# socialtextlytics
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**socialtextlytics** is a Python package that analyzes social‑media post text and returns structured insights about audience engagement patterns. It can identify common themes in highly liked content, suggest language that may drive subscriber growth, and more—without handling media files directly.

## Features

- Extracts categorized feedback from post text, comments, or descriptions.  
- Uses a powerful LLM (ChatLLM7 by default) with regex‑based extraction for reliable outputs.  
- Easily replace the LLM with any LangChain‑compatible model (OpenAI, Anthropic, Google, etc.).  
- Simple, typed API.

## Installation

```bash
pip install socialtextlytics
```

## Quick Start

```python
from socialtextlytics import socialtextlytics

# Example social‑media post text
user_input = """
Just launched our new feature! 🎉 10k likes already.
What do you think? #innovation #tech
"""

# Call the analyzer with default LLM (ChatLLM7)
results = socialtextlytics(user_input)

print(results)
# → ['...extracted insight strings...']
```

## Advanced Usage – Providing Your Own LLM

If you prefer to use a different language model, pass a LangChain `BaseChatModel` instance:

### OpenAI

```python
from langchain_openai import ChatOpenAI
from socialtextlytics import socialtextlytics

llm = ChatOpenAI()          # configure with your OpenAI key as usual
response = socialtextlytics(user_input, llm=llm)
print(response)
```

### Anthropic

```python
from langchain_anthropic import ChatAnthropic
from socialtextlytics import socialtextlytics

llm = ChatAnthropic()
response = socialtextlytics(user_input, llm=llm)
print(response)
```

### Google Generative AI

```python
from langchain_google_genai import ChatGoogleGenerativeAI
from socialtextlytics import socialtextlytics

llm = ChatGoogleGenerativeAI()
response = socialtextlytics(user_input, llm=llm)
print(response)
```

## Parameters

| Name        | Type                     | Description |
|-------------|--------------------------|-------------|
| `user_input`| `str`                    | The text to analyze (post, comment, description, etc.). |
| `llm`       | `Optional[BaseChatModel]`| A LangChain LLM instance. If omitted, the package creates a default **ChatLLM7** instance. |
| `api_key`   | `Optional[str]`         | API key for ChatLLM7. If not supplied, the function reads `LLM7_API_KEY` from the environment. |

### Default LLM – ChatLLM7

When no `llm` is given, `socialtextlytics` creates a `ChatLLM7` instance:

```python
from langchain_llm7 import ChatLLM7
resolved_llm = ChatLLM7(
    api_key=api_key,
    base_url="https://..."
)
```

*ChatLLM7* is available on PyPI: <https://pypi.org/project/langchain-llm7/> (link provided in the source).  
The free tier’s rate limits are sufficient for typical usage. For higher limits, supply your own API key via the `LLM7_API_KEY` environment variable or the `api_key` argument.

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

## How It Works

1. **Prompt Construction** – The package builds system and human prompts (`system_prompt`, `human_prompt`).  
2. **LLM Call** – The LLM processes the prompts and returns a raw response.  
3. **Regex Extraction** – `llmatch` validates the response against a predefined regular expression (`pattern`) and extracts the structured data.  
4. **Result** – A list of extracted insight strings is returned, or an empty list on failure.

## Error Handling

If the LLM call fails or the response does not match the expected pattern, a `RuntimeError` is raised with an informative message.

## Contributing & Issues

Bug reports, feature requests, and pull requests are welcome! Please open an issue at: <https://github.com/chigwell/socialtextlytics/issues>

## License

This project is licensed under the MIT License.

## Author

**Eugene Evstafev**  
Email: <hi@euegne.plus>  
GitHub: <https://github.com/chigwell>

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*Enjoy extracting insights from your social media content with **socialtextlytics**!*