How to Use the Voyage AI (AI Embeddings API) MCP in LlamaIndex

Q: Can I process multimodal content with Voyage AI (AI Embeddings API) in LlamaIndex?

Yes, the createmultimodalembeddings tool lets you embed images or other non-text data types alongside text. This means your resulting knowledge index can be grounded in varied media, not just words.

Q: What is the best way to manage large document uploads with Voyage AI (AI Embeddings API) for LlamaIndex?

Use uploadfile followed by batch processing tools. This keeps your indexing process running in the background, allowing you to monitor progress via getbatch, which is essential when building a massive knowledge index.

Build searchable knowledge bases with LlamaIndex and Voyage AI (AI Embeddings API).

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Connect Voyage AI (AI Embeddings API) MCP to LlamaIndex

Create your Vinkius account to connect Voyage AI (AI Embeddings API) to LlamaIndex and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup Voyage AI (AI Embeddings API) with LlamaIndex

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Create Semantic Search Indexes for LlamaIndex

LlamaIndex requires structured data to build its index, and `create_embeddings` provides the necessary vectors. You can process your raw text using this tool, then pass the resulting embeddings into a vector store. The MCP Server gives you the backbone needed to make unstructured API results searchable.

Manage Source Documents for LlamaIndex

Use `upload_file` and the batch tools (`create_batch`, `get_batch`) when indexing large corpuses of documents. This lets you process many files at once, ensuring your knowledge base stays current with live data. You'll manage the lifecycle of these resources using `list_files`.

Improve Query Precision for LlamaIndex

When a user asks a complex question, don't just search—score it. The `rerank` tool allows your RAG application to compare the initial search results against the actual query, pulling out only the highest-quality snippets. This boosts retrieval accuracy significantly.

Setup guide

Set up Voyage AI (AI Embeddings API) MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all Voyage AI (AI Embeddings API) MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to Voyage AI (AI Embeddings API) tools.",
)
response = await agent.run("List recent Voyage AI (AI Embeddings API) data")

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Common questions about Voyage AI (AI Embeddings API) MCP in LlamaIndex

It provides the embedding vectors that turn unstructured documents into queryable data points. By using `create_embeddings`, you transform raw text into mathematical representations that vector stores can index and search efficiently.

Yes, the `create_multimodal_embeddings` tool lets you embed images or other non-text data types alongside text. This means your resulting knowledge index can be grounded in varied media, not just words.

Use `upload_file` followed by batch processing tools. This keeps your indexing process running in the background, allowing you to monitor progress via `get_batch`, which is essential when building a massive knowledge index.

The MCP Server tracks file metadata and processes embeddings. We recommend using `get_file_content` only when necessary, ensuring you know exactly what data is being indexed into your knowledge base.

This server touches file metadata and embedding vectors. Because the embeddings are derived from source files, always ensure those original documents comply with your organization's data governance rules before indexing.

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