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.

__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")

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.

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.

__init__

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

Arguments:

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