llmatch-validate 2025.12.20175458

A new package designed to process and validate user inputs using advanced language models, ensuring structured and consistent responses. This package leverages the capabilities of llmatch-messages to

llmatch-validate

A Python package for processing and validating user inputs using advanced language models, ensuring structured and consistent responses. Built on top of llmatch-messages, this package extracts and validates key information from text inputs, making it ideal for applications requiring structured data extraction, format validation, or response consistency.

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📦 Installation

Install the package via pip:

pip install llmatch-validate

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🚀 Features

• Structured Data Extraction: Extracts key information from unstructured text inputs.
• Regex Validation: Ensures extracted data matches predefined patterns.
• Flexible LLM Integration: Works with default ChatLLM7 or any LangChain-compatible LLM.
• Error Handling & Diagnostics: Provides clear error messages for failed validations.
• Retry Mechanism: Built-in retry logic for robustness.

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🔧 Usage

Basic Usage (Default LLM: ChatLLM7)

from llmatch_validate import llmatch_validate

response = llmatch_validate(
    user_input="Your input text here..."
)
print(response)  # Returns validated/extracted data as a list

Custom LLM Integration

You can replace the default ChatLLM7 with any LangChain-compatible LLM (e.g., OpenAI, Anthropic, Google Generative AI).

Example: Using OpenAI

from langchain_openai import ChatOpenAI
from llmatch_validate import llmatch_validate

llm = ChatOpenAI()
response = llmatch_validate(
    user_input="Your input text here...",
    llm=llm
)
print(response)

Example: Using Anthropic

from langchain_anthropic import ChatAnthropic
from llmatch_validate import llmatch_validate

llm = ChatAnthropic()
response = llmatch_validate(
    user_input="Your input text here...",
    llm=llm
)
print(response)

Example: Using Google Generative AI

from langchain_google_genai import ChatGoogleGenerativeAI
from llmatch_validate import llmatch_validate

llm = ChatGoogleGenerativeAI()
response = llmatch_validate(
    user_input="Your input text here...",
    llm=llm
)
print(response)

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🔑 API Key Configuration

• Default: Uses LLM7_API_KEY from environment variables.
• Manual Override: Pass the API key directly:

  response = llmatch_validate(
      user_input="Your input text here...",
      api_key="your_llm7_api_key"
  )
  

• Get a Free API Key: Register at LLM7 Token.

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📌 Parameters

Parameter	Type	Description
user_input	str	The input text to process and validate.
api_key	Optional[str]	LLM7 API key (optional if LLM7_API_KEY is set).
llm	Optional[BaseChatModel]	Custom LangChain LLM (optional; defaults to ChatLLM7).

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📝 Default LLM: ChatLLM7

By default, this package uses ChatLLM7 from langchain_llm7. The free tier rate limits are sufficient for most use cases. For higher limits, provide your own API key.

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🔄 Rate Limits

• LLM7 Free Tier: Sufficient for most use cases.
• Custom API Key: Required for higher rate limits (pass via api_key or LLM7_API_KEY).

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📂 License

This project is licensed under the MIT License.

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📧 Support & Issues

For support or bug reports, open an issue on GitHub.

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👤 Author

Eugene Evstafev (LinkedIn) | GitHub

Email: [e-mail]

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📚 Related Packages

• llmatch-messages (Dependency)
• langchain_llm7 (Default LLM)