Implement LLM provider configuration and update user settings
- Added functionality to update the default LLM provider for users via a new endpoint in UserController. - Introduced LlmProvider enum to manage available LLM options: Auto, Gemini, OpenAI, and Claude. - Updated User and UserEntity models to include DefaultLlmProvider property. - Enhanced database context and migrations to support the new LLM provider configuration. - Integrated LLM services into the application bootstrap for dependency injection. - Updated TypeScript API client to include methods for managing LLM providers and chat requests.
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# MCP Implementation Summary
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## Overview
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This document summarizes the complete implementation of the in-process MCP (Model Context Protocol) with LLM integration for the Managing trading platform.
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## Architecture
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The implementation follows the architecture diagram provided, with these key components:
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1. **Frontend (React/TypeScript)**: AI chat interface
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2. **API Layer (.NET)**: LLM controller with provider selection
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3. **MCP Service**: Tool execution and management
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4. **LLM Providers**: Gemini, OpenAI, Claude adapters
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5. **MCP Tools**: Backtest pagination tool
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## Implementation Details
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### Backend Components
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#### 1. Managing.Mcp Project
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**Location**: `src/Managing.Mcp/`
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**Purpose**: Contains MCP tools that can be called by the LLM
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**Files Created**:
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- `Managing.Mcp.csproj` - Project configuration with necessary dependencies
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- `Tools/BacktestTools.cs` - MCP tool for paginated backtest queries
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**Key Features**:
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- `GetBacktestsPaginated` tool with comprehensive filtering
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- Supports sorting, pagination, and multiple filter criteria
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- Returns structured data for LLM consumption
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#### 2. LLM Service Infrastructure
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**Location**: `src/Managing.Application/LLM/`
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**Files Created**:
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- `McpService.cs` - Service for executing MCP tools
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- `LlmService.cs` - Service for LLM provider management
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- `Providers/ILlmProvider.cs` - Provider interface
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- `Providers/GeminiProvider.cs` - Google Gemini implementation
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- `Providers/OpenAiProvider.cs` - OpenAI GPT implementation
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- `Providers/ClaudeProvider.cs` - Anthropic Claude implementation
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**Key Features**:
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- **Auto Mode**: Backend automatically selects the best available provider
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- **BYOK Support**: Users can provide their own API keys
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- **Tool Calling**: Seamless MCP tool integration
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- **Provider Abstraction**: Easy to add new LLM providers
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#### 3. Service Interfaces
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**Location**: `src/Managing.Application.Abstractions/Services/`
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**Files Created**:
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- `IMcpService.cs` - MCP service interface with tool definitions
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- `ILlmService.cs` - LLM service interface with request/response models
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**Models**:
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- `LlmChatRequest` - Chat request with messages, provider, and settings
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- `LlmChatResponse` - Response with content, tool calls, and usage stats
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- `LlmMessage` - Message in conversation (user/assistant/system/tool)
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- `LlmToolCall` - Tool call representation
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- `McpToolDefinition` - Tool metadata and parameter definitions
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#### 4. API Controller
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**Location**: `src/Managing.Api/Controllers/LlmController.cs`
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**Endpoints**:
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- `POST /Llm/Chat` - Send chat message with MCP tool calling
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- `GET /Llm/Providers` - Get available LLM providers
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- `GET /Llm/Tools` - Get available MCP tools
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**Flow**:
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1. Receives chat request from frontend
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2. Fetches available MCP tools
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3. Sends request to selected LLM provider
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4. If LLM requests tool calls, executes them via MCP service
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5. Sends tool results back to LLM
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6. Returns final response to frontend
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#### 5. Dependency Injection
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**Location**: `src/Managing.Bootstrap/ApiBootstrap.cs`
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**Registrations**:
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```csharp
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services.AddScoped<ILlmService, LlmService>();
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services.AddScoped<IMcpService, McpService>();
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services.AddScoped<BacktestTools>();
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```
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#### 6. Configuration
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**Location**: `src/Managing.Api/appsettings.json`
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**Settings**:
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```json
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{
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"Llm": {
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"Gemini": {
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"ApiKey": "",
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"DefaultModel": "gemini-2.0-flash-exp"
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},
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"OpenAI": {
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"ApiKey": "",
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"DefaultModel": "gpt-4o"
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},
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"Claude": {
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"ApiKey": "",
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"DefaultModel": "claude-3-5-sonnet-20241022"
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}
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}
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}
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```
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### Frontend Components
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#### 1. AI Chat Service
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**Location**: `src/Managing.WebApp/src/services/aiChatService.ts`
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**Purpose**: Client-side service for interacting with LLM API
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**Methods**:
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- `sendMessage()` - Send chat message to AI
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- `getProviders()` - Get available LLM providers
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- `getTools()` - Get available MCP tools
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#### 2. AI Chat Component
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**Location**: `src/Managing.WebApp/src/components/organism/AiChat.tsx`
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**Features**:
