Retrieve data and generate AI responses with Amazon Bedrock Knowledge Bases - Amazon Bedrock

Retrieve data and generate AI responses with Amazon Bedrock Knowledge Bases

While foundation models have general knowledge, you can further improve their responses by using Retrieval Augmented Generation (RAG). RAG is a technique that uses information from data sources to improve the relevancy and accuracy of generated responses. With Amazon Bedrock Knowledge Bases, you can integrate proprietary information into your generative-AI applications. When a query is made, a knowledge base searches your data to find relevant information to answer the query. The retrieved information can then be used to improve generated responses. You can build your own RAG-based application by using the capabilities of Amazon Bedrock Knowledge Bases.

With Amazon Bedrock Knowledge Bases, you can:

  • Answer user queries by returning relevant information from data sources.

  • Use retrieved information from data sources to help generate an accurate and relevant response to user queries.

  • Augment your own prompts by feeding the returned relevant information into the prompt.

  • Include citations in the generated response so the original data source can be referenced and accuracy can be checked.

  • Include documents with copious visual resources, from which images can be extracted and retrieved in responses to queries. If you generate a response based on the retrieved data, the model can deliver additional insights based on these images.

  • Convert natural language into queries (such as SQL queries) that are customized for structured databases. These queries are used to retrieve data from structured data stores.

  • Update your data sources and ingest the changes into the knowledge base directly so they can be immediately accessed.

  • Use reranking models to influence the results that are retrieved from your data source.

  • Include the knowledge base in an Amazon Bedrock Agents workflow.

To set up a knowledge base, you must complete the following general steps:

  1. (Optional) If you connect your knowledge base to an unstructured data source, set up your own supported vector store to index the vector embeddings representation of your data. You can skip this step if you plan to use the Amazon Bedrock console to create an Amazon OpenSearch Serverless vector store for you.

  2. Connect your knowledge base to an unstructured or structured data source.

  3. Sync your data source with your knowledge base.

  4. Set up your application or agent to do the following:

    • Query the knowledge base and return relevant sources.

    • Query the knowledge base and generate natural language responses based on the retrieved results.

    • (If you query a knowledge base connected to a structured data store) Transform a query into a structured data language-specific query (such as an SQL query).

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