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Docker with Neo4j , LangChain and Ollama launches new GenAI Stack for developers
Docker , together with partners Neo4j , LangChain and Ollama , has announced 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 , available in the Learning Center in Docker Desktop , 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 .
Docker has also announced its first AI-powered product – Docker AI . Components include :
“ Developers are excited by the possibilities of GenAI , but the rate of change , number of vendors and wide variation in technology stacks makes it challenging to know where and how to start ,” said Docker CEO Scott Johnston . “ This announcement eliminates this dilemma by enabling developers to get started quickly and safely using the Docker tools , content and services they already know and love together with partner technologies on the cutting edge of GenAI app development .”
• 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
• A series of supporting tools , code templates , how-to ’ s and GenAI best practices .
GenAI Stack was designed as an easy out-of-the-box setup empowering 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 .
Developers can do enhanced querying and result enrichment for applications that can both summarize data and showcase knowledge retrieval through flexible knowledge graphs .
They can also generate diverse responses across various formats such as bulleted lists , chain of thought , GitHub issues , PDFs , poems and more .
In addition , developers can compare results achieved between LLMs on their own , LLMs with vector and LLMs with vector and knowledge graph integration . p
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