go-concise-summary 2025.12.21141123
A new package would take a technical description or code snippet related to concurrency in Go and generate a structured summary of the concept, such as a fair, cancelable semaphore. It would extract k
License: MIT
Requires Python: >=3.9

# go-concise-summary
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A lightweight helper library that takes a technical description or code snippet about Go concurrency concepts and returns a structured, concise summary. It is especially useful for extracting key details such as purpose, usage, and implementation notes from a fair, cancelable semaphore or other concurrency patterns.

The package is available on PyPI as `go_concise_summary` and works as a drop‑in function that can use the default **ChatLLM7** model or any LangChain compatible LLM.

---

## Installation

```bash
pip install go_concise_summary
```

---

## Basic Usage

```python
from go_concise_summary import go_concise_summary

user_text = """
A fair, cancelable semaphore in Go can be implemented using a counting semaphore
pattern with a context for cancellation. Its purpose is to limit concurrent
access to a resource while ensuring that waiting goroutines are notified when
the semaphore is released or when the context is cancelled.
"""

summary = go_concise_summary(
    user_input=user_text
)

print(summary)
# Output will be a list of strings matching the expected regex pattern.
```

---

## Function Signature

```python
def go_concise_summary(
    user_input: str,
    api_key: Optional[str] = None,
    llm: Optional[BaseChatModel] = None
) -> List[str]
```

| Argument | Type | Description |
|----------|------|-------------|
| `user_input` | `str` | The Go concurrency description or code that you want summarized. |
| `api_key` | `Optional[str]` | API key for the default **ChatLLM7** service. If omitted, the function will look for an environment variable `LLM7_API_KEY`. |
| `llm` | `Optional[BaseChatModel]` | A LangChain LLM instance. If supplied, the function will use this LLM instead of the default **ChatLLM7**. |

---

## Overriding the LLM

The library ships with a bundled **ChatLLM7** instance, but you can pass any LangChain LLM that implements `BaseChatModel`.

### OpenAI

```python
from langchain_openai import ChatOpenAI
from go_concise_summary import go_concise_summary

llm = ChatOpenAI()
summary = go_concise_summary(user_input=text, llm=llm)
```

### Anthropic

```python
from langchain_anthropic import ChatAnthropic
from go_concise_summary import go_concise_summary

llm = ChatAnthropic()
summary = go_concise_summary(user_input=text, llm=llm)
```

### Google Gemini

```python
from langchain_google_genai import ChatGoogleGenerativeAI
from go_concise_summary import go_concise_summary

llm = ChatGoogleGenerativeAI()
summary = go_concise_summary(user_input=text, llm=llm)
```

---

## Using a Custom LLM7 API Key

If you require higher rate limits than the default free tier, create or export an LLM7 API key:

```bash
export LLM7_API_KEY="your-api-key-here"
```

or pass it directly:

```python
summary = go_concise_summary(user_input=text, api_key="your_api_key_here")
```

Free LLM7 API keys can be obtained at <https://token.llm7.io/>.

---

## How It Works

1. **Prompting** – The function composes a system prompt (`system_prompt`) and a user prompt (`human_prompt`) that instruct the model to produce a concise, structured summary of the Go concurrency snippet.
2. **LLM Call** – It sends the prompts to the configured LLM.
3. **Regex Extraction** – Using the pre‑defined `pattern`, the response is validated and formatted. The extracted information is returned as a `List[str]`.

---

## Issues & Support

Found a bug or have a feature request? Open an issue here:
<https://github.com/chigwell/go-concise-summary/issues>

---

## Author

- **Eugene Evstafev**  
- Email: hi@euegne.plus  
- GitHub: @chigwell