“BeeRad”andClaude Opus 4.6
ec3ab74efe
- Switch rah_search_nodes and rah_search_content from LIKE to FTS5 with word-split LIKE fallback
- Rebuild nodes_fts and chunks_fts indexes on server startup
- Cap chunk field at 10K chars in rah_get_nodes (adds chunk_truncated, chunk_length)
- Multi-word queries now work ("Tesla manufacturing" finds both words anywhere)
- Relevance-ranked results when FTS available
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
104 lines
3.1 KiB
Markdown
104 lines
3.1 KiB
Markdown
---
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name: Schema
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description: Full database schema, tables, columns, query patterns.
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immutable: true
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---
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# Database Schema
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## Tables
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### nodes
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| Column | Type | Notes |
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|--------|------|-------|
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| id | INTEGER | Primary key, auto-increment |
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| title | TEXT | Required |
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| description | TEXT | AI-generated grounding context (~1 sentence) |
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| content | TEXT | User's notes/thoughts (not source content) |
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| chunk | TEXT | Full verbatim source content |
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| chunk_status | TEXT | 'pending', 'chunked', 'failed' |
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| link | TEXT | External URL (only for nodes representing external content) |
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| type | TEXT | Nullable (reserved for future use) |
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| metadata | TEXT | JSON blob (map_position, transcript_length, etc.) |
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| is_pinned | INTEGER | Legacy — use hub node queries instead |
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| created_at | TEXT | ISO timestamp |
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| updated_at | TEXT | ISO timestamp |
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### edges
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| Column | Type | Notes |
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|--------|------|-------|
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| id | INTEGER | Primary key |
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| from_node_id | INTEGER | FK → nodes.id |
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| to_node_id | INTEGER | FK → nodes.id |
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| context | TEXT | JSON: `{ explanation, category, type, confidence, created_via }` |
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| source | TEXT | 'user', 'ai_similarity', or helper name |
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| explanation | TEXT | Human-readable reason for connection |
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| created_at | TEXT | ISO timestamp |
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### dimensions
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| Column | Type | Notes |
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|--------|------|-------|
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| name | TEXT | Primary key |
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| description | TEXT | Purpose description |
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| is_priority | INTEGER | 1 = priority dimension (auto-assigns to new nodes) |
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| updated_at | TEXT | ISO timestamp |
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### node_dimensions (junction)
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| Column | Type |
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|--------|------|
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| node_id | INTEGER FK → nodes.id |
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| dimension | TEXT (dimension name) |
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### chunks (for semantic search)
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| Column | Type | Notes |
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|--------|------|-------|
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| id | INTEGER | Primary key |
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| node_id | INTEGER | FK → nodes.id |
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| chunk_idx | INTEGER | Position in sequence |
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| text | TEXT | Chunk content |
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| created_at | TEXT | ISO timestamp |
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| embedding_type | TEXT | Embedding model used |
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| metadata | TEXT | JSON blob |
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### FTS Tables
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- `chunks_fts` — full-text search on chunk text
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- `nodes_fts` — full-text search on node title + content
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## Common Query Patterns
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**Top connected nodes (hubs):**
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```sql
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SELECT n.id, n.title, n.description, COUNT(DISTINCT e.id) AS edge_count
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FROM nodes n
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LEFT JOIN edges e ON (e.from_node_id = n.id OR e.to_node_id = n.id)
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GROUP BY n.id ORDER BY edge_count DESC LIMIT 5
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```
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**Nodes in a dimension:**
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```sql
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SELECT n.* FROM nodes n
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JOIN node_dimensions nd ON n.id = nd.node_id
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WHERE nd.dimension = ?
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```
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**Edges for a node (both directions):**
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```sql
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SELECT e.*, n1.title as from_title, n2.title as to_title
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FROM edges e
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JOIN nodes n1 ON e.from_node_id = n1.id
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JOIN nodes n2 ON e.to_node_id = n2.id
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WHERE e.from_node_id = ? OR e.to_node_id = ?
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```
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**Search source content (chunks):**
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```sql
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SELECT c.id, c.node_id, c.chunk_idx, c.text, n.title
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FROM chunks c
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JOIN nodes n ON c.node_id = n.id
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WHERE c.text LIKE '%search term%' COLLATE NOCASE
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ORDER BY c.chunk_idx ASC
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LIMIT 10
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```
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**Use `rah_search_content` to search chunks by keyword, or `rah_sqlite_query` for any read operation not covered by structured tools.**
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