f: f.write contents return { "filename": file.filename, "content type": file.content type, "size": len contents , "message": "Upload Successful" } User Uploads File │ ▼ FastAPI Receives Upload │ ▼ UploadFile Object Created │ ▼ Read File │ ▼ Save File │ ▼ AI Processing Begins Uploading files is one of the most important capabilities of modern AI backends. Whether you're building a chatbot over PDFs, a resume analyzer, a legal contract assistant, or a medical report analyzer, every application begins with accepting user files. Today we learned how to: UploadFile objectIt's been some time since I've uploaded. We will continue with our FastAPI series in the upcoming posts.