rag-pinecone

原文涵盖的主题

• Pinecone - Managed Vector Database
• When to use Pinecone
• Quick start
• Installation
• Basic usage
• Core operations
• Create index
• Upsert vectors

The vector database for production AI applications. Use when: Need managed, serverless vector database Production RAG applications Auto-scaling required Low latency critical (<100ms) Don't want to manage infrastructure Need hybrid search (dense + sparse vectors) Metrics: Fully managed SaaS Auto-scales to billions of vectors p95 latency <100ms 99.9% uptime SLA Use alternatives instead: Chroma: Self-hosted, open-source FAISS: Offline, pure similarity search Weaviate: Self-hosted with more features 1. Use serverless - Auto-scaling, cost-effective 2. Batch upserts - More efficient (100-200 per batch) 3. Add metadata - Enable filtering 4. Use namespaces - Isolate data by user/tenant 5. Monitor usage - Check Pinecone dashboard 6. Optimize filters - Index frequently filtered fields 7. Test with free tier - 1 index, 100K vectors free 8. Use hybrid search - Better quality 9. Set appropriate dimensions - Match embedding model 10. Regular backups - Export important data | Operation | Latency | Notes | |-----------|---------|-------| | Upsert | 50-100ms | Per batch | | Query (p50) | 50ms | Depends on index size | | Query (p95) | 100ms | SLA target | | Metadata filter | +10-20ms | Additional ov

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