Index1

index1

AI memory system for coding agents with BM25 + vector hybrid search. Provides 6 MCP tools for intelligent code/doc search and cognitive fact recording.

What it does

• Dual memory: corpus (code index) + cognition (episodic facts)
• Hybrid search: BM25 full-text + vector semantic search with RRF fusion
• Structure-aware chunking: Markdown, Python, Rust, JavaScript, plain text
• MCP Server: 6 tools (recall, learn, read, status, reindex, config)
• CJK optimized: Chinese/Japanese/Korean query detection with dynamic weight tuning
• Built-in ONNX embedding: Vector search works out of the box, no Ollama required
• Graceful degradation: Works without any embedding service (BM25-only mode)

Install

# Recommended
pipx install index1

# Or via pip
pip install index1

# Or via npm (auto-installs Python package)
npx index1@latest

One-click plugin setup:

index1 setup                 # Auto-configure hooks + MCP for Claude Code

Verify:

index1 --version
index1 doctor        # Check environment

Setup MCP

Create .mcp.json in your project root:

{
  "mcpServers": {
    "index1": {
      "type": "stdio",
      "command": "index1",
      "args": ["serve"]
    }
  }
}

If index1 is not in PATH, use the full path from which index1.

Add Search Rules

Add to your project's .claude/CLAUDE.md:

## Search Strategy

This project has index1 MCP Server configured (recall + 5 other tools). When searching code:

1. Known identifiers (function/class/file names) -> Grep/Glob directly (4ms)
2. Exploratory questions ("how does XX work") -> recall first, then Grep for details
3. CJK query for English code -> must use recall (Grep can't cross languages)
4. High-frequency keywords (50+ expected matches) -> prefer recall (saves 90%+ context)

Impact:

Without rules: Grep "search" -> 881 lines -> 35,895 tokens
With rules:    recall        -> 5 summaries -> 460 tokens (97% savings)

Index Your Project

index1 index ./src ./docs    # Index source and docs
index1 status                # Check index stats
index1 search "your query"   # Test search

Optional: Multilingual Enhancement

index1 v2 has built-in ONNX embedding (bge-small-en-v1.5). For better multilingual support:

curl -fsSL https://ollama.com/install.sh | sh
ollama pull nomic-embed-text           # Standard, 270MB
# or
ollama pull bge-m3                     # Best for CJK, 1.2GB

index1 config embed_backend ollama
index1 doctor                          # Verify setup

Without Ollama, ONNX embedding provides vector search out of the box.

Web UI

index1 web                   # Start Web UI on port 6888
index1 web --port 8080       # Custom port

MCP Tools Reference

Troubleshooting