# Python Client for Lantern
## Install
```sh
pip install lantern-client
```
## Basic usage
```python
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)
```
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"description": "# Python Client for Lantern\n\n## Install\n\n```sh\npip install lantern-client\n```\n\n## Basic usage\n\n```python\nfrom lantern import SyncClient\n\n# Initialize the SyncClient with the database URL and table configuration\nDB_URL=\"postgresql://postgres@localhost:5432/lantern\"\nclient = SyncClient(url=DB_URL, table_name=\"small_world\", dimensions=3, distance_type=\"l2sq\", m=12, ef=64, ef_construction=64)\n\n# Attempt to drop the existing table if it exists\ntry:\n client.drop()\nexcept:\n print(f\"An error occurred while dropping the table: {e}\")\n\n# Create a new table\nclient.create_table()\n\n# Bulk insert data into the table\nclient.bulk_insert([\n (\"1\", [0,0,0], { \"name\": \"a\" }),\n (\"2\", [0,1,0], { \"name\": \"b\" }),\n (\"3\", [0,0,1], { \"name\": \"c\" })\n])\n\n# Create an index to optimize search queries\nclient.create_index()\n\n# Retrieve a specific record by ID, selecting only certain fields\nvec_by_id = client.get_by_id(id=\"1\", select_fields=[\"id\", \"metadata\"])\nassert(vec_by_id.embedding is None)\n\n# Retrieve a record by ID without field restrictions\nvec_by_id = client.get_by_id(\"1\")\nassert(vec_by_id.id == \"1\")\n\n# Retrieve multiple records by their IDs\nvectors_by_ids = client.get_by_ids([\"1\", \"3\"])\nassert(len(vectors_by_ids) == 2)\nassert(vectors_by_ids[0].id == \"1\")\nassert(vectors_by_ids[1].id == \"3\")\n\n# Insert or update a single record\nclient.upsert((\"4\", [1,0,0], { \"name\": \"d\" }))\n\n# Update the metadata of a record by its ID\nclient.update_by_id(id=\"4\", metadata={ \"name\": \"4\" })\n\n# Get the total number of records in the table\nrow_count = client.count()\nassert(row_count == 4)\n\n# Retrieve a record by ID, selecting specific fields\nvec_by_id = client.get_by_id(id=\"4\", select_fields=[\"id\", \"embedding\", \"metadata\"])\nassert(vec_by_id.metadata['name'] == \"4\")\n\n# Search for vectors similar to the one with ID \"4\"\nvectors = client.search(query_id=\"4\")\nassert(len(vectors) == row_count)\nassert(vectors[0].id == \"4\")\nassert(vectors[0].distance == 0)\n\n# Search for vectors using the embedding of the vector with ID \"4\"\n# Limit the results to 2 and apply a filter on the metadata\nvectors = client.search(query_embedding=vec_by_id.embedding, limit=2, filter={\"name\": \"a\"}, select_fields=[\"id\"])\nassert(len(vectors) == 1)\nassert(vectors[0].id == \"1\")\nassert(vectors[0].metadata is None)\nassert(vectors[0].embedding is None)\n\n```\n",
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