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https://github.com/NVIDIA/dgx-spark-playbooks.git
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124 lines
4.0 KiB
Python
124 lines
4.0 KiB
Python
#
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# SPDX-FileCopyrightText: Copyright (c) 1993-2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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# SPDX-License-Identifier: Apache-2.0
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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"""
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MCP server providing image understanding and analysis tools.
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This server exposes a `process_image` tool that uses a vision language model to answer queries about images.
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It supports multiple image input formats including URLs, file paths, and base64-encoded images.
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"""
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import asyncio
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import base64
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import os
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import requests
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import sys
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from pathlib import Path
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import time
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from langchain_core.tools import tool, Tool
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from langchain_mcp_adapters.tools import to_fastmcp
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from mcp.server.fastmcp import FastMCP
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from openai import AsyncOpenAI, OpenAI
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project_root = Path(__file__).parent.parent.parent
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sys.path.append(str(project_root))
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from postgres_storage import PostgreSQLConversationStorage
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mcp = FastMCP("image-understanding-server")
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model_name = "Qwen2.5-VL-7B-Instruct"
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model_client = OpenAI(
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base_url=f"http://qwen2.5-vl:8000/v1",
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api_key="api_key"
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)
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POSTGRES_HOST = os.getenv("POSTGRES_HOST", "postgres")
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POSTGRES_PORT = int(os.getenv("POSTGRES_PORT", 5432))
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POSTGRES_DB = os.getenv("POSTGRES_DB", "chatbot")
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POSTGRES_USER = os.getenv("POSTGRES_USER", "chatbot_user")
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POSTGRES_PASSWORD = os.getenv("POSTGRES_PASSWORD", "chatbot_password")
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postgres_storage = PostgreSQLConversationStorage(
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host=POSTGRES_HOST,
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port=POSTGRES_PORT,
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database=POSTGRES_DB,
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user=POSTGRES_USER,
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password=POSTGRES_PASSWORD
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)
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@mcp.tool()
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def explain_image(query: str, image: str):
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"""
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This tool is used to understand an image. It will respond to the user's query based on the image.
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...
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"""
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if not image:
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raise ValueError('Error: explain_image tool received an empty image string.')
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image_url_content = {}
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if image.startswith("http://") or image.startswith("https://"):
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image_url_content = {
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"type": "image_url",
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"image_url": {"url": image}
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}
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else:
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if image.startswith("data:image/"):
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metadata, b64_data = image.split(",", 1)
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filetype = metadata.split(";")[0].split("/")[-1]
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elif os.path.exists(image):
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with open(image, "rb") as image_file:
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filetype = image.split('.')[-1]
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b64_data = base64.b64encode(image_file.read()).decode("utf-8")
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else:
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raise ValueError(f'Invalid image type -- could not be identified as a url or filepath: {image}')
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image_url_content = {
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"type": "image_url",
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"image_url": {
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"url": f"data:image/{filetype if filetype else 'jpeg'};base64,{b64_data}"
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}
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}
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message = [
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{
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"role": "user",
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"content": [
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{"type": "text", "text": query},
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image_url_content
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]
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}
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]
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try:
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print(f"Sending request to vision model: {query}")
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response = model_client.chat.completions.create(
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model=model_name,
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messages=message,
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max_tokens=512,
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temperature=0.1
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)
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print(f"Received response from vision model")
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return response.choices[0].message.content
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except Exception as e:
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print(f"Error calling vision model: {e}")
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raise RuntimeError(f"Failed to process image with vision model: {e}")
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if __name__ == "__main__":
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print(f'running {mcp.name} MCP server')
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mcp.run(transport="stdio") |