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Pipeline Module

pipelines.pipelines.pipelines.standard_pipelines

BaseStandardPipeline

Base class for pre-made standard pipelines pipelines. This class does not inherit from Pipeline.

Source code in pipelines/pipelines/pipelines/standard_pipelines.py 
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class BaseStandardPipeline(ABC):
    """
    Base class for pre-made standard pipelines pipelines.
    This class does not inherit from Pipeline.
    """

    pipeline: Pipeline
    metrics_filter: Optional[Dict[str, List[str]]] = None

    def add_node(self, component, name: str, inputs: List[str]):
        """
        Add a new node to the pipeline.

        :param component: The object to be called when the data is passed to the node. It can be a pipelines component
                          (like Retriever, Reader, or Generator) or a user-defined object that implements a run()
                          method to process incoming data from predecessor node.
        :param name: The name for the node. It must not contain any dots.
        :param 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".
        """
        self.pipeline.add_node(component=component, name=name, inputs=inputs)

    def get_node(self, name: str):
        """
        Get a node from the Pipeline.

        :param name: The name of the node.
        """
        component = self.pipeline.get_node(name)
        return component

    def set_node(self, name: str, component):
        """
        Set the component for a node in the Pipeline.

        :param name: The name of the node.
        :param component: The component object to be set at the node.
        """
        self.pipeline.set_node(name, component)

    def draw(self, path: Path = Path("pipeline.png")):
        """
        Create a Graphviz visualization of the pipeline.

        :param path: the path to save the image.
        """
        self.pipeline.draw(path)

    def save_to_yaml(self, path: Path, return_defaults: bool = False):
        """
        Save a YAML configuration for the Pipeline that can be used with `Pipeline.load_from_yaml()`.

        :param path: path of the output YAML file.
        :param return_defaults: whether to output parameters that have the default values.
        """
        return self.pipeline.save_to_yaml(path, return_defaults)

    @classmethod
    def 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    # pipelines Class name for the component
            |      params:
            |        no_ans_boost: -10
            |        model_name_or_path: ernie-gram-zh-finetuned-dureader-robust
            |    - 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: pipelines_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]
            ```

        :param path: path of the YAML file.
        :param pipeline_name: if the YAML contains multiple pipelines, the pipeline_name to load must be set.
        :param 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.
        """
        standard_pipeline_object = cls.__new__(
            cls
        )  # necessary because we can't call __init__ as we can't provide parameters
        standard_pipeline_object.pipeline = Pipeline.load_from_yaml(path, pipeline_name, overwrite_with_env_variables)
        return standard_pipeline_object

    def get_nodes_by_class(self, class_type) -> List[Any]:
        """
        Gets all nodes in the pipeline that are an instance of a certain class (incl. subclasses).
        This is for example helpful if you loaded a pipeline and then want to interact directly with the document store.
        Example:
        ```python
        | from pipelines.document_stores.base import BaseDocumentStore
        | INDEXING_PIPELINE = Pipeline.load_from_yaml(Path(PIPELINE_YAML_PATH), pipeline_name=INDEXING_PIPELINE_NAME)
        | res = INDEXING_PIPELINE.get_nodes_by_class(class_type=BaseDocumentStore)
        ```
        :return: List of components that are an instance of the requested class
        """
        return self.pipeline.get_nodes_by_class(class_type)

    def get_document_store(self) -> Optional[BaseDocumentStore]:
        """
        Return the document store object used in the current pipeline.

        :return: Instance of DocumentStore or None
        """
        return self.pipeline.get_document_store()

    def run_batch(self, queries: List[str], params: Optional[dict] = None, debug: Optional[bool] = None):
        """
        Run a batch of queries through the pipeline.
        :param queries: List of query strings.
        :param params: Parameters for the individual nodes of the pipeline. For instance,
                       `params={"Retriever": {"top_k": 10}, "Reader": {"top_k": 5}}`
        :param debug: Whether the pipeline should instruct nodes to collect debug information
                      about their execution. By default these include the input parameters
                      they received and the output they generated.
                      All debug information can then be found in the dict returned
                      by this method under the key "_debug"
        """
        output = self.pipeline.run_batch(queries=queries, params=params, debug=debug)
        return output

add_node 

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

Add a new node to the pipeline.

