MKMChat (Mortal Kombat Mobile Assistant)

MKMChat is a premium, local-first AI assistant and Model Context Protocol (MCP) server for Mortal Kombat Mobile. It combines a high-performance Python search backend, advanced RAG retrieval, local Ollama LLM execution, and a stunning Laravel + Livewire web interface.

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🌟 Main Features

• ⚔️ Intelligent Team Suggestion: Assemble custom 3-character team compositions complete with character class analyses, passive synergy ratings, and specific equipment cards recommended for every slot.
• 📖 Mechanic Explanation Flow: Explain complex gameplay mechanics (e.g., Snare, Power Drain, Oblivion) by generating a clear definition and practical combat recommendations in a structured format.
• 💬 AI-Powered Conversation Chat: Enjoy natural, context-aware Q&A about game mechanics, strategy, tier rankings, and character matchups with chat history persistence.
• 🤖 Reasoning Model Support: Optimized dynamically for reasoning models like DeepSeek-R1 and OpenAI o1/o3, adapting parameters (context limits, temperature) for deep analytical outputs.
• 🛠️ MCP-Compatible Tool Server: Exposes rich game tools directly to LLM clients (like Claude Desktop, Cursor, or AI agents) via Model Context Protocol.
• 🎨 Harmonious Light & Dark Modes: Responsive, visual-first Laravel Livewire web interface featuring glassmorphic designs, vibrant color schemes, and seamless dark/light theme toggles.

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🧠 Advanced Hybrid RAG System

The retrieval pipeline has been heavily upgraded to ensure state-of-the-art relevance and precision:

flowchart TD
    A[Game Data<br>TSV + TXT] --> B[Set-Aware Indexer]
    B --> C[Precise Chunking]
    C --> D[Text Normalization]
    D -- "sentence-transformers<br>all-MiniLM-L6-v2" --> E[Embeddings Cache<br>.rag_cache/]
    E --> F[Hybrid Retrieval<br>Semantic + Keyword Boost]
    F --> G[Ollama Assistant<br>Prompt Assembly]

1. Set-Aware Indexing: The indexer automatically scans for character set affiliations (e.g., {{Friendship}} or {{Brutality}} tags) and injects mutual cross-references. Retrieving one item naturally surfaces details and names of its set partners (e.g., retrieving Baraka's Horde Chef's Delight also surfaces Horde Chef's Paraphernalia).
2. True Cosine Similarity: Vector embeddings (both query and document vectors) are mathematically $L_2$-normalized upon creation and query time. Dot-product computation of these normalized vectors yields mathematically precise cosine similarity scores strictly bounded within $[-1.0, 1.0]$.
3. Hybrid Retrieval (Lexical Keyword Boosting): Vector embeddings (all-MiniLM-L6-v2) are combined with a specialized keyword-matching reranker (_apply_keyword_boost()). Exact matches on character names, rarity tiers, and major gameplay terms receive an intelligent boost, ensuring high semantic recall without losing keyword precision.
4. Typography Resiliency: Input queries are automatically normalized (e.g., converting curly quotes ’, “, ” to straight quotes ', ") to prevent matching failures caused by different keyboard inputs.
5. Granular Chunking: Glossary definitions are chunked term-by-term, and gameplay data is chunked line-by-line. This avoids oversized search spaces ("fat chunks") and provides highly targeted context snippets.

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🛠️ Architecture

• mkmchat/: Python core package (Asynchronous FastAPI + Uvicorn HTTP server, MCP server implementation, and local vector RAG system).
• webapp/: Laravel + Livewire web UI consuming the Python API through the secure MkmApiService wrapper.
• docker-compose.yml: Full-stack container orchestration linking ollama, python-api (internal network), and webapp (host exposed).

