surrealdb

name	surrealdb
description	Comprehensive SurrealDB patterns for hybrid RAG, including vector search, graph traversal, timeline queries, and combined semantic-graph-temporal operations.

SurrealDB Skill

This skill provides comprehensive guidance for working with SurrealDB in the Bookstrap framework, covering database patterns, query strategies, and hybrid retrieval-augmented generation (RAG).

SurrealDB Overview

SurrealDB is a multi-model database that combines:

• Document storage: JSON-like records
• Graph database: Native relationships with RELATE statements
• Vector search: Native embedding support with MTREE indexes
• SQL-like queries: Familiar SurrealQL syntax

This makes it ideal for book writing, where you need:

• Semantic search (find similar content)
• Graph traversal (character relationships, citation chains)
• Timeline queries (chronological events, sequences)
• Combined queries (semantic + graph + timeline)

Core Database Patterns

1. Hybrid RAG Architecture

Hybrid RAG combines three retrieval strategies:

Semantic Search    +    Graph Traversal    +    Timeline Queries
     (vectors)              (relationships)           (sequences)
        ↓                        ↓                         ↓
                   Combined Context
                        ↓
                  Writing/Editing

Why Hybrid?

• Semantic alone: Misses explicit relationships
• Graph alone: Misses conceptual similarity
• Timeline alone: Misses both semantics and structure
• Combined: Comprehensive context retrieval

2. Vector Search Pattern

SurrealDB uses MTREE indexes for efficient k-nearest neighbor (kNN) search.

Basic Vector Search:

-- Find similar sections by semantic meaning
SELECT *, vector::similarity::cosine(embedding, $query_vector) AS similarity
FROM section
WHERE vector::similarity::cosine(embedding, $query_vector) > 0.7
ORDER BY similarity DESC
LIMIT 5;

Vector Index Configuration:

DEFINE INDEX idx_section_embedding
    ON section
    FIELDS embedding
    MTREE DIMENSION 1536 DIST COSINE TYPE F32;

Important: Embedding dimensions must match your provider:

• Gemini text-embedding-004: 768 dims
• OpenAI text-embedding-3-small: 1536 dims
• Ollama nomic-embed-text: 768 dims

3. Graph Traversal Pattern

Use -> and <- operators to traverse relationships.

Outbound Traversal (following relationships):

-- Get all sections where character Anna appears
SELECT ->appears_in->section.*
FROM character:anna;

Inbound Traversal (reverse relationships):

-- Get all characters in a section
SELECT <-appears_in<-character.*
FROM section:ch3_s2;

Multi-hop Traversal:

-- Get characters known by Anna's friends
SELECT ->knows->character->knows->character.*
FROM character:anna;

4. Timeline Query Pattern

Use sequence fields and datetime for chronological ordering.

Sequence-Based:

-- Get events before current point
SELECT * FROM event
WHERE sequence < $current_sequence
ORDER BY sequence DESC
LIMIT 10;

Date-Based:

-- Get events in date range
SELECT * FROM event
WHERE date >= $start_date
  AND date <= $end_date
ORDER BY date ASC;

Query Strategy Selection

Choose query strategy based on retrieval goal:

Goal	Strategy	Example Query
Find similar content	Vector search	"Find sections about training"
Get related entities	Graph traversal	"Get Anna's relationships"
Timeline context	Sequence query	"Events before chapter 5"
Comprehensive context	Hybrid (all three)	"Anna's training: similar content + relationships + timeline"

Pre-Write Query Workflow

Before writing a section, gather context using hybrid queries:

Step 1: Semantic Search

Find conceptually similar content:

-- Find related sections by theme
SELECT *, vector::similarity::cosine(embedding, $query_embedding) AS similarity
FROM section
WHERE vector::similarity::cosine(embedding, $query_embedding) > 0.7
ORDER BY similarity DESC
LIMIT 5;

Step 2: Graph Context

Get related entities and relationships:

-- Get characters and their relationships
SELECT
    c.name,
    c.description,
    ->knows->character.name AS knows,
    ->appears_in->section.id AS appears_in
FROM character c
WHERE c.id IN $character_ids;

Step 3: Timeline Context

Get chronological context:

-- Get recent events
SELECT * FROM event
WHERE sequence < $current_sequence
ORDER BY sequence DESC
LIMIT 10;

Step 4: Citation Chain

Get source support:

-- Get sources for concept
SELECT
    <-supports<-source.title,
    <-supports<-source.reliability,
    <-supports<-source.url
FROM concept:wireless_protocols;

Hybrid Query Patterns

Combine multiple strategies for comprehensive context.

Pattern 1: Semantic + Graph

-- Find similar sections that mention Anna
SELECT
    s.*,
    vector::similarity::cosine(s.embedding, $query_embedding) AS similarity
FROM section s
WHERE vector::similarity::cosine(s.embedding, $query_embedding) > 0.7
  AND s->appears_in->character:anna
ORDER BY similarity DESC
LIMIT 5;

Pattern 2: Semantic + Timeline

-- Find similar sections before current point
SELECT
    s.*,
    vector::similarity::cosine(s.embedding, $query_embedding) AS similarity
FROM section s
WHERE vector::similarity::cosine(s.embedding, $query_embedding) > 0.7
  AND s.sequence < $current_sequence
ORDER BY similarity DESC, s.sequence DESC
LIMIT 5;

Pattern 3: Graph + Timeline

-- Get Anna's appearances in chronological order
SELECT
    s.*,
    s.sequence
FROM character:anna->appears_in->section s
WHERE s.sequence < $current_sequence
ORDER BY s.sequence ASC;

Pattern 4: Full Hybrid (Semantic + Graph + Timeline)

-- Comprehensive context query
SELECT
    s.*,
    vector::similarity::cosine(s.embedding, $query_embedding) AS similarity,
    s.sequence
FROM section s
WHERE vector::similarity::cosine(s.embedding, $query_embedding) > 0.7
  AND s->appears_in->character IN $character_ids
  AND s.sequence < $current_sequence
ORDER BY similarity DESC, s.sequence DESC
LIMIT 10;

Entity Extraction and Storage

When ingesting sources or writing sections, extract and store entities.