Parameters:

Name Type Description Default
component

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

 required
name str

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

 required
inputs List[str]

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".

 required
Source code in pipelines/pipelines/pipelines/standard_pipelines.py 
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def add_node(self, component, name: str, inputs: List[str]):
    """
    Add a new node to the pipeline.

    :param component: The object to be called when the data is passed to the node. It can be a pipelines component
                      (like Retriever, Reader, or Generator) or a user-defined object that implements a run()
                      method to process incoming data from predecessor node.
    :param name: The name for the node. It must not contain any dots.
    :param 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".
    """
    self.pipeline.add_node(component=component, name=name, inputs=inputs)

draw 

draw(path: Path = Path('pipeline.png'))

Create a Graphviz visualization of the pipeline.

Parameters:

Name Type Description Default
path Path

the path to save the image.

 Path('pipeline.png')
Source code in pipelines/pipelines/pipelines/standard_pipelines.py 
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def draw(self, path: Path = Path("pipeline.png")):
    """
    Create a Graphviz visualization of the pipeline.

    :param path: the path to save the image.
    """
    self.pipeline.draw(path)

get_document_store 

get_document_store() -> Optional[BaseDocumentStore]

Return the document store object used in the current pipeline.

Returns:

Type Description
Optional[BaseDocumentStore]

Instance of DocumentStore or None
Source code in pipelines/pipelines/pipelines/standard_pipelines.py 
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def get_document_store(self) -> Optional[BaseDocumentStore]:
    """
    Return the document store object used in the current pipeline.

    :return: Instance of DocumentStore or None
    """
    return self.pipeline.get_document_store()

get_node 

get_node(name: str)

Get a node from the Pipeline.

Parameters:

Name Type Description Default
name str

The name of the node.

 required
Source code in pipelines/pipelines/pipelines/standard_pipelines.py 
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def get_node(self, name: str):
    """
    Get a node from the Pipeline.

    :param name: The name of the node.
    """
    component = self.pipeline.get_node(name)
    return component

get_nodes_by_class 

get_nodes_by_class(class_type) -> List[Any]

Gets all nodes in the pipeline that are an instance of a certain class (incl. subclasses). This is for example helpful if you loaded a pipeline and then want to interact directly with the document store. Example: 

| from pipelines.document_stores.base import BaseDocumentStore
| INDEXING_PIPELINE = Pipeline.load_from_yaml(Path(PIPELINE_YAML_PATH), pipeline_name=INDEXING_PIPELINE_NAME)
| res = INDEXING_PIPELINE.get_nodes_by_class(class_type=BaseDocumentStore)

Returns:

Type Description
List[Any]

List of components that are an instance of the requested class
Source code in pipelines/pipelines/pipelines/standard_pipelines.py 
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def get_nodes_by_class(self, class_type) -> List[Any]:
    """
    Gets all nodes in the pipeline that are an instance of a certain class (incl. subclasses).
    This is for example helpful if you loaded a pipeline and then want to interact directly with the document store.
    Example:
    ```python
    | from pipelines.document_stores.base import BaseDocumentStore
    | INDEXING_PIPELINE = Pipeline.load_from_yaml(Path(PIPELINE_YAML_PATH), pipeline_name=INDEXING_PIPELINE_NAME)
    | res = INDEXING_PIPELINE.get_nodes_by_class(class_type=BaseDocumentStore)
    ```
    :return: List of components that are an instance of the requested class
    """
    return self.pipeline.get_nodes_by_class(class_type)

