Features of Amazon Bedrock

Amazon Bedrock provides access to pretrained, powerful AI models from leading providers and a suite of tools to customize, deploy, and integrate these models into diverse workflows. Let’s explore Amazon Bedrock’s standout features, covering how it enables businesses to build scalable AI solutions more easily, securely, and cost-effectively.

Knowledge bases

The knowledge base is a repository that stores up-to-date and proprietary information for easy access by generative AI models. It empowers AI applications to deliver more relevant, accurate, and customized responses on demand. It allows for a quicker time to market by relieving you of the burden of pipeline construction and offering you an end-to-end RAGRetrieval Augmented Generation (RAG) is a widely used method that adds private data from a data store (vector database) to the responses produced by large language models (LLMs). solution to shorten the application’s build time. You don’t need to continually train your model by adding a knowledge base, increasing cost-effectiveness.

How knowledge bases work

Foundation models (FMs) are trained on massive datasets, but these datasets are usually general-purpose and outdated. To allow FMs to grasp context, similarity, and relevance between different data points, you can leverage knowledge bases in Bedrock. This process is invaluable in search, recommendation, personalization, and various NLP applications, where understanding the relationship between terms, phrases, or user preferences is critical.

Each of the steps mentioned in the diagram above is designed to organize and optimize data for effective recovery. A brief description of each is given below:

• Data source: The first step is gathering the data you want in your knowledge base, such as FAQs, product information, or documents. Choosing good, relevant sources is key to ensuring your knowledge base answers questions accurately and thoroughly.

• Chunk creation: The next step is to split large data into smaller chunks (usually of a size such that each represents a coherent unit of information, such as paragraphs) to make retrieval faster and more specific. This improves retrieval efficiency by responding with specific results rather than requiring retrieval of entire documents.

• Embeddings creation: Creating embeddings involves converting text chunks into vector representations that reflect the semantic relationships between them. This step enables the knowledge base to retrieve information based on meaning, not just keywords, improving relevance and accuracy.

• Vector store: The vector store is a specialized storage solution designed for managing and querying embeddings. Once the embeddings are generated, they are stored in a vector database, allowing the system to quickly find the most relevant information against the user’s query.

When a user query is performed, it is first transformed into a vector, taking similar steps as mentioned above, using an embedding model. The vector index is then used to find chunks with semantic similarities to the query in the vector store. ...