close but no cigar

.gitignore

@@ -326,3 +326,4 @@ cython_debug/
 #.idea/
 
 /arxiv_abstracts.zip
+*.parquet

arxiv-import.py

@@ -4,19 +4,21 @@
 ### DO NOT USE = this is my failed attempt to try to get the data into pgvector
 
 import re
+import sys
+
+import numpy as np
 import pyarrow as pa
 import pyarrow.parquet as pq
-import pandas as pd
 import psycopg
 from pgvector.psycopg import register_vector
 from pathlib import Path
 
 # The proper word is arxiv but I messed when creating the folder and project
 
-PARQUET_PATH= Path('../arvix_abstracts/arxiv_abstracts')
+PARQUET_PATH= Path('./')
 DB_NAME= 'arxiv'
 
-conn = psycopg.connect("host=127.0.0.1 user=postgres password='test'", autocommit=True)
+conn = psycopg.connect("host=localhost user=postgres password='letmein'", autocommit=True)
 cursor = conn.cursor()
 
 cursor.execute("SELECT datname FROM pg_database;")
@@ -31,57 +33,45 @@
 
 #Now close the connection and switch DB
 conn.close()
-conn = psycopg.connect("host=127.0.0.1 user=postgres password='test' dbname='arxiv' ")
+
+conn = psycopg.connect("host=127.0.0.1 user=postgres password='letmein' dbname='arxiv' ")
 cursor = conn.cursor()
 
 
 cursor.execute('CREATE EXTENSION IF NOT EXISTS vector')
 register_vector(conn)
+
+conn.execute('DROP TABLE IF EXISTS documents')
 cursor.execute('CREATE TABLE documents (id bigserial PRIMARY KEY, abstract text, embedding vector(768))')
 cursor.connection.commit()
-
+#sys.exit()
+## Loop through the parquet files)
 for path in PARQUET_PATH.rglob('*.parquet'):
     match = re.search('.*(\w{16})\.parquet', str(path))
     data_table = pa.parquet.read_table(path, memory_map=True, columns=['abstract', 'embeddings'])
     data_pandas = data_table.to_pandas()
     #cursor.executemany("INSERT INTO documents (abstract, embedding) VALUES (%s, %s)", data_table.to_pylist())
     print("working on: " + str(path))
     i = 0
-    # for idx, row in data_pandas.iterrows():
-    #     abstract = row['abstract']
-    #     embedding = row['embeddings']
-    #     cursor.executemany("INSERT INTO documents (abstract, embedding) VALUES (%s, %s)", (abstract, embedding.tolist()))
-    #     if (i % 1000 == 0):
-    #         print("working on row: " + str(i))
-    #     i = i + 1
-    # cursor.connection.commit()
 
 
-    with cursor.copy("COPY documents (abstract, embedding) FROM STDIN") as copy:
+
+    with cursor.copy("COPY documents (abstract, embedding) FROM STDIN with (FORMAT BINARY)") as copy:
             print("working on: " + str(path))
-            i = 0
-            for item in data_table.to_pylist():
-                print("working on row: " + str(i))
-                abstract = item['abstract']
-                embedding = item['embeddings']
-                item_tuple = (abstract, embedding)
-                copy.write_row(item_tuple)
+            #for i in range(data_pandas.rows):
+            for i in range(11):
+                copy.write_row([data_pandas.abstract[i], data_pandas.embeddings[i]])
+
+    #copy.set_types(['text', 'vector'])
+            # i = 0
+            # for item in data_table.to_pylist():
+                # print("working on row: " + str(i))
+                # abstract = item['abstract']
+                # embedding = np.asarray(item['embeddings'])
+                # # item_tuple = (abstract, embedding)
+                # copy.write_row([abstract, embedding])
                 i = i + 1
-    cursor.connection.commit()
-        #cursor.mogrify("INSERT INTO documents (abstract, embedding) VALUES (%s, %s)", (abstract, embedding.tolist()))
-        #cursor.execute("INSERT INTO documents (abstract, embedding) VALUES (%s, %s)", (abstract, embedding.tolist()))
-        #print(" ")
-    # for row in data:
-    #     abstract = data['abstract']
-    #     embedding = data['embeddings']
-
-
-
-    #print(item)
-    # data['embeddings'][1]
-    # conn.execute('INSERT INTO documents (content, embedding) VALUES (%s, %s)', (content, embedding))
-    # print("RSS: {}MB".format(pa.total_allocated_bytes() >> 20))
-    #print(path)
+                if i == 11: break
 conn.close()
 print('finished')
 # Should it create the DB and then load the vector extension - yes for now

pgvector_example.py

@@ -0,0 +1,69 @@
+import numpy as np
+from pgvector.psycopg import register_vector
+import psycopg
+
+# generate random data
+rows = 100000
+dimensions = 128
+embeddings = np.random.rand(rows, dimensions)
+categories = np.random.randint(100, size=rows).tolist()
+queries = np.random.rand(10, dimensions)
+
+DB_NAME= 'pgvector_citus'
+
+#Make the table
+conn = psycopg.connect("host=localhost user=postgres password='letmein'", autocommit=True)
+cursor = conn.cursor()
+
+cursor.execute("SELECT datname FROM pg_database;")
+
+list_database = cursor.fetchall()
+
+if ('pgvector_citus',) in list_database:
+    cursor.execute(("DROP database "+ DB_NAME +" with (FORCE);"))
+    cursor.execute("create database " + DB_NAME + ";");
+else:
+    cursor.execute("create database " + DB_NAME + ";");
+
+#Now close the connection and switch DB
+conn.close()
+
+
+# enable extensions
+conn = psycopg.connect("host=localhost user=postgres password='letmein' dbname='pgvector_citus'", autocommit=True)
+conn.execute('CREATE EXTENSION IF NOT EXISTS vector')
+
+# GUC variables set on the session do not propagate to Citus workers
+# https://github.com/citusdata/citus/issues/462
+# you can either:
+# 1. set them on the system, user, or database and reconnect
+# 2. set them for a transaction with SET LOCAL
+# S
+conn.close()
+
+# reconnect for updated GUC variables to take effect
+conn = psycopg.connect("host=localhost user=postgres password='letmein' dbname='pgvector_citus'", autocommit=True)
+register_vector(conn)
+
+print('Creating distributed table')
+conn.execute('DROP TABLE IF EXISTS items')
+conn.execute('CREATE TABLE items (id bigserial, embedding vector(%d), category_id bigint, PRIMARY KEY (id, category_id))' % dimensions)
+#conn.execute('SET citus.shard_count = 4')
+#conn.execute("SELECT create_distributed_table('items', 'category_id')")
+
+print('Loading data in parallel')
+with conn.cursor().copy('COPY items (embedding, category_id) FROM STDIN WITH (FORMAT BINARY)') as copy:
+    copy.set_types(['vector', 'bigint'])
+
+    for i in range(rows):
+        copy.write_row([embeddings[i], categories[i]])
+
+conn.close()
+
+# print('Creating index in parallel')
+# conn.execute('CREATE INDEX ON items USING hnsw (embedding vector_l2_ops)')
+#
+# print('Running distributed queries')
+# for query in queries:
+#     items = conn.execute('SELECT id FROM items ORDER BY embedding <-> %s LIMIT 10', (query,)).fetchall()
+#     print([r[0] for r in items])

requirements.txt

@@ -1,7 +1,3 @@
-numpy
-requests>=2.26.0
-scikit_learn>=1.0.2
-scipy
-sentence_transformers>=2.2.0
-torch
-tqdm
+psycopg
+pyarrow
+pgvector