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.

​
Configuration Schema

{
  "shouldLLMFilter": true,
  "categories": ["feature-request", "bug-report", "positive", "negative"],
  "filterPrompt": "Analyze feedback sentiment and identify feature requests",
  "includeItems": ["critical", "high-priority"],
  "excludeItems": ["spam", "irrelevant"],
  "filterTags": [
    { "tag": "urgent", "score": 0.8 },
    { "tag": "bug", "score": 0.9 }
  ]
}

​
Core Settings

​
shouldLLMFilter

  • Type: boolean
  • Required: No (defaults to false)
  • Description: Master switch for AI-powered content analysis. Must be enabled to use any of the advanced filtering features.

​
categories

  • Type: string[]
  • Limits: Each category must be 1-50 characters
  • Required: No
  • 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.

​
filterPrompt

  • Type: string
  • Limits: 1-750 characters
  • Required: No
  • 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.

​
includeItems & excludeItems

  • Type: string[]
  • Limits: Each item must be 1-20 characters
  • Required: No
  • Description: Fine-tune content filtering by specifying items to explicitly include or exclude during processing.

​
filterTags

  • Type: Array of { tag: string, score: number }
  • Limits: Tags must be 1-50 characters, scores must be ≥ 0
  • Required: No
  • Description: Define weighted tags for more nuanced content classification. Useful for prioritization and advanced filtering.

​
Content Processing Pipeline

When content is ingested with LLM filtering enabled:

  1. Initial Processing

    • Content is extracted and normalized
    • Basic metadata (title, description) is captured

  2. AI Analysis

    • Content is analyzed based on your filterPrompt
    • Categories are assigned (either from your predefined list or auto-generated)
    • Tags are evaluated and scored

  3. Chunking & Indexing

    • Content is split into semantic chunks
    • Each chunk is embedded for efficient search
    • Metadata and classifications are stored

​
Example Use Cases

​
1. Customer Feedback System

{
  "shouldLLMFilter": true,
  "categories": ["positive", "negative", "neutral"],
  "filterPrompt": "Analyze customer sentiment and identify key themes",
  "filterTags": [
    { "tag": "feature-request", "score": 1.0 },
    { "tag": "bug-report", "score": 0.9 },
    { "tag": "praise", "score": 0.8 }
  ]
}

​
2. Content Moderation

{
  "shouldLLMFilter": true,
  "categories": ["safe", "needs-review", "flagged"],
  "filterPrompt": "Identify potentially inappropriate or sensitive content",
  "excludeItems": ["spam", "offensive"],
  "includeItems": ["user-generated"]
}

Important: All filtering features (categories, filterPrompt, includeItems, excludeItems, filterTags) require shouldLLMFilter to be enabled. Attempting to use these features without enabling shouldLLMFilter will result in a 400 error.