txtai-smart-list.py

from txtai import Embeddings

# In-memory data
data = [{"name":"John", "age": 16}, {"name":"Jon", "age": 45},{"name":"Sarah", "age": 18}]

# Vector embeddings index with content storage
embeddings = Embeddings(content=True, columns={"text": "name"})
embeddings.index(data)

# Vector similarity
embeddings.search("SELECT name, age, score FROM txtai WHERE similar('jon')", 2)
# [{'name': 'Jon', 'age': 45, 'score': 1.0},
#  {'name': 'John', 'age': 16, 'score': 0.6010721921920776}]

# String comparison backed by SQLite
embeddings.search("SELECT name, age FROM txtai WHERE name like '%jon%'")
# [{'name': 'Jon', 'age': 45}]

# Vector similarity + SQL filters
embeddings.search("SELECT name, age, score FROM txtai WHERE similar('jon') AND score >= 0.5 AND age < 25")
# [{'name': 'John', 'age': 16, 'score': 0.6010721921920776}]