Pinecone | Skills Pool

技能档案

Pinecone

Pinecone vector database — manage indexes, upsert vectors, query similarity search, manage namespaces, and track collections via the Pinecone API. Build semantic search, recommendation systems, and RAG pipelines with high-performance vector storage. Built for AI agents — Python stdlib only, zero dependencies. Use for vector search, semantic similarity, RAG applications, recommendation engines, and AI memory systems.

openclaw4,189 星标2026年3月7日

职业
分类: 大语言模型与 AI

技能内容

Pinecone vector database — manage indexes, upsert vectors, query similarity search, manage namespaces, and track collections via the Pinecone API.

Features

Index management — create, configure, delete indexes
Vector upsert — insert and update vectors with metadata
Similarity search — query nearest neighbors
Namespace management — organize vectors by namespace
Metadata filtering — filter queries by metadata fields
Collection management — create snapshots of indexes
Batch operations — bulk upsert and delete
Index stats — vector counts, dimensions, usage
Sparse-dense — hybrid search with sparse vectors
Serverless — auto-scaling serverless indexes

Requirements

Variable	Required

相关技能

# List indexes
python3 {baseDir}/scripts/pinecone.py indexes

# Get index details
python3 {baseDir}/scripts/pinecone.py index-get my-index

# Create an index
python3 {baseDir}/scripts/pinecone.py index-create '{"name":"my-index","dimension":1536,"metric":"cosine","spec":{"serverless":{"cloud":"aws","region":"us-east-1"}}}'

# Delete an index
python3 {baseDir}/scripts/pinecone.py index-delete my-index

python3 {baseDir}/scripts/pinecone.py indexes

python3 {baseDir}/scripts/pinecone.py index-get my-index

python3 {baseDir}/scripts/pinecone.py index-create '{"name":"my-index","dimension":1536,"metric":"cosine","spec":{"serverless":{"cloud":"aws","region":"us-east-1"}}}'

python3 {baseDir}/scripts/pinecone.py index-delete my-index

python3 {baseDir}/scripts/pinecone.py upsert --index my-index '{"vectors":[{"id":"vec1","values":[0.1,0.2,...],"metadata":{"text":"hello"}}]}'

python3 {baseDir}/scripts/pinecone.py query --index my-index '{"vector":[0.1,0.2,...],"topK":10,"includeMetadata":true}'

python3 {baseDir}/scripts/pinecone.py fetch --index my-index --ids vec1,vec2,vec3

python3 {baseDir}/scripts/pinecone.py delete --index my-index --ids vec1,vec2

python3 {baseDir}/scripts/pinecone.py delete-namespace --index my-index --namespace docs

python3 {baseDir}/scripts/pinecone.py stats --index my-index

python3 {baseDir}/scripts/pinecone.py collections

python3 {baseDir}/scripts/pinecone.py collection-create '{"name":"backup","source":"my-index"}'

python3 {baseDir}/scripts/pinecone.py namespaces --index my-index

# JSON (default, for programmatic use)
python3 {baseDir}/scripts/pinecone.py indexes --limit 5

# Human-readable
python3 {baseDir}/scripts/pinecone.py indexes --limit 5 --human