cocoindex

name	cocoindex
description	CocoIndex content indexing and search — use when you need to search indexed project content or trigger re-indexing

CocoIndex Skill

When to Use CocoIndex

Use CocoIndex tools and commands when you need to:

1. Search indexed project content — use cocoindex_search instead of content_search or ctx_search
2. Trigger re-indexing — use /unipi:cocoindex-update after significant code changes
3. Initialize indexing — use /unipi:cocoindex-init for new projects

Key Differences from Old FTS5 Content Store

Aspect	Old (FTS5)	New (CocoIndex)
Chunking	Heading/paragraph split	AST-aware code, recursive text
Search	BM25 + trigram fuzzy	Semantic vector + full-text
Incremental	Full re-index every time	Delta-only (changed files)
Scale	Small corpus	Multi-GiB, parallel
Store	SQLite FTS5	LanceDB

Tools

cocoindex_search

Search indexed content. It uses semantic vector search when the LanceDB table has vectors, LanceDB full-text search when an inverted index exists, and a lexical fallback for older text-only indexes.

cocoindex_search({ query: "how authentication works", limit: 10 })

Returns results with title, content, source, rank, contentType, and matchLayer.

cocoindex_status

Check indexing status — pipeline configured, last run, doc count, CLI availability.

cocoindex_status({})

Commands

Command	Description
/unipi:cocoindex-update	Run cocoindex update on current project
/unipi:cocoindex-status	Show indexing status
/unipi:cocoindex-init	Scaffold a default pipeline
/unipi:cocoindex-search <query>	Search indexed codebase semantically
/unipi:cocoindex-settings	Show configuration

Prerequisites

1. Python 3.10+ with cocoindex installed:

   pip install cocoindex
   pip install 'cocoindex[lancedb]'
   

2. Pipeline initialized — run /unipi:cocoindex-init once per project

3. Embedding config — CocoIndex reuses the memory package's embedding settings:

   • API key from ~/.unipi/memory/config.json
   • Same model and dimensions for cross-system search

Architecture

Project files → LocalFile source → SplitRecursively → EmbedText → LanceDB
                                                                              ↓
Agent query ──────────────────────────────→ vector search → ranked results

• Pipeline defined in .unipi/cocoindex/main.py (auto-generated, customizable)
• Data stored in .unipi/cocoindex/.lancedb/
• Search queries LanceDB directly via Node.js SDK (no CLI round-trip)

Troubleshooting

Issue	Fix
"CLI not found"	pip install cocoindex
"Pipeline not initialized"	/unipi:cocoindex-init
"Search unavailable"	npm install @lancedb/lancedb
"No results"	Run /unipi:cocoindex-update first
Embedding errors	Check ~/.unipi/memory/config.json API key