Voyage AI AI Embeddings APIをAIに接続

• 01 / 03文脈を考慮した埋め込みを作成する
• 02 / 03バッチ処理の方法
• 03 / 03検索結果を再ランク付けする方法

最速日

770ms

最遅日

1358ms

14日間の傾向

遅延傾向+12%

Voyage AI Connector

準備完了です。MCPクライアントを選択し、セットアップ手順に従ってください。

コネクタリンクhttps://edge.vinkius.com/vk_preview_x7lwFbbAdz5StvxoOu2Tkh9eDMu9Uh5i3GKObLMP/mcpコピー

Claude Desktop

以下の手順で数秒で接続できます。

1. 1In Claude Desktop, open Settings → Connectors.
2. 2Click “Add custom connector” and paste the connector link above as the remote MCP server URL.
3. 3Click Add and start a new chat — Voyage AI capabilities are ready to use.

設定 · claude_desktop_config.jsonコピー

{
  "mcpServers": {
    "voyage-ai-ai-embeddings-api-mcp": {
      "url": "https://edge.vinkius.com/vk_preview_x7lwFbbAdz5StvxoOu2Tkh9eDMu9Uh5i3GKObLMP/mcp"
    }
  }
}

• Claude
• ChatGPT
• Cursor
• VS Code
• Windsurf
• Claude Code
• JetBrains
• Cline

各クライアントの手順ガイドはこちらです。 接続方法

Claude向けのガイド付きセットアップ？ Voyage AIをClaudeや他のAIに設定する

このConnectorが対応するすべてのAIクライアントを見る ↑

• 01

  AIエンジニア

  標準的な埋め込みモデルよりも優れた検索精度が必要な、プロダクションレベルのRAGパイプラインを構築する方。

• 02

  データサイエンティスト

  マルチモーダル検索や、文脈を考慮したチャンク分割の実験を行う方。

• 03

  開発者

  複雑なインフラを管理することなく、アプリケーションに高精度な検索機能を統合したい方。

• 01

  How do I connect Voyage AI to my AI client?

  You subscribe to the MCP on Vinkius and enter your Voyage AI API key. Once connected, your client can immediately use the capabilities.

• 02

  What makes these embeddings better for RAG?

  The MCP provides contextualized embeddings that look at surrounding text and reranking capabilities that sort results for much higher precision.

• 03

  Can I process large amounts of data at once?

  Yes, you can use the create_batch capability to start large-scale inference jobs and monitor them with get_batch.

• 04

  Does this support images?

  Yes, the create_multimodal_embeddings capability allows you to vectorize text and images into the same space.

• 05

  Which clients are compatible?

  This MCP works with any MCP-compatible client, including Claude, Cursor, and Windsurf.

• 06

  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.

• 07

  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.

• 08

  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.

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