load_from_yaml classmethod 

load_from_yaml(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    # pipelines Class name for the component
|      params:
|        no_ans_boost: -10
|        model_name_or_path: ernie-gram-zh-finetuned-dureader-robust
|    - 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: pipelines_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]
```

Parameters:

Name Type Description Default
path Path

path of the YAML file.

 required
pipeline_name Optional[str]

if the YAML contains multiple pipelines, the pipeline_name to load must be set.

 None
overwrite_with_env_variables bool

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.

 True
Source code in pipelines/pipelines/pipelines/standard_pipelines.py 
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@classmethod
def 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    # pipelines Class name for the component
        |      params:
        |        no_ans_boost: -10
        |        model_name_or_path: ernie-gram-zh-finetuned-dureader-robust
        |    - 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: pipelines_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]
        ```

    :param path: path of the YAML file.
    :param pipeline_name: if the YAML contains multiple pipelines, the pipeline_name to load must be set.
    :param 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.
    """
    standard_pipeline_object = cls.__new__(
        cls
    )  # necessary because we can't call __init__ as we can't provide parameters
    standard_pipeline_object.pipeline = Pipeline.load_from_yaml(path, pipeline_name, overwrite_with_env_variables)
    return standard_pipeline_object

run_batch 

run_batch(queries: List[str], params: Optional[dict] = None, debug: Optional[bool] = None)

Run a batch of queries through the pipeline.

Parameters:

Name Type Description Default
queries List[str]

List of query strings.

 required
params Optional[dict]

Parameters for the individual nodes of the pipeline. For instance, params={"Retriever": {"top_k": 10}, "Reader": {"top_k": 5}}

 None
debug Optional[bool]

Whether the pipeline should instruct nodes to collect debug information about their execution. By default these include the input parameters they received and the output they generated. All debug information can then be found in the dict returned by this method under the key "_debug"

 None
Source code in pipelines/pipelines/pipelines/standard_pipelines.py 
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def run_batch(self, queries: List[str], params: Optional[dict] = None, debug: Optional[bool] = None):
    """
    Run a batch of queries through the pipeline.
    :param queries: List of query strings.
    :param params: Parameters for the individual nodes of the pipeline. For instance,
                   `params={"Retriever": {"top_k": 10}, "Reader": {"top_k": 5}}`
    :param debug: Whether the pipeline should instruct nodes to collect debug information
                  about their execution. By default these include the input parameters
                  they received and the output they generated.
                  All debug information can then be found in the dict returned
                  by this method under the key "_debug"
    """
    output = self.pipeline.run_batch(queries=queries, params=params, debug=debug)
    return output

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().

Parameters:

Name Type Description Default
path Path

path of the output YAML file.

 required
return_defaults bool

whether to output parameters that have the default values.

 False
Source code in pipelines/pipelines/pipelines/standard_pipelines.py 
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def save_to_yaml(self, path: Path, return_defaults: bool = False):
    """
    Save a YAML configuration for the Pipeline that can be used with `Pipeline.load_from_yaml()`.

    :param path: path of the output YAML file.
    :param return_defaults: whether to output parameters that have the default values.
    """
    return self.pipeline.save_to_yaml(path, return_defaults)

set_node 

set_node(name: str, component)

Set the component for a node in the Pipeline.

Parameters:

Name Type Description Default
name str

The name of the node.

 required
component

The component object to be set at the node.

 required
Source code in pipelines/pipelines/pipelines/standard_pipelines.py 
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def set_node(self, name: str, component):
    """
    Set the component for a node in the Pipeline.

    :param name: The name of the node.
    :param component: The component object to be set at the node.
    """
    self.pipeline.set_node(name, component)

DocPipeline

Pipeline for document intelligence.

Source code in pipelines/pipelines/pipelines/standard_pipelines.py 
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class DocPipeline(BaseStandardPipeline):
    """
    Pipeline for document intelligence.
    """

    def __init__(self, preprocessor: BaseComponent, docreader: BaseComponent):
        """
        :param preprocessor: file/image preprocessor instance
        :param docreader: document model runner instance
        """
        self.pipeline = Pipeline()
        self.pipeline.add_node(component=preprocessor, name="PreProcessor", inputs=["Query"])
