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Module pipeline

Pipeline Objects

class Pipeline()

Pipeline brings together building blocks to build a complex search pipeline with Haystack & user-defined components.

Under-the-hood, a pipeline is represented as a directed acyclic graph of component nodes. It enables custom query flows with options to branch queries(eg, extractive qa vs keyword match query), merge candidate documents for a Reader from multiple Retrievers, or re-ranking of candidate documents.

add_node

| add_node(component, name: str, inputs: List[str])

Add a new node to the pipeline.

Arguments:

  • component: The object to be called when the data is passed to the node. It can be a Haystack component (like Retriever, Reader, or Generator) or a user-defined object that implements a run() method to process incoming data from predecessor node.

  • name: The name for the node. It must not contain any dots.

  • inputs: A list of inputs to the node. If the predecessor node has a single outgoing edge, just the name of node is sufficient. For instance, a 'ElasticsearchRetriever' node would always output a single edge with a list of documents. It can be represented as ["ElasticsearchRetriever"].

             In cases when the predecessor node has multiple outputs, e.g., a "QueryClassifier", the output
             must be specified explicitly as "QueryClassifier.output_2".

get_node

| get_node(name: str) -> Optional[BaseComponent]

Get a node from the Pipeline.

Arguments:

  • name: The name of the node.

set_node

| set_node(name: str, component)

Set the component for a node in the Pipeline.

Arguments:

  • name: The name of the node.
  • component: The component object to be set at the node.

draw

| draw(path: Path = Path("pipeline.png"))

Create a Graphviz visualization of the pipeline.

Arguments:

  • path: the path to save the image.

load_from_yaml

| @classmethod
 | load_from_yaml(cls, path: Path, pipeline_name: Optional[str] = None, overwrite_with_env_variables: bool = True)

Load Pipeline from a YAML file defining the individual components and how they're tied together to form a Pipeline. A single YAML can declare multiple Pipelines, in which case an explicit pipeline_name must be passed.

Here's a sample configuration:

```yaml
|   version: '0.8'
|
|    components:    # define all the building-blocks for Pipeline
|    - name: MyReader       # custom-name for the component; helpful for visualization & debugging
|      type: FARMReader    # Haystack Class name for the component
|      params:
|        no_ans_boost: -10
|        model_name_or_path: deepset/roberta-base-squad2
|    - name: MyESRetriever
|      type: ElasticsearchRetriever
|      params:
|        document_store: MyDocumentStore    # params can reference other components defined in the YAML
|        custom_query: null
|    - name: MyDocumentStore
|      type: ElasticsearchDocumentStore
|      params:
|        index: haystack_test
|
|    pipelines:    # multiple Pipelines can be defined using the components from above
|    - name: my_query_pipeline    # a simple extractive-qa Pipeline
|      nodes:
|      - name: MyESRetriever
|        inputs: [Query]
|      - name: MyReader
|        inputs: [MyESRetriever]
```

Arguments:

  • path: path of the YAML file.
  • pipeline_name: if the YAML contains multiple pipelines, the pipeline_name to load must be set.
  • overwrite_with_env_variables: Overwrite the YAML configuration with environment variables. For example, to change index name param for an ElasticsearchDocumentStore, an env variable 'MYDOCSTORE_PARAMS_INDEX=documents-2021' can be set. Note that an _ sign must be used to specify nested hierarchical properties.

save_to_yaml

| save_to_yaml(path: Path, return_defaults: bool = False)

Save a YAML configuration for the Pipeline that can be used with Pipeline.load_from_yaml().

Arguments:

  • path: path of the output YAML file.
  • return_defaults: whether to output parameters that have the default values.

BaseStandardPipeline Objects

class BaseStandardPipeline(ABC)

add_node

| add_node(component, name: str, inputs: List[str])

Add a new node to the pipeline.

Arguments:

  • component: The object to be called when the data is passed to the node. It can be a Haystack component (like Retriever, Reader, or Generator) or a user-defined object that implements a run() method to process incoming data from predecessor node.

  • name: The name for the node. It must not contain any dots.

  • inputs: A list of inputs to the node. If the predecessor node has a single outgoing edge, just the name of node is sufficient. For instance, a 'ElasticsearchRetriever' node would always output a single edge with a list of documents. It can be represented as ["ElasticsearchRetriever"].

             In cases when the predecessor node has multiple outputs, e.g., a "QueryClassifier", the output
             must be specified explicitly as "QueryClassifier.output_2".

get_node

| get_node(name: str)

Get a node from the Pipeline.

Arguments:

  • name: The name of the node.

set_node

| set_node(name: str, component)

Set the component for a node in the Pipeline.

Arguments:

  • name: The name of the node.
  • component: The component object to be set at the node.

draw

| draw(path: Path = Path("pipeline.png"))

Create a Graphviz visualization of the pipeline.

