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Docker Launches New GenAI Stack For Developers


Docker has joined hands with Neo4j, LangChain, and Ollama to announce a new GenAI Stack designed to help developers get a running start with generative AI applications in minutes. Eliminating the need to search for and cobble together and configure technologies from different sources, the GenAI Stack is pre-configured, ready-to-code, and secure with large language models (LLMs) from Ollama, vector and graph databases from Neo4j, and the LangChain framework.

The GenAI Stack addresses popular GenAI use cases using trusted open source content on Docker Hub. Demoed on stage at DockerCon,the GenAI Stack is among a range of new AI/ML capabilities, content, and partnerships announced by Docker with the aim of helping developers quickly and securely take advantage of the power of AI/ML in their applications. Components include:

  • Pre-configured open source LLMs such as Llama 2, Code Llama, Mistral, or private models such as OpenAI’s GPT-3.5 and GPT-4;
  • Help from Ollama for developers to get up and running locally with open source LLMs;
  • Neo4j as the default database for graph and native vector search that uncovers explicit and implicit patterns and relationships in data, making AI/ML models faster and more accurate, and acting as long-term memory for these models;
  • Neo4j knowledge graphs that serve as the knowledge base to ground LLMs for more accurate GenAI predictions and outcomes;
  • LangChain orchestration between the LLM, application, and database with vector index, and serving as the framework for developing context-aware reasoning applications powered by LLMs; and
  • A series of supporting tools, code templates, how-to’s, and GenAI best practices.

Easy out-of-the-box setup empowers developers with numerous capabilities, such as effortless data loading and vector index population, enabling developers to seamlessly import data, create vector indices, embed questions and answers, and store them within the vector index.

The GenAI Stack is now available in the Learning Center in Docker Desktop and in the repository at