pinecone-claude-code-plugin

🥈Silver

The Pinecone Claude Code Plugin integrates Pinecone's vector database into Claude Code for semantic search, index management, and RAG workflows. Operations teams can enhance data retrieval and analysis, connecting Claude Code with Pinecone's vector database for improved information processing.

5710Updated 1mo ago

Intermediate30min to implementautomation

Saves ~30 min per use

Quick InstallView Source

git clone https://github.com/pinecone-io/pinecone-claude-code-plugin.git

Works with:

Claude

Overview

About This Skill

How to Use

["Install the Pinecone Claude Code Plugin in your Claude Code environment using `pip install pinecone-client` and authenticate with your Pinecone API key.","Specify the Pinecone index name in your prompt (e.g., '[INDEX_NAME]') and define the operation (e.g., 'query', 'upsert', 'delete', or 'describe_index_stats').","Include additional context for the operation, such as vector embeddings, metadata filters, or top_k values for queries.","For RAG workflows, use the plugin to fetch relevant documents before generating responses in Claude Code.","Review the output for accuracy and refine your query or embeddings if results are not as expected. Use the `include_metadata=True` flag to get contextual information for downstream tasks."]

Setup & Installation

Quick Install

No install command available. Check the GitHub repository for manual installation instructions.

Alternative Install (Git Clone)

git clone https://github.com/pinecone-io/pinecone-claude-code-plugin

Requirements

Claude Code or compatible AI agent
Works with: Claude

Quick Start Guide

Install the Skill

Copy the install command above and run it in your terminal.

Open Your AI Agent

Launch Claude Code, Cursor, or your preferred AI coding agent.

Try It Out

Use the prompt template or examples below to test the skill.

Customize

Adapt the skill to your specific use case and workflow.

Usage Examples

Prompt Template

Use the Pinecone Claude Code Plugin to [OPERATION] in the Pinecone index '[INDEX_NAME]'. [ADDITIONAL_CONTEXT] Ensure the operation aligns with [BEST_PRACTICES].

Example Output

```python
# Querying Pinecone index 'product-docs' for semantic search of 'AI-powered automation tools'
from pinecone import Pinecone

pc = Pinecone(api_key="your-api-key")
index = pc.Index("product-docs")

response = index.query(
    vector=[0.12, 0.45, 0.78, 0.34],  # Embedding for 'AI-powered automation tools'
    top_k=5,
    include_metadata=True
)

for match in response['matches']:
    print(f"Score: {match['score']:.2f}, ID: {match['id']}")
    print(f"Metadata: {match['metadata']}")
    print("---")
```

**Results:**
- **Top match**: Score 0.94, ID `doc-ai-automation-123` (Metadata: `{'title': 'AI Automation in DevOps', 'category': 'technical-docs'}`)
- **Second match**: Score 0.87, ID `doc-ai-automation-456` (Metadata: `{'title': 'Automating Customer Support with AI', 'category': 'business-docs'}`)
- **Third match**: Score 0.82, ID `doc-ai-automation-789` (Metadata: `{'title': 'AI-Powered Data Pipelines', 'category': 'technical-docs'}`)

**Recommendation**: The top 3 results are highly relevant for building an AI-powered automation workflow. The metadata suggests these documents cover both technical implementation and business use cases. For further refinement, consider filtering by `category` in the query to narrow down to technical documentation.