Pinecone Has a Free API — Here's How to Build Production AI Search at Scale

#pinecone #vectordb #ai #search

Why Pinecone?

Pinecone is a fully managed vector database built for production AI. No infrastructure, no tuning — just fast, scalable similarity search.

Free tier (Starter): 100K vectors, 1 index, 2 GB storage.

Getting Started

pip install pinecone-client

from pinecone import Pinecone, ServerlessSpec

pc = Pinecone(api_key="YOUR_API_KEY")

# Create index
pc.create_index(
    name="products",
    dimension=1536,  # OpenAI ada-002
    metric="cosine",
    spec=ServerlessSpec(cloud="aws", region="us-east-1")
)

index = pc.Index("products")

# Upsert vectors
index.upsert(vectors=[
    ("prod-1", [0.1, 0.2, ...], {"name": "Laptop", "price": 999}),
    ("prod-2", [0.3, 0.4, ...], {"name": "Phone", "price": 699})
])

# Query
results = index.query(vector=[0.1, 0.2, ...], top_k=5, include_metadata=True)
for match in results.matches:
    print(f"{match.metadata[name]}: {match.score:.3f}")

# Filtered query
results = index.query(
    vector=query_vec,
    top_k=5,
    filter={"price": {"$lt": 800}}
)

JavaScript

import { Pinecone } from "@pinecone-database/pinecone";

const pc = new Pinecone({ apiKey: "YOUR_API_KEY" });
const index = pc.index("products");

await index.upsert([{ id: "doc-1", values: [...], metadata: { title: "Hello" } }]);
const results = await index.query({ vector: [...], topK: 5, includeMetadata: true });