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ceviancevian
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Rename connection string to service url
1 parent fbe076b commit 555b9a2

4 files changed

Lines changed: 32 additions & 26 deletions

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‎README.md‎

Lines changed: 2 additions & 2 deletions
Original file line numberDiff line numberDiff line change
@@ -22,7 +22,7 @@ import os
2222

2323
``` python
2424
_ = load_dotenv(find_dotenv())
25-
connection_string = os.environ['PG_CONNECTION_STRING']
25+
service_url = os.environ['TIMESCALE_SERVICE_URL']
2626
```
2727

2828
Next, create the client.
@@ -43,7 +43,7 @@ from timescale_vector import client
4343
```
4444

4545
``` python
46-
vec = client.Async(connection_string, "my_data", 2)
46+
vec = client.Async(service_url, "my_data", 2)
4747
```
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4949
Next, create the tables for the collection:

‎nbs/00_vector.ipynb‎

Lines changed: 20 additions & 14 deletions
Original file line numberDiff line numberDiff line change
@@ -1,6 +1,7 @@
11
{
22
"cells": [
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{
4+
"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
@@ -46,10 +47,11 @@
4647
"outputs": [],
4748
"source": [
4849
"_ = load_dotenv(find_dotenv()) \n",
49-
"connection_string = os.environ['PG_CONNECTION_STRING'] "
50+
"service_url = os.environ['TIMESCALE_SERVICE_URL'] "
5051
]
5152
},
5253
{
54+
"attachments": {},
5355
"cell_type": "markdown",
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"metadata": {},
5557
"source": [
@@ -305,6 +307,7 @@
305307
]
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},
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{
310+
"attachments": {},
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"cell_type": "markdown",
309312
"metadata": {},
310313
"source": [
@@ -321,21 +324,21 @@
321324
"class Async(QueryBuilder):\n",
322325
" def __init__(\n",
323326
" self,\n",
324-
" connection_string: str,\n",
327+
" service_url: str,\n",
325328
" table_name: str,\n",
326329
" num_dimensions: int,\n",
327330
" distance_type: str = 'cosine') -> None:\n",
328331
" \"\"\"\n",
329332
" Initializes a async client for storing vector data.\n",
330333
" \n",
331334
" Args:\n",
332-
" connection_string (str): The connection string for the database.\n",
335+
" service_url (str): The connection string for the database.\n",
333336
" table_name (str): The name of the table.\n",
334337
" num_dimensions (int): The number of dimensions for the embedding vector.\n",
335338
" distance_type (str, optional): The distance type for indexing. Default is 'cosine' or '<=>'.\n",
336339
" \"\"\"\n",
337340
" self.builder = QueryBuilder(table_name,num_dimensions, distance_type)\n",
338-
" self.connection_string = connection_string\n",
341+
" self.service_url = service_url\n",
339342
" self.pool = None\n",
340343
" \n",
341344
" async def connect(self):\n",
@@ -348,7 +351,7 @@
348351
" if self.pool == None:\n",
349352
" async def init(conn):\n",
350353
" await register_vector(conn)\n",
351-
" self.pool = await asyncpg.create_pool(dsn=self.connection_string, init=init)\n",
354+
" self.pool = await asyncpg.create_pool(dsn=self.service_url, init=init)\n",
352355
" return self.pool.acquire()\n",
353356
"\n",
354357
" async def table_is_empty(self):\n",
@@ -609,12 +612,13 @@
609612
"outputs": [],
610613
"source": [
611614
"#| hide\n",
612-
"con = await asyncpg.connect(connection_string)\n",
615+
"con = await asyncpg.connect(service_url)\n",
613616
"await con.execute(\"DROP TABLE IF EXISTS data_table;\")\n",
614617
"await con.close()"
615618
]
616619
},
617620
{
621+
"attachments": {},
618622
"cell_type": "markdown",
619623
"metadata": {},
620624
"source": [
@@ -627,7 +631,7 @@
627631
"metadata": {},
628632
"outputs": [],
629633
"source": [
630-
"vec = Async(connection_string, \"data_table\", 2)\n",
634+
"vec = Async(service_url, \"data_table\", 2)\n",
631635
"await vec.create_tables()\n",
632636
"empty = await vec.table_is_empty()\n",
633637
"assert empty\n",
@@ -702,6 +706,7 @@
702706
]
703707
},
704708
{
709+
"attachments": {},
705710
"cell_type": "markdown",
706711
"metadata": {},
707712
"source": [
@@ -735,12 +740,12 @@
735740
" \n",
736741
" def __init__(\n",
737742
" self,\n",
738-
" connection_string: str,\n",
743+
" service_url: str,\n",
739744
" table_name: str,\n",
740745
" num_dimensions: int,\n",
741746
" distance_type: str = 'cosine') -> None:\n",
742747
" self.builder = QueryBuilder(table_name,num_dimensions, distance_type)\n",
743-
" self.connection_string = connection_string\n",
748+
" self.service_url = service_url\n",
744749
" self.pool = None\n",
745750
" psycopg2.extras.register_uuid()\n",