Character Extraction

-- Create character with embedding
CREATE character SET
    name = "Anna",
    description = "SOE wireless operator, recruited 1942",
    embedding = $character_embedding,
    status = "alive",
    introduced_in = section:ch1_s3;

Location Extraction

-- Create location
CREATE location SET
    name = "Beaulieu Manor",
    description = "SOE training facility in Hampshire",
    embedding = $location_embedding,
    introduced = true;

Event Extraction

-- Create event with timeline info
CREATE event SET
    name = "Anna begins training",
    description = "Wireless operator course starts at Beaulieu",
    embedding = $event_embedding,
    sequence = 5,
    date = "1942-08-15T00:00:00Z";

Relationship Creation

-- Link character to section
RELATE character:anna->appears_in->section:ch3_s2;

-- Link section to location
RELATE section:ch3_s2->located_in->location:beaulieu;

-- Link section to source
RELATE section:ch3_s2->cites->source:soe_manual;

-- Timeline relationship
RELATE event:training_begins->precedes->event:deployment;

Query Optimization

1. Use Vector Indexes

Ensure MTREE indexes exist for all embedding fields:

-- Check existing indexes
INFO FOR TABLE section;

-- Create missing index
DEFINE INDEX idx_section_embedding
    ON section
    FIELDS embedding
    MTREE DIMENSION 1536 DIST COSINE TYPE F32;

2. Filter Before Vector Search

Apply filters before vector operations when possible:

-- Efficient: Filter first, then vector search
SELECT * FROM section
WHERE chapter = $chapter_id
  AND vector::similarity::cosine(embedding, $query_vector) > 0.7;

-- Less efficient: Vector search entire table
SELECT * FROM section
WHERE vector::similarity::cosine(embedding, $query_vector) > 0.7
  AND chapter = $chapter_id;

3. Limit Results Early

Use LIMIT to reduce processing:

SELECT * FROM section
WHERE vector::similarity::cosine(embedding, $query_vector) > 0.7
ORDER BY vector::similarity::cosine(embedding, $query_vector) DESC
LIMIT 5;  -- Only process top 5

4. Use LET for Reusable Queries

Store intermediate results:

-- Store character list
LET $characters = (
    SELECT * FROM character WHERE id IN $character_ids
);

-- Reuse in multiple queries
SELECT * FROM section
WHERE ->appears_in->character IN $characters;

Common Query Patterns

Find Uncited Claims

SELECT * FROM section
WHERE count(->cites->source) = 0
  AND length(content) > 100;

Get Citation Chain

SELECT
    s.id,
    s.content,
    ->cites->source.title AS sources,
    ->cites->source.reliability AS reliability
FROM section s
WHERE s.id = $section_id;

Character Relationship Graph

SELECT
    c1.name AS character,
    ->knows->character.name AS knows,
    <-knows<-character.name AS known_by
FROM character c1;

Timeline Consistency Check

-- Find events with contradictory sequence and dates
SELECT
    e1.name,
    e1.sequence,
    e1.date,
    e2.name,
    e2.sequence,
    e2.date
FROM event e1, event e2
WHERE e1.sequence < e2.sequence
  AND e1.date > e2.date;

Database Schema Reference

See schema.surql for the complete database schema including:

• Table definitions (chapter, section, source, character, location, event, concept)
• Field types and constraints
• Vector indexes (MTREE configuration)
• Edge tables (appears_in, located_in, cites, supports, precedes, follows, knows, related_to)

Query Pattern Files

This skill includes specialized query pattern files:

• semantic.surql: Vector search patterns with examples
• graph.surql: Graph traversal patterns for relationships
• timeline.surql: Timeline and sequence query patterns

Load these files when you need specific query types.

Best Practices

1. Always use embeddings: Generate embeddings for all content
2. Create relationships: Use RELATE for explicit connections
3. Track sequences: Maintain timeline order with sequence fields
4. Verify indexes: Ensure MTREE indexes exist before vector queries
5. Combine strategies: Use hybrid queries for comprehensive context
6. Limit results: Don't retrieve more than you need
7. Store metadata: Track created_at, updated_at for debugging
8. Use transactions: Group related operations for consistency

Error Handling

Common Errors

Vector dimension mismatch:

Error: Vector dimensions do not match (expected 1536, got 768)

Fix: Ensure config embedding dimensions match schema indexes.

Missing index:

Error: No vector index found on field 'embedding'

Fix: Create MTREE index with DEFINE INDEX.

Invalid relationship:

Error: Cannot RELATE records of incompatible types

Fix: Verify edge table schema matches record types.

Supporting Files

• schema.surql — Complete database schema reference
• semantic.surql — Vector search query patterns
• graph.surql — Graph traversal examples
• timeline.surql — Timeline query patterns