        self.pipeline.add_node(component=docreader, name="Reader", inputs=["PreProcessor"])

    def run(self, meta: dict, params: Optional[dict] = None, debug: Optional[bool] = None):
        """
        :param query: the query string.
        :param params: params for the `retriever` and `reader`. For instance, params={"Retriever": {"top_k": 10}}
        :param debug: Whether the pipeline should instruct nodes to collect debug information
              about their execution. By default these include the input parameters
              they received and the output they generated.
              All debug information can then be found in the dict returned
              by this method under the key "_debug"
        """
        output = self.pipeline.run(meta=meta, params=params, debug=debug)
        return output

__init__ 

__init__(preprocessor: BaseComponent, docreader: BaseComponent)

Parameters:

Name Type Description Default
preprocessor BaseComponent

file/image preprocessor instance

 required
docreader BaseComponent

document model runner instance

 required
Source code in pipelines/pipelines/pipelines/standard_pipelines.py 
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def __init__(self, preprocessor: BaseComponent, docreader: BaseComponent):
    """
    :param preprocessor: file/image preprocessor instance
    :param docreader: document model runner instance
    """
    self.pipeline = Pipeline()
    self.pipeline.add_node(component=preprocessor, name="PreProcessor", inputs=["Query"])
    self.pipeline.add_node(component=docreader, name="Reader", inputs=["PreProcessor"])

run 

run(meta: dict, params: Optional[dict] = None, debug: Optional[bool] = None)

Parameters:

Name Type Description Default
query

the query string.

 required
params Optional[dict]

params for the retriever and reader. For instance, params={"Retriever": {"top_k": 10}}

 None
debug Optional[bool]

Whether the pipeline should instruct nodes to collect debug information about their execution. By default these include the input parameters they received and the output they generated. All debug information can then be found in the dict returned by this method under the key "_debug"

 None
Source code in pipelines/pipelines/pipelines/standard_pipelines.py 
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def run(self, meta: dict, params: Optional[dict] = None, debug: Optional[bool] = None):
    """
    :param query: the query string.
    :param params: params for the `retriever` and `reader`. For instance, params={"Retriever": {"top_k": 10}}
    :param debug: Whether the pipeline should instruct nodes to collect debug information
          about their execution. By default these include the input parameters
          they received and the output they generated.
          All debug information can then be found in the dict returned
          by this method under the key "_debug"
    """
    output = self.pipeline.run(meta=meta, params=params, debug=debug)
    return output

ExtractiveQAPipeline

Pipeline for Extractive Question Answering.

Source code in pipelines/pipelines/pipelines/standard_pipelines.py 
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class ExtractiveQAPipeline(BaseStandardPipeline):
    """
    Pipeline for Extractive Question Answering.
    """

    def __init__(self, reader: BaseReader, ranker: BaseRanker, retriever: BaseRetriever):
        """
        :param reader: Reader instance
        :param retriever: Retriever instance
        """
        self.pipeline = Pipeline()
        self.pipeline.add_node(component=retriever, name="Retriever", inputs=["Query"])
        self.pipeline.add_node(component=ranker, name="Ranker", inputs=["Retriever"])
        self.pipeline.add_node(component=reader, name="Reader", inputs=["Ranker"])
        self.metrics_filter = {"Retriever": ["recall_single_hit"]}

    def run(self, query: str, params: Optional[dict] = None, debug: Optional[bool] = None):
        """
        :param query: The search query string.
        :param params: Params for the `retriever` and `reader`. For instance,
                       params={"Retriever": {"top_k": 10}, "Reader": {"top_k": 5}}
        :param debug: Whether the pipeline should instruct nodes to collect debug information
                      about their execution. By default these include the input parameters
                      they received and the output they generated.
                      All debug information can then be found in the dict returned
                      by this method under the key "_debug"
        """
        output = self.pipeline.run(query=query, params=params, debug=debug)
        return output

__init__ 

__init__(reader: BaseReader, ranker: BaseRanker, retriever: BaseRetriever)

Parameters:

Name Type Description Default
reader BaseReader

Reader instance

 required
retriever BaseRetriever

Retriever instance

 required
Source code in pipelines/pipelines/pipelines/standard_pipelines.py 
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def __init__(self, reader: BaseReader, ranker: BaseRanker, retriever: BaseRetriever):
    """
    :param reader: Reader instance
    :param retriever: Retriever instance
    """
    self.pipeline = Pipeline()
    self.pipeline.add_node(component=retriever, name="Retriever", inputs=["Query"])
