Today, Amazon Web Services (AWS) announced the general availability of Amazon Bedrock Knowledge Bases GraphRAG (GraphRAG), a capability in Amazon Bedrock Knowledge Bases that enhances Retrieval-Augmented Generation (RAG) with graph data in Amazon Neptune Analytics. This capability enhances responses from generative AI applications by automatically creating embeddings for semantic search and generating a graph of the entities and relationships extracted from ingested documents. The graph, stored in Amazon Neptune Analytics, provides enriched context during the retrieval phase to deliver more comprehensive, relevant, and explainable responses tailored to customer needs. Developers can enable GraphRAG with just a few clicks on the Amazon Bedrock console to boost the accuracy of generative AI applications without any graph modeling expertise.
In this post, we discuss the benefits of GraphRAG and how to get started with it in Amazon Bedrock Knowledge Bases.
Enhance RAG with graphs for more comprehensive and explainable GenAI applications
Generative AI is transforming how humans interact with technology by having natural conversations that provide helpful, nuanced, and insightful responses. However, a key challenge facing current generative AI systems is providing responses that are comprehensive, relevant, and explainable because data is stored across multiple documents. Without effectively mapping shared context across input data sources, responses risk being incomplete and inaccurate.
To address this, AWS announced a public preview of GraphRAG at re:Invent 2024, and is now announcing its general availability. This new capability integrates the power of graph data modeling with advanced natural language processing (NLP). GraphRAG automatically creates graphs which capture connections between related entities and sections across documents. More specifically, the graph created will connect chunks to documents, and entities to chunks.
During response generation, GraphRAG first does semantic search to find the top k most relevant chunks, and then traverses the surrounding neighborhood of those chunks to retrieve the most relevant content. By linking this contextual information, the generative AI system can provide responses that are more complete, precise, and grounded in source data. Whether answering complex questions across topics or summarizing key details from lengthy reports, GraphRAG delivers the comprehensive and explainable responses needed to enable more helpful, reliable AI conversations.
GraphRAG boosts relevance and accuracy when relevant information is dispersed across multiple sources or documents, which can be seen in the following three use cases.
Streamlining market research to accelerate business decisions
A leading global financial institution sought to enhance insight extraction from its proprietary research. With a vast repository of economic and market research reports, the institution wanted to explore how GraphRAG could improve information retrieval and reasoning for complex financial queries. To evaluate this, they added their proprietary research papers, focusing on critical market trends and economic forecasts.
To evaluate the effectiveness of GraphRAG, the institution partnered with AWS to build a proof-of-concept using Amazon Bedrock Knowledge Bases and Amazon Neptune Analytics. The goal was to determine if GraphRAG could more effectively surface insights compared to traditional retrieval methods. GraphRAG structures knowledge into interconnected entities and relationships, enabling multi-hop reasoning across documents. This capability is crucial for answering intricate questions such as “What are some headwinds and tailwinds to capex growth in the next few years?” or “What is the impact of the ILA strike on international trade?”. Rather than relying solely on keyword matching, GraphRAG allows the model to trace relationships between economic indicators, policy changes, and industry impacts, ensuring responses are contextually rich and data-driven.
When comparing the quality of responses from GraphRAG and other retrieval methods, notable differences emerged in their comprehensiveness, clarity, and relevance. While other retrieval methods delivered straightforward responses, they often lacked deeper insights and broader context. GraphRAG instead provided more nuanced answers by incorporating related factors and offering additional relevant information, which made the responses more comprehensive than the other retrieval methods.
Improving data-driven decision-making in automotive manufacturing
An international auto company manages a large dataset, supporting thousands of use cases across engineering, manufacturing, and customer service. With thousands of users querying different datasets daily, making sure insights are accurate and connected across sources has been a persistent challenge.
To address this, the company worked with AWS to prototype a graph that maps relationships between key data points, such as vehicle performance, supply chain logistics, and customer feedback. This structure allows for more precise results across datasets, rather than relying on disconnected query results.
With Amazon Bedrock Knowledge Bases GraphRAG with Amazon Neptune Analytics automatically constructing a graph from ingested documents, the company can surface relevant insights more efficiently in their RAG applications. This approach helps teams identify patterns in manufacturing quality, predict maintenance needs, and improve supply chain resilience, making data analysis more effective and scalable across the organization.
Enhancing cybersecurity incident analysis
A cybersecurity company is using GraphRAG to improve how its AI-powered assistant analyzes security incidents. Traditional detection methods rely on isolated alerts, often missing the broader context of an attack.
By using a graph, the company connects disparate security signals, such as login anomalies, malware signatures, and network traffic patterns, into a structured representation of threat activity. This allows for faster root cause analysis and more comprehensive security reporting.
Amazon Bedrock Knowledge Bases and Neptune Analytics enable this system to scale while maintaining strict security controls, providing resource isolation. With this approach, the company’s security teams can quickly interpret threats, prioritize responses, and reduce false positives, leading to more efficient incident handling.
