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Choose a vector database based on content type, metadata needs, and query patterns.
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Use this skill when building, debugging, or improving Retrieval-Augmented Generation systems, including chunking, vector database selection, hybrid search, reranking, multimodal RAG, code documentation RAG, retrieval latency, and production RAG architecture.
Chunk nested documents into parent-child levels so retrieval can move from broad sections to fine-grained passages.
Use semantic boundaries and embedding similarity to chunk text for higher-relevance retrieval.
Route RAG chunking decisions across semantic, hierarchical, sliding-window, contextual-header, and framework-selection strategies.
Use overlapping windows to preserve context across chunk boundaries while controlling retrieval size.
Reduce retrieval latency with caching, batching, and index-level optimization.
| name | choosing-vector-db-by-datatype |
| title | Choosing Vector Database by Data Type |
| description | Choose a vector database based on content type, metadata needs, and query patterns. |
| allowed-tools | ["Read","Grep","Glob","Bash"] |
| category | vector-databases |
| tags | ["selection","text","multimodal","code","comparison"] |
Selecting the right vector database depends heavily on your data type (text, images, code, multimodal) and use case requirements. This skill provides a decision framework for choosing between popular vector databases based on data characteristics and operational constraints.
Different vector databases excel at different use cases:
| Database | Strengths | Weaknesses | Best For |
|---|---|---|---|
| Qdrant | Open-source, excellent filtering, hybrid search | Smaller community than Pinecone | Production RAG, cost-sensitive |
| Pinecone | Managed, excellent scalability, easy setup | Expensive, vendor lock-in | Enterprise, zero-ops requirement |
| Weaviate | GraphQL API, modular, schema-first | Learning curve, can be complex | Teams wanting flexible schemas |
| ChromaDB | Simple API, embeds easily, open-source | Less scalable for production | Prototyping, small-medium scale |
| Milvus | Highly scalable, feature-rich | Complex setup, heavy | Enterprise with data team |
| Database | Strengths | Weaknesses | Best For |
|---|---|---|---|
| Qdrant | Great metadata filtering, payload indexing | No code-specific features | Code search with metadata |
| Weaviate | Graph traversals, relationship handling | Setup complexity | Code graph/context search |
| ChromaDB | Simple integration with code tools | Limited scaling | IDE integrations, code assistants |
| Elasticsearch | Text search + vectors, mature ecosystem | Not vector-first | Hybrid code + keyword search |
| Database | Strengths | Weaknesses | Best For |
|---|---|---|---|
| Weaviate | Multi-vector, CLIP support built-in | Performance overhead | Image + text search |
| Qdrant | Multi-vector via named vectors | Manual configuration | Custom multimodal pipelines |
| Pinecone | Multiple namespaces, sparse vectors | Limited multi-vector | Limited multimodal needs |
| Milvus | Multi-vector, hybrid search | Complex setup | Large-scale multimodal systems |
Identify your primary data characteristics.
Why: Data profiling ensures your choice aligns with actual requirements rather than hype or popularity.
Score databases based on your requirements.
Why: A scoring framework provides objective criteria rather than subjective opinions.
Tailor your approach to each data type.
Why: Different data types require fundamentally different approaches for optimal performance.
Create a PoC to validate your choice.
Why: Nothing beats testing with your actual data and queries - PoCs reveal real-world performance characteristics.
Plan for potential future database changes.
Why: An export strategy prevents lock-in and enables future migrations if requirements change.
For comparisons and vendor docs, use ANN Benchmarks, Qdrant documentation, Pinecone documentation, Weaviate documentation, ChromaDB documentation, Milvus documentation, and Elasticsearch documentation.
