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.
git clone https://github.com/pinecone-io/pinecone-claude-code-plugin.gitThe Pinecone Claude Code Plugin connects Pinecone's vector database directly to Claude Code, enabling semantic search, index management, and retrieval-augmented generation (RAG) workflows. It provides slash commands for quick access to common operations, natural language query support, and a fully managed Pinecone Assistant for document Q&A with citations. The plugin includes the full Pinecone MCP Server, allowing you to create indexes, list resources, search records, upsert data, and apply reranking—all without leaving your IDE. Teams building document retrieval systems, knowledge bases, or information-heavy applications can streamline data indexing and querying. Installation requires setting a Pinecone API key and, for Assistant features, the uv Python package manager.
["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."]
Build document Q&A systems with semantic search and citations
Create vector indexes and manage index lifecycles from Claude Code
Implement RAG workflows for knowledge base retrieval
Query vector data using natural language instead of manual embeddings
No install command available. Check the GitHub repository for manual installation instructions.
git clone https://github.com/pinecone-io/pinecone-claude-code-pluginCopy the install command above and run it in your terminal.
Launch Claude Code, Cursor, or your preferred AI coding agent.
Use the prompt template or examples below to test the skill.
Adapt the skill to your specific use case and workflow.
Use the Pinecone Claude Code Plugin to [OPERATION] in the Pinecone index '[INDEX_NAME]'. [ADDITIONAL_CONTEXT] Ensure the operation aligns with [BEST_PRACTICES].
```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.AI assistant built for thoughtful, nuanced conversation
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