Skip to content
Back to skills

Pinecone

ASecurity

Set up Pinecone as the vector DB in a RAG pipeline — index config, batch upsert, semantic query, metadata filters. Use when the user is adding Pinecone or debugging vector search.

  • 2 stars
  • 0 votes
  • 0 copies
  • 1 view
  • Added October 13, 2026
ai-agentstypescriptshellbashnodedebugginggitapidatabase

Works with

  • cli
  • api

Security analysis

A96/100
  • mediumInstalls packages at runtime which could introduce malicious dependencies

Pro shows the line behind each finding and how to fix it

Scanned October 13, 2026

npx -y skills add phucbm/skills --skill pinecone --agent claude-code

Installs into .claude/skills of the current project.

Are you the author of Pinecone?

Add the live security badge to your README. It updates with every re-scan.

Security grade badge for Pinecone
[![Security: A — Skills Directory](https://www.skillsdirectory.com/api/skills/phucbm-pinecone/badge)](https://www.skillsdirectory.com/skills/phucbm-pinecone)

More formats (shields.io, HTML) on the badges page. Keep it an A: scan every change in CI with Pro.

Download with Pro
SKILL.md
---
name: pinecone
description: Set up Pinecone as the vector DB in a RAG pipeline — index config, batch upsert, semantic query, metadata filters. Use when the user is adding Pinecone or debugging vector search.
when_to_use: When the user mentions Pinecone, vector search, or needs a vector DB for RAG.
allowed-tools: Bash Read Write Edit
---

# Pinecone

Managed vector database. Used as the vector store in a RAG pipeline — see the `rag` skill for the full pipeline pattern.

## Install
```shell
pnpm add @pinecone-database/pinecone
```

## Env
```env
PINECONE_API_KEY=
PINECONE_INDEX_NAME=chatbot-knowledge
```

## Client
```typescript
import { Pinecone } from "@pinecone-database/pinecone";

const pinecone = new Pinecone({ apiKey: process.env.PINECONE_API_KEY! });
const index = pinecone.index(process.env.PINECONE_INDEX_NAME ?? "chatbot-knowledge");
```

## Batch upsert
```typescript
const BATCH_SIZE = 100;

export async function upsertVectors(vectors: VectorRecord[], namespace: string) {
  const ns = index.namespace(namespace);
  for (let i = 0; i < vectors.length; i += BATCH_SIZE) {
    await ns.upsert(vectors.slice(i, i + BATCH_SIZE));
  }
}
```

## Query
```typescript
export async function queryVectors(
  embedding: number[],
  topK: number,
  filter?: MetadataFilter,
) {
  const result = await index.namespace("default").query({
    vector: embedding,
    topK,
    includeMetadata: true,
    filter,
  });
  return result.matches;
}
```

## Metadata filter pattern
Store token arrays on each vector for flexible keyword matching:

```typescript
// On ingest
metadata: {
  tokens: ["com", "tam", "com tam"],
  district_tokens: ["quan 1", "quan binh thanh"],
}

// At query time
const filter = { tokens: { $in: tokenize(userQuery) } };
```

If no tokens match, return `{}` to fall through to pure semantic search (no filter applied).
Supported operators: `$in`, `$eq`, `$and`, `$or`.

Increase `topK` when filters are active — filtering reduces recall before ranking.
e.g. `RAG_TOP_K=5` unfiltered, `RAG_TOP_K_FILTERED=10-30` filtered.

## References
- food-tour-sg `lib/rag/pinecone.ts`: https://github.com/phucbm/food-tour-sg/blob/main/lib/rag/pinecone.ts
- food-tour-sg `lib/rag/filters.ts`: https://github.com/phucbm/food-tour-sg/blob/main/lib/rag/filters.ts
- food-tour-sg `lib/rag/normalize.ts`: https://github.com/phucbm/food-tour-sg/blob/main/lib/rag/normalize.ts
- Pinecone Node.js client docs: https://docs.pinecone.io/reference/node-sdk

Attribution

Is this your skill, or is something wrong with this listing? Request removal or report an issue. Author removals are honored within 72 hours.

Comments

Loading comments…

Newsletter

Get the weekly list of safe new skills, and the ones we pulled.