Connectez Voyage AI AI Embeddings API à ChatGPT et Claude

• 01 / 03Créer des embeddings contextualisés
• 02 / 03Traiter des lots
• 03 / 03Réorganiser les résultats de recherche

Jour le plus rapide

770ms

Jour le plus lent

1358ms

Tendance sur 14 jours

En ralentissement+12%

Voyage AI Connector

Tout est prêt. Choisissez votre client MCP et suivez les instructions de configuration.

Lien du connecteurhttps://edge.vinkius.com/vk_preview_x7lwFbbAdz5StvxoOu2Tkh9eDMu9Uh5i3GKObLMP/mcpCopier

Claude Desktop

Suivez les étapes ci-dessous pour vous connecter en quelques secondes.

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.

Configuration · claude_desktop_config.jsonCopier

{
  "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

Les instructions pas à pas pour chaque client sont dans le guide. Comment connecter

Configuration guidée pour Claude ? Configurer Voyage AI avec Claude et les autres IA

Voyez tous les clients IA avec lesquels ce connector fonctionne ↑

• 01

  Ingénieurs IA

  Conçoivent des pipelines RAG de niveau production nécessitant une récupération supérieure aux modèles standards.

• 02

  Data Scientists

  Expérimentent la recherche multimodale et le découpage de données contextualisé.

• 03

  Développeurs

  Intègrent une recherche de haute précision dans leurs applications sans gérer d'infrastructure complexe.

• 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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