haystack/docs/v0.8.0/_src/api/api/pipelines.md

Module pipeline

Pipeline Objects

class Pipeline(ABC)

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: '0.7'
|
|    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.

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.

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.