text-to-struct 2025.12.22084055
text-to-struct converts unstructured text into standardized, structured output
License: MIT
Requires Python: >=3.9

# text-to-struct
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A Python package that transforms unstructured text inputs into structured, standardized outputs using LLM7 and llmatch-messages.

## Overview

`text_to_struct` enables users to process textual descriptions, ideas, or concepts, and generate structured data adhering to a predefined format. It leverages the LLM7 language model via the `langchain_llm7` package, combined with `llmatch-messages` to ensure the output matches specified patterns. This approach promotes consistency and ease of parsing, making the package particularly useful for content creation, brainstorming, and idea management systems.

## Installation

```bash
pip install text_to_struct
```

## Usage

```python
from text_to_struct import text_to_struct

# Example usage with default LLM7
response = text_to_struct(user_input="Describe a new innovative app idea.")
print(response)
```

## Parameters

- `user_input` (str): The unstructured text to be processed.
- `llm` (Optional[BaseChatModel]): An optional langchain LLM instance. If not provided, the default `ChatLLM7` will be used.
- `api_key` (Optional[str]): The API key for LLM7. If not provided, it will be fetched from the `LLM7_API_KEY` environment variable.

## Available LLMs

You can pass your own LLM instances from various providers, such as:

### Using langchain's OpenAI

```python
from langchain_openai import ChatOpenAI
from text_to_struct import text_to_struct

llm = ChatOpenAI()
response = text_to_struct(user_input, llm=llm)
```

### Using langchain's Anthropic

```python
from langchain_anthropic import ChatAnthropic
from text_to_struct import text_to_struct

llm = ChatAnthropic()
response = text_to_struct(user_input, llm=llm)
```

### Using langchain's Google Generative AI

```python
from langchain_google_genai import ChatGoogleGenerativeAI
from text_to_struct import text_to_struct

llm = ChatGoogleGenerativeAI()
response = text_to_struct(user_input, llm=llm)
```

## API Key & Rate Limits

The default free-tier rate limits for LLM7 are sufficient for most use cases. To access higher rate limits, set your API key via:

- Environment variable: `LLM7_API_KEY`
- Or pass directly: `text_to_struct(user_input, api_key="your_api_key")`

You can obtain a free API key at https://token.llm7.io/.

## Development & Support

- **GitHub issues:** [https://github.com/chigwell/text_to_struct/issues](https://github.com/chigwell/text_to_struct/issues)
- **Author:** Eugene Evstafev
- **Email:** hi@eugene.plus
- **GitHub:** [@chigwell](https://github.com/chigwell)

## Source Code

The core functionality is built on the `langchain_llm7` package (available on PyPI), which interfaces with LLM7. The package also uses `llmatch_messages` for pattern matching and ensuring output structure.

---

**Note:** The `text_to_struct` function relies on a pattern matching regex and specific prompts (`system_prompt` and `human_prompt`) to guide the LLM output. Make sure your input and patterns align with your expected structured output.