A zero-dependency Pinecone vector database manager for building RAG pipelines, semantic search, and AI memory systems.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install pinecone
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install pinecone using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Pinecone?
This Pinecone skill enables agents to interact seamlessly with Pinecone vector databases using only the Python standard library. It provides a comprehensive interface for managing indexes, upserting high-dimensional vectors, and performing low-latency similarity searches. Designed for maximum efficiency and security, it allows developers to build advanced Retrieval-Augmented Generation (RAG) applications and recommendation engines without complex dependency chains. This tool is a cornerstone for developers building persistent AI memory and semantic search capabilities within Openclaw Skills.
Pinecone Use Cases
- Building Retrieval-Augmented Generation (RAG) pipelines for AI agents.
- Implementing long-term memory for conversational AI agents.
- Developing high-performance semantic search and recommendation systems.
- Managing vector namespaces and collections for multi-tenant AI applications.
- Automating index lifecycle management including creation, deletion, and scaling.
How Pinecone Works
- The agent authenticates via the PINECONE_API_KEY provided in the environment variables.
- Users or agents issue commands through the CLI script to interact with the Pinecone REST API.
- For storage, vectors and metadata are upserted into specific indexes or namespaces.
- For retrieval, query vectors are sent to the API to find the top-K nearest neighbors based on cosine, dot product, or euclidean similarity metrics.
- Results are returned in JSON format for the agent to process or in human-readable format for developers.
Pinecone Setup
First, ensure you have your Pinecone API key ready and exported to your environment.
export PINECONE_API_KEY='your_api_key_here'
You can then verify the setup by listing your current indexes using the human-readable flag:
python3 {baseDir}/scripts/pinecone.py indexes --human
Pinecone Data Schema & Taxonomy
The skill organizes data primarily within Pinecone's cloud infrastructure. No data is stored locally.
| Entity | Description |
|---|---|
| Index | The top-level container for vector data, defined by dimensions and similarity metrics. |
| Vector | Includes a unique ID, an array of floats (values), and optional metadata. |
| Metadata | Key-value pairs attached to vectors used for filtering during queries. |
| Namespace | Logical partitions within an index to isolate data subsets. |
| Collection | Static, read-only snapshots of an index for backup or migration. |
Pinecone Advanced Features
- Sparse-Dense Hybrid Search: Support for complex search queries combining vector similarity and keyword-based sparse vectors.
- Serverless Indexing: Native support for auto-scaling serverless configurations on AWS or GCP.
- Batch Operations: High-throughput bulk upserting and deletion of vector records for large datasets.
- Metadata Filtering: Granular query filtering using metadata fields to narrow search results based on specific criteria.
- Zero-Dependency Architecture: Built exclusively using the Python standard library for maximum compatibility within Openclaw Skills environments.
SKILL.md
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