Query Types

There are three query types:

• Keyword queries
• Questions
• Statements

Keyword queries are just keywords. They don't have a sentence structure and the order of words doesn't matter, for example:

• last year results
• results 2022
• USA president

Questions, on the other hand, are complete, grammatical sentences, such as:

• What were the results last year?
• What were the results in 2022?
• Who is the president of the USA

(Pipelines in deepset Cloud don't need a question mark to process a query.)

Statements are declarative sentences, such as:

• Last year results were good.
• Results in 2022 were not satisfying.
• The president of the USA is Joe Biden.

Optimizing the Pipeline To Handle All Query Types

You can configure you pipeline so that each query type is routed to a node that's best at handling it, at the same time saving on GPU resources. For example, you can route questions and statements to a dense Retriever, such as DensePassageRetriever, and keywords to a sparse Retriever, such as BM25Retriever. deepset Cloud offers a node called QueryClassifier that's designed to do just that.

Here's what an example pipeline with this setup would look like:

And here's the pipeline code:

components:
#here's how you specify QueryClassifier:
  - name: QueryClassifier
    type: TransformersQueryClassifier
    params:
      model_name_or_path: shahrukhx01/bert-mini-finetune-question-detection
  - name: DocumentStore
    type: DeepsetCloudDocumentStore
  - name: DenseRetriever
    type: EmbeddingRetriever 
    params:
      document_store: DocumentStore
      embedding_model: sentence-transformers/multi-qa-mpnet-base-dot-v1 
      model_format: sentence_transformers
      top_k: 20 
  - name: SparseRetriever
    type: BM25Retriever
    params:
      document_store: DocumentStore
  - name: Reader
    type: FARMReader
    params:
      model: deepset/deberta-v3-base-squad2
      use_gpu: True

pipelines:
  - name: query
    nodes: 
      - name: QueryClassifier
        inputs: [Query]
      - name: DenseRetriever
        inputs: [QueryClassifier.output_1]
      - name: SparseRetriever
        inputs: [QueryClassifier.output_2]
      - name: Reader
        inputs: [DenseRetriever]
 ... #here you'd need to specify the indexing pipeline