    self.pipeline.add_node(component=ranker, name="Ranker", inputs=["Retriever"])
    self.pipeline.add_node(component=reader, name="Reader", inputs=["Ranker"])
    self.metrics_filter = {"Retriever": ["recall_single_hit"]}

run 

run(query: str, params: Optional[dict] = None, debug: Optional[bool] = None)

Parameters:

Name Type Description Default
query str

The search query string.

 required
params Optional[dict]

Params for the retriever and reader. For instance, params={"Retriever": {"top_k": 10}, "Reader": {"top_k": 5}}

 None
debug Optional[bool]

Whether the pipeline should instruct nodes to collect debug information about their execution. By default these include the input parameters they received and the output they generated. All debug information can then be found in the dict returned by this method under the key "_debug"

 None
Source code in pipelines/pipelines/pipelines/standard_pipelines.py 
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def run(self, query: str, params: Optional[dict] = None, debug: Optional[bool] = None):
    """
    :param query: The search query string.
    :param params: Params for the `retriever` and `reader`. For instance,
                   params={"Retriever": {"top_k": 10}, "Reader": {"top_k": 5}}
    :param debug: Whether the pipeline should instruct nodes to collect debug information
                  about their execution. By default these include the input parameters
                  they received and the output they generated.
                  All debug information can then be found in the dict returned
                  by this method under the key "_debug"
    """
    output = self.pipeline.run(query=query, params=params, debug=debug)
    return output

QAGenerationPipeline

Pipeline for semantic search.

Source code in pipelines/pipelines/pipelines/standard_pipelines.py 
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class QAGenerationPipeline(BaseStandardPipeline):
    """
    Pipeline for semantic search.
    """

    def __init__(self, answer_extractor: AnswerExtractor, question_generator: QuestionGenerator, qa_filter: QAFilter):
        """
        :param retriever: Retriever instance
        """
        self.pipeline = Pipeline()
        self.pipeline.add_node(component=answer_extractor, name="AnswerExtractor", inputs=["Query"])
        self.pipeline.add_node(component=question_generator, name="QuestionGenerator", inputs=["AnswerExtractor"])
        self.pipeline.add_node(component=qa_filter, name="QAFilter", inputs=["QuestionGenerator"])

    def run(self, meta: List[str], params: Optional[dict] = None, debug: Optional[bool] = None):
        """
        :param query: the query string.
        :param params: params for the `retriever` and `reader`. For instance, params={"Retriever": {"top_k": 10}}
        :param debug: Whether the pipeline should instruct nodes to collect debug information
              about their execution. By default these include the input parameters
              they received and the output they generated.
              All debug information can then be found in the dict returned
              by this method under the key "_debug"
        """
        output = self.pipeline.run(meta=meta, params=params, debug=debug)
        return output

__init__ 

__init__(answer_extractor: AnswerExtractor, question_generator: QuestionGenerator, qa_filter: QAFilter)

Parameters:

Name Type Description Default
retriever

Retriever instance

 required
Source code in pipelines/pipelines/pipelines/standard_pipelines.py 
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def __init__(self, answer_extractor: AnswerExtractor, question_generator: QuestionGenerator, qa_filter: QAFilter):
    """
    :param retriever: Retriever instance
    """
    self.pipeline = Pipeline()
    self.pipeline.add_node(component=answer_extractor, name="AnswerExtractor", inputs=["Query"])
    self.pipeline.add_node(component=question_generator, name="QuestionGenerator", inputs=["AnswerExtractor"])
    self.pipeline.add_node(component=qa_filter, name="QAFilter", inputs=["QuestionGenerator"])

run 

run(meta: List[str], params: Optional[dict] = None, debug: Optional[bool] = None)

Parameters:

Name Type Description Default
query

the query string.

 required
params Optional[dict]

params for the retriever and reader. For instance, params={"Retriever": {"top_k": 10}}

 None
debug Optional[bool]

Whether the pipeline should instruct nodes to collect debug information about their execution. By default these include the input parameters they received and the output they generated. All debug information can then be found in the dict returned by this method under the key "_debug"

 None
Source code in pipelines/pipelines/pipelines/standard_pipelines.py 
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def run(self, meta: List[str], params: Optional[dict] = None, debug: Optional[bool] = None):
    """
    :param query: the query string.
    :param params: params for the `retriever` and `reader`. For instance, params={"Retriever": {"top_k": 10}}
    :param debug: Whether the pipeline should instruct nodes to collect debug information
          about their execution. By default these include the input parameters
          they received and the output they generated.