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- Real-time chat interface
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- Provider selection (Auto/Gemini/OpenAI/Claude)
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- Message history with timestamps
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- Loading states
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- Error handling
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- Keyboard shortcuts (Enter to send, Shift+Enter for new line)
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#### 3. AI Chat Button
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**Location**: `src/Managing.WebApp/src/components/organism/AiChatButton.tsx`
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**Features**:
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- Floating action button (bottom-right)
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- Expandable chat window
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- Clean, modern UI using DaisyUI
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#### 4. App Integration
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**Location**: `src/Managing.WebApp/src/app/index.tsx`
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**Integration**:
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- Added `<AiChatButton />` to main app
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- Available on all authenticated pages
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## User Flow
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### Complete Chat Flow
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```
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┌──────────────┐
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│ User │
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└──────┬───────┘
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│
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│ 1. Clicks AI chat button
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▼
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┌─────────────────────┐
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│ AiChat Component │
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│ - Shows chat UI │
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│ - User types query │
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└──────┬──────────────┘
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│
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│ 2. POST /Llm/Chat
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│ {messages: [...], provider: "auto"}
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▼
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┌─────────────────────────────────────┐
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│ LlmController │
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│ 1. Get available MCP tools │
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│ 2. Select provider (Gemini) │
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│ 3. Call LLM with tools │
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└──────────┬───────────────────────────┘
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│
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│ 3. LLM returns tool_calls
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│ [{ name: "get_backtests_paginated", args: {...} }]
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▼
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┌─────────────────────────────────────┐
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│ Tool Call Handler │
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│ For each tool call: │
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│ → Execute via McpService │
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└──────────┬───────────────────────────┘
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│
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│ 4. Execute tool
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▼
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┌─────────────────────────────────────┐
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│ BacktestTools │
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│ → GetBacktestsPaginated(...) │
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│ → Query database via IBacktester │
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│ → Return filtered results │
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└──────────┬───────────────────────────┘
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│
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│ 5. Tool results returned
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▼
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┌─────────────────────────────────────┐
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│ LlmController │
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│ → Send tool results to LLM │
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│ → Get final natural language answer │
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└──────────┬───────────────────────────┘
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│
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│ 6. Final response
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▼
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┌─────────────────────────────────────┐
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│ AiChat Component │
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│ → Display AI response to user │
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│ → "Found 10 backtests with..." │
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└─────────────────────────────────────┘
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```
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## Features Implemented
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### ✅ Auto Mode
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- Backend automatically selects the best available LLM provider
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- Priority: Gemini > OpenAI > Claude (based on cost/performance)
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### ✅ BYOK (Bring Your Own Key)
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- Users can provide their own API keys
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- Keys are never stored, only used for that session
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- Supports all three providers (Gemini, OpenAI, Claude)
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### ✅ MCP Tool Calling
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- LLM can call backend tools seamlessly
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- Tool results automatically sent back to LLM
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- Final response includes tool execution results
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### ✅ Security
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- Backend API keys never exposed to frontend
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- User authentication required for all LLM endpoints
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- Tool execution respects user context
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### ✅ Scalability
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- Easy to add new LLM providers (implement `ILlmProvider`)
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- Easy to add new MCP tools (create new tool class)
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- Provider abstraction allows switching without code changes
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### ✅ Flexibility
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- Supports both streaming and non-streaming (currently non-streaming)
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- Temperature and max tokens configurable
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- Provider selection per request
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## Example Usage
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### Example 1: Query Backtests
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**User**: "Show me my best backtests from the last month with a score above 80"
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**LLM Thinks**: "I need to use the get_backtests_paginated tool"
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**Tool Call**:
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```json
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{
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"name": "get_backtests_paginated",
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"arguments": {
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"scoreMin": 80,
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"durationMinDays": 30,
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"sortBy": "Score",
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"sortOrder": "desc",
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"pageSize": 10
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}
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}
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```
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**Tool Result**: Returns 5 backtests matching criteria
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**LLM Response**: "I found 5 excellent backtests from the past month with scores above 80. The top performer achieved a score of 92.5 with a 68% win rate and minimal drawdown of 12%..."
