memory-index

---

name: memory-index description: Index a folder's contents into the MemPalace semantic memory for search and retrieval. Use this skill whenever the user asks to "index a folder", "index a directory", "index memory", "mine a project into memory", "make a folder searchable", "embed a folder", "ingest code into the palace", or "index this directory for semantic search". Covers the full flow: model download, mempalace.yaml room configuration, mining, and verification via search. Also trigger when the user says "memory index", "index memory of folder", or asks how to make a project's code semantically searchable with pi-go's MemPalace.

Index a Folder into MemPalace

This skill walks through indexing a folder's source code into pi-go's MemPalace — the 4-layer semantic memory system with SQLite storage and embedding-based search.

Once indexed, the agent can search the folder's contents by meaning, not just keywords, using the palace-search tool or pi memory search CLI.

Prerequisites

The embedding model (all-MiniLM-L6-v2) must be downloaded. Check with:

pi memory model status

If not downloaded:

pi memory model download

This fetches ~90 MB of ONNX model files to ~/.pi-go/models/. It auto-selects the optimal ONNX variant for the platform (quantized ARM64 on Apple Silicon, base on x86_64).

The model is optional. Without it, search falls back to FTS5 keyword matching. Semantic search requires the model; keyword search works without it.

Workflow

1. Verify the model is ready

pi memory model status

Output should show Model: all-MiniLM-L6-v2 with a path and size. If it says Model: not downloaded, run pi memory model download first.

2. Configure rooms (optional but recommended)

Create a mempalace.yaml at the repo root to map file patterns to semantic "rooms". This narrows the search space before embeddings fire, improving retrieval significantly (benchmark: 60.9% recall unfiltered → 94.8% with wing+room filtering).

wing: my-project          # top-level name (defaults to directory basename)

rooms:
  - name: api
    patterns:
      - "internal/api/**"
      - "internal/handler/**"
    keywords:
      - http
      - handler
      - route
  - name: models
    patterns:
      - "internal/models/**"
    keywords:
      - schema
      - struct
      - database
  - name: tests
    patterns:
      - "**/*_test.go"
    keywords:
      - test
      - mock
      - fixture

Rules for good rooms:

• 3–8 rooms is the sweet spot. Too few = no filtering power. Too many = sparse drawers.
• Use patterns (glob) for path-based assignment. Use keywords for content-based hints.
• Files not matching any room go to a default room named general.
• The wing defaults to the directory basename if omitted.

3. Mine the folder

pi memory mine .

This command:

• Walks the directory recursively
• Respects .gitignore and skips node_modules/, vendor/, .git/, dist/, build/, etc.
• Chunks each supported file into semantic units
• Embeds each chunk with all-MiniLM-L6-v2 (384-dim vectors)
• Stores drawers in .pi-go/palace.db (SQLite + FTS5 + embedding BLOBs)
• Detects and skips near-duplicate content (cosine similarity > threshold)

Flags:

pi memory mine .                    # mine source files (default)
pi memory mine                      # same as above — defaults to current directory
pi memory mine . --wing myapp       # override wing name
pi memory mine . --convos            # mine conversation files (.jsonl, .txt, .md) instead of source

Supported file extensions (source mode): .go, .py, .js, .ts, .tsx, .jsx, .java, .c, .cpp, .h, .hpp, .rs, .rb, .php, .swift, .kt, .scala, .md, .txt, .json, .yaml, .yml, .toml, .xml, .html, .css, .scss, .sql, .sh, .bash, .zsh, .fish.

File size limit: 512 KB per file. Larger files are skipped silently.

Progress: The command shows a live spinner with phase-aware progress (scan → embed → insert), per-file counts, chunk counts, elapsed time, and a final summary with palace status:

 ✓  done     [████████████████████████████████]  142/142 files, 138 chunks  3m12s

Mining complete:
  Processed: 142
  Added:     138
  Skipped:   4 (duplicates)
  Errors:    0

Palace status:
  Drawers: 138
  Wings:   1
  Rooms:   5

4. Verify with search

Search the indexed content semantically:

pi memory search "authentication flow" --limit 5
pi memory search "database connection pool" --wing myapp --room models

Or from within an agent session, the palace-search tool is available automatically when a palace database exists at .pi-go/palace.db.

5. Check status

pi memory status

Shows drawer counts per wing/room, database size, and model status.

Re-indexing

Mining is idempotent for identical content — duplicates are detected by embedding similarity and skipped. To re-index after significant changes:

pi memory mine .

Changed files will be re-embedded; unchanged files will be skipped as duplicates. For a full clean re-index, delete the database first:

rm .pi-go/palace.db
pi memory mine .

Tips

• Index at the repo root so .gitignore and mempalace.yaml are picked up.
• Run from the project directory — the palace database is created at .pi-go/palace.db relative to the mined directory.
• Index conversations separately with --convos to search past agent sessions.
• Search with filters — --wing and --room dramatically improve recall by narrowing the search space before similarity ranking.
• No model? Still works. If the embedding model isn't loaded, search falls back to FTS5 keyword matching automatically. Semantic search is better, keyword is the floor.