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| .. | ||
| app | ||
| compose | ||
| services | ||
| README.md | ||
Deployment Configuration
This directory contains all deployment-related configuration for the txt2kg project.
Structure
-
compose/: Docker Compose files for local development and testing
docker-compose.yml: Minimal Docker Compose configuration (Ollama + ArangoDB + Next.js)docker-compose.complete.yml: Complete stack with optional services (vLLM, Pinecone, Sentence Transformers)docker-compose.optional.yml: Additional optional servicesdocker-compose.vllm.yml: Legacy vLLM configuration (use--completeflag instead)
-
app/: Frontend application Docker configuration
- Dockerfile for Next.js application
-
services/: Containerized services
- ollama/: Ollama LLM inference service with GPU support
- sentence-transformers/: Sentence transformer service for embeddings (optional)
- vllm/: vLLM inference service with FP8 quantization (optional)
- gpu-viz/: GPU-accelerated graph visualization services (optional, run separately)
- gnn_model/: Graph Neural Network model service (experimental, not in default compose files)
Usage
Recommended: Use the start script
# Minimal setup (Ollama + ArangoDB + Next.js frontend)
./start.sh
# Complete stack (includes vLLM, Pinecone, Sentence Transformers)
./start.sh --complete
# Development mode (run frontend without Docker)
./start.sh --dev-frontend
Manual Docker Compose commands:
To start the minimal services:
docker compose -f deploy/compose/docker-compose.yml up -d
To start the complete stack:
docker compose -f deploy/compose/docker-compose.complete.yml up -d
Services Included
Minimal Stack (default)
- Next.js App: Web UI on port 3001
- ArangoDB: Graph database on port 8529
- Ollama: Local LLM inference on port 11434
Complete Stack (--complete flag)
All minimal services plus:
- vLLM: Advanced LLM inference on port 8001
- Pinecone (Local): Vector embeddings on port 5081
- Sentence Transformers: Embedding generation on port 8000
Optional Services (run separately)
- GPU-Viz Services: See
services/gpu-viz/README.mdfor GPU-accelerated visualization - GNN Model Service: See
services/gnn_model/README.mdfor experimental GNN-based RAG