[Draft]add MCP notebooks

.ci/skipped_notebooks.yml

@@ -556,3 +556,9 @@
         - macos-13
         - ubuntu-22.04
         - windows-2019
+- notebook: notebooks/llm-agent-mcp/llm-agent-mcp.ipynb
+  skips:
+    - os:
+        - macos-13
+        - ubuntu-22.04
+        - windows-2019

.ci/spellcheck/.pyspelling.wordlist.txt

@@ -549,6 +549,7 @@ matplotlib
 MathVista
 MatMul
 MBs
+MCP
 md
 MediaPipe
 medprob

notebooks/llm-agent-mcp/README.md

@@ -0,0 +1,33 @@
+# Create MCP Agent using OpenVINO and Qwen-Agent
+
+MCP is an open protocol that standardizes how applications provide context to LLMs. Think of MCP like a USB-C port for AI applications. Just as USB-C provides a standardized way to connect your devices to various peripherals and accessories, MCP provides a standardized way to connect AI models to different data sources and tools.
+
+MCP helps you build agents and complex workflows on top of LLMs. LLMs frequently need to integrate with data and tools, and MCP provides:
+
+- A growing list of pre-built integration that your LLM can directly plug into
+- The flexibility to switch between LLM providers and vendors
+- Best practices for securing your data within your infrastructure
+
+![Image](https://github.com/user-attachments/assets/dfe1aa42-cae9-4356-be81-f010462d78a8)
+
+[Qwen-Agent](https://github.com/QwenLM/Qwen-Agent) is a framework for developing LLM applications based on the instruction following, tool usage, planning, and memory capabilities of Qwen. It also comes with example applications such as Browser Assistant, Code Interpreter, and Custom Assistant.
+
+This notebook explores how to create a MCP Agent step by step using OpenVINO and Qwen-Agent.
+
+### Notebook Contents
+
+The tutorial consists of the following steps:
+
+- Install prerequisites
+- Download and convert the model from a public source using the [OpenVINO integration with Hugging Face Optimum](https://huggingface.co/blog/openvino).
+- Compress model weights to INT4 or INT8 precision using [NNCF](https://github.com/openvinotoolkit/nncf)
+- Create an Agent
+- Interactive Demo
+
+
+## Installation Instructions
+
+This is a self-contained example that relies solely on its own code.</br>
+We recommend  running the notebook in a virtual environment. You only need a Jupyter server to start.
+For details, please refer to [Installation Guide](../../README.md).
+<img referrerpolicy="no-referrer-when-downgrade" src="https://static.scarf.sh/a.png?x-pxid=5b5a4db0-7875-4bfb-bdbd-01698b5b1a77&file=notebooks/llm-agent-mcp/README.md" />

notebooks/llm-agent-mcp/gradio_helper.py

@@ -0,0 +1,141 @@
+import os
+from typing import List
+from qwen_agent.gui.utils import convert_history_to_chatbot
+from qwen_agent.llm.schema import Message
+from qwen_agent.gui import WebUI
+
+
+class OpenVINOUI(WebUI):
+    def run(
+        self,
+        messages: List[Message] = None,
+        share: bool = False,
+        server_name: str = None,
+        server_port: int = None,
+        concurrency_limit: int = 10,
+        enable_mention: bool = False,
+        **kwargs
+    ):
+        self.run_kwargs = kwargs
+
+        from qwen_agent.gui.gradio_dep import gr, mgr, ms
+
+        customTheme = gr.themes.Default(
+            primary_hue=gr.themes.utils.colors.blue,
+            radius_size=gr.themes.utils.sizes.radius_none,
+        )
+
+        with gr.Blocks(
+            css=os.path.join(os.path.dirname(__file__), "assets/appBot.css"),
+            theme=customTheme,
+        ) as demo:
+            history = gr.State([])
+            with ms.Application():
+                with gr.Row(elem_classes="container"):
+                    with gr.Column(scale=4):
+                        chatbot = mgr.Chatbot(
+                            value=convert_history_to_chatbot(messages=messages),
+                            avatar_images=[
+                                self.user_config,
+                                self.agent_config_list,
+                            ],
+                            height=850,
+                            avatar_image_width=80,
+                            flushing=False,
+                            show_copy_button=True,
+                            latex_delimiters=[
+                                {"left": "\\(", "right": "\\)", "display": True},
+                                {"left": "\\begin{equation}", "right": "\\end{equation}", "display": True},
+                                {"left": "\\begin{align}", "right": "\\end{align}", "display": True},
+                                {"left": "\\begin{alignat}", "right": "\\end{alignat}", "display": True},
+                                {"left": "\\begin{gather}", "right": "\\end{gather}", "display": True},
+                                {"left": "\\begin{CD}", "right": "\\end{CD}", "display": True},
+                                {"left": "\\[", "right": "\\]", "display": True},
+                            ],
+                        )
+
+                        input = mgr.MultimodalInput(
+                            placeholder=self.input_placeholder,
+                        )
+                        audio_input = gr.Audio(sources=["microphone"], type="filepath")
+
+                    with gr.Column(scale=1):
+                        if len(self.agent_list) > 1:
+                            agent_selector = gr.Dropdown(
+                                [(agent.name, i) for i, agent in enumerate(self.agent_list)],
+                                label="Agents",
+                                info="Select an Agent",
+                                value=0,
+                                interactive=True,
+                            )
+
+                        agent_info_block = self._create_agent_info_block()
+
+                        agent_plugins_block = self._create_agent_plugins_block()
+
+                        if self.prompt_suggestions:
+                            gr.Examples(
+                                label="Example",
+                                examples=self.prompt_suggestions,
+                                inputs=[input],
+                            )
+
+                    if len(self.agent_list) > 1:
+                        agent_selector.change(
+                            fn=self.change_agent,
+                            inputs=[agent_selector],
+                            outputs=[agent_selector, agent_info_block, agent_plugins_block],
+                            queue=False,
+                        )
+
+                    input_promise = input.submit(
+                        fn=self.add_text,
+                        inputs=[input, audio_input, chatbot, history],
+                        outputs=[input, audio_input, chatbot, history],
+                        queue=False,
+                    )
+
+                    if len(self.agent_list) > 1 and enable_mention:
+                        input_promise = input_promise.then(
+                            self.add_mention,
+                            [chatbot, agent_selector],
+                            [chatbot, agent_selector],
+                        ).then(
+                            self.agent_run,
+                            [chatbot, history, agent_selector],
+                            [chatbot, history, agent_selector],
+                        )
+                    else:
+                        input_promise = input_promise.then(
+                            self.agent_run,
+                            [chatbot, history],
+                            [chatbot, history],
+                        )
+
+                    input_promise.then(self.flushed, None, [input])
+
+            demo.load(None)
+
+        demo.queue(default_concurrency_limit=concurrency_limit).launch(share=share, server_name=server_name, server_port=server_port)
+
+    def _create_agent_plugins_block(self, agent_index=0):
+        from qwen_agent.gui.gradio_dep import gr
+
+        agent_interactive = self.agent_list[agent_index]
+
+        if agent_interactive.function_map:
+            capabilities = [key for key in agent_interactive.function_map.keys()]
+            return gr.CheckboxGroup(
+                label="Plugins",
+                value=capabilities,
+                choices=capabilities,
+                interactive=False,
+            )
+
+        else:
+            return gr.CheckboxGroup(
+                label="Plugins",
+                value=[],
+                choices=[],
+                interactive=False,
+            )