Performance test for pgvector HNSW index build and queries (#7873)

## Problem

We want to regularly verify the performance of pgvector HNSW parallel
index builds and parallel similarity search using HNSW indexes.
The first release that considerably improved the index-build parallelism
was pgvector 0.7.0 and we want to make sure that we do not regress by
our neon compute VM settings (swap, memory over commit, pg conf etc.)

## Summary of changes

Prepare a Neon project with 1 million openAI vector embeddings (vector
size 1536).
Run HNSW indexing operations in the regression test for the various
distance metrics.
Run similarity queries using pgbench with 100 concurrent clients.

I have also added the relevant metrics to the grafana dashboards pgbench
and olape

---------

Co-authored-by: Alexander Bayandin <alexander@neon.tech>

test_runner/performance/pgvector/loaddata.py

@@ -0,0 +1,72 @@
+import sys
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+import psycopg2
+from pgvector.psycopg2 import register_vector
+from psycopg2.extras import execute_values
+
+
+def print_usage():
+    print("Usage: loaddata.py <CONNSTR> <DATADIR>")
+
+
+def main(conn_str, directory_path):
+    # Connection to PostgreSQL
+    with psycopg2.connect(conn_str) as conn:
+        with conn.cursor() as cursor:
+            # Run SQL statements
+            cursor.execute("CREATE EXTENSION IF NOT EXISTS vector;")
+            register_vector(conn)
+            cursor.execute("DROP TABLE IF EXISTS documents;")
+            cursor.execute(
+                """
+                CREATE TABLE documents (
+                    _id TEXT PRIMARY KEY,
+                    title TEXT,
+                    text TEXT,
+                    embeddings vector(1536) -- text-embedding-3-large-1536-embedding (OpenAI)
+                );
+            """
+            )
+            conn.commit()
+
+            # List and sort Parquet files
+            parquet_files = sorted(Path(directory_path).glob("*.parquet"))
+
+            for file in parquet_files:
+                print(f"Loading {file} into PostgreSQL")
+                df = pd.read_parquet(file)
+
+                print(df.head())
+
+                data_list = [
+                    (
+                        row["_id"],
+                        row["title"],
+                        row["text"],
+                        np.array(row["text-embedding-3-large-1536-embedding"]),
+                    )
+                    for index, row in df.iterrows()
+                ]
+                # Use execute_values to perform batch insertion
+                execute_values(
+                    cursor,
+                    "INSERT INTO documents (_id, title, text, embeddings) VALUES %s",
+                    data_list,
+                )
+                # Commit after we insert all embeddings
+                conn.commit()
+
+                print(f"Loaded {file} into PostgreSQL")
+
+
+if __name__ == "__main__":
+    if len(sys.argv) != 3:
+        print_usage()
+        sys.exit(1)
+
+    conn_str = sys.argv[1]
+    directory_path = sys.argv[2]
+    main(conn_str, directory_path)