August 2021 Summaries

A query classifier in Haystack helps distinguish between keyword-based and natural language queries, routing them accordingly to optimize performance and accuracy. The classifier can be integrated into various pipelines, including question answering systems, to make smarter decisions before triggering the full pipeline. By using a machine learning model trained on a binary classification task, the classifier can handle complex use cases such as distinguishing between questions and statements, or adapting to different retrieval methods for dense and sparse queries. The query classifier can save time and resources by rerouting keyword queries to a regular document search instead of employing the full semantic search pipeline, resulting in significant computational and time savings. By incorporating a query classifier into question answering pipelines, systems can maximize accuracy while minimizing computation effort and cost.

Building a REST API for Question Answering With Haystack involves creating an application programming interface (API) that allows individual computer systems to interact with each other. A question answering API is a production-ready way to embed semantic search capabilities into a website or mobile app, and can be used for various use cases such as question answering functionality on websites or in mobile apps, generative question answering, FAQ search, and document search. To deploy a Haystack API, one needs to initialize the Haystack infrastructure, add data to the underlying datastore like Elasticsearch, start the Haystack API container, and query the API using HTTP requests. The Haystack API can be exposed on a public IP address by modifying the AWS security group associated with the EC2 instance, and custom API endpoints can be added using FastAPI.

Haystack Pipelines are tools that allow users to piece together different components of a question answering system, automating data flow and eliminating computational heavy lifting. However, debugging pipelines can be challenging due to their complexity. To troubleshoot common issues, it's essential to run each component individually to isolate problems, check the error printout for human-readable messages, and visualize the pipeline graph to identify logical errors. By following these solutions, users can simplify debugging and build bug-free programs with Haystack question answering systems.

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.