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86 lines
2.9 KiB
Text
86 lines
2.9 KiB
Text
---
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title: "Cleaning and Categorizing"
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description: "Document Cleaning Summaries in supermemory"
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icon: "washing-machine"
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---
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supermemory provides advanced configuration options to customize your content processing pipeline. At its core is an AI-powered system that can automatically analyze, categorize, and filter your content based on your specific needs.
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## Configuration Schema
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```json
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{
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"shouldLLMFilter": true,
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"categories": ["feature-request", "bug-report", "positive", "negative"],
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"filterPrompt": "Analyze feedback sentiment and identify feature requests",
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"includeItems": ["critical", "high-priority"],
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"excludeItems": ["spam", "irrelevant"]
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}
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```
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## Core Settings
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### shouldLLMFilter
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- **Type**: `boolean`
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- **Required**: No (defaults to `false`)
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- **Description**: Master switch for AI-powered content analysis. Must be enabled to use any of the advanced filtering features.
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### categories
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- **Type**: `string[]`
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- **Limits**: Each category must be 1-50 characters
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- **Required**: No
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- **Description**: Define custom categories for content classification. When specified, the AI will only use these categories. If not specified, it will generate 3-5 relevant categories automatically.
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### filterPrompt
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- **Type**: `string`
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- **Limits**: 1-750 characters
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- **Required**: No
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- **Description**: Custom instructions for the AI on how to analyze and categorize content. Use this to guide the categorization process based on your specific needs.
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### includeItems & excludeItems
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- **Type**: `string[]`
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- **Limits**: Each item must be 1-20 characters
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- **Required**: No
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- **Description**: Fine-tune content filtering by specifying items to explicitly include or exclude during processing.
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## Content Processing Pipeline
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When content is ingested with LLM filtering enabled:
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1. **Initial Processing**
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- Content is extracted and normalized
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- Basic metadata (title, description) is captured
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2. **AI Analysis**
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- Content is analyzed based on your `filterPrompt`
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- Categories are assigned (either from your predefined list or auto-generated)
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- Tags are evaluated and scored
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3. **Chunking & Indexing**
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- Content is split into semantic chunks
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- Each chunk is embedded for efficient search
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- Metadata and classifications are stored
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## Example Use Cases
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### 1. Customer Feedback System
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```json
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{
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"shouldLLMFilter": true,
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"categories": ["positive", "negative", "neutral"],
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"filterPrompt": "Analyze customer sentiment and identify key themes",
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}
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```
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### 2. Content Moderation
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```json
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{
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"shouldLLMFilter": true,
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"categories": ["safe", "needs-review", "flagged"],
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"filterPrompt": "Identify potentially inappropriate or sensitive content",
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"excludeItems": ["spam", "offensive"],
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"includeItems": ["user-generated"]
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}
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```
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> **Important**: All filtering features (`categories`, `filterPrompt`, `includeItems`, `excludeItems`) require `shouldLLMFilter` to be enabled. Attempting to use these features without enabling `shouldLLMFilter` will result in a 400 error.
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