Which version of Ernie Bot should I use for chat?

For high performance and reasoning, use `ernie-4.0-8k`. For faster response times and cost efficiency, `ernie-speed-128k` or `ernie-lite-8k` are excellent choices.

Can I automatically generate embeddings for RAG?

Yes! Use the `get_embeddings` tool with your text input. The agent will retrieve the vector representations from Baidu's embedding models, ready for indexing in your vector database.

How do I use prompt templates from the console?

Use the `list_prompt_templates` tool to find your configured templates. You can then retrieve specific details using `get_prompt_template` to maintain consistency across your AI workflows.

Baidu Qianfan MCP Server for LlamaIndex 6 tools — connect in under 2 minutes

Name: Baidu Qianfan
Availability: InStock
Author: Vinkius

docs.llamaindex.ai/en/stable/module_guides/tools/mcp

Built by Vinkius GDPR 6 Tools Framework

LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Baidu Qianfan as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

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

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
    mcp_tool_spec = McpToolSpec(client=mcp_client)
    tools = await mcp_tool_spec.to_tool_list_async()

    agent = FunctionAgent(
        tools=tools,
        llm=OpenAI(model="gpt-4o"),
        system_prompt=(
            "You are an assistant with access to Baidu Qianfan. "
            "You have 6 tools available."
        ),
    )

    response = await agent.run(
        "What tools are available in Baidu Qianfan?"
    )
    print(response)

asyncio.run(main())

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About Baidu Qianfan MCP Server

Connect your AI agents to Baidu Qianfan (百度千帆), the enterprise-grade LLM platform. This MCP provides 10 tools to automate interactions with Ernie Bot and other foundation models, including chat completions, vector embeddings, and prompt engineering.

LlamaIndex agents combine Baidu Qianfan tool responses with indexed documents for comprehensive, grounded answers. Connect 6 tools through Vinkius and query live data alongside vector stores and SQL databases in a single turn. ideal for hybrid search, data enrichment, and analytical workflows.

What you can do

Model Interaction — Trigger chat completions with Ernie Bot (Turbo/Speed/4.0) using persistent context
Vector Embeddings — Generate semantic embeddings for text to power RAG and search workflows
Prompt Engineering — Manage and retrieve centralized prompt templates for consistent model outputs
Image Generation — Trigger Text-to-Image tasks using Baidu's advanced diffusion models
Usage Monitoring — Track token consumption and manage model service status programmatically

The Baidu Qianfan MCP Server exposes 6 tools through the Vinkius. Connect it to LlamaIndex in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

How to Connect Baidu Qianfan to LlamaIndex via MCP

Follow these steps to integrate the Baidu Qianfan MCP Server with LlamaIndex.

Install dependencies

Run pip install llama-index-tools-mcp llama-index-llms-openai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save to agent.py and run: python agent.py

Explore tools

The agent discovers 6 tools from Baidu Qianfan

Why Use LlamaIndex with the Baidu Qianfan MCP Server

LlamaIndex provides unique advantages when paired with Baidu Qianfan through the Model Context Protocol.

Data-first architecture: LlamaIndex agents combine Baidu Qianfan tool responses with indexed documents for comprehensive, grounded answers

Query pipeline framework lets you chain Baidu Qianfan tool calls with transformations, filters, and re-rankers in a typed pipeline

Multi-source reasoning: agents can query Baidu Qianfan, a vector store, and a SQL database in a single turn and synthesize results

Observability integrations show exactly what Baidu Qianfan tools were called, what data was returned, and how it influenced the final answer

Baidu Qianfan + LlamaIndex Use Cases

Practical scenarios where LlamaIndex combined with the Baidu Qianfan MCP Server delivers measurable value.

Hybrid search: combine Baidu Qianfan real-time data with embedded document indexes for answers that are both current and comprehensive

Data enrichment: query Baidu Qianfan to augment indexed data with live information before generating user-facing responses

Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying Baidu Qianfan for fresh data

Analytical workflows: chain Baidu Qianfan queries with LlamaIndex's data connectors to build multi-source analytical reports

Baidu Qianfan MCP Tools for LlamaIndex (6)

These 6 tools become available when you connect Baidu Qianfan to LlamaIndex via MCP:

chat_completions

Requires model endpoint name. Send a message to a Baidu Qianfan model

get_embeddings

Generate vector embeddings for text

list_datasets

List uploaded datasets

list_models

List available model services

list_train_jobs

List model training jobs

text_to_image

Generate an image from a text prompt

Example Prompts for Baidu Qianfan in LlamaIndex

Ready-to-use prompts you can give your LlamaIndex agent to start working with Baidu Qianfan immediately.

"Chat with Ernie Bot 4.0 and ask 'Write a formal apology letter for a late shipment'."

"Generate embeddings for the text 'The quick brown fox jumps over the lazy dog'."

"List all my prompt templates in Qianfan."

Troubleshooting Baidu Qianfan MCP Server with LlamaIndex

Common issues when connecting Baidu Qianfan to LlamaIndex through the Vinkius, and how to resolve them.

BasicMCPClient not found

Install: pip install llama-index-tools-mcp

Baidu Qianfan + LlamaIndex FAQ

Common questions about integrating Baidu Qianfan MCP Server with LlamaIndex.

How does LlamaIndex connect to MCP servers?

Use the MCP client adapter to create a connection. LlamaIndex discovers all tools and wraps them as query engine tools compatible with any LlamaIndex agent.

Can I combine MCP tools with vector stores?

Yes. LlamaIndex agents can query Baidu Qianfan tools and vector store indexes in the same turn, combining real-time and embedded data for grounded responses.