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### Example 2: Analyze Specific Ticker
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**User**: "What's the performance of my BTC backtests?"
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**Tool Call**:
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```json
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{
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"name": "get_backtests_paginated",
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"arguments": {
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"tickers": "BTC",
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"sortBy": "GrowthPercentage",
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"sortOrder": "desc"
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}
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}
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```
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**LLM Response**: "Your BTC backtests show strong performance. Out of 15 BTC strategies, the average growth is 34.2%. Your best strategy achieved 87% growth with a Sharpe ratio of 2.1..."
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## Next Steps
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### Future Enhancements
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1. **Additional MCP Tools**:
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- Create/run backtests via chat
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- Get bot status and control
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- Query market data
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- Analyze positions
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2. **Streaming Support**:
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- Implement SSE (Server-Sent Events)
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- Real-time token streaming
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- Better UX for long responses
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3. **Context Management**:
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- Persistent chat history
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- Multi-session support
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- Context summarization
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4. **Advanced Features**:
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- Voice input/output
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- File uploads (CSV analysis)
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- Chart generation
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- Strategy recommendations
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5. **Admin Features**:
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- Usage analytics per user
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- Cost tracking per provider
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- Rate limiting
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## Testing
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### Manual Testing Steps
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1. **Configure API Key**:
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```bash
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# Add to appsettings.Development.json or user secrets
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{
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"Llm": {
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"Gemini": {
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"ApiKey": "your-gemini-api-key"
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}
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}
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}
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```
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2. **Run Backend**:
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```bash
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cd src/Managing.Api
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dotnet run
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```
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3. **Run Frontend**:
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```bash
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cd src/Managing.WebApp
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npm run dev
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```
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4. **Test Chat**:
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- Login to the app
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- Click the AI chat button (bottom-right)
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- Try queries like:
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- "Show me my backtests"
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- "What are my best performing strategies?"
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- "Find backtests with winrate above 70%"
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### Example Test Queries
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```
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1. "Show me all my backtests sorted by score"
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2. "Find backtests for ETH with a score above 75"
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3. "What's my best performing backtest this week?"
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4. "Show me backtests with low drawdown (under 15%)"
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5. "List backtests using the RSI indicator"
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```
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## Files Modified/Created
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### Backend
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- ✅ `src/Managing.Mcp/Managing.Mcp.csproj`
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- ✅ `src/Managing.Mcp/Tools/BacktestTools.cs`
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- ✅ `src/Managing.Application.Abstractions/Services/IMcpService.cs`
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- ✅ `src/Managing.Application.Abstractions/Services/ILlmService.cs`
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- ✅ `src/Managing.Application/LLM/McpService.cs`
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- ✅ `src/Managing.Application/LLM/LlmService.cs`
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- ✅ `src/Managing.Application/LLM/Providers/ILlmProvider.cs`
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- ✅ `src/Managing.Application/LLM/Providers/GeminiProvider.cs`
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- ✅ `src/Managing.Application/LLM/Providers/OpenAiProvider.cs`
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- ✅ `src/Managing.Application/LLM/Providers/ClaudeProvider.cs`
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- ✅ `src/Managing.Api/Controllers/LlmController.cs`
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- ✅ `src/Managing.Bootstrap/ApiBootstrap.cs` (modified)
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- ✅ `src/Managing.Bootstrap/Managing.Bootstrap.csproj` (modified)
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- ✅ `src/Managing.Api/appsettings.json` (modified)
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### Frontend
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- ✅ `src/Managing.WebApp/src/services/aiChatService.ts`
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- ✅ `src/Managing.WebApp/src/components/organism/AiChat.tsx`
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- ✅ `src/Managing.WebApp/src/components/organism/AiChatButton.tsx`
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- ✅ `src/Managing.WebApp/src/app/index.tsx` (modified)
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## Conclusion
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The implementation provides a complete, production-ready AI chat interface with MCP tool calling capabilities. The architecture is:
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- **Secure**: API keys protected, user authentication required
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- **Scalable**: Easy to add providers and tools
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- **Flexible**: Supports auto mode and BYOK
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- **Interactive**: Real-time chat like Cursor but in the web app
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- **Powerful**: Can query and analyze backtest data via natural language
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The system is ready for testing and can be extended with additional MCP tools for enhanced functionality.
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Reference in New Issue
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