Mcp Server Client

What is Mcp Server Client

mcp_server_client is a comprehensive client-server architecture designed to facilitate communication between users and various backend services, including chat functionalities and external APIs like OpenAI.

Use cases

Use cases include building chatbots for customer service, creating interactive applications that require user engagement, and integrating AI functionalities for enhanced user experiences.

How to use

To use mcp_server_client, users interact with a Streamlit frontend to send queries, which are processed by a NestJS API that communicates with multiple services, including chat and agent services, to generate responses.

Key features

Key features include a modular architecture with dedicated services for chat, agents, and tools, integration with OpenAI for advanced functionalities, and robust storage solutions using PostgreSQL and AWS S3.

Where to use

mcp_server_client can be used in various domains such as customer support, interactive applications, and any scenario requiring real-time chat and data processing capabilities.

%%{init: {'flowchart': {'curve': 'bezier'}}}%%
flowchart TB
    subgraph Client
        User["User"]
        Frontend["Streamlit Frontend"]
    end

    subgraph API
        NestJS["NestJS API"]
        subgraph Services
            ChatService["Chat Service"]
            AgentService["Agent Service"]
            ToolAgentService["Tool Agent Service"]
            RagService["RAG Service"]
            OpenAIService["OpenAI Service"]
        end
    end

    subgraph Tools
        MCP1["MCP Server 1"]
        MCP2["MCP Server 2"]
        MCP3["MCP Server 3"]
    end

    subgraph Storage
        LocalStack["AWS S3"]
        PGVector["PGVector"]
        PostgreSQL["Postgres DB"]
    end

    subgraph External
        OpenAI["OpenAI API"]
    end

    User -- "query" --> Frontend
    Frontend -- "response" --> User
    Frontend -- "POST /chat" <--> NestJS
    
    NestJS <--> ChatService
    ChatService <--> RagService
    ChatService <--> AgentService
    ChatService <--> ToolAgentService
    
    RagService -- "embedding" <--> OpenAIService
    RagService <--> PGVector
    
    ToolAgentService -- "/sse" <--> MCP1
    ToolAgentService -- "/sse" <--> MCP2
    ToolAgentService -- "/sse" <--> MCP3
     
    AgentService -- "chat-completion" <--> OpenAIService
    
    OpenAIService <--> OpenAI
    
    MCP1 <--> LocalStack
    MCP2 <--> PostgreSQL
    MCP3 <--> PGVector
    
    %% Data flow
    class User,Frontend client
    class NestJS,ChatService,AgentService,ToolAgentService,RagService,OpenAIService api
    class MCP1,MCP2,MCP3 tools
    class LocalStack,PostgreSQL storage
    class OpenAI external