https://github.com/promptengineer48/mcp_basic_example
https://github.com/promptengineer48/mcp_basic_example
Last synced: 8 months ago
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- Host: GitHub
- URL: https://github.com/promptengineer48/mcp_basic_example
- Owner: PromptEngineer48
- Created: 2025-06-28T07:45:27.000Z (9 months ago)
- Default Branch: main
- Last Pushed: 2025-06-28T07:49:10.000Z (9 months ago)
- Last Synced: 2025-06-28T08:32:41.996Z (9 months ago)
- Language: Python
- Size: 0 Bytes
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# 🕸️ LLM Web Search App
This Streamlit app enables users to scrape web content from a given URL using Bright Data’s Multi-Server MCP and extract insights by querying a Large Language Model (LLM). Ideal for summarizing or analyzing content from platforms like Reddit, LinkedIn, or general web pages.
## 🚀 Features
- 🔍 Scrape structured content from websites using Bright Data’s MCP tools
- 🤖 Generate intelligent responses using Ollama's local LLM (`gemma3n:latest`)
- 🧠 Automatically selects the right scraper based on URL
- 🖥️ Clean, simple UI built with Streamlit
---
## 🛠️ Tech Stack
- [Streamlit](https://streamlit.io/) – for the frontend interface
- [LangChain](https://www.langchain.com/) – to manage tools and workflows
- [Bright Data MCP](https://brightdata.com/) – for scraping tools
- [Ollama](https://ollama.com/) – to run local LLMs like `gemma3n`
- Python `asyncio` – for asynchronous execution
---
## 🧾 Prerequisites
- Python 3.8+
- Node.js (for running MCP tools)
- [Bright Data](https://brightdata.com/) API Token
- [Ollama](https://ollama.com/) installed with the `gemma3n` model
---
## 🔧 Installation
```bash
# Clone the repo
git clone https://PromptEngineer48/mcp_basic_example.git
cd mcp_basic_example
# Create virtual environment
python -m venv venv
source venv/bin/activate # On Windows use `venv\Scripts\activate`
# Install dependencies
pip install -r requirements.txt