# Module pipeline
## Pipeline Objects
```python
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
```python
| 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
```python
| get_node(name: str)
```
Get a node from the Pipeline.
**Arguments**:
- `name`: The name of the node.
#### set\_node
```python
| 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
```python
| draw(path: Path = Path("pipeline.png"))
```
Create a Graphviz visualization of the pipeline.
**Arguments**:
- `path`: the path to save the image.
## BaseStandardPipeline Objects
```python
class BaseStandardPipeline()
```
#### add\_node
```python
| 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
```python
| get_node(name: str)
```
Get a node from the Pipeline.
**Arguments**:
- `name`: The name of the node.
#### set\_node
```python
| 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
```python
| draw(path: Path = Path("pipeline.png"))
```
Create a Graphviz visualization of the pipeline.
**Arguments**:
- `path`: the path to save the image.
## ExtractiveQAPipeline Objects
```python
class ExtractiveQAPipeline(BaseStandardPipeline)
```
#### \_\_init\_\_
```python
| __init__(reader: BaseReader, retriever: BaseRetriever)
```
Initialize a Pipeline for Extractive Question Answering.
**Arguments**:
- `reader`: Reader instance
- `retriever`: Retriever instance
## DocumentSearchPipeline Objects
```python
class DocumentSearchPipeline(BaseStandardPipeline)
```
#### \_\_init\_\_
```python
| __init__(retriever: BaseRetriever)
```
Initialize a Pipeline for semantic document search.
**Arguments**:
- `retriever`: Retriever instance
## GenerativeQAPipeline Objects
```python
class GenerativeQAPipeline(BaseStandardPipeline)
```
#### \_\_init\_\_
```python
| __init__(generator: BaseGenerator, retriever: BaseRetriever)
```
Initialize a Pipeline for Generative Question Answering.
**Arguments**:
- `generator`: Generator instance
- `retriever`: Retriever instance
## FAQPipeline Objects
```python
class FAQPipeline(BaseStandardPipeline)
```
#### \_\_init\_\_
```python
| __init__(retriever: BaseRetriever)
```
Initialize a Pipeline for finding similar FAQs using semantic document search.
**Arguments**:
- `retriever`: Retriever instance
## JoinDocuments Objects
```python
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\_\_
```python
| __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.