Marketlens Llm

DeepSeek LLM semantic enrichment pipeline exposed via a FastMCP Model Context Protocol server. Performs sentiment analysis, category mapping, and attribute extraction on raw product data.

Architecture

This microservice is part of the MarketLens AI Platform. It is designed to be decoupled from the scraping layer, communicating via standardized schemas and file-based or artifact-based storage.

• Enricher Agent: Uses LangChain and DeepSeek to process raw product data into enriched metadata.
• MCP Server: Provides a Model Context Protocol interface to expose product intelligence tools.

MCP Tools Exposed

• get_top_products(limit: int): Returns the top ranked products.
• get_cluster_summary(cluster_id: int): Provides aggregate metrics for specific product clusters.

Quick Start

1. Clone the repository.
2. Install dependencies:

   pip install -r requirements.txt
   

3. Set your environment variables (see .env.example).
4. Run the enrichment pipeline:

   PYTHONPATH=. python llm_agents/main.py
   

5. Start the MCP server:

   PYTHONPATH=. python llm_agents/mcp_server.py
   

Environment Variables

• DEEPSEEK_API_KEY: Required for LLM enrichment.
• TOP_PRODUCTS_PATH: (Optional) Override path for the ranked products dataset.
• MCP_TRANSPORT: (Optional) stdio (default) or sse.

Docker Usage

Build the image:

docker build -t marketlens-llm-mcp .

Run the container:

docker run -e DEEPSEEK_API_KEY=your_key marketlens-llm-mcp

Part of the MarketLens AI Platform

This service integrates with the wider MarketLens ecosystem:

1. Scraping (Source)
2. Enrichment (This service)
3. ML Pipeline (Training & Ranking)
4. Dashboard (UI)

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Author: Yassine Kamouss — FST Tanger, LSI 2, 2025/2026