Chatbot Assistant with Gemini + gRPC

@omjamnekar

About Chatbot Assistant with Gemini + gRPC

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

Config

Add this server to your MCP-compatible client using the configuration below.

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

Tools

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Overview

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

FAQ from Chatbot Assistant with Gemini + gRPC

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.

Frequently asked questions

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.

Comments

Basic information

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