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Vinkius

Voyage AI (AI Embeddings API) Connector for AI agents.

13 live capabilities

Build high-precision RAG systems with contextualized embeddings and reranking.

Live agent request Voyage AI (AI Embeddings API) / Connector

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Why people use Voyage AI (AI Embeddings API)

Voyage AI for High-Precision RAG Retrieval

This Connector changes that by letting your agent use Voyage AI's contextualized models. Instead of looking at a chunk in isolation, it understands the flow of the document. You get much more accurate answers and less time spent cleaning up the noise.

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

What Vinkius changes

You get production-ready search results without managing the complex math behind the embeddings.

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

One account · 5,900+ Connectors

  1. Real-world use case 01

    Improving RAG accuracy for long documents

    A user asks about a specific clause in a 100-page contract.

  2. Real-world use case 02

    Visual search for e-commerce

    A shopper uploads a photo of a chair.

  3. Real-world use case 03

    Processing massive datasets

    A data scientist needs to vectorize 1 million product descriptions.

Complete set · 13capabilities

The complete Voyage AI (AI Embeddings API) capability set.

These are the exact actions your AI can choose when you ask it to work with Voyage AI (AI Embeddings API).

Capability set01 / 04

01—04

4 capabilities in this set.

Part of 13 available through Voyage AI (AI Embeddings API).

  1. 01 Capability

    Cancel batch

    Stop a batch job if it's no longer needed. This saves on costs and resources when you need to pivot quickly.

  2. 02 Capability

    Create batch

    Start a background job for large-scale data processing. This is the best way to handle thousands of items at once.

  3. 03 Capability

    Create contextualized embeddings

    Create embeddings that keep the surrounding context of a document chunk. This significantly reduces errors in long-form text retrieval.

  4. 04 Capability

    Create embeddings

    Generate vectors for text or code. It's the standard way to turn your data into numbers for search.

Capability set02 / 04

05—07

3 capabilities in this set.

Part of 13 available through Voyage AI (AI Embeddings API).

  1. 05 Capability

    Create multimodal embeddings

    Turn images and text into a single vector space. Use this for visual search across different types of media.

  2. 06 Capability

    Delete file

    Remove a specific file from your storage. This helps keep your data clean during batch processing.

  3. 07 Capability

    Get batch

    Check the status of a background job. Use this to see if your large-scale embedding task is finished.

Capability set03 / 04

08—10

3 capabilities in this set.

Part of 13 available through Voyage AI (AI Embeddings API).

  1. 08 Capability

    Get file content

    Download the content of a specific file. This lets your agent read data directly from your storage.

  2. 09 Capability

    Get file

    Retrieve metadata about a file. Use this to check file details without downloading the whole thing.

  3. 10 Capability

    List batches

    See a list of all your current and past batch jobs. It's great for monitoring high-volume data updates.

Capability set04 / 04

11—13

3 capabilities in this set.

Part of 13 available through Voyage AI (AI Embeddings API).

  1. 11 Capability

    Rerank

    Take a list of search results and reorder them by relevance. It ensures your agent sees the most important info first.

  2. 12 Capability

    Upload file

    Send a file to the system for batch inference. This is how you start processing large amounts of data at once.

  3. 13 Capability

    List files

    View all files in your storage. Use this to see what's available for your agent to process.

Set up in minutes

One URL. Then ask Voyage AI (AI Embeddings API) to work.

Claude and ChatGPT only need the Connector URL. Copy it once, add it in settings, and use Voyage AI (AI Embeddings API) 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_x7lwFbbAdz5StvxoOu2Tkh9eDMu9Uh5i3GKObLMP/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 Voyage AI (AI Embeddings API), and paste the URL above.

  3. Step 03

    Turn it on in chat

    Select +, open Connectors, and enable Voyage AI (AI Embeddings API) for the conversation.

Where the request belongs

Work Voyage AI can move forward.

