arcaneum-marketplace

Arcaneum

CLI tools and Claude Code plugins for semantic and full-text search.

pipx install "https://github.com/cwensel/arcaneum/releases/download/v0.2.0/arcaneum-0.2.0-py3-none-any.whl"

Overview

Arcaneum helps you discover and understand project dependencies, documentation, and reference implementations. By indexing libraries, frameworks, and technical papers, you can semantically search for patterns, APIs, and concepts when building new projects. Works especially well with the RDR (Recommendation Data Record) model for AI-assisted development planning.

The system supports PDF documents and source code with git-aware, AST-based chunking.

Currently Available:

• Semantic search with Qdrant (vector embeddings)
• Full-text search with MeiliSearch (exact phrase matching)
• Dual indexing workflow for comprehensive search

Features

Search Capabilities

• Semantic Search (Qdrant): Find conceptually similar content using vector embeddings
• Full-Text Search (MeiliSearch): Exact phrase matching, keyword search, and typo-tolerant queries

Indexing

• PDF Indexing: OCR support for scanned documents, page-level metadata, parallel processing
• Source Code Indexing: Git-aware with AST chunking, multi-branch support, 165+ languages
• Markdown Indexing: YAML frontmatter extraction, semantic chunking, incremental sync
• Dual Indexing: Single command to index to both search engines
• Performance Tuning: Granular control over workers, batch sizes, and process priority via --embedding-batch-size and --process-priority flags

Multiple Embedding Models

• stella (1024D) - High-quality general purpose embeddings, optimized for PDFs and documents
• jina-code-0.5b (896D) - RECOMMENDED for code - SOTA Sept 2025, 32K context, fast (default)
• jina-code-1.5b (1536D) - Highest quality code embeddings, SOTA Sept 2025
• codesage-large (1024D) - CodeSage V2, 9 programming languages
• bge-large (1024D) - BGE large embeddings, balanced performance
• jina-v3 (1024D) - Multilingual embeddings with extended 8K context
• bge-base (768D) - BGE base embeddings, balanced performance and speed
• bge-small (384D) - BGE small embeddings, fastest for size-constrained scenarios

See arc models list for complete model information and recommendations.

GPU Acceleration

• Enabled by default for 1.5-3x faster embedding generation
• Supports Apple Silicon (MPS) and NVIDIA GPUs (CUDA)
• Automatic CPU fallback when GPU unavailable
• Use --no-gpu flag to force CPU-only mode

CLI-First Design

• All operations via command-line interface
• JSON output mode for automation
• Structured error messages with exit codes
• Python >= 3.12 required

Claude Code Integration

• Slash commands for all operations (/arc:search, /arc:index, /arc:collection, etc.)
• Discoverable via /help or /commands in Claude Code
• No MCP overhead - direct CLI execution

Quick Start

Get started with Arcaneum in just a few commands:

# 1. Install
pipx install "https://github.com/cwensel/arcaneum/releases/download/v0.2.0/arcaneum-0.2.0-py3-none-any.whl"

# 2. Install Claude Code plugin (optional, in Claude Code)
# /plugin install cwensel/arcaneum

# 3. Verify and start services
arc doctor
arc container start

# 4. Create a corpus and sync content (indexes to both Qdrant and MeiliSearch)
arc corpus create Frameworks --type code
arc corpus sync Frameworks ~/libs/fastapi ~/libs/sqlalchemy

# 5. Search with semantic or full-text queries
arc search semantic "dependency injection pattern" --corpus Frameworks
arc search text "async def" --corpus Frameworks

First time? Run arc doctor to check prerequisites and get setup guidance.

👉 Full Quick Start Guide - Detailed walkthrough with troubleshooting

Quick Reference

# Service Management
arc container start          # Start Qdrant and MeiliSearch
arc container status         # Check service health
arc doctor                   # Verify setup

# Corpus (Recommended - Dual Indexing to Both Systems)
arc corpus create NAME --type TYPE       # pdf, code, or markdown
arc corpus list                          # List all corpora
arc corpus sync NAME PATH [PATH...]      # Sync one or more directories
arc corpus items NAME                    # List items with parity status
arc corpus parity NAME                   # Check/restore parity between systems
arc corpus delete NAME                   # Delete both collection and index