Neo4j powers RAG systems for enhanced LLM context

This article details how to implement a retrieval-augmented generation (RAG) system using Neo4j as a vector database. RAG enhances Large Language Models (LLMs) by providing them with up-to-date and specific context from private data sources. The process involves a retriever that converts user queries into embeddings, performs vector searches against a vector index, and fetches relevant data to augment the LLM's prompt, thereby enabling more accurate and context-aware responses. AI

IMPACT Enables LLMs to access and utilize private, up-to-date data for more relevant and accurate responses.

RANK_REASON Article describes implementation of a RAG system using specific tools like Neo4j and SentenceTransformer.

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• embeddings
• LLM
• Neo4j
• Ollama
• retrieval-augmented generation
• Retriever
• SentenceTransformer
• vector search

• product
• infra

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