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Chatbot Assistant with Gemini + gRPC MCP Server

@omjamnekar

リポジトリ

基本情報

カテゴリ

AI とエージェント

トランスポート

stdio

作者

omjamnekar

Chatbot Assistant with Gemini + gRPC MCP Server について

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

#chatbot#grpc#gemini

Chatbot Assistant with Gemini + gRPC MCP Server の設定

以下の設定を使って、このサーバーを MCP 対応クライアントに追加してください。

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

Chatbot Assistant with Gemini + gRPC MCP Server のツール

ツール一覧はまだありません

この掲載には表示できるツール一覧がまだありません。サーバーにツールがないという意味ではありません。対応機能はプロジェクトのドキュメントをご確認ください。

ツールは README から自動的に抽出されます。メンテナーは ## Tools という見出しの下に記載することで、このタブに反映できます。

概要

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?

  1. Install dependencies: pip install -r requirements.txt.
  2. Create a .env file with your GEMINI_API key.
  3. Start the server: python server.py.
  4. 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

よくある質問

1

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`.

2

What Python version is required?

Python 3.10 or higher is recommended.

3

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.

4

How is chat context managed?

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

5

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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