QueryClassifier distinguishes between different types of queries and routes them to the pipeline branch that can handle them best. It can categorize queries into keyword-based and natural language queries.


A common use case for QueryClassifier is in a question answering pipeline where it routes keyword queries to a less computationally expensive sparse Retriever and natural language questions to a dense Retriever. This helps you save time and can produce better results for your keyword queries.

To handle these tasks, QueryClassifier uses a classification model.

Basic Information

  • Position in a pipeline: Use QueryClassifier at the beginning of the query pipeline.
  • Input and output: QueryClassifier takes a query as input and returns a classified query as output.
  • Available classes: There are two types of QueryClassifier: TransformersQueryClassifier and SklearnQueryClassifier

When used in a pipeline, it acts as a decision node, which means it routes the queries to a specific node, depending on how the query is classified.


This QueryClassifier is sensitive to the syntax of a sentence as it uses a transformers model to classify queries.

The default model for TransformersQueryClassifier is shahrukhx01/bert-mini-finetune-question-detection. It was trained using the mini BERT architecture of about 50 MB in size, which allows relatively fast inference on the CPU.

Main features:

  • Uses a transformers model to classify an incoming query
  • More accurate than SklearnQueryClassifier
  • Supports zero-shot classification


These are the parameters you can specify for TransformersQueryClassifier:

ParameterTypeMandatoryPossible ValuesDescription
model_name_or_pathStringYes-Specfies the model you want to use. You can either type a path to the model stored on your computer or the name of a public model from Hugging Face.
We recommend the shahrukhx01/bert-mini-finetune-question-detection model. It was trained on the mini BERT architecture and can distinguish between natural language queries and questions.
model_versionStringNoTag name
Branch name
Commit hash
The version of the model from Hugging Face.
tokenizerStringNoThe name of the tokenizer usually the same as the model name.
Default: True
Specifies if GPU should be used.
Specifies the type of classification the node should perform.
labelsA list of stringsYesIf you choose text-classification as task and provide an ordered label, the first label corresponds to output_1, the second label corresponds to output_2, and so on. The labels must match the model labels; only their order can differ.
If you selected zero-shot-classification as task, these are the candidate labels.
batch_sizeIntegerYesDefault: 16The number of queries you want to process at one time.
Default: True
Shows the progress bar when processing queries.
use_auth_tokenString or BooleanNo-Specifies the API token used to download private models from Hugging Face. If you set it to True, it uses the token generated when running transformers-cli login.`
devicesString or torch.deviceNo-A list of torch devices such as cuda, cpu, mps, to limit inference to specific devices.
Example: [torch.device(cuda:0), "mps, "cuda:1"
If you set use_gpu to False, this parameter is not used and a single cpu device is used for inference.


This QueryClassifier class uses a lightweight sklearn model to classify queries. It's less accurate than TransformersQueryClassifier but needs fewer resources.

Main Features

  • Lightweight
  • Uses a sklearn model


Here are the parameters you can specify for SklearnQueryClassifier:

ParameterTypeMandatoryPossible ValuesDescription
model_name_or_pathStringYes-A gradient boosting-based binary classifier to classify between keywords and statements or questions, or statements and questions.
You can use the following pre-trained query classifier: https://ext-models-haystack.s3.eu-central-1.amazonaws.com/gradboost_query_classifier/model.pickle
To learn how it was trained and how it performed, see readme.
vectorizer_name_or_pathStringYes-An ngram-based TFIDF vectorizer for extracting features from the query.

You can use the following pre-trained query vectorizer: https://ext-models-haystack.s3.eu-central-1.amazonaws.com/gradboost_query_classifier/vectorizer.pickle
To learn how it was trained and how it performed, see readme.
batch_sizeIntegerNoSpecifies the number of queries you want to process at one time.
Default: True
Shows a progress bar when processing the queries.

Handling Different Query Types with QueryClassifier

Queries come in different shapes—keywords, questions, and statements. You can optimize your search by routing each query type to a node that handles it best and saving time and resources at the same time.

Query Types

There are two main query types you may want to distinguish between: keywords and natural language queries. 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

Natural language queries can be questions or statements. They're complete, grammatical sentences, such as:

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


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

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

Optimizing the Pipeline to Handle Each Query Type

You can adjust the architecture of your pipeline so that only statements and questions are routed to the Reader, while for keywords, the pipeline performs a regular document search. This way, you save time and computational resources.

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

A diagram showing the query pipeline that starts with a query which is then routed to the query classifier. The query classifier then routes output 1 to embedding retriever and output 2 to bm25 retriever. Then the embedding retriever output is routed further to a farm reader.A diagram showing the query pipeline that starts with a query which is then routed to the query classifier. The query classifier then routes output 1 to embedding retriever and output 2 to bm25 retriever. Then the embedding retriever output is routed further to a farm reader.

And here's the pipeline code:

import os
os.environ["DEEPSET_CLOUD_API_KEY"] = "api_eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJzdWIiOiJiNTEzZTFmNi03YzA3LTRhMzUtOTczZS00Zjg4NGIxY2JkMDV8NjJjNTUzMjI0MWJhMDExZjIzM2IwNWIzIiwiZXhwIjoxNjY2MzU1Mjg3LCJhdWQiOlsiaHR0cHM6Ly9hcGkuY2xvdWQuZGVlcHNldC5haSJdfQ.QZfTPKL12ea_tDK6WhZPyPiHJ92znYDHAM4wxa03TUc"
os.environ["DEEPSET_CLOUD_API_ENDPOINT"] = "https://api.cloud.deepset.ai/api/v1"

from haystack import Pipeline
from haystack.nodes import TransformersQueryClassifier, FARMReader, BM25Retriever, EmbeddingRetriever
from haystack.document_stores import DeepsetCloudDocumentStore

document_store = DeepsetCloudDocumentStore()
query_classifier = TransformersQueryClassifier(model_name_or_path="shahrukhx01/bert-mini-finetune-question-detection")
embedding_retriever = EmbeddingRetriever(
bm25_retriever = BM25Retriever(document_store=document_store)
reader = FARMReader(model_name_or_path="deepset/deberta-v3-base-squad2", use_gpu="True")

pipe = Pipeline()
pipe.add_node(component=query_classifier, name="QueryClassifier", inputs=["Query"])
pipe.add_node(component=embedding_retriever, name="EmbeddingRetriever", inputs=["QueryClassifier.output_1"])
pipe.add_node(component=bm25_retriever, name="BM25", inputs=["QueryClassifier.output_2"])
pipe.add_node(component=reader, name="QAReader", inputs=["EmbeddingRetriever"])

# Pass a question -> run DPR + QA -> return answers
res_1 = pipe.run(query="Who is the father of Arya Stark?")

# Pass keywords -> run only BM25Retriever -> return Documents
res_2 = pipe.run(query="arya stark father")
# This example contains just the query pipeline, without the indexing pipeline
version: 1.9.1
name: QueryClassifierPipeline

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

  - name: query
      - name: QueryClassifier
        inputs: [Query]
      - name: DenseRetriever
        inputs: [QueryClassifier.output_1]
      - name: SparseRetriever
        inputs: [QueryClassifier.output_2]
      - name: Reader
        inputs: [DenseRetriever]