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```python
# AI Dev Tool: VectorDB FastAPI PostgreSQL Vector Search Extension
# Description: A production-ready boilerplate for building fast and scalable vector search APIs
# using FastAPI, PostgreSQL, and the PostgreSQL Vector Search Extension.

from fastapi import FastAPI, HTTPException
from fastapi.responses import JSONResponse
from fastapi.requests import Request
from pydantic import BaseModel
from typing import List, Optional
import psycopg2
import psycopg2.extras
import numpy as np
from sklearn.metrics.pairwise import cosine_similarity

# Define the FastAPI app
app = FastAPI()

# Define the PostgreSQL connection settings
POSTGRES_HOST = "localhost"
POSTGRES_DB = "vector_db"
POSTGRES_USER = "vector_user"
POSTGRES_PASSWORD = "vector_password"

# Define the PostgreSQL vector search extension settings
VECTOR_SEARCH_TABLE = "vector_search_table"
VECTOR_SEARCH_COLUMN = "vector_search_column"

# Define the vector search model
class VectorSearchModel(BaseModel):
    vector: List[float]
    metadata: Optional[dict]

# Define the vector search response model
class VectorSearchResponse(BaseModel):
    results: List[dict]

# Create a PostgreSQL connection pool
def create_connection_pool():
    return psycopg2.pool.Threaded

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