PineconeRequest AccessVector DB

Pinecone

Vector database for AI applications

Store and query vector embeddings at scale with Pinecone. Ideal for RAG pipelines and semantic search in AI agents.

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Features

✓Vector similarity search
✓Metadata filtering
✓Namespace isolation
✓Real-time indexing
✓Hybrid search

Integration Example

Use Pinecone through Keystore with zero code changes. Keys are resolved from the vault and injected at request time.

pinecone-example.ts
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import Keystore from "@keystore/sdk";

const ks = new Keystore({ agentToken: process.env.KS_TOKEN! });
ks.interceptAll();

// Query vectors through the Keystore proxy
const res = await fetch(
  "https://my-index.svc.pinecone.io/query",
  {
    method: "POST",
    headers: { "Content-Type": "application/json" },
    body: JSON.stringify({
      vector: [0.1, 0.2, 0.3],
      topK: 10,
      includeMetadata: true,
    }),
  }
);
const results = await res.json();

Use Cases

RAG (Retrieval-Augmented Generation)
Semantic search engines
Recommendation systems
Anomaly detection

Ready to use Pinecone?

Request access and our concierge team will provision credentials for you — usually within 24 hours. No setup on your end.

Request Access