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```python
# AI Dev Tool: FastAPI Vector Database Starter
# =============================================
# A production-ready starter boilerplate for building vector database-powered micro-SaaS applications
# using FastAPI, Pydantic, and Faiss.

from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
from typing import List, Optional
import faiss
import numpy as np

app = FastAPI()

# Define a Pydantic model for vector data
class VectorData(BaseModel):
    """Vector data model"""
    id: int
    vector: List[float]

# Initialize a Faiss index
index = faiss.IndexFlatL2(128)  # 128-dimensional vector space

# Define a route for adding vectors to the index
@app.post("/vectors/")
async def add_vector(vector_data: VectorData):
    """
    Add a vector to the index.

Args:
    - vector_data (VectorData): The vector data to add.

Returns:
    - A success message.
    """
    try:
        # Convert the vector to a NumPy array
        vector = np.array(vector_data.vector).astype("float32")
        # Add the vector to the index
        index.add_vectors([vector])
        return

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