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375 lines
11 KiB
Markdown
375 lines
11 KiB
Markdown
<a name="pipeline"></a>
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# Module pipeline
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<a name="pipeline.Pipeline"></a>
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## Pipeline Objects
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```python
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class Pipeline(ABC)
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```
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Pipeline brings together building blocks to build a complex search pipeline with Haystack & user-defined components.
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Under-the-hood, a pipeline is represented as a directed acyclic graph of component nodes. It enables custom query
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flows with options to branch queries(eg, extractive qa vs keyword match query), merge candidate documents for a
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Reader from multiple Retrievers, or re-ranking of candidate documents.
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<a name="pipeline.Pipeline.add_node"></a>
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#### add\_node
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```python
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| add_node(component, name: str, inputs: List[str])
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```
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Add a new node to the pipeline.
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**Arguments**:
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- `component`: The object to be called when the data is passed to the node. It can be a Haystack component
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(like Retriever, Reader, or Generator) or a user-defined object that implements a run()
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method to process incoming data from predecessor node.
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- `name`: The name for the node. It must not contain any dots.
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- `inputs`: A list of inputs to the node. If the predecessor node has a single outgoing edge, just the name
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of node is sufficient. For instance, a 'ElasticsearchRetriever' node would always output a single
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edge with a list of documents. It can be represented as ["ElasticsearchRetriever"].
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In cases when the predecessor node has multiple outputs, e.g., a "QueryClassifier", the output
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must be specified explicitly as "QueryClassifier.output_2".
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<a name="pipeline.Pipeline.get_node"></a>
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#### get\_node
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```python
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| get_node(name: str) -> Optional[BaseComponent]
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```
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Get a node from the Pipeline.
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**Arguments**:
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- `name`: The name of the node.
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<a name="pipeline.Pipeline.set_node"></a>
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#### set\_node
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```python
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| set_node(name: str, component)
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```
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Set the component for a node in the Pipeline.
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**Arguments**:
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- `name`: The name of the node.
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- `component`: The component object to be set at the node.
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<a name="pipeline.Pipeline.draw"></a>
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#### draw
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```python
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| draw(path: Path = Path("pipeline.png"))
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```
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Create a Graphviz visualization of the pipeline.
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**Arguments**:
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- `path`: the path to save the image.
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<a name="pipeline.Pipeline.load_from_yaml"></a>
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#### load\_from\_yaml
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```python
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| @classmethod
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| load_from_yaml(cls, path: Path, pipeline_name: Optional[str] = None, overwrite_with_env_variables: bool = True)
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```
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Load Pipeline from a YAML file defining the individual components and how they're tied together to form
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a Pipeline. A single YAML can declare multiple Pipelines, in which case an explicit `pipeline_name` must
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be passed.
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Here's a sample configuration:
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```yaml
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| version: '0.7'
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| components: # define all the building-blocks for Pipeline
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| - name: MyReader # custom-name for the component; helpful for visualization & debugging
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| type: FARMReader # Haystack Class name for the component
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| params:
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| no_ans_boost: -10
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| model_name_or_path: deepset/roberta-base-squad2
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| - name: MyESRetriever
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| type: ElasticsearchRetriever
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| params:
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| document_store: MyDocumentStore # params can reference other components defined in the YAML
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| custom_query: null
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| - name: MyDocumentStore
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| type: ElasticsearchDocumentStore
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| params:
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| index: haystack_test
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| pipelines: # multiple Pipelines can be defined using the components from above
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| - name: my_query_pipeline # a simple extractive-qa Pipeline
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| nodes:
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| - name: MyESRetriever
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| inputs: [Query]
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| - name: MyReader
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| inputs: [MyESRetriever]
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```
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**Arguments**:
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- `path`: path of the YAML file.
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- `pipeline_name`: if the YAML contains multiple pipelines, the pipeline_name to load must be set.
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- `overwrite_with_env_variables`: Overwrite the YAML configuration with environment variables. For example,
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to change index name param for an ElasticsearchDocumentStore, an env
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variable 'MYDOCSTORE_PARAMS_INDEX=documents-2021' can be set. Note that an
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`_` sign must be used to specify nested hierarchical properties.
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<a name="pipeline.BaseStandardPipeline"></a>
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## BaseStandardPipeline Objects
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```python
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class BaseStandardPipeline(ABC)
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```
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<a name="pipeline.BaseStandardPipeline.add_node"></a>
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#### add\_node
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```python
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| add_node(component, name: str, inputs: List[str])
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```
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Add a new node to the pipeline.
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**Arguments**:
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- `component`: The object to be called when the data is passed to the node. It can be a Haystack component
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(like Retriever, Reader, or Generator) or a user-defined object that implements a run()
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method to process incoming data from predecessor node.
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- `name`: The name for the node. It must not contain any dots.
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- `inputs`: A list of inputs to the node. If the predecessor node has a single outgoing edge, just the name
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of node is sufficient. For instance, a 'ElasticsearchRetriever' node would always output a single
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edge with a list of documents. It can be represented as ["ElasticsearchRetriever"].
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In cases when the predecessor node has multiple outputs, e.g., a "QueryClassifier", the output
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must be specified explicitly as "QueryClassifier.output_2".
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<a name="pipeline.BaseStandardPipeline.get_node"></a>
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#### get\_node
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```python
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| get_node(name: str)
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```
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Get a node from the Pipeline.
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**Arguments**:
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- `name`: The name of the node.
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<a name="pipeline.BaseStandardPipeline.set_node"></a>
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#### set\_node
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```python
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| set_node(name: str, component)
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```
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Set the component for a node in the Pipeline.
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**Arguments**:
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- `name`: The name of the node.
