ProphecyPerfect
A Python package for analyzing and verifying if textual statements correctly express prophetic or visionary statements in the perfect tense.
Installation
pip install prophecyperfect
Overview
The prophecyperfect package takes a brief textual description, prompt, or statement from the user and analyzes it to determine if it correctly expresses a prophetic or visionary statement in the perfect tense. Using pattern matching and structured responses, it classifies the input as prophetic, reaffirming its status, or identifies inaccuracies or non-prophetic phrasing.
Usage
Basic Usage
from prophecyperfect import prophecyperfect
# Analyze a prophetic statement
user_input = "Thus saith the Lord: I will surely bless you, and I will multiply you exceedingly."
response = prophecyperfect(user_input)
print(response)
Using Custom LLM
You can also use your own LLM instance from LangChain. Here are examples with different providers:
OpenAI
from langchain_openai import ChatOpenAI
from prophecyperfect import prophecyperfect
llm = ChatOpenAI()
response = prophecyperfect(user_input, llm=llm)
Anthropic
from langchain_anthropic import ChatAnthropic
from prophecyperfect import prophecyperfect
llm = ChatAnthropic()
response = prophecyperfect(user_input, llm=llm)
from langchain_google_genai import ChatGoogleGenerativeAI
from prophecyperfect import prophecyperfect
llm = ChatGoogleGenerativeAI()
response = prophecyperfect(user_input, llm=llm)
API Key Configuration
The default rate limits for LLM7 free tier are sufficient for most use cases. If you need higher rate limits, you can:
- Set the API key as an environment variable:
export LLM7_API_KEY="your_api_key_here"
- Or pass it directly to the function:
response = prophecyperfect(user_input, api_key="your_api_key_here")
You can obtain a free API key by registering at https://token.llm7.io/
Parameters
user_input(str): The user input text to processllm(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, it will use the environment variableLLM7_API_KEYor the default free tier.
Author
- Eugene Evstafev - hi@euegne.plus
- GitHub Nickname: chigwell
Issues
For any issues or to contribute, please visit the GitHub repository: https://github.com/chigwell/prophecyperfect
Metadata
Release files for prophecyperfect 2025.12.21163318
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| prophecyperfect-2025.12.21163318.tar.gz | 4.2 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| prophecyperfect-2025.12.21163318-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 9.0 kB
Release files / prophecyperfect-2025.12.21163318.tar.gz
| Download URL | prophecyperfect-2025.12.21163318.tar.gz |
|---|---|
| Size | 4.2 kB |
| Tags | Source |
|
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Release files / prophecyperfect-2025.12.21163318-py3-none-any.whl
| Download URL | prophecyperfect-2025.12.21163318-py3-none-any.whl |
|---|---|
| Size | 4.8 kB |
| Tags | Python 3 |
|
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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