How to Use the Gradient AI (LLM API & Finetuning) MCP in LlamaIndex

Q: How does LlamaIndex index data from Gradient AI (LLM API & Finetuning)?

The LlamaIndex agent calls generateembeddings to vectorize your documents. LlamaIndex then stores these vectors in an index, allowing you to run semantic search queries across your Gradient AI knowledge base.

Q: Can I use Gradient AI (LLM API & Finetuning) to parse PDFs in LlamaIndex?

Yes. Your LlamaIndex pipeline uses extractpdf to pull text from raw files, which LlamaIndex then parses into nodes for indexing and retrieval on Gradient AI.

Q: How does LlamaIndex fetch available Gradient AI models?

The LlamaIndex agent calls listmodels to fetch the active list of foundational and custom-tuned models. LlamaIndex uses this list to validate model configurations dynamically before running queries over the MCP connection.

Index tool outputs and query fine-tuned models directly within your LlamaIndex RAG pipelines.

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Connect Gradient AI (LLM API & Finetuning) MCP to LlamaIndex

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Setup Gradient AI (LLM API & Finetuning) with LlamaIndex

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Semantic Search with Custom Embeddings

The `generate_embeddings` tool converts raw text inputs into vector representations for indexing in your LlamaIndex MCP workflows. LlamaIndex uses these Gradient AI vectors to populate local or cloud-hosted index structures. When a query comes in, the LlamaIndex agent calls `list_embeddings` to find the correct Gradient AI model and executes a semantic search against your indexed data. It then generates the query embedding to pull the most relevant nodes.

Structured Document Extraction in LlamaIndex

Pulling clean JSON from messy text is where the `extract_entity` tool shines on Gradient AI documents. LlamaIndex ingestion pipelines run this MCP tool to clean up messy Gradient AI data sources. This raw Gradient AI data is then converted into LlamaIndex nodes. By structuring the Gradient AI data first, your LlamaIndex queries return precise facts instead of loose text matches.

Dynamic Question Answering and Summarization

Direct question answering without heavy database overhead is handled by the `answer_question` tool. LlamaIndex routes short-context questions to this Gradient AI tool for rapid evaluation. For longer files, the LlamaIndex agent invokes `summarize_document` to create a concise Gradient AI overview. This summary is indexed as LlamaIndex metadata, making the source Gradient AI document easier to retrieve later.

Setup guide

Set up Gradient AI (LLM API & Finetuning) 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 Gradient AI (LLM API & Finetuning) 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 Gradient AI (LLM API & Finetuning) tools.",
)
response = await agent.run("List recent Gradient AI (LLM API & Finetuning) data")

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Common questions about Gradient AI (LLM API & Finetuning) MCP in LlamaIndex

The LlamaIndex agent calls `generate_embeddings` to vectorize your documents. LlamaIndex then stores these vectors in an index, allowing you to run semantic search queries across your Gradient AI knowledge base.

Yes. Your LlamaIndex pipeline uses `extract_pdf` to pull text from raw files, which LlamaIndex then parses into nodes for indexing and retrieval on Gradient AI.

You specify the model ID in your LlamaIndex LLM configuration and call `complete_model`. The LlamaIndex agent sends the prompt to your custom-tuned Gradient AI weights and returns the structured response.

The LlamaIndex agent calls `list_models` to fetch the active list of foundational and custom-tuned models. LlamaIndex uses this list to validate model configurations dynamically before running queries over the MCP connection.

Your PDFs and generated embeddings are processed in memory within Vinkius's secure sandboxed environment. No data is stored on the server host, and all API calls to Gradient AI use encrypted HTTPS channels.

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