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Vinkius

Vector Index Estimator Connector for AI agents.

3 live capabilities

Predict vector search performance and memory requirements for your production database.

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Why people use Vector Index Estimator

Vector Index Recall Estimator for Vector Search Performance Analysis

This Connector lets you do that work in a chat interface. You describe your dataset dimensions, count, and precision, and get a clear picture of the performance landscape. You can see the trade-offs between different algorithms like HNSW and IVF instantly.

  • Claude
  • ChatGPT
  • Gemini
  • Cursor
  • Visual Studio Code
  • Windsurf

What Vinkius changes

You get a data-backed blueprint for your vector database configuration before you deploy a single line of production code.

Use it from Claude, ChatGPT, Cursor or another AI client you already have.

One account · 6,100+ Connectors

  1. Real-world use case 01

    Sizing a production cluster

    An engineer needs to know if 10 million vectors will fit in a 32GB instance.

  2. Real-world use case 02

    Finding the right nprobe value

    A team wants 95% recall but doesn't know which nprobe value to set for their IVF index.

  3. Real-world use case 03

    Comparing HNSW vs IVF

    A startup needs to choose between HNSW and IVF for low-latency requirements.

Complete set · 3capabilities

The complete Vector Index Estimator capability set.

These are the exact actions your AI can choose when you ask it to work with Vector Index Estimator.

Capability set01 / 01

01—03

3 capabilities in this set.

Part of 3 available through Vector Index Estimator.

  1. 01 Capability

    Get parameter recommendations

    Provides the ideal ranges for settings like efSearch and nprobe to hit your accuracy targets. This replaces manual trial and error.

  2. 02 Capability

    Calculate memory usage

    Predicts how much RAM your vector index will consume based on your data's scale and precision. Use this to size your cloud instances correctly.

  3. 03 Capability

    Estimate search performance

    Generates specific tradeoff points between search speed and recall for different algorithms. It helps you find the balance for your users.

Set up in minutes

One URL. Then ask Vector Index Estimator to work.

Claude and ChatGPT only need the Connector URL. Copy it once, add it in settings, and use Vector Index Estimator from the conversation.

Choose your client

Live preview
Advanced clients IDE · CLI

Claude · Web + desktop

Official guide ↗

Connector URL · ready to paste

Streamable HTTP
https://edge.vinkius.com/vk_preview_N33Tt9r0kNlk9h9CQjDLut4LHlCfuA7wPCcdfONP/mcp
  1. Step 01

    Open Connectors

    In Claude Web or Claude Desktop, open Settings and choose Connectors.

  2. Step 02

    Add the URL

    Choose Add custom connector, name it Vector Index Estimator, and paste the URL above.

  3. Step 03

    Turn it on in chat

    Select +, open Connectors, and enable Vector Index Estimator for the conversation.

Where the request belongs

Work Vector Index Estimator can move forward.

Built around the request

This is for the ML engineer who needs to justify infrastructure costs to management or the backend dev who is tired of seeing out of memory errors during deployment.

01

ML Engineer

Validating search accuracy against latency requirements during the R&D phase.

02

Platform Architect

Planning hardware capacity for high-scale vector similarity search across multiple clusters.

03

Backend Developer

Tuning production parameters to prevent system bottlenecks during high-traffic periods.

Build the capability set

Add more capabilities.

Each Connector adds new actions and data without changing how you work.

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01 6 capabilities

Redis Vector

Equip your AI to autonomously manage embeddings, run KNN similarity searches, and administrate vector indexes natively inside your Redis stack.

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02 6 capabilities

Vald

Power your agent with Vald. query, insert, and manage dense vectors on a highly scalable, distributed nearest-neighbor engine.

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03 7 capabilities

Milvus (Open-Source Vector Database)

Manage vector storage via Milvus. perform ANN searches, query scalar entities, and audit collections.

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04 6 capabilities

Elasticsearch Vector

Empower vector search via Elasticsearch. perform dense vector kNN searches, handle index mappings, and index embedding documents directly from any AI agent.

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05 6 capabilities

Vertex AI Vector Search

Bring Google's massive vector matching power to your AI agent. Search billions of semantic embeddings and administer Vertex Index endpoints directly in chat.

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OpenSearch Vector logo
06 6 capabilities

OpenSearch Vector

Run k-NN vector searches on OpenSearch. create indexes, upsert embeddings, query similar documents, and manage your vector store from any AI agent.

View Connector

Bring your own AI

Change the model, client or framework. Keep Vector Index Estimator connected.

  • Claude
  • ChatGPT
  • Gemini
  • Cursor
  • VS Code
  • Windsurf
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  • Zed
  • Continue
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Before you connect

Questions about Vector Index Estimator.

The practical details behind the request, access and result.

Can the Vector Index Recall Estimator help me save money on cloud costs?

Yes. By using calculate_memory_usage, you can determine the exact RAM requirements for your data. This prevents you from over-provisioning expensive cloud instances.

Does the Vector Index Recall Estimator support HNSW and IVF?

Yes, it provides specific performance estimates and parameter recommendations for both HNSW and IVF algorithms.

How accurate are the memory estimates?

The estimates are based on standard vector precision tiers and algorithm overhead. They provide a reliable blueprint for hardware capacity planning.

Can I use the Vector Index Recall Estimator to find the best nprobe value?

Yes, you can ask for recommendations based on your target recall percentage and dataset scale to find the optimal nprobe range.

What's the difference between HNSW and IVF performance?

The capability allows you to compare both, showing you how HNSW handles recall-vs-latency trade-offs compared to the parameter-dependent scaling of IVF.

Do I need to have my data ready to use the Vector Index Recall Estimator?

No, you don't need your actual data. You only need to know your expected vector count, dimensions, and the precision tier you plan to use.

What algorithms are supported?

The server supports HNSW (Hierarchical Navigable Small World) and IVF (Inverted File Index) algorithms.

How can I estimate the RAM needed for my index?

Use the calculate_memory_usage capability. You will need to provide the total vector count, dimensions per vector, and the precision tier (float32, float16, or int8).

Can I get parameter suggestions for a specific recall target?

Yes, the get_parameter_recommendations capability provides suggested ranges for parameters like efSearch or nprobe to help you reach your target recall percentage.

One connection away

Give your agent a direct line to Vector Index Estimator.

Connect Vector Index Estimator once. Keep it beside 6,100+ managed Connectors when the next task needs more.

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