480626dc99
…tch]: import models from community ran ```bash git grep -l 'from langchain\.chat_models' | xargs -L 1 sed -i '' "s/from\ langchain\.chat_models/from\ langchain_community.chat_models/g" git grep -l 'from langchain\.llms' | xargs -L 1 sed -i '' "s/from\ langchain\.llms/from\ langchain_community.llms/g" git grep -l 'from langchain\.embeddings' | xargs -L 1 sed -i '' "s/from\ langchain\.embeddings/from\ langchain_community.embeddings/g" git checkout master libs/langchain/tests/unit_tests/llms git checkout master libs/langchain/tests/unit_tests/chat_models git checkout master libs/langchain/tests/unit_tests/embeddings/test_imports.py make format cd libs/langchain; make format cd ../experimental; make format cd ../core; make format ``` |
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| .. | ||
| python_lint | ||
| tests | ||
| .gitignore | ||
| LICENSE | ||
| pyproject.toml | ||
| README.md | ||
python-lint
This agent specializes in generating high-quality Python code with a focus on proper formatting and linting. It uses black, ruff, and mypy to ensure the code meets standard quality checks.
This streamlines the coding process by integrating and responding to these checks, resulting in reliable and consistent code output.
It cannot actually execute the code it writes, as code execution may introduce additional dependencies and potential security vulnerabilities. This makes the agent both a secure and efficient solution for code generation tasks.
You can use it to generate Python code directly, or network it with planning and execution agents.
Environment Setup
- Install
black,ruff, andmypy:pip install -U black ruff mypy - Set
OPENAI_API_KEYenvironment variable.
Usage
To use this package, you should first have the LangChain CLI installed:
pip install -U langchain-cli
To create a new LangChain project and install this as the only package, you can do:
langchain app new my-app --package python-lint
If you want to add this to an existing project, you can just run:
langchain app add python-lint
And add the following code to your server.py file:
from python_lint import agent_executor as python_lint_agent
add_routes(app, python_lint_agent, path="/python-lint")
(Optional) Let's now configure LangSmith. LangSmith will help us trace, monitor and debug LangChain applications. LangSmith is currently in private beta, you can sign up here. If you don't have access, you can skip this section
export LANGCHAIN_TRACING_V2=true
export LANGCHAIN_API_KEY=<your-api-key>
export LANGCHAIN_PROJECT=<your-project> # if not specified, defaults to "default"
If you are inside this directory, then you can spin up a LangServe instance directly by:
langchain serve
This will start the FastAPI app with a server is running locally at http://localhost:8000
We can see all templates at http://127.0.0.1:8000/docs We can access the playground at http://127.0.0.1:8000/python-lint/playground
We can access the template from code with:
from langserve.client import RemoteRunnable
runnable = RemoteRunnable("http://localhost:8000/python-lint")