37 lines
1.7 KiB
Markdown
37 lines
1.7 KiB
Markdown
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Collection: knowledge_base
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Metadata Schema:
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{
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"subject": "Machine Learning", // Primary topic/theme
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"subjects": ["AI", "NLP"], // Related subjects for cross-linking
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"category": "reference", // reference | code | notes | documentation
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"path": "AI/ML/Transformers", // Hierarchical location (like filesystem)
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"level": 2, // Depth: 0=root, 1=section, 2=chunk
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"parent_id": "abc-123", // Parent document ID (for chunks/children)
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"content_type": "web_page", // web_page | pdf | code | markdown | note
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"language": "python", // For code/docs (optional)
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"project": "llm-research", // Optional project tag
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"checksum": "sha256:abc...", // For duplicate detection
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"source_url": "https://...", // Optional reference (not primary org)
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"title": "Understanding Transformers", // Display name
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"concepts": ["attention", "bert"], // Auto-extracted key concepts
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"date_added": "2026-02-05",
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"date_updated": "2026-02-05"
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}
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Key Design Decisions:
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- Subject-first: Organize by topic, not by where it came from
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- Path-based hierarchy: Navigate "AI/ML/Transformers" or "Projects/HomeLab/Docker"
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- Separate from memories: knowledge_base and openclaw_memories don't mix
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- Duplicate handling: Checksum comparison → overwrite if changed, skip if same
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- No retention limits
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Use Cases:
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- Web scrape → path: "Research/Web/<topic>", subject: extracted topic
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- Project docs → path: "Projects/<project-name>/<doc>", project tag
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- Code reference → path: "Code/<language>/<topic>", language field
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- Personal notes → path: "Notes/<category>/<note>"
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