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- `component`: The component object to be set at the node.
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<a name="pipeline.BaseStandardPipeline.draw"></a>
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#### draw
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```python
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| draw(path: Path = Path("pipeline.png"))
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```
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Create a Graphviz visualization of the pipeline.
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**Arguments**:
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- `path`: the path to save the image.
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<a name="pipeline.ExtractiveQAPipeline"></a>
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## ExtractiveQAPipeline Objects
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```python
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class ExtractiveQAPipeline(BaseStandardPipeline)
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```
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<a name="pipeline.ExtractiveQAPipeline.__init__"></a>
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#### \_\_init\_\_
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```python
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| __init__(reader: BaseReader, retriever: BaseRetriever)
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```
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Initialize a Pipeline for Extractive Question Answering.
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**Arguments**:
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- `reader`: Reader instance
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- `retriever`: Retriever instance
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<a name="pipeline.DocumentSearchPipeline"></a>
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## DocumentSearchPipeline Objects
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```python
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class DocumentSearchPipeline(BaseStandardPipeline)
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```
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<a name="pipeline.DocumentSearchPipeline.__init__"></a>
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#### \_\_init\_\_
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```python
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| __init__(retriever: BaseRetriever)
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```
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Initialize a Pipeline for semantic document search.
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**Arguments**:
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- `retriever`: Retriever instance
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<a name="pipeline.GenerativeQAPipeline"></a>
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## GenerativeQAPipeline Objects
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```python
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class GenerativeQAPipeline(BaseStandardPipeline)
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```
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<a name="pipeline.GenerativeQAPipeline.__init__"></a>
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#### \_\_init\_\_
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```python
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| __init__(generator: BaseGenerator, retriever: BaseRetriever)
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```
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Initialize a Pipeline for Generative Question Answering.
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**Arguments**:
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- `generator`: Generator instance
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- `retriever`: Retriever instance
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<a name="pipeline.SearchSummarizationPipeline"></a>
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## SearchSummarizationPipeline Objects
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```python
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class SearchSummarizationPipeline(BaseStandardPipeline)
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```
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<a name="pipeline.SearchSummarizationPipeline.__init__"></a>
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#### \_\_init\_\_
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```python
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| __init__(summarizer: BaseSummarizer, retriever: BaseRetriever)
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```
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Initialize a Pipeline that retrieves documents for a query and then summarizes those documents.
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**Arguments**:
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- `summarizer`: Summarizer instance
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- `retriever`: Retriever instance
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<a name="pipeline.SearchSummarizationPipeline.run"></a>
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#### run
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```python
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| 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)
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```
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**Arguments**:
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- `query`: Your search query
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- `filters`:
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- `top_k_retriever`: Number of top docs the retriever should pass to the summarizer.
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The higher this value, the slower your pipeline.
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- `generate_single_summary`: Whether to generate single summary from all retrieved docs (True) or one per doc (False).
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- `return_in_answer_format`: Whether the results should be returned as documents (False) or in the answer format used in other QA pipelines (True).
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With the latter, you can use this pipeline as a "drop-in replacement" for other QA pipelines.
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<a name="pipeline.FAQPipeline"></a>
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## FAQPipeline Objects
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```python
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class FAQPipeline(BaseStandardPipeline)
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```
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<a name="pipeline.FAQPipeline.__init__"></a>
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#### \_\_init\_\_
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```python
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| __init__(retriever: BaseRetriever)
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```
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Initialize a Pipeline for finding similar FAQs using semantic document search.
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**Arguments**:
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- `retriever`: Retriever instance
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<a name="pipeline.TranslationWrapperPipeline"></a>
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## TranslationWrapperPipeline Objects
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```python
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class TranslationWrapperPipeline(BaseStandardPipeline)
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```
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Takes an existing search pipeline and adds one "input translation node" after the Query and one
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"output translation" node just before returning the results
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<a name="pipeline.TranslationWrapperPipeline.__init__"></a>
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#### \_\_init\_\_
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```python
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| __init__(input_translator: BaseTranslator, output_translator: BaseTranslator, pipeline: BaseStandardPipeline)
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```
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Wrap a given `pipeline` with the `input_translator` and `output_translator`.
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**Arguments**:
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- `input_translator`: A Translator node that shall translate the input query from language A to B
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- `output_translator`: A Translator node that shall translate the pipeline results from language B to A
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- `pipeline`: The pipeline object (e.g. ExtractiveQAPipeline) you want to "wrap".
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Note that pipelines with split or merge nodes are currently not supported.
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<a name="pipeline.JoinDocuments"></a>
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## JoinDocuments Objects
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```python
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class JoinDocuments(BaseComponent)
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```
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A node to join documents outputted by multiple retriever nodes.
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The node allows multiple join modes:
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* concatenate: combine the documents from multiple nodes. Any duplicate documents are discarded.
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* merge: merge scores of documents from multiple nodes. Optionally, each input score can be given a different
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`weight` & a `top_k` limit can be set. This mode can also be used for "reranking" retrieved documents.
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<a name="pipeline.JoinDocuments.__init__"></a>
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#### \_\_init\_\_
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```python
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| __init__(join_mode: str = "concatenate", weights: Optional[List[float]] = None, top_k_join: Optional[int] = None)
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```
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**Arguments**:
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- `join_mode`: `concatenate` to combine documents from multiple retrievers or `merge` to aggregate scores of
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individual documents.
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- `weights`: A node-wise list(length of list must be equal to the number of input nodes) of weights for
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adjusting document scores when using the `merge` join_mode. By default, equal weight is given
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to each retriever score. This param is not compatible with the `concatenate` join_mode.
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- `top_k_join`: Limit documents to top_k based on the resulting scores of the join.
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