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pinecone-cli

pinecone-cli

github.com/pinecone-ioData & databases16283 installsUpdated 6w ago
Repository

About pinecone-cli

pinecone-cli is a command-line tool for managing Pinecone, for developers handling vector operations, index administration, and automation. It works across all index types and provides JSON output for scripts and CI/CD.

Key features

  1. 01Manage indexes, namespaces, and backups
  2. 02Upsert, query, fetch, update, and delete vectors
  3. 03Query indexes with raw vector values
  4. 04Output JSON for script processing
  5. 05Configure service-account authentication for automation

Use cases

  1. 01Process vectors across different index types
  2. 02Automate vector uploads in CI/CD
  3. 03Back up before a migration and restore to a new index

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/pinecone-io/skills --skill pinecone-cli

After installing, type/pinecone-cliin Claude Code to confirm it loaded.

Bundled filesreferences/command-reference.mdreferences/troubleshooting.md

Details

From the project README

Pinecone CLI (pc)

Manage Pinecone from the terminal. The CLI is especially valuable for vector operations across all index types — something the MCP currently can't do.

CLI vs MCP

CLIMCP
Index typesAll (standard, integrated, sparse)Integrated only
Vector ops (upsert, query, fetch, update, delete)✅❌
Text search on integrated indexes✅✅
Backups, namespaces, org/project mgmt✅❌
CI/CD / scripting✅❌

Setup

Install (macOS)
brew tap pinecone-io/tap
brew install pinecone-io/tap/pinecone

Other platforms (Linux, Windows) — download from GitHub Releases.

Authenticate
# Interactive (recommended for local dev)
pc login
pc target -o "my-org" -p "my-project"

# Service account (recommended for CI/CD)
pc auth configure --client-id "$PINECONE_CLIENT_ID" --client-secret "$PINECONE_CLIENT_SECRET"

# API key (quick testing)
pc config set-api-key $PINECONE_API_KEY

Check status: pc auth status · pc target --show

Note for agent sessions: If you need to run pc login inside an agent loop, the browser auth link may not surface correctly. It's best to authenticate before starting an agent session. Run pc login in your terminal directly, then invoke the agent once you're authenticated.

Authenticating the CLI does not set PINECONE_API_KEY

pc login authenticates the CLI tool itself — it does not set PINECONE_API_KEY in your environment. Python scripts, Node.js SDKs, and other tools that use the Pinecone SDK need PINECONE_API_KEY set separately.

Use the CLI to create a key and export it in one step:

KEY=$(pc api-key create --name agent-sdk-key --json | jq -r '.value')
export PINECONE_API_KEY="$KEY"

Without jq: run pc api-key create --name agent-sdk-key --json and copy the "value" field manually.


Common Commands

TaskCommand
List indexespc index list
Create serverless indexpc index create -n my-index -d 1536 -m cosine -c aws -r us-east-1
Index statspc index stats -n my-index
Upload vectors from filepc index vector upsert -n my-index --file ./vectors.json
Query by vectorpc index vector query -n my-index --vector '[0.1, ...]' -k 10 --include-metadata
Query by vector IDpc index vector query -n my-index --id "doc-123" -k 10
Fetch vectors by IDpc index vector fetch -n my-index --ids '["vec1","vec2"]'
List vector IDspc index vector list -n my-index
Delete vectors by filterpc index vector delete -n my-index --filter '{"genre":"classical"}'
List namespacespc index namespace list -n my-index
Create backuppc backup create -i my-index -n "my-backup"
JSON output (for scripting)Add -j to any command

Interesting Things You Can Do

Query with custom vectors (not just text)

Unlike the MCP, the CLI lets you query any index with raw vector values — useful when you generate embeddings externally (OpenAI, HuggingFace, etc.):

pc index vector query -n my-index \
  --vector '[0.1, 0.2, ..., 0.9]' \
  --filter '{"source":{"$eq":"docs"}}' \
  -k 20 --include-metadata
Pipe embeddings directly into queries
jq -c '.embedding' doc.json | pc index vector query -n my-index --vector - -k 10
Bulk metadata update with preview
# Preview first
pc index vector update -n my-index \
  --filter '{"env":{"$eq":"staging"}}' \
  --metadata '{"env":"production"}' \
  --dry-run

# Apply
pc index vector update -n my-index \
  --filter '{"env":{"$eq":"staging"}}' \
  --metadata '{"env":"production"}'
Backup and restore
# Snapshot before a migration
pc backup create -i my-index -n "pre-migration"

# Restore to a new index if something goes wrong
pc backup restore -i <backup-uuid> -n my-index-restored
Automate in CI/CD
export PINECONE_CLIENT_ID="..."
export PINECONE_CLIENT_SECRET="..."
pc auth configure --client-id "$PINECONE_CLIENT_ID" --client-secret "$PINECONE_CLIENT_SECRET"
pc index vector upsert -n my-index --file ./vectors.jsonl --batch-size 1000
Script against JSON output
# Get all index names as a list
pc index list -j | jq -r '.[] | .name'

# Check if an index exists before creating
if ! pc index describe -n my-index -j 2>/dev/null | jq -e '.name' > /dev/null; then
  pc index create -n my-index -d 1536 -m cosine -c aws -r us-east-1
fi

Reference Files

Documentation

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