Docker introduced a brand new GenAI Stack in partnership with Neo4j, LangChain, and Ollama throughout its annual DockerCon developer convention keynote. This GenAI Stack is designed to assist builders rapidly and simply construct generative AI purposes with out looking for and configuring varied applied sciences.
It consists of pre-configured parts like giant language fashions (LLMs) from Ollama, vector and graph databases from Neo4j, and the LangChain framework. Docker additionally launched its first AI-powered product, Docker AI.
The GenAI Stack addresses standard use circumstances for generative AI and is on the market within the Docker Studying Middle and on GitHub. It presents pre-configured open-source LLMs, help from Ollama for establishing LLMs, Neo4j because the default database for improved AI/ML mannequin efficiency, data graphs to reinforce GenAI predictions, LangChain orchestration for context-aware reasoning purposes, and varied supporting instruments and assets. This initiative goals to empower builders to leverage AI/ML capabilities of their purposes effectively and securely.
“Builders are excited by the probabilities of GenAI, however the fee of change, variety of distributors, and vast variation in expertise stacks makes it difficult to know the place and begin,” mentioned Scott Johnston, CEO of Docker CEO Scott Johnston. “Right this moment’s announcement eliminates this dilemma by enabling builders to get began rapidly and safely utilizing the Docker instruments, content material, and companies they already know and love along with accomplice applied sciences on the chopping fringe of GenAI app improvement.”
Builders are supplied with simple setup choices that provide varied capabilities, together with easy information loading and vector index creation. This enables builders to import information, create vector indices, add questions and solutions, and retailer them inside the vector index.
This setup permits enhanced querying, consequence enrichment, and the creation of versatile data graphs. Builders can generate numerous responses in several codecs, equivalent to bulleted lists, chain of thought, GitHub points, PDFs, poems, and extra. Moreover, builders can examine outcomes achieved between completely different configurations, together with LLMs on their very own, LLMs with vectors, and LLMs with vector and data graph integration.