The GenAI Stack is a great way to quickly get started building GenAI-backed applications. It includes Neo4j as the default database for vector search and knowledge graphs, and it’s available completely for free.
It comes bundled with the core components you need to get started, already integrated and set up for you in opens in new tabDocker containers
It makes it really easy to experiment with new models, hosted locally on your machine (such as opens in new tabLlama2) or via APIs (like OpenAI’s opens in new tabGPT)
It is already set up to help you use the opens in new tabRetrieval Augmented Generationopens in new tab (RAG) architecture for LLM apps, which, in my opinion, is the easiest way to integrate an LLM into an application and give it access to your own data
All of this, available at your fingertips with a simple docker compose up!
The backend thoughts
The powerful combination of graphs and LLMs is why we’ve seen a huge uptake and adoption of Neo4j to build LLM-backed applications. Usage skyrocketed when we opens in new tabadded native vector search as part of our core capability, combining the implicit relationships uncovered by vectors with the explicit and factual relationships and patterns illuminated by graphs.
Neo4j also allows users to create knowledge graphs, which ground LLMs in these factual relationships, enable customers to get richer insights from semantic search and generative AI applications, and improve accuracy. While LLMs are great at language skills, they hallucinate because they lack grounding in truth. Knowledge graphs solve this problem.
references:
https://neo4j.com/labs/genai-ecosystem/genai-stack/
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