          All debug information can then be found in the dict returned
          by this method under the key "_debug"
    """
    output = self.pipeline.run(meta=meta, params=params, debug=debug)
    return output

SemanticSearchPipeline

Pipeline for semantic search.

Source code in pipelines/pipelines/pipelines/standard_pipelines.py 
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class SemanticSearchPipeline(BaseStandardPipeline):
    """
    Pipeline for semantic search.
    """

    def __init__(self, retriever: BaseRetriever, ranker: Optional[BaseRanker] = None):
        """
        :param retriever: Retriever instance
        """
        self.pipeline = Pipeline()
        self.pipeline.add_node(component=retriever, name="Retriever", inputs=["Query"])
        if ranker:
            self.pipeline.add_node(component=ranker, name="Ranker", inputs=["Retriever"])

    def run(self, query: str, params: Optional[dict] = None, debug: Optional[bool] = None):
        """
        :param query: the query string.
        :param params: params for the `retriever` and `reader`. For instance, params={"Retriever": {"top_k": 10}}
        :param debug: Whether the pipeline should instruct nodes to collect debug information
              about their execution. By default these include the input parameters
              they received and the output they generated.
              All debug information can then be found in the dict returned
              by this method under the key "_debug"
        """
        output = self.pipeline.run(query=query, params=params, debug=debug)
        return output

__init__ 

__init__(retriever: BaseRetriever, ranker: Optional[BaseRanker] = None)

Parameters:

Name Type Description Default
retriever BaseRetriever

Retriever instance

 required
Source code in pipelines/pipelines/pipelines/standard_pipelines.py 
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def __init__(self, retriever: BaseRetriever, ranker: Optional[BaseRanker] = None):
    """
    :param retriever: Retriever instance
    """
    self.pipeline = Pipeline()
    self.pipeline.add_node(component=retriever, name="Retriever", inputs=["Query"])
    if ranker:
        self.pipeline.add_node(component=ranker, name="Ranker", inputs=["Retriever"])

run 

run(query: str, params: Optional[dict] = None, debug: Optional[bool] = None)

Parameters:

Name Type Description Default
query str

the query string.

 required
params Optional[dict]

params for the retriever and reader. For instance, params={"Retriever": {"top_k": 10}}

 None
debug Optional[bool]

Whether the pipeline should instruct nodes to collect debug information about their execution. By default these include the input parameters they received and the output they generated. All debug information can then be found in the dict returned by this method under the key "_debug"

 None
Source code in pipelines/pipelines/pipelines/standard_pipelines.py 
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def run(self, query: str, params: Optional[dict] = None, debug: Optional[bool] = None):
    """
    :param query: the query string.
    :param params: params for the `retriever` and `reader`. For instance, params={"Retriever": {"top_k": 10}}
    :param debug: Whether the pipeline should instruct nodes to collect debug information
          about their execution. By default these include the input parameters
          they received and the output they generated.
          All debug information can then be found in the dict returned
          by this method under the key "_debug"
    """
    output = self.pipeline.run(query=query, params=params, debug=debug)
    return output

SentaPipeline

Pipeline for document intelligence.

Source code in pipelines/pipelines/pipelines/standard_pipelines.py 
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class SentaPipeline(BaseStandardPipeline):
    """
    Pipeline for document intelligence.
    """

    def __init__(self, preprocessor: BaseComponent, senta: BaseComponent, visualization: BaseComponent):
        """
        :param preprocessor: file preprocessor instance
        :param senta: senta model instance
        """
        self.pipeline = Pipeline()
        self.pipeline.add_node(component=preprocessor, name="PreProcessor", inputs=["File"])
        self.pipeline.add_node(component=senta, name="Senta", inputs=["PreProcessor"])
        self.pipeline.add_node(component=visualization, name="Visualization", inputs=["Senta"])

    def run(self, meta: dict, params: Optional[dict] = None, debug: Optional[bool] = None):
        """
        :param query: the query string.
        :param params: params for the `retriever` and `reader`. For instance, params={"Retriever": {"top_k": 10}}
        :param debug: Whether the pipeline should instruct nodes to collect debug information
              about their execution. By default these include the input parameters
              they received and the output they generated.
              All debug information can then be found in the dict returned
              by this method under the key "_debug"
        """
        output = self.pipeline.run(meta=meta, params=params, debug=debug)
        if "examples" in output:
            output.pop("examples")
        return output

__init__ 

__init__(preprocessor: BaseComponent, senta: BaseComponent, visualization: BaseComponent)

Parameters:

