routerflu – Structured LLM Response Extractor

Streamline interactions with large language models (LLMs) like Claude via OpenRouter by processing natural language inputs into structured, pattern-matched outputs. routerflu ensures consistent, extractable responses for programming, data querying, or content creation tasks.

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📌 Key Features

✅ Pattern-Matched Outputs – Forces LLM responses to follow strict regex patterns for reliability. ✅ Flexible LLM Integration – Works with LLM7 (default), OpenAI, Anthropic, Google, or any BaseChatModel. ✅ Environment-Aware – Uses LLM7_API_KEY from env vars or accepts direct API keys. ✅ Minimal Dependencies – Built on langchain and llmatch_messages.

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🚀 Installation

pip install routerflu

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🔧 Usage Examples

1. Basic Usage (Default: LLM7)

from routerflu import routerflu

response = routerflu(
    user_input="Write a Python function to reverse a string."
)
print(response)  # Structured output matching predefined patterns

2. Custom LLM Integration

OpenAI

from langchain_openai import ChatOpenAI
from routerflu import routerflu

llm = ChatOpenAI()
response = routerflu(user_input="Explain how REST APIs work.", llm=llm)

Anthropic (Claude)

from langchain_anthropic import ChatAnthropic
from routerflu import routerflu

llm = ChatAnthropic()
response = routerflu(user_input="Debug this SQL query.", llm=llm)

Google Vertex AI

from langchain_google_genai import ChatGoogleGenerativeAI
from routerflu import routerflu

llm = ChatGoogleGenerativeAI()
response = routerflu(user_input="Summarize this document.", llm=llm)

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🔑 Configuration

API Key

• Default: Uses LLM7_API_KEY from environment variables.
• Manual Override:

  routerflu(user_input="...", api_key="your_llm7_api_key")
  

• Get a Free Key: LLM7 Token Registration

Rate Limits

• LLM7 Free Tier: Sufficient for most use cases.
• Upgrade: Use a custom API key or switch to a paid plan.

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📦 Dependencies

• langchain-core (for BaseChatModel)
• llmatch_messages (for pattern extraction)
• langchain_llm7 (default LLM provider)

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📝 Function Signature

routerflu(
    user_input: str,
    api_key: Optional[str] = None,
    llm: Optional[BaseChatModel] = None
) -> List[str]

• user_input (str): Natural language prompt for the LLM.
• api_key (Optional[str]): LLM7 API key (falls back to env var LLM7_API_KEY).
• llm (Optional[BaseChatModel]): Custom LLM (e.g., ChatOpenAI, ChatAnthropic).

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🔄 How It Works

1. System Prompt: Guides the LLM to format responses strictly.
2. Pattern Matching: Uses regex to extract structured data from responses.
3. Error Handling: Raises RuntimeError if LLM fails to comply.

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📜 License

MIT

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📢 Support & Issues

• GitHub Issues: routerflu GitHub
• Author: Eugene Evstafev
• GitHub: @chigwell

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