bookmark-analyzer 2025.12.22110020
Organize bookmarks and browsing history with automated tagging and categorization.
License: MIT
Requires Python: >=3.9

# bookmark-analyzer
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**bookmark-analyzer** is a lightweight Python package that turns unstructured bookmark titles or user‑written descriptions into structured tags, categories, or summaries. It leverages a LLM (by default **ChatLLM7**) to understand the meaning of a bookmark and return machine‑readable metadata, making it far easier to search, filter, and rediscover saved content.

## 🚀 Installation

```bash
pip install bookmark_analyzer
```

## 📖 Quick Start

```python
from bookmark_analyzer import bookmark_analyzer

# Simple usage – the default ChatLLM7 will be used
tags = bookmark_analyzer(
    user_input="How to bake a perfect sourdough loaf – an in‑depth guide with photos"
)

print(tags)   # e.g. ['baking', 'sourdough', 'cooking', 'guide']
```

### Using Your Own LLM

If you prefer another LangChain‑compatible model (OpenAI, Anthropic, Google, …) just pass the instance:

#### OpenAI

```python
from langchain_openai import ChatOpenAI
from bookmark_analyzer import bookmark_analyzer

llm = ChatOpenAI(model="gpt-4o-mini")
tags = bookmark_analyzer(
    user_input="Why the 2024 election matters for climate policy",
    llm=llm,
)
print(tags)
```

#### Anthropic

```python
from langchain_anthropic import ChatAnthropic
from bookmark_analyzer import bookmark_analyzer

llm = ChatAnthropic(model="claude-3-haiku-20240307")
tags = bookmark_analyzer(
    user_input="Best practices for micro‑frontends in a large SaaS product",
    llm=llm,
)
print(tags)
```

#### Google Gemini

```python
from langchain_google_genai import ChatGoogleGenerativeAI
from bookmark_analyzer import bookmark_analyzer

llm = ChatGoogleGenerativeAI(model="gemini-1.5-flash")
tags = bookmark_analyzer(
    user_input="Top 10 must‑read sci‑fi novels of the 21st century",
    llm=llm,
)
print(tags)
```

## 🛠️ Parameters

| Parameter   | Type                         | Description |
|-------------|------------------------------|-------------|
| `user_input`| `str`                        | The bookmark title or free‑form description you want to analyze. |
| `llm`       | `Optional[BaseChatModel]`    | A LangChain chat model. If omitted, the package creates a default `ChatLLM7` instance. |
| `api_key`   | `Optional[str]`              | API key for **ChatLLM7**. If not supplied, the function looks for `LLM7_API_KEY` in the environment; if still missing, it falls back to a placeholder `"None"` (which will trigger an authentication error from the service). |

## 🔑 API Key for the Default Model

- **ChatLLM7** is the out‑of‑the‑box model used by `bookmark-analyzer`.
- Free tier rate limits are sufficient for typical personal use.
- To obtain a free key, register at <https://token.llm7.io/>.
- You can provide the key either:
  - Via the environment variable `LLM7_API_KEY`, **or**
  - Directly when calling the function:

```python
tags = bookmark_analyzer(
    user_input="My favorite podcasts about AI",
    api_key="sk-xxxxxx"
)
```

## 📚 How It Works

`bookmark_analyzer` builds a prompt (see the internal `prompts` module) and sends it to the LLM together with a regular‑expression pattern that describes the expected output format. The response is validated against that pattern and the extracted tags are returned as a list of strings.

## 🐞 Issues & Contributions

If you encounter any bugs or have feature requests, please open an issue:

> <https://github.com/chigwell/bookmark-analyzer/issues>

Feel free to fork the repository, submit pull requests, or improve documentation.

## ✍️ Author

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

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Happy bookmarking! 🎉