/models

Manage and view available embedding models for vector search.

IMPORTANT: You must specify a subcommand (currently only list is available).

Subcommands (required):

• list: List all available embedding models with details

Options:

• --json: Output in JSON format

Examples:

/models list
/models list --json

Execution:

arc models $ARGUMENTS

Available Models:

The list command shows:

• Model name (for --model flags)
• Dimensions (vector size)
• Backend (fastembed, sentence-transformers)
• Best use case (PDFs, code, general)
• Model ID (HuggingFace identifier)

Current Models:

For Documents/PDFs:

• stella (1024D): Best for documents, PDFs, general text
• bge-large (1024D): General purpose, high quality
• modernbert (1024D): Newer general-purpose model

For Source Code:

• jina-code-0.5b (896D): RECOMMENDED - SOTA Sept 2025, 32K context, fast (default)
• jina-code-1.5b (1536D): SOTA Sept 2025, 32K context, highest quality
• codesage-large (1024D): CodeSage V2, Dec 2024, 9 languages
• jina-code (768D): Legacy v2 model, 8K context
• nomic-code (3584D): 7B params, 6 languages, slower but comprehensive

For General Use:

• bge (1024D): High-quality general embeddings
• bge-small (384D): Faster, smaller, lower quality

Model Selection Tips:

1. Match content type:

   • PDFs/docs → stella or modernbert
   • Source code → jina-code-0.5b (fast) or jina-code-1.5b (quality)
   • Mixed → stella or bge

2. Consider dimensions:

   • Higher dimensions (1024D) = better quality, more storage
   • Lower dimensions (384D, 768D) = faster, less storage

3. Backend matters:

   • fastembed: Faster, optimized, limited models
   • sentence-transformers: More models, HuggingFace ecosystem

4. Collection consistency:

   • Use same model for all documents in a collection
   • Cannot mix dimensions in one vector space

Downloading Models:

Models auto-download on first use (~1-2GB):

• Cached in ~/.cache/arcaneum/models (XDG-compliant)
• Reused across indexing operations
• Use --offline flag to require cached models

Pre-download for offline use:

# Download the recommended code model
python -c "from sentence_transformers import SentenceTransformer; SentenceTransformer('jinaai/jina-code-embeddings-0.5b')"

# Or the legacy v2 model
python -c "from sentence_transformers import SentenceTransformer; SentenceTransformer('jinaai/jina-embeddings-v2-base-code')"

Related Commands:

• /collection create - Create collection with specific model
• /index pdf - Index with model selection
• /index code - Index with model selection

Implementation:

• RDR-002: Embedding client architecture
• RDR-006: Model listing CLI
• arcaneum-142: Multi-backend support