Introduction to Amazon Bedrock

Amazon Bedrock is an advanced generative artificial intelligence (AI) platform launched by Amazon Web Services (AWS) aimed at helping businesses easily build, train, and deploy large-scale generative AI models.

Amazon Bedrock Innovations in RAG Applications

By integrating various pre-trained language models, Amazon Bedrock provides users with flexible and scalable AI solutions that support natural language processing, text generation, image generation, and many other application scenarios.

It offers high-performance foundational models (FM) from several leading AI companies, including Amazon Web Services, through a single API, along with a wide range of features for building generative AI applications with security, privacy, and responsibility.

Amazon Bedrock Knowledge Base

The Amazon Bedrock Knowledge Base is a fully managed service that helps developers create high-precision, low-latency, secure, and customizable generative AI applications in a cost-effective manner. The Amazon Bedrock Knowledge Base utilizes Retrieval-Augmented Generation (RAG) to connect foundational models (FM) with internal enterprise data. RAG helps FM provide more relevant, accurate, and customized responses.

Custom Connectors and Streaming Data Ingestion

When developing RAG-based generative AI applications, enterprises often face challenges with updating knowledge base data. Current data synchronization requires complete syncing every time data is added or deleted, which is time-consuming and labor-intensive. Additionally, when users integrate datasets from unsupported sources into the knowledge base, they must first move them to supported sources before initiating the ingestion process, leading to increased costs and delays in data access. Furthermore, the use of streaming data also faces delays in the availability of real-time data due to the need to store data in supported data sources before ingestion.

The newly added support for custom connectors and streaming data ingestion in the Amazon Bedrock Knowledge Base addresses these challenges: with just one API call, data can be efficiently and economically ingested, updated, or deleted without the need for regular or complete synchronization of data sources after each change, ensuring that the knowledge base is updated in real-time and queryable.

Re-ranking Model

Amazon Web Services has introduced the Rerank API in Amazon Bedrock to help developers use re-ranking models to enhance the relevance and accuracy of responses, thereby improving RAG application performance. By employing vector embedding-driven semantic search, documents are mapped to a semantic high-dimensional space, where semantically related texts exhibit semantic similarity, allowing for similar items to be returned even without shared vocabulary with the search query. Since the relevance of retrieved documents to the query is crucial for providing accurate responses, RAG applications retrieve a series of relevant documents from vector storage, thus semantic search is utilized in RAG applications.

However, semantic search has limitations when it comes to ranking documents based on user preferences or query context, especially when queries are complex, ambiguous, or nuanced, which may result in retrieved documents having low relevance to user questions, leading to incorrect citation and attribution to the correct source, affecting the credibility and transparency of RAG applications.

The advanced re-ranking model is undoubtedly a solution to this challenge: it prioritizes the most relevant content in the knowledge base, ensuring that the foundational model receives the most pertinent information to generate precise responses, while also prioritizing the information sent to reduce the cost of response generation.

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