textstructparser 2025.12.20202423

A new package designed to parse and structure information from unstructured text. It takes textual input and utilizes advanced language models with pattern matching to extract specific data points, en

textstructparser 🔍

Parse and structure information from unstructured text using advanced language models.

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Overview

textstructparser is a Python package designed to extract structured data from unstructured text using pattern matching and large language models (LLMs). It ensures consistent, machine-readable output for downstream applications like data processing, NLP pipelines, or automation workflows.

By default, it uses ChatLLM7 (via langchain_llm7), but developers can easily integrate their preferred LLM (e.g., OpenAI, Anthropic, Google) for flexibility.

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Installation

pip install textstructparser

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Features

✅ Flexible LLM Integration – Works with any BaseChatModel from LangChain. ✅ Pattern-Based Extraction – Uses regex constraints for structured output. ✅ Default LLM7 Support – Free-tier rate limits sufficient for most use cases. ✅ Customizable – Override defaults with your own API keys or LLMs.

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Usage Examples

Basic Usage (Default LLM7)

from textstructparser import textstructparser

user_input = "Extract dates and names from this text: John Doe visited New York on 2023-10-15."
response = textstructparser(user_input)
print(response)  # Returns structured data matching the predefined pattern

Using a Custom LLM (OpenAI)

from langchain_openai import ChatOpenAI
from textstructparser import textstructparser

llm = ChatOpenAI(model="gpt-4")
response = textstructparser(user_input, llm=llm)

Using a Custom LLM (Anthropic)

from langchain_anthropic import ChatAnthropic
from textstructparser import textstructparser

llm = ChatAnthropic(model="claude-2")
response = textstructparser(user_input, llm=llm)

Using a Custom LLM (Google Vertex AI)

from langchain_google_genai import ChatGoogleGenerativeAI
from textstructparser import textstructparser

llm = ChatGoogleGenerativeAI(model="gemini-pro")
response = textstructparser(user_input, llm=llm)

Passing a Custom API Key (LLM7)

from textstructparser import textstructparser

# Option 1: Via environment variable
import os
os.environ["LLM7_API_KEY"] = "your_api_key_here"

# Option 2: Directly in function call
response = textstructparser(user_input, api_key="your_api_key_here")

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Parameters

Parameter	Type	Description
user_input	str	The unstructured text to parse.
api_key	Optional[str]	LLM7 API key (defaults to LLM7_API_KEY env var).
llm	Optional[BaseChatModel]	Custom LangChain LLM (defaults to ChatLLM7).

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How It Works

1. Input Processing: Takes raw text and applies a predefined regex pattern for structured extraction.
2. LLM Integration: Uses llmatch (from llmatch_messages) to query the LLM with system/human prompts.
3. Output Validation: Ensures extracted data matches the regex pattern before returning.

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Default LLM (LLM7)

• Provider: LLM7
• Free Tier: Sufficient for most use cases (rate limits apply).
• Upgrade: Pass a custom API key via api_key or LLM7_API_KEY env var.

Get a free API key: https://token.llm7.io/

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Custom LLM Support

For OpenAI, Anthropic, Google, or other LangChain-compatible LLMs, simply pass your model instance:

from langchain_openai import ChatOpenAI
from textstructparser import textstructparser

llm = ChatOpenAI(model="gpt-3.5-turbo")
response = textstructparser(user_input, llm=llm)

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Contributing

Contributions are welcome! Please open an issue or submit a PR: 🔗 GitHub Issues

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License

MIT – See LICENSE for details.

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Author

👤 Eugene Evstafev 📧 [e-mail] 🔗 LinkedIn 🐙 GitHub

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