GraphRAG applications using Amazon Bedrock Knowledge Bases
Turning documents into decisions with GraphRAG and Amazon Bedrock for smarter AI
Retrieval-augmented generation (RAG) transforms how organizations use large language models (LLMs). By combining LLMs with external data sources, RAG enables more accurate and contextually relevant answers than models can provide. Traditional RAG is helpful for simple queries that require pulling relevant text chunks. It relies on vector search from a vector database to retrieve semantically similar text chunks where the information can be found within a single or a few closely related documents. However, it struggles when queries require connecting facts spread across multiple sources or understanding complex relationships between entities and concepts.
This newsletter introduces GraphRAG, a graph-based approach designed to overcome these limitations.
Why traditional RAG hits a wall#
The traditional RAG has significantly improved the handling of many standard queries, especially simpler questions where pulling relevant text chunks and conditioning a model works well. In a standard RAG pipeline, incoming queries are first converted into dense vector representations (embeddings). These query embeddings are then compared against embeddings of pre-indexed text chunks stored in a vector database. Using similarity search, the system retrieves the most relevant text segments. Because these segments typically come from a single document or a small set of closely related documents, the model can efficiently ground its responses in a highly relevant context before generating an answer.
Imagine a leading global financial institution struggling to extract deep insights from its massive proprietary research library. While traditional retrieval methods could handle simple queries, the results for complex questions that required connecting information scattered across multiple documents were often shallow answers lacking the context and synthesis needed for high-stakes decision-making.
