open-spec-correct 2025.12.21155728

A new package designed to streamline and automate the correction of imperfect OpenAPI specifications by analyzing user-provided textual descriptions and extracting structured insights. It processes te

Open Spec Correct

Automate and streamline the correction of imperfect OpenAPI specifications by extracting structured insights from textual descriptions.

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📌 Overview

Open Spec Correct is a Python package designed to analyze user-provided textual descriptions of APIs and generate structured corrections for OpenAPI specifications. It leverages pattern matching and AI-driven insights to identify inconsistencies, errors, or missing details in API documentation, helping developers create more accurate and maintainable OpenAPI specs faster.

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🚀 Features

• Text-to-Spec Correction: Extracts structured insights from unstructured textual descriptions.
• Pattern Matching: Uses regex-based validation to ensure output correctness.
• LLM Flexibility: Works with LLM7 by default (free tier included) or supports custom LLMs (OpenAI, Anthropic, Google, etc.).
• Retry Mechanisms: Ensures reliable extraction and correction.
• Environment-Aware: Respects LLM7_API_KEY environment variable for seamless integration.

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📦 Installation

pip install open_spec_correct

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🔧 Usage

Basic Usage (Default LLM7)

from open_spec_correct import open_spec_correct

user_input = """
API endpoint for fetching user data.
Path: /users/{id}
Method: GET
Query params: ?name=string, ?age=int
Response: 200 OK with JSON body { "id": int, "name": string }
"""

response = open_spec_correct(user_input)
print(response)

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 open_spec_correct import open_spec_correct

llm = ChatOpenAI(model_name="gpt-4")  # Replace with your preferred model
response = open_spec_correct(user_input, llm=llm)
print(response)

Example: Using Anthropic

from langchain_anthropic import ChatAnthropic
from open_spec_correct import open_spec_correct

llm = ChatAnthropic(model="claude-2")
response = open_spec_correct(user_input, llm=llm)
print(response)

Example: Using Google Generative AI

from langchain_google_genai import ChatGoogleGenerativeAI
from open_spec_correct import open_spec_correct

llm = ChatGoogleGenerativeAI(model="gemini-pro")
response = open_spec_correct(user_input, 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 = open_spec_correct(user_input, api_key="your_llm7_api_key")
  

• Free API Key: Register at LLM7 Token to get started.

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⚠️ Rate Limits

• LLM7 Free Tier: Sufficient for most use cases.
• Custom Rate Limits: Upgrade via LLM7 dashboard or use a different LLM.

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📝 Input Parameters

Parameter	Type	Description
user_input	str	Textual description of the API to correct.
api_key	Optional[str]	LLM7 API key (optional if LLM7_API_KEY is set in environment).
llm	Optional[BaseChatModel]	Custom LangChain LLM (e.g., ChatOpenAI, ChatAnthropic). Defaults to ChatLLM7.

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🔄 Output

Returns a list of structured corrections (e.g., OpenAPI-compatible snippets) extracted from the input text.

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📜 License

MIT License (see LICENSE).

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🐛 Issues & Support

Report bugs or request features at: 🔗 GitHub Issues

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👤 Author

Eugene Evstafev 📧 [e-mail] 🌐 GitHub: chigwell

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