Built around the request

This is for the AI engineer tired of seeing irrelevant search results in their RAG app or the data scientist trying to make sense of mixed image and text data.

01

AI Engineer

Building production RAG pipelines where retrieval accuracy is the top priority.

02

Data Scientist

Experimenting with multimodal search and contextualized chunking for complex datasets.

03

Backend Developer

Integrating high-precision search into an app without building a custom vector engine.

Build the capability set

Add more capabilities.

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

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01 5 capabilities

Cohere (Embed & Rerank)

Empower RAG via Cohere. generate high-quality text embeddings, rerank documents for better accuracy, and perform AI classification directly from any AI agent.

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02 5 capabilities

Cohere (AI Platform)

Power enterprise AI via Cohere. generate text, perform chat completions, reorder documents, and manage embeddings directly from any AI agent.

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Gradient AI (LLM API & Finetuning) logo
03 19 capabilities

Gradient AI (LLM API & Finetuning)

Access powerful LLMs, fine-tune models on your own data, and generate embeddings directly through your AI agent.

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04 6 capabilities

Cohere

Access Cohere AI models via API. chat with Command models, generate embeddings, rerank documents and tokenize text from any AI agent.

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Mistral AI (Frontier LLMs & Embeddings) logo
05 7 capabilities

Mistral AI (Frontier LLMs & Embeddings)

Manage AI inference via Mistral. execute chat completions, generate RAG embeddings, and audit frontier models.

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Haystack (deepset Cloud) logo
06 7 capabilities

Haystack (deepset Cloud)

Build and manage AI-powered search and RAG pipelines via deepset Cloud. search documents, run pipelines, and manage workspaces.

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Bring your own AI

Change the model, client or framework. Keep Voyage AI connected.

  • Claude
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Before you connect

Questions about Voyage AI.

The practical details behind the request, access and result.

How does Voyage AI MCP improve my RAG system's accuracy?

It provides contextualized embeddings that keep the surrounding information of a document chunk in mind. This prevents the AI from getting confused by isolated sentences and leads to much more relevant answers.

Can I use Voyage AI MCP to search through images and text together?

Yes, the multimodal capabilities allow your agent to search across different types of media in one go. This is perfect for visual search or catalogs with mixed content.

Is Voyage AI MCP good for handling very large datasets?

Yes, it includes dedicated batch capabilities. You can upload files for background processing, which is the standard way to handle thousands of items without hitting rate limits.

What is the difference between standard embeddings and Voyage AI's contextualized ones?

Standard embeddings treat every chunk as a standalone island. Voyage AI's contextualized versions look at the surrounding text to ensure the meaning stays consistent, which is vital for long-form content.

How does reranking help my AI agent's responses?

Reranking takes the top results from a search and re-scores them for relevance. It ensures that the absolute best match is moved to the top, so your agent always sees the most important information first.

Can I manage my batch jobs using the Voyage AI MCP?

Yes, you can list all your batches and check the status of specific jobs. This makes it easy to monitor large data updates as they happen in the background.

How does reranking improve my RAG system's accuracy?

By using the rerank capability, your agent can take a list of potentially relevant documents and re-score them using a powerful cross-encoder model. This ensures that the most semantically relevant pieces of information are ranked first, providing better context for the LLM to answer queries.

What is the benefit of using contextualized embeddings?

The create_contextualized_embeddings capability allows you to embed chunks of text while considering the surrounding content of the same document. This prevents loss of meaning that often happens with standard chunking, leading to much higher retrieval precision.

Can I process images and text in the same vector space?

Yes! With create_multimodal_embeddings, you can provide interleaved sequences of text and image URLs. Voyage AI will generate a single embedding that represents the combined semantic meaning, perfect for visual or hybrid search.

One connection away

Give your agent a direct line to Voyage AI.

Connect Voyage AI once. Keep it beside 5,900+ managed Connectors when the next task needs more.

Explore every Connector No credit card required · Free tier available