structuredxtract 2025.12.21145156

A new package is designed to simplify the process of extracting structured information from user-provided text inputs by leveraging a language model with pattern matching capabilities. The system prom

structuredxtract

Extract structured information from unstructured text with pattern-matching precision.

A Python package that simplifies structured data extraction from plain text inputs using a language model with pattern-matching capabilities. Ideal for surveys, feedback analysis, and report generation where consistent, well-formatted outputs are required.

---

🚀 Features

• Pattern-based extraction: Uses regex patterns to enforce structured output formats.
• Flexible LLM integration: Works with default ChatLLM7 or any LangChain-compatible model.
• No multimedia support: Focuses solely on text-based inputs for reliability.
• Consistent formatting: Ensures responses match expected schemas (tables, summaries, key-value pairs).
• Easy customization: Replace default LLM with OpenAI, Anthropic, Google, or any other LangChain model.

---

📦 Installation

pip install structuredxtract

---

🔧 Usage

Basic Usage (Default LLM7)

from structuredxtract import structuredxtract

user_input = """
Name: John Doe
Age: 30
Occupation: Software Engineer
"""

response = structuredxtract(user_input)
print(response)  # Structured output based on predefined patterns

Custom LLM Integration

Replace the default ChatLLM7 with your preferred model:

OpenAI

from langchain_openai import ChatOpenAI
from structuredxtract import structuredxtract

llm = ChatOpenAI()
response = structuredxtract(user_input, llm=llm)

Anthropic

from langchain_anthropic import ChatAnthropic
from structuredxtract import structuredxtract

llm = ChatAnthropic()
response = structuredxtract(user_input, llm=llm)

Google Vertex AI

from langchain_google_genai import ChatGoogleGenerativeAI
from structuredxtract import structuredxtract

llm = ChatGoogleGenerativeAI()
response = structuredxtract(user_input, llm=llm)

---

🔑 API Key

• Default: Uses LLM7_API_KEY from environment variables.
• Manual override: Pass via api_key parameter or set LLM7_API_KEY before importing.

  import os
  os.environ["LLM7_API_KEY"] = "your_api_key_here"
  

Get a free API key at LLM7 Token.

---

📜 Parameters

Parameter	Type	Description
user_input	str	Plain text input to extract structured data from.
api_key	Optional[str]	LLM7 API key (optional if using environment variable).
llm	Optional[BaseChatModel]	Custom LangChain LLM (e.g., ChatOpenAI, ChatAnthropic). Defaults to ChatLLM7.

---

📊 Output

Returns a List[str] of extracted data matching predefined patterns. Example:

[
    {"Name": "John Doe", "Age": "30", "Occupation": "Software Engineer"},
    {"Key1": "Value1", "Key2": "Value2"}
]

---

🔄 Rate Limits

• LLM7 Free Tier: Sufficient for most use cases.
• Custom API Key: For higher limits, pass via api_key or environment variable.

---

📝 License

MIT

---

📢 Support & Issues

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

---

👤 Author

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