/store

Store agent-generated content (research, analysis, synthesized information) with rich metadata. Content is persisted to disk for re-indexing and full-text retrieval, then indexed to Qdrant for semantic search.

Tip: For full search capabilities (semantic + full-text), first create a corpus with /arc:corpus create Memory --type markdown, then use this command with --collection Memory. The stored content will be searchable via both /arc:search semantic and /arc:search text.

Storage Location: ~/.local/share/arcaneum/agent-memory/{collection}/

Options:

• --collection: Target collection (required)
• --model: Embedding model (default: stella for documents)
• --title: Document title (added to frontmatter)
• --category: Document category (e.g., research, security, analysis)
• --tags: Comma-separated tags
• --metadata: Additional metadata as JSON
• --chunk-size: Target chunk size in tokens (overrides model default)
• --chunk-overlap: Overlap between chunks in tokens
• --verbose: Show detailed progress
• --json: Output in JSON format

Examples:

/store analysis.md --collection Memory --title "Security Analysis" --category security
/store - --collection Research --title "Findings" --tags "research,important"

Execution:

arc store $ARGUMENTS

How It Works:

1. Accept content from file or stdin (-)
2. Extract/add rich metadata (title, category, tags, custom fields)
3. Semantic chunking preserving document structure
4. Generate embeddings (stella default: 1024D for documents)
5. Upload to Qdrant with metadata
6. Persist to disk: ~/.local/share/arcaneum/agent-memory/{collection}/{date}_{agent}_{slug}.md
7. Generate YAML frontmatter with injection metadata (injection_id, injected_at, injected_by)

Persistence:

Content is always persisted for durability. This enables:

• Re-indexing: Update embeddings without losing original content
• Full-text retrieval: Access complete original documents
• Audit trail: Track what was stored and when (injection_id, timestamps)

Filename Format:

YYYYMMDD_agent_slug.md (e.g., 20251030_claude_security-analysis.md)

Use Cases:

• AI agents storing research findings
• Preserving analysis results
• Collecting synthesized information
• Building knowledge bases from agent workflows

Default Model:

• stella (1024D, document-optimized)

Related Commands:

• /arc:corpus create - Create corpus for dual search (recommended: --type markdown)
• /arc:collection create - Create collection for semantic search only
• /arc:search semantic - Search stored content semantically
• /arc:search text - Search stored content with full-text (requires corpus)
• /arc:index markdown - For indexing existing markdown directories (different use case)

Implementation:

• RDR-014: Markdown content indexing
• arcaneum-204: Direct injection persistence module