-- Dense search
QUERY 'emergency care' FROM docs LIMIT 5

-- Hybrid with parameterized RRF
QUERY 'emergency care' FROM docs LIMIT 5 USING HYBRID WITH (rrf_k = 30, rrf_weights = [0.7, 0.3])

-- Multi-stage retrieval with CTEs
WITH dense AS (QUERY 'care' USING dense LIMIT 200),
     sparse AS (QUERY 'care' USING sparse LIMIT 300)
QUERY 'care' FROM docs LIMIT 10 PREFETCH (dense, sparse) FUSION RRF

-- Pure fusion (no search target, just combine CTE results)
WITH dense AS (QUERY 'care' USING dense LIMIT 200),
     sparse AS (QUERY 'care' USING sparse LIMIT 300)
FUSION RRF LIMIT 10 PREFETCH (dense, sparse)

-- Recommendation by example
QUERY RECOMMEND WITH (positive = ('id-1', 'id-2'), negative = ('id-3')) FROM docs LIMIT 5

-- Score shaping with BOOST
QUERY 'emergency' FROM docs LIMIT 10
  BOOST (CASE WHEN priority = 'high' THEN 2.0 ELSE 1.0 END + GAUSS_DECAY(date, target: datetime('2026-01-01'), scale: 30d))

-- Create collection with multivector (HNSW disabled for reranking)
CREATE COLLECTION pdf_retrieval (
  original VECTOR(128, COSINE) WITH MULTIVECTOR (comparator = 'max_sim') WITH HNSW (m = 0),
  mean_pooling_columns VECTOR(128, COSINE) WITH MULTIVECTOR (comparator = 'max_sim'),
  mean_pooling_rows VECTOR(128, COSINE) WITH MULTIVECTOR (comparator = 'max_sim')
)

-- Insert with named vectors
INSERT INTO pdf_retrieval VALUES {'id': 1, 'vector': {'original': [[0.1, 0.2], [0.3, 0.4]], 'mean_pooling_columns': [[0.1, 0.2]], 'mean_pooling_rows': [[0.3, 0.4]]}}

-- Two-stage retrieval: prefetch with mean-pooled, rerank with original
WITH
  _pf0 AS (QUERY [0.1, 0.2, 0.3] USING 'mean_pooling_columns' LIMIT 100),
  _pf1 AS (QUERY [0.1, 0.2, 0.3] USING 'mean_pooling_rows' LIMIT 100)
QUERY [0.1, 0.2, 0.3] FROM pdf_retrieval USING 'original' LIMIT 10
  PREFETCH (_pf0, _pf1)

CREATE COLLECTION docs HYBRID
  WITH HNSW (m = 32, ef_construct = 100)
  WITH QUANTIZATION (type = 'scalar', quantile = 0.95, always_ram = true)

ALTER COLLECTION docs WITH VECTORS (on_disk = true)

CREATE INDEX ON docs FOR tags TYPE keyword WITH (is_tenant = true)

SHOW COLLECTION docs

INSERT INTO docs VALUES {'id': 1, 'text': 'hello', 'topic': 'search'} USING HYBRID
INSERT INTO docs VALUES {'id': 1, 'vector': {'dense': [0.1, 0.2, 0.3], 'colbert': [[0.1, 0.2], [0.3, 0.4]]}}
SELECT * FROM docs WHERE id = 1
SCROLL FROM docs WHERE topic = 'search' LIMIT 10
UPDATE docs SET VECTOR 'colbert' = [0.1, 0.2, 0.3] WHERE id = 1
UPDATE docs SET PAYLOAD = {'status': 'reviewed'} WHERE id = 1
DELETE FROM docs WHERE status = 'archived'

QUERY 'emergency care' FROM docs LIMIT 10
  BOOST (
    $score * 2
    + CASE WHEN priority = 'high' THEN 10 ELSE 0 END
    + ABS(GEO_DISTANCE({lat: 40.7, lon: -74.0}, location)) * -0.1
  )
  DEFAULTS (priority_weight = 1.5)

WHERE status = 'active' AND year >= 2024
WHERE priority IN ('high', 'medium') AND tags IS NOT NULL
WHERE (team = 'search' OR team = 'infra') AND severity BETWEEN 3 AND 5
WHERE title MATCH PHRASE 'vector search'