django-idea-analyzer

django-idea-analyzer is a tiny helper library that leverages LLM7 (or any LangChain‑compatible LLM) to evaluate textual descriptions of Django web‑application ideas.
It returns a structured list of feedback items covering:

• Feasibility of the proposed feature
• Typical implementation challenges
• Recommended best practices for a Django‑centric solution

The package lets you get a quick sanity‑check on a Django concept before you start writing code.

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Installation

pip install django_idea_analyzer

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Quick start

from django_idea_analyzer import django_idea_analyzer

# A short description of the idea you want to evaluate
idea = """
I want a blogging platform where users can write posts,
add tags, and have a realtime comment section powered by websockets.
"""

# Use the default LLM7 backend (API key taken from LLM7_API_KEY env var)
feedback = django_idea_analyzer(user_input=idea)

print(feedback)

Typical output (list of strings):

[
  "The core blog model is straightforward in Django and can be built with the standard ORM.",
  "Using Django Channels for realtime comments is feasible, but you need to configure a channel layer (e.g., Redis).",
  "Tagging can be implemented with a ManyToMany field or a dedicated package like django‑tag‑git.",
  "Consider adding pagination and caching for performance on large comment streams.",
  "Make sure to handle authentication and permissions for comment creation."
]

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API reference

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

Parameter	Type	Description
user_input	str	The textual description of the Django feature or project idea you want to analyze.
llm	Optional[BaseChatModel]	A LangChain LLM instance. If omitted, the function creates a ChatLLM7 instance automatically.
api_key	Optional[str]	API key for LLM7. If not supplied, the function reads LLM7_API_KEY from the environment.

The function returns a list of feedback strings extracted from the LLM response.

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Using a custom LLM

You can plug any LangChain‑compatible chat model instead of the default LLM7. This is handy if you prefer OpenAI, Anthropic, Google Gemini, or a self‑hosted model.

OpenAI

from langchain_openai import ChatOpenAI
from django_idea_analyzer import django_idea_analyzer

my_llm = ChatOpenAI(model="gpt-4o-mini")
response = django_idea_analyzer(user_input=idea, llm=my_llm)

Anthropic

from langchain_anthropic import ChatAnthropic
from django_idea_analyzer import django_idea_analyzer

my_llm = ChatAnthropic(model="claude-3-haiku-20240307")
response = django_idea_analyzer(user_input=idea, llm=my_llm)

Google Generative AI

from langchain_google_genai import ChatGoogleGenerativeAI
from django_idea_analyzer import django_idea_analyzer

my_llm = ChatGoogleGenerativeAI(model="gemini-1.5-flash")
response = django_idea_analyzer(user_input=idea, llm=my_llm)

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LLM7 default configuration

• Package: langchain_llm7 – pip install langchain-llm7
• Default model: The free tier of LLM7 provides generous limits suitable for most development and testing scenarios.
• Obtaining an API key: Register at https://token.llm7.io/ to receive a free key.
• Overriding the key: Pass it directly via the api_key argument or set the environment variable LLM7_API_KEY.

export LLM7_API_KEY="your-llm7-api-key"

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Contributing & Support

• Issue tracker: https://github.com/chigwell/django_idea_analyzer/issues
• Author: Eugene Evstafev
• Email: [email protected]
• GitHub: https://github.com/chigwell

Feel free to open issues, submit pull requests, or ask questions. Happy coding!

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License

This project is licensed under the MIT License. See the LICENSE file for details.