Streamline RAG applications with intelligent metadata filtering using Amazon Bedrock

Retrieval Augmented Generation (RAG) has become a crucial technique for improving the accuracy and relevance of AI-generated responses. The effectiveness of RAG heavily depends on the quality of context provided to the large language model (LLM), which is typically retrieved from vector stores based on user queries. The relevance of this context directly impacts the model’s ability to generate accurate and contextually appropriate responses.

One effective way to improve context relevance is through metadata filtering, which allows you to refine search results by pre-filtering the vector store based on custom metadata attributes. By narrowing down the search space to the most relevant documents or chunks, metadata filtering reduces noise and irrelevant information, enabling the LLM to focus on the most relevant content.

In some use cases, particularly those involving complex user queries or a large number of metadata attributes, manually constructing metadata filters can become challenging and potentially error-prone ...