How to Use the pgvector (Vector Database) MCP in LlamaIndex

Q: How does LlamaIndex handle index updates for pgvector (Vector Database)?

Your agent triggers createindex when necessary. This allows you to optimize search performance as your data volume grows.

Ground your LlamaIndex knowledge base in live PostgreSQL data using this MCP Server.

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Connect pgvector (Vector Database) MCP to LlamaIndex

Create your Vinkius account to connect pgvector (Vector Database) to LlamaIndex — we handle the hosting, security, and runtime updates so you don't have to. No server setup required.

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Setup pgvector (Vector Database) with LlamaIndex

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Index PostgreSQL data into LlamaIndex

Use `search_vectors` to retrieve relevant context that becomes part of your searchable index. It bridges your database and your agent's knowledge base. This makes your RAG pipeline more accurate. You stop hallucinating by forcing your agent to reference actual records stored in your tables.

Build dynamic pipelines for LlamaIndex

Call `create_table` to define new buckets for your retrieved documents. You can structure your database to match the specific needs of your current query session. It removes the need for static, pre-built stores. Your agent decides where to put data based on the structure of the incoming information.

Maintain vector stores via LlamaIndex

Use `delete_vector` to purge outdated records during your indexing process. It keeps your search results clean and relevant to the user request. Your agent manages the lifecycle of these embeddings. You maintain control over what stays in your database and what gets cleared.

Setup guide

Set up pgvector (Vector Database) 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 pgvector (Vector Database) 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 pgvector (Vector Database) tools.",
)
response = await agent.run("List recent pgvector (Vector Database) data")

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