Module base

BaseQueryClassifier

class BaseQueryClassifier(BaseComponent)

Abstract class for Query Classifiers

Module sklearn

SklearnQueryClassifier

class SklearnQueryClassifier(BaseQueryClassifier)

A node to classify an incoming query into one of two categories using a lightweight sklearn model. Depending on the result, the query flows to a different branch in your pipeline and the further processing can be customized. You can define this by connecting the further pipeline to either output_1 or output_2 from this node.

Example:

|{
|pipe = Pipeline()
|pipe.add_node(component=SklearnQueryClassifier(), name="QueryClassifier", inputs=["Query"])
|pipe.add_node(component=elastic_retriever, name="ElasticRetriever", inputs=["QueryClassifier.output_2"])
|pipe.add_node(component=dpr_retriever, name="DPRRetriever", inputs=["QueryClassifier.output_1"])

|# Keyword queries will use the ElasticRetriever
|pipe.run("kubernetes aws")

|# Semantic queries (questions, statements, sentences ...) will leverage the DPR retriever
|pipe.run("How to manage kubernetes on aws")

Models:

Pass your own Sklearn binary classification model or use one of the following pretrained ones:

1. Keywords vs. Questions/Statements (Default) query_classifier can be found here query_vectorizer can be found here output_1 => question/statement output_2 => keyword query Readme

2. Questions vs. Statements query_classifier can be found here query_vectorizer can be found here output_1 => question output_2 => statement Readme

See also the tutorial on pipelines.

SklearnQueryClassifier.__init__

def __init__(model_name_or_path: Union[
            str, Any
        ] = "https://ext-models-haystack.s3.eu-central-1.amazonaws.com/gradboost_query_classifier/model.pickle", vectorizer_name_or_path: Union[
            str, Any
        ] = "https://ext-models-haystack.s3.eu-central-1.amazonaws.com/gradboost_query_classifier/vectorizer.pickle", batch_size: Optional[int] = None)

Arguments:

• model_name_or_path: Gradient boosting based binary classifier to classify between keyword vs statement/question queries or statement vs question queries.
• vectorizer_name_or_path: A ngram based Tfidf vectorizer for extracting features from query.
• batch_size: Number of queries to process at a time.

Module transformers

TransformersQueryClassifier

class TransformersQueryClassifier(BaseQueryClassifier)

A node to classify an incoming query into one of two categories using a (small) BERT transformer model. Depending on the result, the query flows to a different branch in your pipeline and the further processing can be customized. You can define this by connecting the further pipeline to either output_1 or output_2 from this node.

Example:

|{
|pipe = Pipeline()
|pipe.add_node(component=TransformersQueryClassifier(), name="QueryClassifier", inputs=["Query"])
|pipe.add_node(component=elastic_retriever, name="ElasticRetriever", inputs=["QueryClassifier.output_2"])
|pipe.add_node(component=dpr_retriever, name="DPRRetriever", inputs=["QueryClassifier.output_1"])

|# Keyword queries will use the ElasticRetriever
|pipe.run("kubernetes aws")

|# Semantic queries (questions, statements, sentences ...) will leverage the DPR retriever
|pipe.run("How to manage kubernetes on aws")

Models:

Pass your own Transformer binary classification model from file/huggingface or use one of the following pretrained ones hosted on Huggingface:

1. Keywords vs. Questions/Statements (Default) model_name_or_path="shahrukhx01/bert-mini-finetune-question-detection" output_1 => question/statement output_2 => keyword query Readme

2. Questions vs. Statements model_name_or_path="shahrukhx01/question-vs-statement-classifier" output_1 => question output_2 => statement Readme

See also the tutorial on pipelines.

TransformersQueryClassifier.__init__

def __init__(model_name_or_path: Union[Path, str] = "shahrukhx01/bert-mini-finetune-question-detection", use_gpu: bool = True, batch_size: int = 16)

Arguments:

• model_name_or_path: Transformer based fine tuned mini bert model for query classification
• use_gpu: Whether to use GPU (if available).