Solution overview
In this post, we provide a walkthrough to build Amazon Bedrock Knowledge Bases GraphRAG with Amazon Neptune Analytics, using files in an Amazon Simple Storage Service (Amazon S3) bucket. Running this example will incur costs in Amazon Neptune Analytics, Amazon S3, and Amazon Bedrock. Amazon Neptune Analytics costs for this example will be approximately $0.48 per hour. Amazon S3 costs will vary depending on how large your dataset is, and more details on Amazon S3 pricing can be found here. Amazon Bedrock costs will vary depending on the embeddings model and chunking strategy you select, and more details on Bedrock pricing can be found here.
Prerequisites
To follow along with this post, you need an AWS account with the necessary permissions to access Amazon Bedrock, and an Amazon S3 bucket containing data to serve as your knowledge base. Also ensure that you have enabled model access to Claude 3 Haiku (anthropic.claude-3-haiku-20240307-v1:0) and any other models that you wish to use as your embeddings model. For more details on how to enable model access, refer to the documentation here.
Build Amazon Bedrock Knowledge Bases GraphRAG with Amazon Neptune Analytics
To get started, complete the following steps:
- On the Amazon Bedrock console, choose Knowledge Bases under Builder tools in the navigation pane.
- In the Knowledge Bases section, choose Create and Knowledge Base with vector store.
- For Knowledge Base details, enter a name and an optional description.
- For IAM permissions, select Create and use a new service role to create a new AWS Identity and Access Management (IAM) role.
- For Data source details, select Amazon S3 as your data source.
- Choose Next.
- For S3 URI, choose Browse S3 and choose the appropriate S3 bucket.
- For Parsing strategy, select Amazon Bedrock default parser.
- For Chunking strategy, choose Default chunking (recommended for GraphRAG) or any other strategy as you wish.
- Choose Next.
- For Embeddings model, choose an embeddings model, such as Amazon Titan Text Embeddings v2.
- For Vector database, select Quick create a new vector store and then select Amazon Neptune Analytics (GraphRAG).
- Choose Next.
- Review the configuration details and choose Create Knowledge Base.
Sync the data source
- Once the knowledge base is created, click Sync under the Data source section. The data sync can take a few minutes to a few hours, depending on how many source documents you have and how big each one is.
Test the knowledge base
Once the data sync is complete:
- Choose the expansion icon to expand the full view of the testing area.
- Configure your knowledge base by adding filters or guardrails.
- We encourage you to enable reranking (For information about pricing for reranking models, see Amazon Bedrock Pricing) to fully take advantage of the capabilities of GraphRAG. Reranking allows GraphRAG to refine and optimize search results.
- You can also supply a custom metadata file (each up to 10 KB) for each document in the knowledge base. You can apply filters to your retrievals, instructing the vector store to pre-filter based on document metadata and then search for relevant documents. This way, you have control over the retrieved documents, especially if your queries are ambiguous. Note that the
listtype is not supported.
- Use the chat area in the right pane to ask questions about the documents from your Amazon S3 bucket.
The responses will use GraphRAG and provide references to chunks and documents in their response.
Now that you’ve enabled GraphRAG, test it out by querying your generative AI application and observe how the responses have improved compared to baseline RAG approaches. You can monitor the Amazon CloudWatch logs for performance metrics on indexing, query latency, and accuracy.
Clean up
When you’re done exploring the solution, make sure to clean up by deleting any resources you created. Resources to clean up include the Amazon Bedrock knowledge base, the associated AWS IAM role that the Amazon Bedrock knowledge base uses, and the Amazon S3 bucket that was used for the source documents.
You will also need to separately delete the Amazon Neptune Analytics graph that was created on your behalf, by Amazon Bedrock Knowledge Bases.
Conclusion
In this post, we discussed how to get started with Amazon Bedrock Knowledge Bases GraphRAG with Amazon Neptune. For further experimentation, check out the Amazon Bedrock Knowledge Bases Retrieval APIs to use the power of GraphRAG in your own applications. Refer to our documentation for code samples and best practices.
About the authors
Denise Gosnell is a Principal Product Manager for Amazon Neptune, focusing on generative AI infrastructure and graph data applications that enable scalable, cutting-edge solutions across industry verticals.
Melissa Kwok is a Senior Neptune Specialist Solutions Architect at AWS, where she helps customers of all sizes and verticals build cloud solutions according to best practices. When she’s not at her desk you can find her in the kitchen experimenting with new recipes or reading a cookbook.
Ozan Eken is a Product Manager at AWS, passionate about building cutting-edge Generative AI and Graph Analytics products. With a focus on simplifying complex data challenges, Ozan helps customers unlock deeper insights and accelerate innovation. Outside of work, he enjoys trying new foods, exploring different countries, and watching soccer.
Harsh Singh is a Principal Product Manager Technical at AWS AI. Harsh enjoys building products that bring AI to software developers and everyday users to improve their productivity.
Mani Khanuja is a Tech Lead – Generative AI Specialists, author of the book Applied Machine Learning and High-Performance Computing on AWS, and a member of the Board of Directors for Women in Manufacturing Education Foundation Board. She leads machine learning projects in various domains such as computer vision, natural language processing, and generative AI. She speaks at internal and external conferences such AWS re:Invent, Women in Manufacturing West, YouTube webinars, and GHC 23. In her free time, she likes to go for long runs along the beach.