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Requirements.txt

As explained in Build Financial Software with Generative AI (From Scratch)

requirements.txt is a conventional Python configuration file that declares installable package dependencies and, optionally, their versions.

requirements.txt is the conventional Python file for listing the packages an application needs. It gives local development tools and Docker builds a shared dependency checklist instead of relying on packages already installed on a server. section 3.2.3 section 4.4.1

A practical dependency workflow looks like this:

  • Install the packages required by the project, such as FastAPI, Uvicorn, pytest, and httpx. section 4.4.1 section 4.3
  • Generate the file with pip freeze > requirements.txt, or use an IDE that supports requirements-file management. section 4.4.1 section 4.3
  • Review the generated entries and confirm that intended packages, including FastAPI and Uvicorn, are present; transitive dependencies may also appear. section 4.4.1
  • Copy the file into a Docker image and run pip during the image build to install its packages. section 3.2.3

FastAPI container setup

After installing Uvicorn alongside FastAPI, generate requirements.txt and check that both packages appear. A Dockerfile can then copy the file into the image and run pip to install the declared dependencies before starting the API. section 3.2.3 section 4.4.1

How Build Financial Software with Generative AI (From Scratch) covers it

How deep it goes
Explains generation, review, and Docker installation, but does not detail the file syntax or dependency-resolution internals.
Examples
Uses FastAPI projects containing Uvicorn, pytest, and httpx, then generates and reviews a requirements.txt for Docker.
What it assumes
Assumes basic Python package installation and introduces requirements generation while connecting it to Docker image setup.
What you can do after
Operate/configure a reproducible Dockerized Python setup by generating, checking, and installing declared project dependencies.
How it connects
Connects Python package management with pip, Docker, Dockerfiles, FastAPI, Uvicorn, and IDE tooling.

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