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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)

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.

BaseStandardPipeline Objects

class BaseStandardPipeline()

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: int = 10, generate_single_summary: bool = False, return_in_answer_format=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

JoinDocuments Objects

class JoinDocuments()

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.