What is Bedrock Mcp Streamlit

bedrock-mcp-streamlit is a chat application built with Streamlit that integrates with the Model Context Protocol (MCP) tool, allowing users to interact with chat models using Langchain and Bedrock.

Use cases

Use cases include creating customer service chatbots, developing educational tutoring systems, and building interactive conversational agents for websites.

How to use

To use bedrock-mcp-streamlit, install the required dependencies using ‘pip install streamlit langchain langchain-aws langchain_mcp_adapters’, configure the MCP server in ‘mcp_config.json’, and run the application with ‘streamlit run src/main.py’.

Key features

Key features include a chat interface using Streamlit, integration with MCP tools, configuration options for LLM models in ‘config.json’, chat history storage in YAML format, and the ability to change settings via the Streamlit sidebar.

Where to use

bedrock-mcp-streamlit can be used in various fields such as customer support, educational tools, and any application requiring interactive chat functionalities with advanced language models.

Bedrock Chat with MCP tool

This is a chat application built with Streamlit and integrated with the MCP (Model Context Protocol) tool.

Overview

Bedrock Chat with MCP tool is a chat application built with Streamlit and integrated with the MCP (Model Context Protocol) tool.

This application uses Langchain and Bedrock to create a chat model and uses the model specified in config.json as a parameter in Langchain’s init_chat_model function (https://python.langchain.com/docs/how_to/chat_models_universal_init/). It interacts with the MCP (Model Context Protocol) server defined in mcp_config.json and accesses various tools. MCP is an open protocol that standardizes how applications provide context to LLM (https://modelcontextprotocol.io/). Chat history is stored in a YAML file. The util.py module defines MessageProcessor and its subclasses to handle message processing using different models.

The config.json file allows you to configure the LLM model to use, where the chat history files are stored, etc.
The mcp_config.json file describes the configuration of the MCP server.

In the Streamlit sidebar, you can configure the following:

• Select LLM model
• Change chat history directory
• Change MCP configuration file
• Select past chat history

Features

• Chat interface using Streamlit
• Integration with MCP tools
• Use Langchain and Bedrock for chat model
• LLM model etc. can be configured in config.json
• MCP tool integration
• Use Langchain and Bedrock
• Read MCP server settings from mcp_config.json
• Save chat history in YAML format
• Can change settings from Streamlit sidebar

Setup

1. Install dependencies:

   pip install streamlit langchain langchain-aws langchain_mcp_adapters
   

2. Configure MCP server in mcp_config.json.

3. Run the application.

   streamlit run src/main.py
   

Configuration

The config.json file is where you configure the LLM model and other settings.

The mcp_config.json file contains the settings for the MCP server.

Please note that transport is required.

{
  "mcpServers": {
    "server1": {
      "command": "...",
      "args": [
        "..."
      ],
      "env": {
        "API_KEY": "..."
      },
      "transport": "..."
    }
  }
}

Usage

To run the Streamlit application, run the following command.

streamlit run src/main.py

Once the application is running, enter a message in the chat input box.
After sending, the chat model and MCP tool will generate a response.

In the sidebar, you can configure the LLM model, chat history directory, and MCP config file.
You can also select past chat history and resume the conversation.

Notes

• Write the MCP server configuration in mcp_config.json.
• To use Bedrock, you need an AWS account.
• Chat history is stored in a YAML file.
• config.json and mcp_config.json must be in the same directory as your application.