Name Type Description Default
preprocessor BaseComponent

file preprocessor instance

 required
senta BaseComponent

senta model instance

 required
Source code in pipelines/pipelines/pipelines/standard_pipelines.py 
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def __init__(self, preprocessor: BaseComponent, senta: BaseComponent, visualization: BaseComponent):
    """
    :param preprocessor: file preprocessor instance
    :param senta: senta model instance
    """
    self.pipeline = Pipeline()
    self.pipeline.add_node(component=preprocessor, name="PreProcessor", inputs=["File"])
    self.pipeline.add_node(component=senta, name="Senta", inputs=["PreProcessor"])
    self.pipeline.add_node(component=visualization, name="Visualization", inputs=["Senta"])

run 

run(meta: dict, params: Optional[dict] = None, debug: Optional[bool] = None)

Parameters:

Name Type Description Default
query

the query string.

 required
params Optional[dict]

params for the retriever and reader. For instance, params={"Retriever": {"top_k": 10}}

 None
debug Optional[bool]

Whether the pipeline should instruct nodes to collect debug information about their execution. By default these include the input parameters they received and the output they generated. All debug information can then be found in the dict returned by this method under the key "_debug"

 None
Source code in pipelines/pipelines/pipelines/standard_pipelines.py 
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def run(self, meta: dict, params: Optional[dict] = None, debug: Optional[bool] = None):
    """
    :param query: the query string.
    :param params: params for the `retriever` and `reader`. For instance, params={"Retriever": {"top_k": 10}}
    :param debug: Whether the pipeline should instruct nodes to collect debug information
          about their execution. By default these include the input parameters
          they received and the output they generated.
          All debug information can then be found in the dict returned
          by this method under the key "_debug"
    """
    output = self.pipeline.run(meta=meta, params=params, debug=debug)
    if "examples" in output:
        output.pop("examples")
    return output

TextToImagePipeline

A simple pipeline that takes prompt texts as input and generates images.

Source code in pipelines/pipelines/pipelines/standard_pipelines.py 
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class TextToImagePipeline(BaseStandardPipeline):
    """
    A simple pipeline that takes prompt texts as input and generates
    images.
    """

    def __init__(self, text_to_image_generator: ErnieTextToImageGenerator):
        self.pipeline = Pipeline()
        self.pipeline.add_node(component=text_to_image_generator, name="TextToImageGenerator", inputs=["Query"])

    def run(self, query: str, params: Optional[dict] = None, debug: Optional[bool] = None):
        output = self.pipeline.run(query=query, params=params, debug=debug)
        return output

    def run_batch(
        self,
        documents: List[Document],
        params: Optional[dict] = None,
        debug: Optional[bool] = None,
    ):
        output = self.pipeline.run_batch(documents=documents, params=params, debug=debug)
        return output

WebQAPipeline

Pipeline for Generative Question Answering performed based on Documents returned from a web search engine.

Source code in pipelines/pipelines/pipelines/standard_pipelines.py 
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class WebQAPipeline(BaseStandardPipeline):
    """
    Pipeline for Generative Question Answering performed based on Documents returned from a web search engine.
    """

    def __init__(
        self,
        retriever: WebRetriever,
        prompt_node: PromptNode,
        sampler: Optional[BaseRanker] = None,
        shaper: Optional[Shaper] = None,
    ):
        """
        :param retriever: The WebRetriever used for retrieving documents from a web search engine.
        :param prompt_node: The PromptNode used for generating the answer based on retrieved documents.
        :param shaper: The Shaper used for transforming the documents and scores into a format that can be used by the PromptNode. Optional.
        """
        if not shaper:
            shaper = Shaper(func="join_documents_and_scores", inputs={"documents": "documents"}, outputs=["documents"])
        if not sampler and retriever.mode != "snippets":
            # Documents returned by WebRetriever in mode "snippets" already have scores.
            # For other modes, we need to add a sampler if none is provided to compute the scores.
            # TODO(wugaosheng): Add topsampler into WebQAPipeline
            sampler = ErnieRanker("rocketqa-zh-dureader-cross-encoder", top_k=2)

        self.pipeline = Pipeline()
        self.pipeline.add_node(component=retriever, name="Retriever", inputs=["Query"])
        if sampler:
            self.pipeline.add_node(component=sampler, name="Sampler", inputs=["Retriever"])
            self.pipeline.add_node(component=shaper, name="Shaper", inputs=["Sampler"])
        else:
            self.pipeline.add_node(component=shaper, name="Shaper", inputs=["Retriever"])
        self.pipeline.add_node(component=prompt_node, name="PromptNode", inputs=["Shaper"])
