FastAPI File Uploads: The RAG Ingestion Pattern You Need

Key Takeaways

Hey, and welcome back. So far, every endpoint we have built receives data either through the URL or through a JSON body, but there are three more ways data comes into your API: headers, forms, and file uploads. And if you're building an ingestion endpoint for current track system, you're going to need all three. Let's break them down one by one. Look at the three cards on the screen. These are the three request patterns you need beyond path, query, and JSON body. First, headers. On the left, notice the example. It's a get request with an authorization bearer token and a custom X-Request-ID. That's typically for API authentication and request tracing. Second, form data. In the middle, the example shows a post with application form sending query parameters as a flat key-value string. And third, file upload. On the right, that's multipart boundary in the example is what happens under the hood when you send a PDF. You don't write that manually. FastAPI handles it. Each of these has specific use cases listed at the bottom. Know which tool to reach out for you, and you will handle any request pattern your API needs. Now, headers travel with every HTTP request, but outside the body. The most common use case in AI backends is passing an API key. Look at the code. You import headers from FastAPI and use it as default value for your parameter. FastAPI automatically maps the snake case parameter name, your API key, to HTTP header. Notice the endpoint returns an empty document list if the key is valid and raises a 401 if it's not. Clean one-liner authentication check. We go much deeper on authentication in module four, but this is the pattern everything builds on. And now, form data is a different encoding from JSON. Instead of a JSON object in the body, the client sends a flat key-value pair. Look at the code. You import from FastAPI and use it instead of Pydantic model. Username and password come in as form fields. Simple and readable. And one rule you need to remember is it's on the slide. You cannot mix form field and adjacent body in the same endpoint. FastAPI doesn't allow it. If you need both structured data and file together, that's handled differently, which is exactly what the next slide shows. For file uploads, FastAPI gives you upload file. Import it along with file. Use them as a parameter default and you are set. Look at the code. Three things you will use every time. file.filename gives you the original name of the file. Three things you will use every time. First, file.filename. It gives you the original name of the file the caller uploaded. Second, file.content_type. It tells you what kind of file it is. And third, await file.read. It gives you the raw bytes. Notice the await. Reading file content is input-output bound, so it is async. The return shows all three plus the size. Once you have the bytes, you can pass them straight into your ingestion pipeline. And here's the slide that ties everything together for current track system. A real ingestion endpoint needs a file and a metadata in the same request. Look at the code. The file comes in as upload file. Title is a required form field and source is optional. FastAPI handles the multipart encoding automatically. You don't configure anything extra. The function reads the file, returns the file name, title, source, and the size. This is exactly what the {slash} documents {slash} ingest endpoint in module six will look like. You already know how it works before you get there. A quick recap. Headers carry metadata and authentication token. Use headers to read them. And form data handles key-value pair from form clients. Use form. File upload use upload file to get file content and the metadata. And when you need both a file and some metadata in the same request, you can combine file and form fields cleanly. In the next video, we will look at the HTTP methods through real JNAI examples. Get, post, put, patch, delete in realistic AI back-end scenario instead of abstract route. See you there.