Metadata Filtering in Haystack

Metadata filtering can significantly boost the quality of answers in Haystack question answering systems by quickly providing preselected sections of data to deep language models. Metadata, which refers to "data about data," can be represented as structured data such as Booleans, integers, floating point numbers, or categorical data. By passing a filter to the retriever-reader pipeline, metadata filtering reduces the search space for the rest of the pipeline, resulting in improved search speed and increased likelihood of high-quality answers. The article demonstrates how to implement metadata filtering using Elasticsearch and Weaviate document stores with Haystack, showcasing its potential in scenarios such as competitor analysis or general class product queries. By leveraging metadata filtering, users can enhance their question answering systems and explore the possibilities of filtering with Haystack.

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