        self.metrics_filter = {"Retriever": ["recall_single_hit"]}

    def run(self, query: str, params: Optional[dict] = None, debug: Optional[bool] = None):
        """
        :param query: The search query string.
        :param params: Params for the `Retriever`, `Sampler`, `Shaper`, and ``PromptNode. For instance,
                       params={"Retriever": {"top_k": 3}, "Sampler": {"top_p": 0.8}}. See the API documentation of each node for available parameters and their descriptions.
        :param debug: Whether the pipeline should instruct nodes to collect debug information
                      about their execution. By default, these include the input parameters
                      they received and the output they generated.
                      YOu can then find all debug information in the dict thia method returns
                      under the key "_debug".
        """
        output = self.pipeline.run(query=query, params=params, debug=debug)
        # Extract the answer from the last line of the PromptNode's output
        output["answers"] = [Answer(answer=output["results"][0].split("\n")[-1], type="generative")]
        return output

__init__ 

__init__(retriever: WebRetriever, prompt_node: PromptNode, sampler: Optional[BaseRanker] = None, shaper: Optional[Shaper] = None)

Parameters:

Name Type Description Default
retriever WebRetriever

The WebRetriever used for retrieving documents from a web search engine.

 required
prompt_node PromptNode

The PromptNode used for generating the answer based on retrieved documents.

 required
shaper Optional[Shaper]

The Shaper used for transforming the documents and scores into a format that can be used by the PromptNode. Optional.

 None
Source code in pipelines/pipelines/pipelines/standard_pipelines.py 
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def __init__(
    self,
    retriever: WebRetriever,
    prompt_node: PromptNode,
    sampler: Optional[BaseRanker] = None,
    shaper: Optional[Shaper] = None,
):
    """
    :param retriever: The WebRetriever used for retrieving documents from a web search engine.
    :param prompt_node: The PromptNode used for generating the answer based on retrieved documents.
    :param shaper: The Shaper used for transforming the documents and scores into a format that can be used by the PromptNode. Optional.
    """
    if not shaper:
        shaper = Shaper(func="join_documents_and_scores", inputs={"documents": "documents"}, outputs=["documents"])
    if not sampler and retriever.mode != "snippets":
        # Documents returned by WebRetriever in mode "snippets" already have scores.
        # For other modes, we need to add a sampler if none is provided to compute the scores.
        # TODO(wugaosheng): Add topsampler into WebQAPipeline
        sampler = ErnieRanker("rocketqa-zh-dureader-cross-encoder", top_k=2)

    self.pipeline = Pipeline()
    self.pipeline.add_node(component=retriever, name="Retriever", inputs=["Query"])
    if sampler:
        self.pipeline.add_node(component=sampler, name="Sampler", inputs=["Retriever"])
        self.pipeline.add_node(component=shaper, name="Shaper", inputs=["Sampler"])
    else:
        self.pipeline.add_node(component=shaper, name="Shaper", inputs=["Retriever"])
    self.pipeline.add_node(component=prompt_node, name="PromptNode", inputs=["Shaper"])
    self.metrics_filter = {"Retriever": ["recall_single_hit"]}

run 

run(query: str, params: Optional[dict] = None, debug: Optional[bool] = None)

Parameters:

Name Type Description Default
query str

The search query string.

 required
params Optional[dict]

Params for the Retriever, Sampler, Shaper, and ``PromptNode. For instance, params={"Retriever": {"top_k": 3}, "Sampler": {"top_p": 0.8}}. See the API documentation of each node for available parameters and their descriptions.

 None
debug Optional[bool]

Whether the pipeline should instruct nodes to collect debug information about their execution. By default, these include the input parameters they received and the output they generated. YOu can then find all debug information in the dict thia method returns under the key "_debug".

 None
Source code in pipelines/pipelines/pipelines/standard_pipelines.py 
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def run(self, query: str, params: Optional[dict] = None, debug: Optional[bool] = None):
    """
    :param query: The search query string.
    :param params: Params for the `Retriever`, `Sampler`, `Shaper`, and ``PromptNode. For instance,
                   params={"Retriever": {"top_k": 3}, "Sampler": {"top_p": 0.8}}. See the API documentation of each node for available parameters and their descriptions.
    :param debug: Whether the pipeline should instruct nodes to collect debug information
                  about their execution. By default, these include the input parameters
                  they received and the output they generated.
                  YOu can then find all debug information in the dict thia method returns
                  under the key "_debug".
    """
    output = self.pipeline.run(query=query, params=params, debug=debug)
    # Extract the answer from the last line of the PromptNode's output
    output["answers"] = [Answer(answer=output["results"][0].split("\n")[-1], type="generative")]
    return output