Chatbot Assistant with Gemini + gRPC MCP Server

@omjamnekar

代码仓库

基本信息

分类

AI 与智能体

传输方式

stdio

作者

omjamnekar

关于 Chatbot Assistant with Gemini + gRPC MCP Server

grcp chatmodel with mcp server model integrated with gemini model 5o-flash

#chatbot #grpc #gemini

Chatbot Assistant with Gemini + gRPC MCP Server 配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

{
  "mcpServers": {
    "grcp-chatmodel": {
      "command": "python",
      "args": [
        "server.py"
      ]
    }
  }
}

Chatbot Assistant with Gemini + gRPC MCP Server 工具

工具列表暂未提供

当前收录暂时没有可展示的工具列表，不代表服务没有工具。请查阅项目文档了解支持的能力。

工具是从 README 中自动提取的。维护者可以在 ## Tools 标题下列出工具,即可填充这部分内容。

概览

What is Chatbot Assistant with Gemini + gRPC?

A simple chatbot backend powered by Google's Gemini API that uses gRPC for client‑server communication. It maintains conversation context per session and follows a clean modular design (client, server, context manager, inference engine). Designed for developers building interactive conversational experiences with gRPC and Gemini.

How to use Chatbot Assistant with Gemini + gRPC?

Install dependencies: pip install -r requirements.txt.
Create a .env file with your GEMINI_API key.
Start the server: python server.py.
Run the client: python client.py. Type messages and receive AI replies; type exit to stop.

Key features of Chatbot Assistant with Gemini + gRPC

gRPC‑based communication between client and server
Integration with Google Gemini API for AI replies
Session‑based context management per chat
Modular code (client, server, context manager, inference engine)
Clean project structure with .proto definitions

Use cases of Chatbot Assistant with Gemini + gRPC

Building a prototype chatbot with stateful conversation history
Learning how to combine gRPC with a modern LLM API
Experimenting with session‑aware AI assistants in Python
Demonstrating a full‑stack gRPC application with Gemini backend

常见问题

How do I set up the Gemini API key?

Create a `.env` file in the project root and add `GEMINI_API=your_api_key_here`.

What Python version is required?

Python 3.10 or higher is recommended.

Do I need to compile the .proto files myself?

No – the repository includes pre‑generated files (`assistant_pb2.py` and `assistant_pb2_grpc.py`). Ensure they match your `.proto` definitions if you modify them.

How is chat context managed?

The `context_manager.py` module maintains context per session, so the conversation history is preserved for each client.

What transport and authentication does the server use?

Communication uses gRPC. The Gemini API is authenticated via the `GEMINI_API` key stored in the `.env` file.

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