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```python
# AI Dev Tool: CloudVector PostgreSQL Vector Search Extension Boilerplate
# Description: A production-ready boilerplate for building scalable vector search applications
#              using PostgreSQL and Rust WebAssembly Edge Functions.

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

# Define the PostgreSQL connection settings
POSTGRES_HOST = os.environ.get("POSTGRES_HOST", "localhost")
POSTGRES_PORT = os.environ.get("POSTGRES_PORT", 5432)
POSTGRES_USER = os.environ.get("POSTGRES_USER", "postgres")
POSTGRES_PASSWORD = os.environ.get("POSTGRES_PASSWORD", "postgres")
POSTGRES_DB = os.environ.get("POSTGRES_DB", "vector_search")

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

# Create a FastAPI application instance
app = FastAPI()

# Define a Pydantic model for vector search requests
class VectorSearchRequest(BaseModel):
    query: str
    limit: int = 10

# Define a Pydantic model for vector search responses
class VectorSearchResponse(BaseModel

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