Product MCP Server

A microservice-based product management system using MCP (Model Context Protocol) with intelligent tool selection and argument extraction.

Architecture

• MCP Server: FastAPI-based product CRUD operations (Port 9000)

• Chat API: Natural language interface with multiple classification methods (Port 8000)

• Ollama: Local LLM server for embeddings and text generation (Port 11434)

• PostgreSQL: Database for product storage (Port 5432)

Models Used

• nomic-embed-text: For vector embeddings and semantic search

• llama3.1:8b: For tool selection and argument extraction from natural language

• CSV-based ML Classifier: Fast pattern-based classification with dynamic argument extraction

• Joint Intent-Slot Classifier: Advanced ML-based classification for complex queries

API Endpoints

Chat API (Port 8000)

• POST /chat/v1 - Uses configurable NLP model (llama3.1:8b, qwen2.5:3b-instruct-q4_K_M, etc.) for tool selection and argument extraction

• POST /chat/v2 - Uses joint intent-slot classification with advanced ML approach

• POST /chat/v3 - Uses CSV-based ML classifier with dynamic argument extraction

• GET /chat/tools - List available tools

• GET /chat/test - Health check

Endpoint Evolution: v1 → v2 → v3

The chat endpoints represent an evolution in classification approaches:

v1 (Configurable NLP):

• Why: Unified approach using configurable NLP models via environment variables

• Pros: Flexible model selection (llama3.1:8b, qwen2.5:3b-instruct-q4_K_M, etc.), handles all scenarios

• Cons: Speed depends on chosen model (llama3.1:8b = slow, qwen2.5:3b = fast)

• Use case: All scenarios - model choice based on requirements

v2 (Joint Intent-Slot Classification):

• Why: Needed faster, more structured approach than v1

• Pros: Fast, handles intent classification and argument extraction simultaneously

• Cons: Requires pattern training, less flexible than LLM

• Use case: Standard queries with predictable patterns

v3 (CSV-based ML Classifier):

• Why: Needed fastest possible response for production use

• Pros: Sub-second response, rule-based with ML fallback

• Cons: Requires explicit pattern training, least flexible

• Use case: High-throughput production scenarios

MCP Server (Port 9000)

• Product CRUD operations via MCP protocol

• OpenAPI spec available at /openapi.json

• Health check at /health

Quick Start

1. Build and start services:

   docker-compose up -d

2. Test the v1 endpoint (configurable NLP model):

   curl -X POST "http://localhost:8000/chat/v1" \
     -H "Content-Type: application/json" \
     -d '{"message": "add Smartphone Pro, with touchscreen, price $1249"}'

3. Expected response (minimal format for all endpoints):

   {
     "message": "product created",
     "data": {
       "id": 1,
       "name": "Smartphone Pro",
       "price": 1249.0,
       "description": "with touchscreen"
     }
   }

Features

Multiple Classification Methods

• v1: Configurable NLP model classification (llama3.1:8b, qwen2.5:3b-instruct-q4_K_M, etc.) with configurable timeout

• v2: Joint intent-slot classification with advanced ML approach

• v3: CSV-based ML classifier with dynamic argument extraction (fastest)

Response Formats

All endpoints now use the minimal format for clean, consistent responses:

{
  "message": "product created|updated|deleted|retrieved|products retrieved",
  "data": { /* actual product data */ }
}

Tool Selection Capabilities

• ✅ Intelligent tool selection using multiple ML approaches

• ✅ Automatic argument extraction from natural language

• ✅ Dynamic argument extraction with regex patterns

• ✅ Recent product filtering for "show recent" queries

• ✅ Fast CSV-based classification for quick responses

• ✅ Advanced joint classification for complex queries

Supported Operations

• product.create: Add new products with name, price, description

• product.list: List all products (with pagination and recent_only filter)

• product.get: Get specific product by ID

• product.update: Update existing products

• product.delete: Delete products

Special Features

• Recent Products: Queries like "show recent product created" return only the most recent product

• Dynamic Extraction: Automatically extracts names, prices, IDs, and descriptions from natural language

• Fallback Handling: Graceful fallbacks when primary classification methods fail

Performance Optimizations

Recent Improvements

• ✅ Unified response format: All endpoints use minimal response format

• ✅ Automated model training: CSV classifier trained during Docker build

• ✅ Dynamic argument extraction: Enhanced with regex-based extraction

• ✅ Recent product support: Special handling for "recent" queries

• ✅ Optimized timeouts: 300-second timeout for Llama 3.1 operations

• ✅ Pre-trained models: Uses existing csv_classifier.pkl with latest patterns

Build Performance

• Model Training: Automated during container build process

• Pattern Updates: Latest patterns automatically included in builds

• Fast Startup: Pre-loaded models for immediate availability

Development

Rebuilding after changes:

# Stop and rebuild specific service
docker-compose stop chat-api
docker-compose build chat-api
docker-compose up -d chat-api

# Or rebuild all services
docker-compose down
docker-compose up --build -d

Performance Comparison

Response Time Analysis (Query: "add Ismartphone Xy5, best one yet and price is $1592")

Performance Characteristics

v1 (Llama 3.1):

• Response Time: ~3m 1s

• Status: ✅ Success (with 5-minute timeout)

• Product Accuracy: Perfect (name: ✓, price: ✓, description: ✓)

• Use Case: Complex queries requiring deep reasoning

• Trade-off: Maximum intelligence, slowest speed

v2 (Joint Intent-Slot):

• Response Time: ~0.21s

• Status: ✅ Success

• Product Accuracy: Good (name: ✓, price: ✓, description: ✗ - includes price info)

• Use Case: Standard queries with predictable patterns

• Trade-off: Fast, requires pattern training

v3 (CSV-based ML):

• Response Time: ~0.19s

• Status: ✅ Success

• Product Accuracy: Good (name: ✓, price: ✓, description: ✗ - includes price info)

• Use Case: High-throughput production scenarios

• Trade-off: Fastest, least flexible

v4 (Qwen2.5-3B q4):

• Response Time: ~1m 36s

• Status: ✅ Success

• Product Accuracy: Perfect (name: ✓, price: ✓, description: ✓)

• Use Case: Balanced scenarios requiring both speed and intelligence

• Trade-off: Moderate speed, good flexibility, reasonable resource usage

Product Creation Analysis

Query: "add Ismartphone Xy5, best one yet and price is $1592"

Note: All endpoints successfully created products in the database. v1 and v4 correctly extracted the description as "best one yet". v2 and v3 incorrectly included price information in the description.

Endpoint Evolution Summary

Troubleshooting

Common Issues

1. Model not found: Ensure Ollama is running and llama3.1:8b is pulled

2. Timeout errors: v1 endpoint has 300-second timeout for complex queries

3. Build issues: Check that all services are properly stopped before rebuilding

Logs

# View chat-api logs
docker-compose logs -f chat-api

# View mcp-api logs
docker-compose logs -f mcp-api

# View ollama logs
docker-compose logs -f ollama