autodeploydocker 2025.12.21144536

A new package that simplifies the deployment of Docker and Docker Compose applications to servers without requiring configuration changes or causing downtime. It automates the process, making it acces

autodeploydocker

autodeploydocker is a tiny Python package that makes it easy to automate the deployment of Docker and Docker‑Compose applications to remote servers.
It drives a language model (LLM) to generate the exact deployment steps, ensuring zero‑downtime deployments without the need for manual configuration changes.

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Installation

pip install autodeploydocker

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Quick start

from autodeploydocker import autodeploydocker

# Minimal usage – the package will create a ChatLLM7 instance for you.
response = autodeploydocker(
    user_input="Deploy the latest version of my web‑app using Docker Compose on server X."
)

print(response)   # -> list of strings extracted from the LLM response

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Function signature

def autodeploydocker(
    user_input: str,
    api_key: Optional[str] = None,
    llm: Optional[BaseChatModel] = None,
) -> List[str]:
    ...

Parameter	Type	Description
user_input	str	The natural‑language description of the deployment you want to perform.
api_key	Optional[str]	API key for the default ChatLLM7 backend. If omitted, the function reads LLM7_API_KEY from the environment.
llm	Optional[BaseChatModel]	A LangChain‑compatible LLM instance. If provided, it overrides the default ChatLLM7.

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How it works

The function builds a system prompt (system_prompt) and a human prompt (human_prompt) and sends them to the selected LLM.
The LLM’s output is then validated against a regular‑expression pattern defined in prompts.pattern.
If the output matches, the extracted data (a List[str]) is returned; otherwise a RuntimeError is raised.

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Using a custom LLM

You can safely supply any LangChain LLM that follows the BaseChatModel interface.

OpenAI

from langchain_openai import ChatOpenAI
from autodeploydocker import autodeploydocker

llm = ChatOpenAI(model="gpt-4o")   # configure as you need
response = autodeploydocker(
    user_input="Deploy the staging environment with Docker Compose.",
    llm=llm,
)

Anthropic

from langchain_anthropic import ChatAnthropic
from autodeploydocker import autodeploydocker

llm = ChatAnthropic(model="claude-3-5-sonnet-20240620")
response = autodeploydocker(
    user_input="Roll out a new version of the API service.",
    llm=llm,
)

Google Generative AI

from langchain_google_genai import ChatGoogleGenerativeAI
from autodeploydocker import autodeploydocker

llm = ChatGoogleGenerativeAI(model="gemini-1.5-pro")
response = autodeploydocker(
    user_input="Update the production stack using Docker Compose.",
    llm=llm,
)

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Default LLM – ChatLLM7

If you do not pass an llm instance, autodeploydocker falls back to ChatLLM7 from the langchain_llm7 package:

pip install langchain_llm7

ChatLLM7 works out‑of‑the‑box with a free tier that is sufficient for most use cases.
To use a personal key, set the environment variable LLM7_API_KEY or pass the key directly:

response = autodeploydocker(
    user_input="Deploy …",
    api_key="my-llm7-key",
)

You can obtain a free API key by registering at https://token.llm7.io/.

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Contributing & Support

If you encounter any issues, have a feature request, or want to contribute, please open an issue on GitHub:

https://github....

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Author

Eugene Evstafev – chigwell
✉️ Email: [e-mail]

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License

This project is licensed under the MIT License.