82 lines
2.5 KiB
Markdown
82 lines
2.5 KiB
Markdown
---
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description: Store agent-generated content for long-term memory
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argument-hint: <file|-for-stdin> --collection <name> [options]
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---
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Store agent-generated content (research, analysis, synthesized information) with rich
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metadata. Content is persisted to disk for re-indexing and full-text retrieval, then
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indexed to Qdrant for semantic search.
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**Storage Location:** `~/.arcaneum/agent-memory/{collection}/`
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**Options:**
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- --collection: Target collection (required)
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- --model: Embedding model (default: stella for documents)
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- --title: Document title (added to frontmatter)
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- --category: Document category (e.g., research, security, analysis)
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- --tags: Comma-separated tags
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- --metadata: Additional metadata as JSON
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- --chunk-size: Target chunk size in tokens (overrides model default)
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- --chunk-overlap: Overlap between chunks in tokens
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- --verbose: Show detailed progress
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- --json: Output in JSON format
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**Examples:**
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```text
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/store analysis.md --collection Memory --title "Security Analysis" --category security
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/store - --collection Research --title "Findings" --tags "research,important"
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```
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**Execution:**
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```bash
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cd ${CLAUDE_PLUGIN_ROOT}
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arc store $ARGUMENTS
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```
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**How It Works:**
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1. Accept content from file or stdin (`-`)
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2. Extract/add rich metadata (title, category, tags, custom fields)
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3. Semantic chunking preserving document structure
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4. Generate embeddings (stella default: 1024D for documents)
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5. Upload to Qdrant with metadata
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6. Persist to disk: `~/.arcaneum/agent-memory/{collection}/{date}_{agent}_{slug}.md`
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7. Generate YAML frontmatter with injection metadata (injection_id, injected_at, injected_by)
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**Persistence:**
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Content is always persisted for durability. This enables:
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- Re-indexing: Update embeddings without losing original content
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- Full-text retrieval: Access complete original documents
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- Audit trail: Track what was stored and when (injection_id, timestamps)
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**Filename Format:**
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`YYYYMMDD_agent_slug.md` (e.g., `20251030_claude_security-analysis.md`)
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**Use Cases:**
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- AI agents storing research findings
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- Preserving analysis results
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- Collecting synthesized information
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- Building knowledge bases from agent workflows
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**Default Model:**
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- stella (1024D, document-optimized)
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**Related Commands:**
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- /collection create - Create collection before storing (use --type markdown)
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- /search semantic - Search stored content
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- /index markdown - For indexing existing markdown directories (different use case)
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**Implementation:**
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- RDR-014: Markdown content indexing
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- arcaneum-204: Direct injection persistence module
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