Ollama Pydantic Project

Name: Ollama Pydantic Project
Author: jageenshukla

@jageenshukla

代码仓库

关于 Ollama Pydantic Project

Created sample project for pydantic agent with local ollama model with mcp server integration.

#ollama #pydantic #chatbot #streamlit

基本信息

分类

AI 与智能体

运行时

python

传输方式

stdio

发布者

jageenshukla

配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

{
  "mcpServers": {
    "ollama-pydantic-project": {
      "command": "python3",
      "args": [
        "-m",
        "venv",
        "venv"
      ]
    }
  }
}

工具

未检测到工具

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 Ollama Pydantic Project?

Ollama Pydantic Project is a demonstration that integrates a local Ollama model with the Pydantic agent framework and an MCP (Model Context Protocol) server to create an intelligent agent. It provides a user-friendly web-based chatbot interface using Streamlit, and is intended for developers exploring local LLM-based agents with tool integration.

How to use Ollama Pydantic Project?

Install Python 3.8+, run the Ollama server locally on http://localhost:11434/v1, and set up a separate MCP server (a sample is referenced). Clone the repository, create a virtual environment, install dependencies (pip install -r requirements.txt), then start the application with streamlit run src/streamlit_app.py. Open the provided URL (typically http://localhost:8501) to interact with the chatbot.

Key features of Ollama Pydantic Project

Integrates a local Ollama model for response generation.
Uses Pydantic agent framework for data validation.
Connects to an MCP server to enable tool use.
Provides a Streamlit-based web chatbot interface.
Ensures type safety and data validation.

Use cases of Ollama Pydantic Project

Building a chatbot powered by a locally hosted LLM.
Creating an agent that uses external tools via MCP.
Prototyping an AI assistant with structured data handling.
Demonstrating integration of Ollama, Pydantic, and Streamlit.

FAQ from Ollama Pydantic Project

What are the prerequisites to run the project?

You need Python 3.8 or higher, the Ollama server running locally on http://localhost:11434/v1, and an MCP server set up as described in the referenced sample.

How do I start the application?

After cloning the repository and installing dependencies, ensure the Ollama server is running, then execute streamlit run src/streamlit_app.py. The application will be available at http://localhost:8501.

What should I do if I encounter issues?

Verify that the Ollama server is running on the correct port and that your virtual environment is activated. Ensure you are using Python 3.8 or higher. For MCP server problems, refer to the MCP Server Sample repository.