bedrock-mcp-streamlit

# 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/](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/](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: ```bash pip install streamlit langchain langchain-aws langchain_mcp_adapters ``` 2. Configure MCP server in `mcp_config.json`. 3. Run the application. ```bash streamlit run src/main.py ``` ## Configuration The `config.json` file is where you configure the LLM model and other settings. ```json { "chat_history_dir": "chat_history", "mcp_config_file": "mcp_config.json", "models": { "Claude 3.7 Sonnet": { "model_provider": "bedrock_converse", "model": "us.anthropic.claude-3-7-sonnet-20250219-v1:0" }, "Amazon Nova Pro": { "model_provider": "bedrock_converse", "model": "us.amazon.nova-pro-v1:0" }, }, } ``` The `mcp_config.json` file contains the settings for the MCP server. Please note that `transport` is required. ```json { "mcpServers": { "server1": { "command": "...", "args": ["..."], "env": { "API_KEY": "..." }, "transport": "..." } } } ``` ## Usage To run the Streamlit application, run the following command. ```bash 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.