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The output can be used for personal journaling, social‑media sharing or building collections of meaningful life moments.\n\n---\n\n## Installation\n\n```bash\npip install lifelogger_ai\n```\n\n---\n\n## Quick Start\n\n```python\nfrom lifelogger_ai import lifelogger_ai\n\n# Text you want to process\nuser_input = \"\"\"\nI spent the afternoon hiking up Blue Ridge Mountain, feeling a mix of triumph and exhaustion.\nThe wind was blowing strong and the view from the summit was breathtaking. I took a photo of a golden sunrise over the valley.\n\"\"\"\n\n# Run the lifestream summarization\nsummary = lifelogger_ai(user_input)\n\nprint(summary)\n# ['Summarised sentences here...']\n```\n\nThe function returns a list of strings – one or more sentences that capture the essence of the input.\n\n---\n\n## Parameters\n\n| Name      | Type                | Description                                                                                                                                   |\n|-----------|---------------------|-----------------------------------------------------------------------------------------------------------------------------------------------|\n| `user_input` | `str` | The raw text describing a personal moment, event or thought.                                                                                 |\n| `llm`      | `Optional[BaseChatModel]` | A LangChain language‑model instance to use for processing. If omitted, the default **ChatLLM7** from `langchain_llm7` is used.               |\n| `api_key`  | `Optional[str]`    | The API key for LLM7. If omitted, the package first looks for the `LLM7_API_KEY` environment variable; otherwise a free‑tier key is used.     |\n\n---\n\n## Using Your Own LLM\n\n`lifelogger-ai` is LLM‑agnostic. Pass any LangChain model that implements `BaseChatModel` to the function:\n\n```python\n# Example 1 – OpenAI\nfrom langchain_openai import ChatOpenAI\nfrom lifelogger_ai import lifelogger_ai\n\nllm = ChatOpenAI(model=\"gpt-4o-mini\")\nresponse = lifelogger_ai(\"Your text here\", llm=llm)\n```\n\n```python\n# Example 2 – Anthropic\nfrom langchain_anthropic import ChatAnthropic\nfrom lifelogger_ai import lifelogger_ai\n\nllm = ChatAnthropic()\nresponse = lifelogger_ai(\"Your text here\", llm=llm)\n```\n\n```python\n# Example 3 – Google Gemini\nfrom langchain_google_genai import ChatGoogleGenerativeAI\nfrom lifelogger_ai import lifelogger_ai\n\nllm = ChatGoogleGenerativeAI()\nresponse = lifelogger_ai(\"Your text here\", llm=llm)\n```\n\n---\n\n## Rate Limits \u0026 API Key for LLM7\n\nThe free tier of LLM7 offers rate limits that generally suffice for typical usage.  \nIf you need higher limits:\n\n* **Environment variable**: `export LLM7_API_KEY=your_key_here`\n* **Explicit argument**: `lifelogger_ai(user_input, api_key=\"your_key_here\")`\n\nGet a free key at \u003chttps://token.llm7.io/\u003e\n\n---\n\n## Contributing \u0026 Issues\n\nFeel free to open issues or submit pull requests on GitHub: \u003chttps://github.com/chigwell/lifelogger-ai\u003e\n\n---\n\n## Author\n\nEugene Evstafev  \nEmail: hi@euegne.plus  \nGitHub: **chigwell**\n\n---","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fchigwell%2Flifelogger-ai","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fchigwell%2Flifelogger-ai","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fchigwell%2Flifelogger-ai/lists"}