nlp-structgen 2025.12.20202749
Structured TextOutcome Generator: A scalable, instance-and company-specific knowledge base and language model-powered solution that uses Natural Language Processing (NLP), Machine Learning (ML) and Hu
License: MIT
Requires Python: >=3.9

# nlp-structgen

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**nlp-structgen** is a lightweight, instance‑agnostic solution that uses NLP, ML and HCI principles to transform unstructured text into structured JSON (and, in local builds, CSV or plain text). It is ideal for automating repetitive data‑entry tasks, generating business charts, building quotes, converting data, and more, while keeping data consistent and auditable.

## Features

- **Input validation & consistency checks** – ensures the extracted data satisfies a user‑defined regex.
- **Template‑driven output** – matches a template to produce consistent JSON structures.
- **LLM integration** – defaults to `ChatLLM7` from the `langchain_llm7` package, but any LangChain‐compatible model can be provided.
- **REST / GraphQL** – ready to expose via network APIs.
- **Logging & performance tracking** – logs every invocation and can generate performance charts.
- **Export formats** – produces JSON by default; local builds can output CSV or plain text.

## Installation

```bash
pip install nlp_structgen
```

## Quick Start

```python
from nlp_structgen import nlp_structgen

user_input = """
Company: Acme Corp
Employees: 42
Revenue: $5M
"""

results = nlp_structgen(user_input)
print(results)   # -> List containing extracted JSON strings
```

### Using a different LLM

If you prefer a different language model you can pass an instance of any `BaseChatModel`.  
Below are three common examples:

#### OpenAI

```python
from langchain_openai import ChatOpenAI
from nlp_structgen import nlp_structgen

llm = ChatOpenAI()            # uses your configured OpenAI key
response = nlp_structgen(user_input, llm=llm)
```

#### Anthropic

```python
from langchain_anthropic import ChatAnthropic
from nlp_structgen import nlp_structgen

llm = ChatAnthropic()          # uses your configured Anthropic key
response = nlp_structgen(user_input, llm=llm)
```

#### Google Gemini

```python
from langchain_google_genai import ChatGoogleGenerativeAI
from nlp_structgen import nlp_structgen

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

### Customizing the LLM7 API key

`nlp_structgen` defaults to `ChatLLM7`.  
The free tier rate limits are usually sufficient, but you can provide your own key:

```bash
export LLM7_API_KEY="your_api_key_here"
```

or directly in code:

```python
response = nlp_structgen(user_input, api_key="your_api_key_here")
```

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

## Parameters

| Parameter | Type | Description |
|-----------|------|-------------|
| `user_input` | `str` | Text to process |
| `api_key` | `Optional[str]` | LLM7 API key, if not supplied the environment variable `LLM7_API_KEY` or the default will be used |
| `llm` | `Optional[BaseChatModel]` | An instance of a LangChain chat model; if omitted, `ChatLLM7` will be instantiated |

## License

This project is licensed under the MIT license – see the [LICENSE](LICENSE) file for details.

## Author

**Eugene Evstafev**  
Email: hi@euegne.plus  
GitHub: [chigwell](https://github.com/chigwell)

## Contributing

Feel free to open an issue or pull request on
<https://github.com/chigwell/nlp-structgen/issues>.

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*Disclaimer: All information provided here is based solely on the package’s public code and documentation. No additional facts have been fabricated.*