lantern-client 0.0.6

Python client for Lantern

Python Client for Lantern

Install

pip install lantern-client

Basic usage

from lantern import SyncClient

# Initialize the SyncClient with the database URL and table configuration
DB_URL="postgresql://postgres@localhost:5432/lantern"
client = SyncClient(url=DB_URL, table_name="small_world", dimensions=3, distance_type="l2sq", m=12, ef=64, ef_construction=64)

# Attempt to drop the existing table if it exists
try:
  client.drop()
except:
  print(f"An error occurred while dropping the table: {e}")

# Create a new table
client.create_table()

# Bulk insert data into the table
client.bulk_insert([
  ("1", [0,0,0], { "name": "a" }),
  ("2", [0,1,0], { "name": "b" }),
  ("3", [0,0,1], { "name": "c" })
])

# Create an index to optimize search queries
client.create_index()

# Retrieve a specific record by ID, selecting only certain fields
vec_by_id = client.get_by_id(id="1", select_fields=["id", "metadata"])
assert(vec_by_id.embedding is None)

# Retrieve a record by ID without field restrictions
vec_by_id = client.get_by_id("1")
assert(vec_by_id.id == "1")

# Retrieve multiple records by their IDs
vectors_by_ids = client.get_by_ids(["1", "3"])
assert(len(vectors_by_ids) == 2)
assert(vectors_by_ids[0].id == "1")
assert(vectors_by_ids[1].id == "3")

# Insert or update a single record
client.upsert(("4", [1,0,0], { "name": "d" }))

# Update the metadata of a record by its ID
client.update_by_id(id="4", metadata={ "name": "4" })

# Get the total number of records in the table
row_count = client.count()
assert(row_count == 4)

# Retrieve a record by ID, selecting specific fields
vec_by_id = client.get_by_id(id="4", select_fields=["id", "embedding", "metadata"])
assert(vec_by_id.metadata['name'] == "4")

# Search for vectors similar to the one with ID "4"
vectors = client.search(query_id="4")
assert(len(vectors) == row_count)
assert(vectors[0].id == "4")
assert(vectors[0].distance == 0)

# Search for vectors using the embedding of the vector with ID "4"
# Limit the results to 2 and apply a filter on the metadata
vectors = client.search(query_embedding=vec_by_id.embedding, limit=2, filter={"name": "a"}, select_fields=["id"])
assert(len(vectors) == 1)
assert(vectors[0].id == "1")
assert(vectors[0].metadata is None)
assert(vectors[0].embedding is None)