"Each piece requires its own setup, its own environment variables, and its own compatibility headaches. Tired of reinventing the wheel for every project, I decided to create a definitive solution: a complete, robust, and ready-to-use stack with Docker Compose. An environment I could spin up with a single command, giving me everything needed to experiment and build RAG systems locally and privately."
"AI & Processing: Ollama: To run language models like Llama 3.1 locally, with CPU or GPU support. Total privacy and control. Qdrant: An incredibly fast and efficient vector database. It's the heart of our semantic search. Storage & Relationships: MongoDB: Perfect for flexibly storing original documents and their metadata. Redis: An ultra-fast cache that speeds up responses and reduces load. Neo4j: This is the crown jewel. A graph database that lets us model and query complex relationships between documents, entities, and concepts,"
Retrieval-Augmented Generation connects language models to private knowledge bases but requires complex infrastructure and integration. Multiple services—vector databases, LLM servers, workflow orchestrators, data stores, authentication, and caching—create setup and compatibility headaches. A Docker Compose stack consolidates these components into a single-command environment for local, private experimentation and development. Core components include Ollama for local LLMs, Qdrant for semantic vector search, MongoDB for document storage, Redis for caching, Neo4j for modeling relationships, Keycloak for identity, Mongo Express for DB management, and n8n for automation and orchestration.
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