schedule-llm-query 2025.12.20185642

A new package that processes user queries about event schedules, such as FOSDEM 2026, and returns structured information like session times, locations, and descriptions. It uses an LLM to interpret na

schedule-llm-query

A Python package for processing natural language queries about event schedules (e.g., FOSDEM 2026) and extracting structured information like session times, locations, and descriptions using an LLM.

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📌 Overview

This package interprets user queries (e.g., "What talks are on Sunday afternoon?") and extracts structured schedule data using pattern matching. The LLM is guided by a system prompt to format responses in a predefined structure, ensuring consistent and reliable output for applications.

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🚀 Installation

pip install schedule-llm-query

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🔧 Usage

Basic Usage (Default LLM: ChatLLM7)

from schedule_llm_query import schedule_llm_query

response = schedule_llm_query(
    user_input="What talks are on Sunday afternoon?",
    api_key="your_llm7_api_key"  # Optional (falls back to env var LLM7_API_KEY)
)
print(response)

Custom LLM Integration

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

Example: Using OpenAI

from langchain_openai import ChatOpenAI
from schedule_llm_query import schedule_llm_query

llm = ChatOpenAI()
response = schedule_llm_query(
    user_input="Show me all Python talks on Saturday",
    llm=llm
)
print(response)

Example: Using Anthropic

from langchain_anthropic import ChatAnthropic
from schedule_llm_query import schedule_llm_query

llm = ChatAnthropic()
response = schedule_llm_query(
    user_input="List all keynote sessions",
    llm=llm
)
print(response)

Example: Using Google Generative AI

from langchain_google_genai import ChatGoogleGenerativeAI
from schedule_llm_query import schedule_llm_query

llm = ChatGoogleGenerativeAI()
response = schedule_llm_query(
    user_input="What are the talks at Hall 1?",
    llm=llm
)
print(response)

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🔑 API Key

• Default LLM: Uses ChatLLM7 (from langchain_llm7).
• Free Tier: Sufficient for most use cases (rate limits apply).
• Custom Key: Pass via api_key parameter or LLM7_API_KEY environment variable.

  schedule_llm_query(user_input="...", api_key="your_api_key")
  

• Get a Key: Register at llm7.io.

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📝 Parameters

Parameter	Type	Description
user_input	str	The natural language query to process (e.g., "What talks are on Sunday?").
api_key	Optional[str]	LLM7 API key (optional if using env var or custom LLM).
llm	Optional[BaseChatModel]	Custom LangChain LLM (e.g., ChatOpenAI). Falls back to ChatLLM7 if None.

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🔄 Output Structure

The package returns structured data matching the regex pattern:

[
  {
    "title": "Talk Title",
    "time": "14:00-15:30",
    "location": "Hall 2",
    "description": "Brief description..."
  }
]

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📦 Dependencies

• langchain-core (for LLM integration)
• langchain_llm7 (default LLM, optional for custom LLM)
• llmatch (for pattern extraction)

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📜 License

MIT

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📢 Support & Issues

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

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👤 Author

Eugene Evstafev 📧 [e-mail] 🔗 LinkedIn

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