@Robinson777-prog
基本信息
4-openai-integration
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This example demonstrates how to integrate the Model Context Protocol (MCP) with OpenAI’s API, allowing OpenAI to dynamically use tools exposed by an MCP server. It is intended for developers building AI‑powered applications that need to access backend knowledge bases or other tool‑based systems.
Install the required dependencies, set your OpenAI API key in a .env file, and run the client with python client.py. The MCP server is automatically launched as a subprocess via stdio transport, so no separate server startup is needed. For a split client‑server architecture, switch to SSE transport as described in the documentation.
get_knowledge_base tool that retrieves Q&A pairsIt uses **stdio transport**, meaning the client and server run in the same process and the client launches the server as a subprocess. If you need separate applications (e.g., running the server on a different machine), use the **SSE (Server‑Sent Events) transport** instead.
OpenAI’s function‑calling mechanism works through four steps: the MCP client registers tools as OpenAI functions, OpenAI chooses which tool to use based on the query, the client executes the tool via MCP, and OpenAI incorporates the result into its final response.
MCP acts as a standardized, secure bridge between AI models and your backend systems. It abstracts away backend complexity, provides a consistent interface for tool interactions, and lets you control exactly which tools and data are exposed.
It contains Q&A pairs about company policies stored in a JSON file (`data/kb.json`). The MCP server retrieves these pairs through the `get_knowledge_base` tool.
No. With the stdio transport used here, the client automatically starts the server as a subprocess. For an SSE‑based setup, you would run the server independently.
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