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pinecone

Manage vector indexes, upserts, and hybrid queries with the Pinecone API.

Updated Jun 5, 2026
Search Tags:#semantic search#embeddings#rag#vector database#hybrid search#similarity search#pinecone
One-click install
npx skills add https://github.com/xu1713/openhorse --skill pinecone-xu1713
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: pinecone
Source: https://github.com/xu1713/openhorse/tree/main/openhorse/openhorse/optional-skills/mlops/pinecone
Command: npx skills add https://github.com/xu1713/openhorse --skill pinecone-xu1713

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pinecone-client, and includes references (resource) components.

What problem does it solve? Building production RAG, semantic search, or recommendation systems requires a scalable vector database, but managing infrastructure, scaling, and latency yourself is costly and complex. ## Core Features & Use Cases - Managed Vector Operations: Create serverless or pod-based indexes, upsert vectors in batches, and query with metadata filtering and namespaces. - Hybrid Search: Combine dense and sparse vectors with an alpha parameter for semantic plus keyword retrieval. - Framework Integration: Connect with LangChain and LlamaIndex for retrieval pipelines. - Use Case: Build a multi-tenant RAG application where each user's documents are isolated in namespaces, queried with metadata filters, and served with p95 latency under 100ms. ## Quick Start Install pinecone-client, then ask the assistant to create a serverless Pinecone index and upsert your document embeddings for semantic search.

Frequently Asked Questions about pinecone

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I create a Pinecone index in Python?▼

Install pinecone-client, initialize the Pinecone client with your API key, and call create_index with a name, dimension matching your embedding model, metric such as cosine, and a ServerlessSpec or PodSpec. Then connect with pc.Index(name).

How to query Pinecone with metadata filtering?▼

Pass a filter dictionary to index.query alongside your vector and top_k. Filters support operators like $eq, $gte, $lte, $in, and logical $and or $or combinations to narrow results by metadata fields.

Pinecone vs Chroma vs FAISS for vector search?▼

Pinecone is a fully managed, auto-scaling SaaS suited for production workloads with low latency requirements. Chroma is self-hosted and open-source, while FAISS is an offline library for pure similarity search without a server.

Does Pinecone support hybrid search with sparse vectors?▼

Yes, Pinecone supports hybrid search by upserting both dense values and sparse_values with token indices and weights. Query with both vector and sparse_vector, and set the alpha parameter to balance dense versus sparse relevance.

When should I use pod-based instead of serverless Pinecone indexes?▼

Use pod-based indexes when you need consistent p95 latency, predictable performance, and high throughput for production workloads. Serverless is better for variable traffic, cost optimization, and development.

How do Pinecone namespaces work for multi-tenancy?▼

Namespaces partition vectors within a single index by a namespace string such as a user ID. Upsert and query operations accept a namespace parameter, isolating each tenant's data without creating separate indexes.

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