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structquery is a lightweight Python package that converts unstructured text queries into highly structured, search‐optimized outputs. By leveraging the llmatch‐messages utility and a default LLM7 model, the package extracts key elements from user input (e.g., location, difficulty, length) and returns them in a format that is ready for downstream search engines or information‐retrieval pipelines.

• One‐line transformation from free‐form text to a list of structured fields.
• Built‐in LLM7 support (ChatLLM7 from langchain_llm7) with automatic API‐key handling.
• Pluggable LLMs – you can swap in any LangChain‐compatible chat model (OpenAI, Anthropic, Google, etc.).
• Regex‐driven output validation via llmatch to guarantee format correctness.
• Zero‐config defaults – works out‐of‐the‐box for common use‐cases.

pip install structquery

from structquery import structquery
# Simple call – uses the default ChatLLM7 model
response = structquery(
 user_input="best hiking trails in Colorado"
)
print(response) # → List of extracted structured strings

You can provide any LangChain‐compatible chat model that inherits from BaseChatModel. Below are examples for the most popular providers.

from langchain_openai import ChatOpenAI
from structquery import structquery
llm = ChatOpenAI(model="gpt-4o-mini")
response = structquery(
 user_input="latest smartphones under 500ドル",
 llm=llm
)

from langchain_anthropic import ChatAnthropic
from structquery import structquery
llm = ChatAnthropic(model_name="claude-3-haiku-20240307")
response = structquery(
 user_input="affordable vegan meal plan",
 llm=llm
)

from langchain_google_genai import ChatGoogleGenerativeAI
from structquery import structquery
llm = ChatGoogleGenerativeAI(model="gemini-1.5-flash")
response = structquery(
 user_input="top-rated bike touring routes in Europe",
 llm=llm
)

• The free tier of LLM7 supplies generous rate limits that satisfy most experimentation and production needs.
• To obtain a free API key, register at https://token.llm7.io/ .
• You can provide the key in three ways:
  1. Environment variable: export LLM7_API_KEY=your_key
  2. Direct argument: structquery(user_input, api_key="your_key")
  3. Pass a pre‐configured ChatLLM7 instance via the llm argument.

• Bug reports / feature requests: https://github.com/chigwell/structquery/issues
• Contributions are welcome! Fork the repository, make your changes, and submit a pull request.

Distributed under the MIT License. See the LICENSE file for details.

Eugene Evstafev – hi@euegne.plus
GitHub: https://github.com/chigwell