https://github.com/chigwell/tech-summary
tech-summary processes text to extract structured summaries of technical concepts, ensuring consistent and reliable output for developers, educators, and writers.
https://github.com/chigwell/tech-summary
automation comparison-of-features concise-explanations consistent-output data-extraction developer-tools educational-aids formalization manual-reformatting-avoidance pattern-matching programming-languages reliable-formatting structured-summaries technical-concepts
Last synced: 30 days ago
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tech-summary processes text to extract structured summaries of technical concepts, ensuring consistent and reliable output for developers, educators, and writers.
- Host: GitHub
- URL: https://github.com/chigwell/tech-summary
- Owner: chigwell
- Created: 2025-12-21T23:43:16.000Z (about 2 months ago)
- Default Branch: main
- Last Pushed: 2025-12-21T23:43:23.000Z (about 2 months ago)
- Last Synced: 2025-12-23T10:41:31.981Z (about 2 months ago)
- Topics: automation, comparison-of-features, concise-explanations, consistent-output, data-extraction, developer-tools, educational-aids, formalization, manual-reformatting-avoidance, pattern-matching, programming-languages, reliable-formatting, structured-summaries, technical-concepts
- Language: Python
- Homepage: https://pypi.org/project/tech-summary/
- Size: 2.93 KB
- Stars: 1
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# tech-summary
[](https://badge.fury.io/py/tech-summary)
[](https://opensource.org/licenses/MIT)
[](https://pepy.tech/project/tech-summary)
[](https://www.linkedin.com/in/eugene-evstafev-716669181/)
Package to extract structured summaries of technical concepts from text input.
## Overview
This package uses pattern matching to ensure output consistency and reliability, avoiding unstructured or ambiguous responses. It's useful for developers, educators, or technical writers who need concise, formatted explanations without manual reformatting.
## Installation
```bash
pip install tech_summary
```
## Usage
```python
from tech_summary import tech_summary
user_input = "Compare garbage collection and move semantics in programming languages."
response = tech_summary(user_input)
print(response)
```
You can also pass a LangChain LLM instance to use:
```python
from langchain_llm7 import ChatLLM7
from tech_summary import tech_summary
llm = ChatLLM7()
response = tech_summary(user_input, llm=llm)
print(response)
```
You can also use another LLM instance (e.g. OpenAI, Anthropic, Google Generative AI) by passing your own instance:
```python
from langchain_openai import ChatOpenAI
from tech_summary import tech_summary
llm = ChatOpenAI()
response = tech_summary(user_input, llm=llm)
print(response)
from langchain_anthropic import ChatAnthropic
from tech_summary import tech_summary
llm = ChatAnthropic()
response = tech_summary(user_input, llm=llm)
print(response)
from langchain_google_genai import ChatGoogleGenerativeAI
from tech_summary import tech_summary
llm = ChatGoogleGenerativeAI()
response = tech_summary(user_input, llm=llm)
print(response)
```
## Configuration
You can configure the LLM7 API key by setting the `LLM7_API_KEY` environment variable or passing it directly to the `tech_summary` function:
```python
tech_summary(user_input, api_key="your_api_key")
```
If you haven't registered for an API key, you can get one for free at https://token.llm7.io/.
## GitHub
Raise issues at https://github.tech-summary.
## Author
Eugene Evstafev
hi@euegne.plus
## Changelog
This package is under development. See GitHub for updates.
## Acknowledgments
This package uses ChatLLM7 (https://pypi.org/project/langchain-llm7/) by default.