The setup is effortless, and all the components are guaranteed to run and work together. You can use the GenAI Stack to quickly experiment with building and running GenAI apps in a trusted environment with ready-to-use, code-first examples. ![]() Docker compose has a watch mode setup that rebuilds relevant containers any time you make a change to the application code, allowing for fast feedback loops and a good developer experience. These containers are tied together with Docker compose. If you’re on MacOS, install Ollama outside of Docker. LLM container Ollama (if you’re on Linux).Database container with vector index and graph search (Neo4j).Application containers (the application logic in Python built with LangChain for the orchestration and Streamlit for the UI).You can experiment with importing different information in the knowledge graph and examine how the variety in underlying grounding information affects the generated responses by the LLM in the user interface. The containers provide a dev environment of a pre-built, support agent app with data import and response generation use-cases. The GenAI Stack is a set of Docker containers that are orchestrated by Docker Compose which includes a management tool for local LLMs ( Ollama), a database for grounding ( Neo4j), and GenAI apps based on LangChain. The maintainers behind the Ollama project have recognized the opportunity of open source LLMs by providing a seamless solution to set up and run local LLMs on your own infrastructure or even a laptop. ![]() A significant benefit of using open source LLMs is removing the dependency to an external LLM provider while retaining complete control over the data flows and how the data is being shared and stored. Models like Llama2 and Mistral are showing impressive levels of accuracy and performance, making them a viable alternative for their commercial counterparts. Open-source LLM research has significantly advanced in recent times. Finally, the context information from the database is combined with the user question and additional instructions into a prompt that is passed to an LLM to generate the final answer, which is then sent to the user.Once the relevant nodes are identified using vector search, the application is designed to retrieve additional information from the nodes themselves and also by traversing the relationships in the graph.The next step is to find the most relevant nodes in the database by comparing the cosine similarity of the embedding values of the user’s question and the documents in the database.When a user asks the support agent a question, the question first goes through an embedding model to calculate its vector representation.The idea behind RAG applications is to provide LLMs with additional context at query time for answering the user’s question. Augmenting LLMs with additional information by combining vector search and context from the knowledge graph.Using plain LLM and relying on their internal knowledge.Follow along to experiment with two approaches to information retrieval: In this blog, we walk you through using the GenAI Stack to explore the approaches of using retrieval augmented generation (RAG) to improve accuracy, relevance, and provenance compared to relying on the internal knowledge of an LLM. Simply developing a wrapper around an LLM API doesn’t guarantee success with generated responses because well-known challenges with accuracy and knowledge cut-off go unaddressed. In this blog, you will learn how to implement a support agent that relies on information from Stack Overflow by following best practices and using trusted components. ![]() To accelerate GenAI experimentation and learning, Neo4j has partnered with Docker, LangChain, and Ollama to announce the GenAI Stack – a pre-built development environment environment for creating GenAI applications. Interest in GenAI remains high, with new innovations emerging daily.
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