git:20260507.50823d8 to git:20260507.44b06c0

---

name: pinecone-integration

description: Pinecone vector database setup, configuration, and operations for RAG applications

allowed-tools:

- Read

- Write

- Edit

- Bash

- Glob

- Grep

graph:

domains: [domain:software-engineering]

specializations: [specialization:ai-agents-conversational]

roles: [role:ml-engineer, role:backend-engineer]

workflows: [workflow:ml-model-lifecycle, workflow:feature-development]

---

# Pinecone Integration Skill

## Capabilities

- Set up Pinecone index and environment

- Configure index parameters and pods

- Implement upsert and query operations

- Design namespace strategies for multi-tenancy

- Configure metadata filtering

- Implement batch operations and optimization

## Target Processes

- vector-database-setup

- rag-pipeline-implementation

## Implementation Details

### Core Operations

1. **Index Management**: Create, configure, delete indices

2. **Upsert**: Single and batch vector uploads

3. **Query**: Similarity search with metadata filters

4. **Fetch/Delete**: Direct vector operations

5. **Index Stats**: Monitor index usage

### Configuration Options

- Index dimension and metric

- Pod type and replicas

- Serverless vs pod-based deployment

- Namespace configuration

- Metadata schema design

### Best Practices

- Use appropriate metric for embeddings

- Design namespaces for isolation

- Batch upserts for efficiency

- Implement proper error handling

- Monitor index performance

### Dependencies

- pinecone-client

- langchain-pinecone