# Local RAG Index

The local RAG index is the first retrieval layer over canonical Markdown memory.

## Purpose

It helps AI clients quickly find relevant snippets without loading the whole project knowledge vault into context.

```text
project-knowledge/ Markdown
        ↓
scripts/aiw/indexer.py
        ↓
.aiw/indexes/<profile>/project-knowledge.jsonl
        ↓
MCP tool: memory_hybrid_search
```

## Current Implementation

The current indexer is dependency-free and lexical. It is intentionally simple so it can run on a new machine without a vector database.

Build:

```bash
python3 scripts/aiw/indexer.py build --profile fidelity
```

Status:

```bash
python3 scripts/aiw/indexer.py status --profile fidelity
```

Search:

```bash
python3 scripts/aiw/indexer.py search "dismissal lifecycle" --profile fidelity
```

## What It Stores

- source path;
- heading;
- text chunk;
- mtime;
- content hash;
- chunk id.

## What It Does Not Do

- It does not replace Markdown.
- It does not write project facts.
- It does not index templates as real notes.
- It does not send data to a cloud service.

## Future Options

Future phases may add:

- better full-text ranking;
- semantic embeddings;
- Qdrant or Chroma as optional local vector stores;
- hybrid lexical + semantic search;
- index status in the menu bar app.

Keep this as a derived layer. The project knowledge vault remains canonical.