verify-response 2025.12.21152239

A new package enables users to provide text inputs and receive reliably structured responses that clearly present key information with confidence indicators, reducing misunderstanding and overconfiden

verify_response

A Python package that ensures structured, verified, and reliable responses from language models by enforcing strict output formatting and confidence indicators. This package helps reduce ambiguity and overconfidence in AI-generated outputs, making it ideal for applications requiring precise data extraction, summaries, or structured insights.

---

📦 Installation

Install the package via pip:

pip install verify_response

---

🚀 Features

• Structured Outputs: Enforces strict regex-based response formatting to ensure consistency.
• Confidence Indicators: Provides clear indicators of response reliability.
• Flexible LLM Support: Works with default ChatLLM7 or any LangChain-compatible LLM.
• No Multimedia Processing: Focuses solely on text inputs and structured outputs.
• Transparency: Reduces false confidence by validating output against predefined patterns.

---

🔧 Usage

Basic Usage (Default LLM7)

from verify_response import verify_response

response = verify_response(user_input="What is the capital of France?")
print(response)  # Structured, verified output

Custom LLM (OpenAI)

from langchain_openai import ChatOpenAI
from verify_response import verify_response

llm = ChatOpenAI()
response = verify_response(user_input="Summarize this text...", llm=llm)
print(response)

Custom LLM (Anthropic)

from langchain_anthropic import ChatAnthropic
from verify_response import verify_response

llm = ChatAnthropic()
response = verify_response(user_input="Extract key points...", llm=llm)
print(response)

Custom LLM (Google Generative AI)

from langchain_google_genai import ChatGoogleGenerativeAI
from verify_response import verify_response

llm = ChatGoogleGenerativeAI()
response = verify_response(user_input="Analyze this data...", llm=llm)
print(response)

---

🔑 API Key Configuration

Default (LLM7 Free Tier)

The package defaults to ChatLLM7 with the API key loaded from the environment variable LLM7_API_KEY. If not set, it falls back to a default key (not recommended for production).

Custom API Key

Pass your API key directly or via environment variable:

# Directly
verify_response(user_input="...", api_key="your_llm7_api_key")

# Via environment variable
export LLM7_API_KEY="your_llm7_api_key"
verify_response(user_input="...")

Get a free API key: LLM7 Token Registration

---

📝 Parameters

Parameter	Type	Description
user_input	str	The input text to process.
api_key	Optional[str]	LLM7 API key (defaults to LLM7_API_KEY env var).
llm	Optional[BaseChatModel]	Custom LangChain LLM (e.g., ChatOpenAI, ChatAnthropic). Defaults to ChatLLM7.

---

📊 Rate Limits

The default ChatLLM7 free tier supports most use cases. For higher limits, use your own API key or upgrade via LLM7.

---

📜 License

MIT

---

📢 Support & Issues

For bugs or feature requests, open an issue on GitHub.

---

👤 Author

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

---