Connect Voyage AI MCP for AI Agents
Building high-precision, multimodal document search systems
We take care of the infrastructure, maintenance, security, and governance. Works with:
Claude 
ChatGPT 
Cursor 
Gemini 
Windsurf 
VS Code 
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What AI agents can do with Voyage AI Embeddings: 13 Tools for Vector Search and RAG
These tools let you generate vectors, manage batch jobs, handle file uploads, and refine search results across text, code, and images.
Create embeddings
Creates standard vector representations for general blocks of text.
Create multimodal embeddings
Converts mixed files containing both images and text into a single, searchable vector.
Delete file
Removes uploaded source files from the system to manage storage.
Get batch
Retrieves the current status and results of a previously started batch job using its ID.
Get file content
Downloads the actual content from an uploaded source file for inspection or processing.
Get file
Retrieves metadata, like size and type, about an uploaded source file without downloading it.
List batches
Shows a list of all batch jobs that have been created on the account.
List files
Lists all source files currently stored in the system.
Rerank
Takes a set of documents and scores them against a query to prioritize relevance for your AI agent.
Upload file
Uploads one or more source files specifically intended for batch embedding processing.
Cancel batch
Stops an existing batch embedding job immediately when you no longer need it.
Create batch
Initiates a large-scale, asynchronous job to process and embed many files at once.
Create contextualized embeddings
Generates embeddings for text chunks while preserving the surrounding document context.
Frequently Asked Questions
How does Voyage AI help with searching documents that include both images and text? +
It vectorizes images and text together into one searchable format. This means your AI client doesn't treat them separately; it understands the relationship, allowing you to search for a concept using either an image or descriptive text.
Is Voyage AI suitable for very large document collections? +
Yes. It includes batch tools that let your agent process thousands of files asynchronously. You submit the work and check back later, preventing timeouts and keeping your main application running smoothly.
What’s the difference between standard embeddings and contextualized ones? +
Standard embeddings treat chunks in isolation. Contextualized embeddings analyze the chunk while considering its surrounding text, significantly improving accuracy by preventing the loss of vital context during retrieval.
Do I need to use Voyage AI for every single search query? +
No. You run it when your agent needs specialized embedding or reranking power. It’s a tool you call only for complex retrieval, not a constant background service.
Can I improve the quality of my AI's answers using Voyage AI? +
Yes, especially by running retrieved documents through reranking. This process scores and orders the context, guaranteeing your agent reads the most accurate information first, leading to better final outputs.
How does reranking improve my RAG system's accuracy? +
By using the rerank tool, 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 tool 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.
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