txtai-query-sql.py

# Create an index for the list of text
embeddings.index([(uid, {"text": text, "length": len(text)}, None) for uid, text in enumerate(data)])

# Filter by score
print(embeddings.search("select text, score from txtai where similar('hiking danger') and score >= 0.15"))

# Filter by metadata field 'length'
print(embeddings.search("select text, length, score from txtai where similar('feel good story') and score >= 0.05 and length >= 40"))

# Run aggregate queries
print(embeddings.search("select count(*), min(length), max(length), sum(length) from txtai"))
print()
for x in embeddings.search("select count(*), min(length), max(length), sum(length), text, score from txtai group by text limit 10"):
  print(x)