Vector Database Patterns
Comprehensive guide to vector databases including Pinecone, Qdrant, Weaviate, embedding strategies, and similarity search.
npx skills add majiayu000/claude-skill-registry --skill vector-database-amnadtaowsoam-cerebraskills --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
What it does
Explains how to work with vector databases, covering multiple platforms (Pinecone, Qdrant, Weaviate), embeddings, similarity search, and related concepts. It describes setup, upserting vectors, querying, deleting, and filtering across different implementations in Python and TypeScript, including batch operations and namespace usage.
How it works
Outlines concrete code examples for:
- Pinecone: setup, connect, create index, describe stats, upsert vectors (single and batch), query with various filters, delete vectors (single, multiple, by namespace).
- Qdrant: create collections, insert points (single and batch), query with filters and named vectors, hybrid search, and filtering examples.
- Weaviate: (section begins but not shown in excerpt; implies schema setup and further steps would be described). For each tool, the skill provides code snippets in Python and TypeScript demonstrating the exact function calls and parameters to use, including vector dimensions, distances, metadata/payload handling, and batch processing.
When to use it
Use when planning or implementing a vector database-backed search or retrieval system, including semantic search, recommendations, anomaly detection, or knowledge retrieval in RAG pipelines. Trains on prerequisites like embeddings, distance metrics, and basic database concepts.
What it can touch
The skill declares the tool "claude-code" for executing or illustrating code in examples. It includes code blocks that show usage for Pinecone, Qdrant, and Weaviate across Python and TypeScript environments.
Caveats
License is MIT. The material shows example API keys and endpoints as placeholders; real usage requires proper credentials and configuration. It presents both managed and self-hosted options (e.g., Pinecone as managed, Qdrant as self-hosted).
# Vector Database Patterns ## Overview Vector databases are specialized databases designed to store, index, and query high-dimensional vectors efficiently. They enable similarity search by finding vectors that are "closest" to a query vector using various distance metrics. This skill covers Pinecone, Qdrant, Weaviate, embedding strategies, similarity search, performance optimization, and production considerations. ## Prerequisites - Understanding of vectors and embeddings - Knowledge of machine learning concepts - Familiarity with Python or TypeScript - Understanding of similarity metrics (cosine, Euclidean, dot product) - Basic knowledge of database concepts ## Key Concepts ### Vector Database Fundamentals - **Vectors**: Numerical representations of data (text, images, audio) in high-dimensional space - **Embeddings**: Vectors generated by machine learning models that capture semantic meaning - **Distance Metrics**: Measures of similarity between vectors (cosine, Euclidean, dot product) - **Indexing**: Data structures that enable fast similarity search - **Metadata**: Additional information associated with vectors for filtering ### Vector Database Types - **Pinecone**: Managed ser
- Overview
- Prerequisites
- Key Concepts
- Vector Database Fundamentals
- Vector Database Types
- Use Cases
- Implementation Guide
- Pinecone
- Qdrant
- Weaviate
- Embedding Strategies
- Similarity Search
- Performance Optimization
- Production Considerations
What does the Vector Database Patterns skill do?
Comprehensive guide to vector databases including Pinecone, Qdrant, Weaviate, embedding strategies, and similarity search.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill vector-database-amnadtaowsoam-cerebraskills --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.