Arguments:

  • path: the path to save the image.

ExtractiveQAPipeline Objects

class ExtractiveQAPipeline(BaseStandardPipeline)

__init__

| __init__(reader: BaseReader, retriever: BaseRetriever)

Initialize a Pipeline for Extractive Question Answering.

Arguments:

  • reader: Reader instance
  • retriever: Retriever instance

DocumentSearchPipeline Objects

class DocumentSearchPipeline(BaseStandardPipeline)

__init__

| __init__(retriever: BaseRetriever)

Initialize a Pipeline for semantic document search.

Arguments:

  • retriever: Retriever instance

GenerativeQAPipeline Objects

class GenerativeQAPipeline(BaseStandardPipeline)

__init__

| __init__(generator: BaseGenerator, retriever: BaseRetriever)

Initialize a Pipeline for Generative Question Answering.

Arguments:

  • generator: Generator instance
  • retriever: Retriever instance

SearchSummarizationPipeline Objects

class SearchSummarizationPipeline(BaseStandardPipeline)

__init__

| __init__(summarizer: BaseSummarizer, retriever: BaseRetriever)

Initialize a Pipeline that retrieves documents for a query and then summarizes those documents.

Arguments:

  • summarizer: Summarizer instance
  • retriever: Retriever instance

run

| run(query: str, filters: Optional[Dict] = None, top_k_retriever: Optional[int] = None, generate_single_summary: Optional[bool] = None, return_in_answer_format: bool = False)

Arguments:

  • query: Your search query
  • filters:
  • top_k_retriever: Number of top docs the retriever should pass to the summarizer. The higher this value, the slower your pipeline.
  • generate_single_summary: Whether to generate single summary from all retrieved docs (True) or one per doc (False).
  • return_in_answer_format: Whether the results should be returned as documents (False) or in the answer format used in other QA pipelines (True). With the latter, you can use this pipeline as a "drop-in replacement" for other QA pipelines.

FAQPipeline Objects

class FAQPipeline(BaseStandardPipeline)

__init__

| __init__(retriever: BaseRetriever)

Initialize a Pipeline for finding similar FAQs using semantic document search.

Arguments:

  • retriever: Retriever instance

TranslationWrapperPipeline Objects

class TranslationWrapperPipeline(BaseStandardPipeline)

Takes an existing search pipeline and adds one "input translation node" after the Query and one "output translation" node just before returning the results

__init__

| __init__(input_translator: BaseTranslator, output_translator: BaseTranslator, pipeline: BaseStandardPipeline)

Wrap a given pipeline with the input_translator and output_translator.

Arguments:

  • input_translator: A Translator node that shall translate the input query from language A to B
  • output_translator: A Translator node that shall translate the pipeline results from language B to A
  • pipeline: The pipeline object (e.g. ExtractiveQAPipeline) you want to "wrap". Note that pipelines with split or merge nodes are currently not supported.

SklearnQueryClassifier Objects

class SklearnQueryClassifier(BaseComponent)

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="https://ext-models-haystack.s3.eu-central-1.amazonaws.com/gradboost_query_classifier/model.pickle" query_vectorizer="https://ext-models-haystack.s3.eu-central-1.amazonaws.com/gradboost_query_classifier/vectorizer.pickle" output_1 => question/statement output_2 => keyword query Readme

  2. Questions vs. Statements query_classifier="https://ext-models-haystack.s3.eu-central-1.amazonaws.com/gradboost_query_classifier_statements/model.pickle" query_vectorizer="https://ext-models-haystack.s3.eu-central-1.amazonaws.com/gradboost_query_classifier_statements/vectorizer.pickle" output_1 => question output_2 => statement Readme

See also the tutorial on pipelines.

__init__

| __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.

TransformersQueryClassifier Objects

class TransformersQueryClassifier(BaseComponent)

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__

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

Arguments:

  • model_name_or_path: Transformer based fine tuned mini bert model for query classification

JoinDocuments Objects

class JoinDocuments(BaseComponent)

A node to join documents outputted by multiple retriever nodes.

The node allows multiple join modes:

  • concatenate: combine the documents from multiple nodes. Any duplicate documents are discarded.
  • merge: merge scores of documents from multiple nodes. Optionally, each input score can be given a different weight & a top_k limit can be set. This mode can also be used for "reranking" retrieved documents.

__init__

| __init__(join_mode: str = "concatenate", weights: Optional[List[float]] = None, top_k_join: Optional[int] = None)

Arguments:

  • join_mode: concatenate to combine documents from multiple retrievers or merge to aggregate scores of individual documents.
  • weights: A node-wise list(length of list must be equal to the number of input nodes) of weights for adjusting document scores when using the merge join_mode. By default, equal weight is given to each retriever score. This param is not compatible with the concatenate join_mode.
  • top_k_join: Limit documents to top_k based on the resulting scores of the join.