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

nlp-structgen

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

pip install nlp_structgen

Quick Start

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

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

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

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:

export LLM7_API_KEY="your_api_key_here"

or directly in code:

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 file for details.

Author

Eugene Evstafev
Email: [e-mail]
GitHub: 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.