# Use Case - open-domain question-answering

The open-domain question-answering use case is an example of an end-to-end question-answering application. Taking a textual question, it returns a plain-text answer to the question. To start the application, please follow the instructions in the README.

This sample application is an implementation of the Dense Passage Retriever system. Its full version contains 21 million passages from Wikipedia that are retrieved using either a sparse retrieval (BM25) or a dense retrieval (ANN). It then uses a BERT model to re-rank the passages and extract the most probable correct answer.

After deploying the application, you can ask questions like this:

http://localhost:8080/search/?query=what+is+the+boiling+point+of+ethanol%3F


And Vespa will return the exact answer: 78.37 C. This application uses the Natural Questions dataset.

### Highlighted features

• Approximate nearest neighbors using an HNSW index

Vespa supports approximate nearest neighbors (ANN) by using Hierarchical Navigable Small World (HNSW) indexes. This allows for efficient similarity search in large collections. Vespa implements a modified HNSW index that allows for index building during feeding, so one does not have to build the index offline. It also supports additional query filters directly, thus avoiding the sub-optimal filtering after the ANN search.

• Ranking with Transformer models

The Transformer architecture has revolutionized multiple fields after its introduction, starting with natural language understanding (NLU). This application uses two BERT models: one to transform the question to a representation vector for ANN search, the other to re-rank and extract the actual answer to the question.

• ONNX model evaulation

The Transformer models are exported from HuggingFace’s Transformers library to ONNX models. The Open Neural Network Exchange (ONNX) is an open standard for distributing machine-learned models between different systems. Vespa imports ONNX models and evaluates them using ONNX Runtime, ensuring efficient and correct inference.

• Container components

In Vespa, you can set up custom document or search processors to perform any extra processing during document feeding or during a query. This application uses this feature to generate token sequences from a WordPiece tokenizer. During feeding of a passage, its text is converted to a token sequence, which is stored along with the document. Likewise, the text of a query is converted to a token representation, which is used as input to the Transformer ONNX model inference.

• Custom configuration

When creating custom components in Vespa, for instance, document processors, searchers, or handlers, one can use custom configuration to inject config parameters into the components. This involves defining a config definition (a .def file), which creates a config class. You can instantiate this class with data in services.xml, and the resulting object is dependency injected to the component during construction. This application uses custom config to set up the token vocabulary used in tokenization.

Vespa supports using multiple threads per query. This means that the ranking computation for handling a query can be split into multiple threads. This is useful for cases where ranking is computationally expensive. Vespa takes care of balancing the load between available threads.

Note that this is different from multi-threaded ranking, where multiple queries are processed in parallel.

• Text retrieval with BM25

In addition to dense retrieval using ANN, this application shows, for comparison, text retrieval using BM25. The fields that have enabled a BM25 index (enable-bm25) use this index for retrieval.

• Multi-phased ranking

Vespa supports ranking over multiple phases. This is useful for expensive computation in ranking, for instance, when evaluating a large machine-learned model. In these cases, one can perform a fast first phase ranking and only perform the expensive computation on a smaller subset. This application first uses a euclidean score from the ANN and only evaluates the large Transformer model on the top 10 candidates.

• Summary features

Summary features allow for customizing what is included with each hit. This application uses this to return the start and end indexes of the potential answer to a custom searcher component that extracts the most probably answer substring.