data-convertible
A Python package that provides a structured and reliable way to process user input related to common developer utilities such as JSON, Base64, URL, and hash operations. It uses an LLM to interpret user requests and return formatted, validated outputs, ensuring consistency and correctness in the results.
Installation
You can install the package via pip:
pip install data_convertible
Usage
Here's a basic example of how to use the package:
from data_convertible import data_convertible
# Process user input
response = data_convertible("Convert 'hello' to base64")
print(response)
Parameters
user_input(str): The user input text to process.llm(Optional[BaseChatModel]): The LangChain LLM instance to use. If not provided, the default ChatLLM7 will be used.api_key(Optional[str]): The API key for LLM7. If not provided, the environment variableLLM7_API_KEYwill be used, or a default free tier key will be used.
Using a Custom LLM
You can pass your own LangChain LLM instance if you want to use another LLM provider. For example, to use OpenAI:
from langchain_openai import ChatOpenAI
from data_convertible import data_convertible
llm = ChatOpenAI()
response = data_convertible("Validate this JSON: {'name': 'John'}", llm=llm)
To use Anthropic:
from langchain_anthropic import ChatAnthropic
from data_convertible import data_convertible
llm = ChatAnthropic()
response = data_convertible("Encode this URL: example.com?q=test", llm=llm)
To use Google Generative AI:
from langchain_google_genai import ChatGoogleGenerativeAI
from data_convertible import data_convertible
llm = ChatGoogleGenerativeAI()
response = data_convertible("Compute SHA256 of 'hello'", llm=llm)
API Key for LLM7
By default, the package uses ChatLLM7 from langchain_llm7 with a free tier API key. The default rate limits are sufficient for most use cases. If you need higher rate limits, you can:
- Set the environment variable
LLM7_API_KEYto your API key. - Pass the API key directly:
data_convertible(..., api_key="your_api_key").
You can get a free API key by registering at https://token.llm7.io/.
Contributing
If you encounter any issues or have suggestions for improvements, please open an issue on GitHub.
Author
- Eugene Evstafev - hi@euegne.plus
- GitHub: chigwell
Metadata
Release files for data-convertible 2025.12.21173829
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | Provenance | Actions |
|---|---|---|---|---|
| data_convertible-2025.12.21173829.tar.gz | 4.9 kB | Without provenance | Details for data_convertible-2025.12.21173829.tar.gz |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Actions Reset |
|---|---|---|---|---|
| data_convertible-2025.12.21173829-py3-none-any.whl | Python 3 | none | any | Details for data_convertible-2025.12.21173829-py3-none-any.whl |
Total release size: 10.3 kB
Release files / data_convertible-2025.12.21173829.tar.gz
| Download URL | data_convertible-2025.12.21173829.tar.gz |
|---|---|
| Size | 4.9 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/6.2.0 CPython/3.12.1
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Release files / data_convertible-2025.12.21173829-py3-none-any.whl
| Download URL | data_convertible-2025.12.21173829-py3-none-any.whl |
|---|---|
| Size | 5.5 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
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BLAKE2b-256 checksum How to use checksums |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/6.2.0 CPython/3.12.1
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