This repository contains the following packages with Azure integrations with LangChain:

• langchain-azure-ai
• langchain-azure-dynamic-sessions
• langchain-sqlserver

Note: This repository will replace all Azure integrations currently present in the langchain-community package. Users are encouraged to migrate to this repository as soon as possible.

Hi there! Thank you for even being interested in contributing to LangChain-Azure. As an open-source project in a rapidly developing field, we are extremely open to contributions, whether they involve new features, improved infrastructure, better documentation, or bug fixes.

To contribute to this project, please follow the "fork and pull request" workflow.

Please follow the checked-in pull request template when opening pull requests. Note related issues and tag relevant maintainers.

Pull requests cannot land without passing the formatting, linting, and testing checks first. See Testing and Formatting and Linting for how to run these checks locally.

It's essential that we maintain great documentation and testing. If you:

• Fix a bug
  • Add a relevant unit or integration test when possible.
• Make an improvement
  • Update unit and integration tests when relevant.
• Add a feature
  • Add unit and integration tests.

If there's something you'd like to add or change, opening a pull request is the best way to get our attention. Please tag one of our maintainers for review.

This project utilizes Poetry v1.7.1+ as a dependency manager.

❗Note: Before installing Poetry, if you use Conda, create and activate a new Conda env (e.g. conda create -n langchain python=3.9)

Install Poetry: documentation on how to install it.

❗Note: If you use Conda or Pyenv as your environment/package manager, after installing Poetry, tell Poetry to use the virtualenv python environment (poetry config virtualenvs.prefer-active-python true)

This repository contains three packages with Azure integrations with LangChain:

• langchain-azure-ai
• langchain-azure-dynamic-sessions
• langchain-sqlserver

Each of these has its own development environment. Docs are run from the top-level makefile, but development is split across separate test & release flows.

If you plan on contributing to LangChain-Google code or documentation, it can be useful to understand the high level structure of the repository.

LangChain-Azure is organized as a monorepo that contains multiple packages.

Here's the structure visualized as a tree:

.
├── libs
│   ├── azure-ai
│   ├── azure-dynamic-sessions
│   ├── langchain-sqlserver

Install development requirements (for running langchain, running examples, linting, formatting, tests, and coverage):

poetry install --with lint,typing,test,test_integration

Then verify dependency installation:

make test

If during installation you receive a WheelFileValidationError for debugpy, please make sure you are running Poetry v1.6.1+. This bug was present in older versions of Poetry (e.g. 1.4.1) and has been resolved in newer releases. If you are still seeing this bug on v1.6.1+, you may also try disabling "modern installation" (poetry config installer.modern-installation false) and re-installing requirements. See this debugpy issue for more details.

Formatting for this project is done via ruff.

To run formatting for a library, run the same command from the relevant library directory:

cd libs/{LIBRARY}
make format

Additionally, you can run the formatter only on the files that have been modified in your current branch as compared to the master branch using the format_diff command:

make format_diff

This is especially useful when you have made changes to a subset of the project and want to ensure your changes are properly formatted without affecting the rest of the codebase.

Linting for this project is done via a combination of ruff and mypy.

To run linting for docs, cookbook and templates:

make lint

To run linting for a library, run the same command from the relevant library directory:

cd libs/{LIBRARY}
make lint

In addition, you can run the linter only on the files that have been modified in your current branch as compared to the master branch using the lint_diff command:

make lint_diff

This can be very helpful when you've made changes to only certain parts of the project and want to ensure your changes meet the linting standards without having to check the entire codebase.

We recognize linting can be annoying - if you do not want to do it, please contact a project maintainer, and they can help you with it. We do not want this to be a blocker for good code getting contributed.

Spellchecking for this project is done via codespell. Note that codespell finds common typos, so it could have false-positive (correctly spelled but rarely used) and false-negatives (not finding misspelled) words.

To check spelling for this project:

make spell_check

To fix spelling in place:

make spell_fix

If codespell is incorrectly flagging a word, you can skip spellcheck for that word by adding it to the codespell config in the pyproject.toml file.

[tool.codespell]
...
# Add here:
ignore-words-list =...

All of our packages have unit tests and integration tests, and we favor unit tests over integration tests.

Unit tests run on every pull request, so they should be fast and reliable.

Integration tests run once a day, and they require more setup, so they should be reserved for confirming interface points with external services.

Unit tests cover modular logic that does not require calls to outside APIs. If you add new logic, please add a unit test. In unit tests we check pre/post processing and mocking all external dependencies.

To install dependencies for unit tests:

poetry install --with test

To run unit tests:

make test

To run unit tests in Docker:

make docker_tests

To run a specific test:

TEST_FILE=tests/unit_tests/test_imports.py make test

Integration tests cover logic that requires making calls to outside APIs (often integration with other services). If you add support for a new external API, please add a new integration test.

Warning: Almost no tests should be integration tests.

Tests that require making network connections make it difficult for other developers to test the code.

Instead favor relying on responses library and/or mock.patch to mock requests using small fixtures.

To install dependencies for integration tests:

poetry install --with test,test_integration

To run integration tests:

make integration_tests

For detailed information on how to contribute, see LangChain contribution guide.