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Title *

Description

## Overview
Summarize documents and web pages using LLM, with email delivery of results.

## App Metadata
- **App Name:** doc_summarizer
- **Publisher:** platform
- **Version:** 1.0.0
- **Priority:** High
- **Complexity:** Low
- **Phase:** 1 (Quick wins for beta demo)

## Actions

### 1. summarize_text
**Description:** Summarize plain text content using LLM
**Parameters:**
- text (str, required): Text content to summarize
- max_length (int, default=500): Maximum summary length in words
- style (str, default='concise'): Summary style (concise|detailed|bullet_points)
- language (str, default='english'): Output language

**Returns:**
{
  "summary": "...",
  "original_length": 5000,
  "summary_length": 450,
  "key_points": ["point1", "point2"],
  "model_used": "qwen3-vl:235b-instruct-cloud"
}

### 2. summarize_url
**Description:** Fetch URL content and summarize
**Parameters:**
- url (str, required): URL to fetch and summarize
- max_length (int, default=500): Maximum summary length
- style (str, default='concise'): Summary style
- extract_mode (str, default='article'): Content extraction (article|full|main)

**Returns:** Same as summarize_text plus {"source_url": "...", "page_title": "..."}

### 3. summarize_and_email
**Description:** Summarize content and email the result
**Parameters:**
- text (str, optional): Text to summarize (either text or url required)
- url (str, optional): URL to summarize
- recipient_email (str, required): Email to send summary to
- subject_prefix (str, default='Summary'): Email subject prefix
- include_original (bool, default=False): Include original text in email

**Returns:** {"summary": "...", "email_sent": true, "recipient": "..."}

## Example Workflow
builder = WorkflowBuilder(name='summarize_article', version='1.0.0')
builder.task(
    task_id='summarize',
    function='apps.platform.doc_summarizer.summarize_url',
    kwargs={
        'url': 'https://example.com/long-article',
        'style': 'bullet_points',
        'max_length': 300
    },
    result_key='summary_result'
)
builder.task(
    task_id='email_summary',
    function='apps.platform.doc_summarizer.summarize_and_email',
    kwargs={
        'text': '{{summary_result.summary}}',
        'recipient_email': 'user@example.com',
        'subject_prefix': 'Article Summary'
    }
)

## Implementation Notes
- Use existing LLM tool (engine/tools/llm.py) for summarization
- Use httpx/BeautifulSoup for URL content extraction
- Consider readability library for article extraction
- Rate limit LLM calls to avoid quota issues
- Cache summaries by content hash to avoid re-processing

## LLM Prompt Template
"""
Summarize the following text in {style} format. 
Maximum length: {max_length} words.
Language: {language}

Text:
{text}

Provide:
1. A {style} summary
2. 3-5 key points as bullet points
"""

## Configuration Schema
{
  "default_max_length": 500,
  "default_style": "concise",
  "llm_model": "qwen3-vl:235b-instruct-cloud",
  "llm_temperature": 0.3
}

## Secrets Config
None (LLM config from environment: OLLAMA_API_KEY, OLLAMA_BASE_URL)

## Dependencies
- engine/tools/llm.py (existing LLM integration)
- httpx for URL fetching
- beautifulsoup4 for HTML parsing
- Email sending (reuse SMTP config)

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Edit Issue #427