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🚀 High-Performance Asynchronous Architecture

The Python API has been completely migrated to a fully asynchronous runtime stack:

• Asynchronous Web Core: Replaced custom BaseHTTPRequestHandler with FastAPI running under Uvicorn. All API routes (/suggest-team, /ask-question, /explain-mechanic, /chat, /health, /) run asynchronously (async def) and non-blockingly.
• Concurrency Verification: Multiple parallel requests are executed in parallel on Uvicorn's event loop. Under load tests, executing 5 concurrent API requests yields a total wall-clock execution time that matches the latency of a single request (~4.75 seconds), achieving 80%+ concurrency latency savings compared to blocking synchronous designs.
• Security & Reliability: Implemented native FastAPI exception handlers for exact HTTP contract safety ({"error": "details"} output format), client IP-based custom sliding rate limiting dependencies, and secure API key authentication headers.

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🐳 Running with Docker (Recommended)

1) Prepare Environment Files

From the project root:

cp .env.docker.example .env.docker
cp webapp/.env.docker.example webapp/.env.docker

2) Configure Your Environment

At a minimum, ensure these match where required:

• MKM_API_KEY: A strong random string shared by the backend and Laravel services for authentication.
• OLLAMA_MODEL: The default model tag to use (e.g., llama3.2:3b or deepseek-r1:14b-fit).
• MKM_DEBUG_PROMPTS: Set to true to write detailed, fully redacted LLM prompts and responses to debug_llm.log for easy tuning.

3) Start the Stack

docker compose up -d --build

Dedicated GPU / High VRAM Tuning (WSL & Linux)

To deploy a high-performance DeepSeek-R1 (14B) model optimized for local consumption on GPU-enabled environments:

docker compose exec ollama sh -lc "cat > /tmp/Modelfile.deepseek14b-fit << 'EOF'
FROM deepseek-r1:14b
PARAMETER num_ctx 512
PARAMETER num_batch 32
PARAMETER num_predict 800
PARAMETER use_mmap true
PARAMETER temperature 0.2
EOF
ollama create deepseek-r1:14b-fit -f /tmp/Modelfile.deepseek14b-fit"

Once generated, select deepseek-r1:14b-fit in the web application model selector dropdown!

4) Endpoints

• Web Application: http://localhost:8000
• Ollama Interface: http://localhost:11434

Note: The Python API is internal to the Docker network (http://python-api:8080) and is not published directly to the host for maximum container-level security.

5) Operations & Logs

docker compose ps               # Check service status
docker compose logs -f python-api  # Live Python server logs
docker compose logs -f webapp      # Live Web UI logs
docker compose logs -f ollama      # Live Ollama inference logs
docker compose down             # Tear down all containers

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💻 Running Without Docker

1. Python API & RAG Backend

Ensure you have Python 3.10+ installed:

python -m venv .venv
source .venv/bin/activate   # Linux/macOS
# .venv\Scripts\activate    # Windows

pip install -e .
ollama pull llama3.2:3b
python -m mkmchat http

2. Laravel Web Application

Ensure PHP 8.2+ and Composer are installed:

cd webapp
cp .env.example .env
composer install
npm install
npm run build
php artisan key:generate
php artisan migrate
php artisan serve

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⚙️ Environment Variables

Core Variables (.env.docker)

Variable	Description	Default
OLLAMA_BASE_URL	Base URL of the Ollama server	http://ollama:11434
OLLAMA_MODEL	Default LLM model tag	llama3.2:3b
MKM_HTTP_HOST	Host binding for Python API	0.0.0.0
MKM_API_KEY	Strong bearer authorization key	change-me-in-production
MKM_DEBUG_PROMPTS	Write fully redacted prompt history to log file	false
MKM_MECHANIC_RAG_TOP_K	Number of passages to search for explanations	16
MKM_MECHANIC_RAG_MAX_PASSAGES	Max context passages to include	8

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🧪 Testing

Run RAG and model connection checks directly inside the containers:

Quick Smoke Test

docker compose exec webapp curl -X POST http://python-api:8080/explain-mechanic \
  -H "Content-Type: application/json" \
  -H "X-API-Key: <your-mkm-api-key>" \
  -d '{"mechanic":"power drain","model":"llama3.2:3b"}'

Python RAG Verification

To manually test semantic search quality, cache validation, and keyword boosting:

# Inside the virtual environment
python tests/test_rag.py

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📜 License

This project is licensed under the GNU GPL v3. See the LICENSE file for details.