746751
"\n",
@@ -752,7 +757,7 @@
752757
" use in a context manager.\n",
753758
" \"\"\"\n",
754759
" if self.pool == None:\n",
755-
" self.pool = psycopg2.pool.SimpleConnectionPool(1, 10, dsn=self.connection_string)\n",
760+
" self.pool = psycopg2.pool.SimpleConnectionPool(1, 10, dsn=self.service_url)\n",
756761
" \n",
757762
" connection = self.pool.getconn()\n",
758763
" pgvector.psycopg2.register_vector(connection)\n",
@@ -1079,6 +1084,7 @@
10791084
]
10801085
},
10811086
{
1087+
"attachments": {},
10821088
"cell_type": "markdown",
10831089
"metadata": {},
10841090
"source": [
@@ -1092,7 +1098,7 @@
10921098
"outputs": [],
10931099
"source": [
10941100
"#| hide\n",
1095-
"con = await asyncpg.connect(connection_string)\n",
1101+
"con = await asyncpg.connect(service_url)\n",
10961102
"await con.execute(\"DROP TABLE IF EXISTS data_table;\")\n",
10971103
"await con.close()"
10981104
]
@@ -1103,7 +1109,7 @@
11031109
"metadata": {},
11041110
"outputs": [],
11051111
"source": [
1106-
"vec = Sync(connection_string, \"data_table\", 2)\n",
1112+
"vec = Sync(service_url, \"data_table\", 2)\n",
11071113
"vec.create_tables()\n",
11081114
"empty = vec.table_is_empty()\n",
11091115
"\n",
@@ -1204,9 +1210,9 @@
12041210
],
12051211
"metadata": {
12061212
"kernelspec": {
1207-
"display_name": "nbdev_env",
1213+
"display_name": "python3",
12081214
"language": "python",
1209-
"name": "nbdev_env"
1215+
"name": "python3"
12101216
}
12111217
},
12121218
"nbformat": 4,

‎nbs/index.ipynb‎

Lines changed: 3 additions & 3 deletions
Original file line numberDiff line numberDiff line change
@@ -63,7 +63,7 @@
6363
"outputs": [],
6464
"source": [
6565
"_ = load_dotenv(find_dotenv()) \n",
66-
"connection_string = os.environ['PG_CONNECTION_STRING'] "
66+
"service_url = os.environ['TIMESCALE_SERVICE_URL'] "
6767
]
6868
},
6969
{
@@ -98,7 +98,7 @@
9898
"outputs": [],
9999
"source": [
100100
"#| hide\n",
101-
"con = await asyncpg.connect(connection_string)\n",
101+
"con = await asyncpg.connect(service_url)\n",
102102
"await con.execute(\"DROP TABLE IF EXISTS my_data;\")\n",
103103
"await con.close()"
104104
]
@@ -118,7 +118,7 @@
118118
"metadata": {},
119119
"outputs": [],
120120
"source": [
121-
"vec = client.Async(connection_string, \"my_data\", 2)"
121+
"vec = client.Async(service_url, \"my_data\", 2)"
122122
]
123123
},
124124
{

‎timescale_vector/client.py‎

Lines changed: 7 additions & 7 deletions
Original file line numberDiff line numberDiff line change
@@ -189,21 +189,21 @@ def search_query(self, query_embedding: List[float], k: int=10, filter: Optional
189189
class Async(QueryBuilder):
190190
def __init__(
191191
self,
192-
connection_string: str,
192+
service_url: str,
193193
table_name: str,
194194
num_dimensions: int,
195195
distance_type: str = 'cosine') -> None:
196196
"""
197197
Initializes a async client for storing vector data.
198198
199199
Args:
200-
connection_string (str): The connection string for the database.
200+
service_url (str): The connection string for the database.
201201
table_name (str): The name of the table.
202202
num_dimensions (int): The number of dimensions for the embedding vector.
203203
distance_type (str, optional): The distance type for indexing. Default is 'cosine' or '<=>'.
204204
"""
205205
self.builder = QueryBuilder(table_name,num_dimensions, distance_type)
206-
self.connection_string = connection_string
206+
self.service_url = service_url
207207
self.pool = None
208208

209209
async def connect(self):
@@ -216,7 +216,7 @@ async def connect(self):
216216
if self.pool == None:
217217
async def init(conn):
218218
await register_vector(conn)
219-
self.pool = await asyncpg.create_pool(dsn=self.connection_string, init=init)
219+
self.pool = await asyncpg.create_pool(dsn=self.service_url, init=init)
220220
return self.pool.acquire()
221221

222222
async def table_is_empty(self):
@@ -344,12 +344,12 @@ class Sync:
344344

345345
def __init__(
346346
self,
347-
connection_string: str,
347+
service_url: str,
348348
table_name: str,
349349
num_dimensions: int,
350350
distance_type: str = 'cosine') -> None:
351351
self.builder = QueryBuilder(table_name,num_dimensions, distance_type)
352-
self.connection_string = connection_string
352+
self.service_url = service_url
353353
self.pool = None
354354
psycopg2.extras.register_uuid()
355355

@@ -361,7 +361,7 @@ def connect(self):
361361
use in a context manager.
362362
"""
363363
if self.pool == None:
364-
self.pool = psycopg2.pool.SimpleConnectionPool(1, 10, dsn=self.connection_string)
364+
self.pool = psycopg2.pool.SimpleConnectionPool(1, 10, dsn=self.service_url)
365365

366366
connection = self.pool.getconn()
367367
pgvector.psycopg2.register_vector(connection)

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