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```python
# AI Dev Tool: Vector Search API Integration Wrapper
# ================================================
# This is a high-demand, production-ready developer code asset.
# It provides a PostgreSQL Vector Search Extension API integration wrapper.
# The wrapper is built using FastAPI and provides a simple, robust interface
# for searching vectors in a PostgreSQL database.

from fastapi import FastAPI, HTTPException
from fastapi.responses import JSONResponse
from pydantic import BaseModel
from typing import List, Optional
import psycopg2
import psycopg2.extras
import json

# Define the FastAPI application
app = FastAPI()

# Define the PostgreSQL connection parameters
POSTGRES_HOST = "localhost"
POSTGRES_PORT = 5432
POSTGRES_DB = "vector_search_db"
POSTGRES_USER = "vector_search_user"
POSTGRES_PASSWORD = "vector_search_password"

# Define the vector search model
class VectorSearchModel(BaseModel):
    """Vector search model"""
    vector: List[float]
    limit: Optional[int] = 10

# Define the vector search response model
class VectorSearchResponseModel(BaseModel):
    """Vector search response model"""
    results: List[List[float]]

# Create a PostgreSQL connection pool
def create_postgres_connection():
    """Create a

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