How do I use Vector Search Optimization on Zeplik?

Sign in to Zeplik and ask in plain language, or type /vector-search-optimization in any chat to invoke it directly. The skill applies its method and returns a result you can refine in the same conversation.

Which AI model does the Vector Search Optimization skill use?

Any model you choose. Zeplik works across every model in one chat, so the Vector Search Optimization skill runs on your preferred model for the task.

Where does the Vector Search Optimization skill come from?

The Vector Search Optimization skill is adapted from the open-source wshobson/agents project (MIT) and tuned to run natively on Zeplik. The original source is linked on this page.

How much does the Vector Search Optimization skill cost?

Using the skill is free to start. You only spend Zeplik credits when the assistant runs, and new accounts begin with free credits.

Vector Search Optimization

Software development skill, available on Zeplik

Vector Search Optimization is a ready-to-run software development skill on Zeplik. Not for chunking (use embedding-strategies) or fusion (use hybrid-search-implementation). Ask in plain language and Zeplik applies the skill's method for you inside the conversation, on whichever AI model you prefer.

The Vector Search Optimization skill loads automatically when your request matches it, or you can invoke it directly by typing /vector-search-optimization in any chat. It works with attachments, connectors, and any model that supports the task, so you get the same expert method every time without setting anything up.

What the Vector Search Optimization skill can do

Tune HNSW and IVF parameters for recall versus latency tradeoffs
Recommend similarity metric and exact versus ANN search strategy
Plan memory sizing and capacity for large vector collections
Diagnose filtered search performance issues across major vector stores

Try these prompts on Zeplik

Pick a prompt to open it in the Zeplik app. If you are not signed in yet, your prompt is waiting for you the moment you do.

How the Vector Search Optimization skill works

/vector-search-optimization

Umbrella skill for production vector search performance: index selection and parameter tuning, recall/latency trade-offs, similarity metric choice, filtered search, and capacity planning across pgvector, FAISS, Qdrant, Weaviate, Milvus, and managed stores. The user describes their store, scale, and symptom; deliver concrete parameters, benchmarks to run, and migration steps. For what to embed and how to chunk use embedding-strategies; for combining keyword and vector ranking use hybrid-search-implementation; for whole-RAG architecture use rag-implementation.

Dispatch table

Pick the reference file(s) that match the request, read them, then answer.

Topic	Read
Similarity search design: metrics, exact vs ANN, filtering, scaling patterns	references/similarity-search-patterns.md (+ --details.md)
Index tuning: HNSW/IVF parameters, recall vs latency, memory sizing, benchmarks	references/vector-index-tuning.md (+ --details.md)

How to work

Establish store, vector count, dimensionality, update rate, and the symptom (slow, low recall, memory pressure). Ask for the missing number rather than guessing scale.
Read the matching reference file(s) from the table above before answering.
Deliver concrete settings with the reasoning (for example, m=16 / ef_construction=64 as a starting point and what to measure before raising them), plus the query or benchmark to verify.
If the problem turns out to be chunking, embedding model choice, or rank fusion rather than the index, route to embedding-strategies or hybrid-search-implementation and say so.

Usage

/vector-search-optimization $ARGUMENTS

How to use the Vector Search Optimization skill

Sign in to Zeplik
Create a free Zeplik account or sign in. New accounts start with free credits, so you can try the Vector Search Optimization skill right away.
Describe your software development task
Ask in plain language, or type /vector-search-optimization to invoke the skill directly. Zeplik recognizes the Vector Search Optimization skill and applies its method.
Review and refine the result
Zeplik returns a clear, structured answer. Ask follow-ups in the same chat to refine it or take the next step.

Source and credit

Author: anthropic
License: MIT

Adapted from the open-source wshobson/agents project and tuned to run natively on Zeplik. View source on GitHub.

Frequently asked questions

What is the Vector Search Optimization skill?: Vector Search Optimization is a ready-to-run software development skill on Zeplik. Not for chunking (use embedding-strategies) or fusion (use hybrid-search-implementation). Ask in plain language and Zeplik applies the skill's method for you inside the conversation, on whichever AI model you prefer.
How do I use Vector Search Optimization on Zeplik?: Sign in to Zeplik and ask in plain language, or type /vector-search-optimization in any chat to invoke it directly. The skill applies its method and returns a result you can refine in the same conversation.
Which AI model does the Vector Search Optimization skill use?: Any model you choose. Zeplik works across every model in one chat, so the Vector Search Optimization skill runs on your preferred model for the task.
Where does the Vector Search Optimization skill come from?: The Vector Search Optimization skill is adapted from the open-source wshobson/agents project (MIT) and tuned to run natively on Zeplik. The original source is linked on this page.
How much does the Vector Search Optimization skill cost?: Using the skill is free to start. You only spend Zeplik credits when the assistant runs, and new accounts begin with free credits.

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