<!---
title: "Pipelines"
metaTitle: "Pipelines"
metaDescription: ""
slug: "/docs/pipelines"
date: "2020-11-05"
id: "pipelinesmd"
--->

# Pipelines


<div class="recommendation">

The new Pipelines class was added in Haystack 0.6.0 to give a more flexible way of defining your processing steps. 
It replaces the Finder class which will be deprecated in the next version.

</div>


### Flexible Pipelines powered by DAGs
In order to build modern search pipelines, you need two things: powerful building blocks and a flexible way to stick them together.
The `Pipeline` class is exactly build for this purpose and enables many search scenarios beyond QA. The core idea: you can build a Directed Acyclic Graph (DAG) where each node is one "building block" (Reader, Retriever, Generator ...). Here's a simple example for a "standard" Open-Domain QA Pipeline: 

```python
p = Pipeline()
p.add_node(component=retriever, name="ESRetriever1", inputs=["Query"])
p.add_node(component=reader, name="QAReader", inputs=["ESRetriever1"])
res = p.run(query="What did Einstein work on?", top_k_retriever=1)

```

You can **draw the DAG** to better inspect what you are building:
```python
p.draw(path="custom_pipe.png")
```
![image](https://user-images.githubusercontent.com/1563902/102451716-54813700-4039-11eb-881e-f3c01b47ca15.png)

### Multiple retrievers
You can now also use multiple Retrievers and join their results: 
```python
p = Pipeline()
p.add_node(component=es_retriever, name="ESRetriever", inputs=["Query"])
p.add_node(component=dpr_retriever, name="DPRRetriever", inputs=["Query"])
p.add_node(component=JoinDocuments(join_mode="concatenate"), name="JoinResults", inputs=["ESRetriever", "DPRRetriever"])
p.add_node(component=reader, name="QAReader", inputs=["JoinResults"])
res = p.run(query="What did Einstein work on?", top_k_retriever=1)
```
![image](https://user-images.githubusercontent.com/1563902/102451782-7bd80400-4039-11eb-9046-01b002a783f8.png)

### Custom nodes
You can easily build your own custom nodes. Just respect the following requirements: 

1. Add a method `run(self, **kwargs)` to your class. `**kwargs` will contain the output from the previous node in your graph.
2. Do whatever you want within `run()` (e.g. reformatting the query)
3. Return a tuple that contains your output data (for the next node) and the name of the outgoing edge `output_dict, "output_1`
4. Add a class attribute `outgoing_edges = 1` that defines the number of output options from your node. You only need a higher number here if you have a decision node (see below).

### Decision nodes
Or you can add decision nodes where only one "branch" is executed afterwards. This allows, for example, to classify an incoming query and depending on the result routing it to different modules: 
![image](https://user-images.githubusercontent.com/1563902/102452199-41229b80-403a-11eb-9365-7038697e7c3e.png)
```python 
    class QueryClassifier():
        outgoing_edges = 2

        def run(self, **kwargs):
            if "?" in kwargs["query"]:
                return (kwargs, "output_1")

            else:
                return (kwargs, "output_2")

    p = Pipeline()
    p.add_node(component=QueryClassifier(), name="QueryClassifier", inputs=["Query"])
    p.add_node(component=es_retriever, name="ESRetriever", inputs=["QueryClassifier.output_1"])
    p.add_node(component=dpr_retriever, name="DPRRetriever", inputs=["QueryClassifier.output_2"])
    p.add_node(component=JoinDocuments(join_mode="concatenate"), name="JoinResults",
                  inputs=["ESRetriever", "DPRRetriever"])
    p.add_node(component=reader, name="QAReader", inputs=["JoinResults"])
    res = p.run(query="What did Einstein work on?", top_k_retriever=1)
```

### Default Pipelines (replacing the "Finder")
Last but not least, we added some "Default Pipelines" that allow you to run standard patterns with very few lines of code.
This is replacing the `Finder` class which is now deprecated.

```
from haystack.pipeline import DocumentSearchPipeline, ExtractiveQAPipeline, Pipeline, JoinDocuments

# Extractive QA
qa_pipe = ExtractiveQAPipeline(reader=reader, retriever=retriever)
res = qa_pipe.run(query="When was Kant born?", top_k_retriever=3, top_k_reader=5)

# Document Search
doc_pipe = DocumentSearchPipeline(retriever=retriever)
res = doc_pipe.run(query="Physics Einstein", top_k_retriever=1)

# Generative QA
doc_pipe = GenerativeQAPipeline(generator=rag_generator, retriever=retriever)
res = doc_pipe.run(query="Physics Einstein", top_k_retriever=1)

# FAQ based QA
doc_pipe = FAQPipeline(retriever=retriever)
res = doc_pipe.run(query="How can I change my address?", top_k_retriever=3)

```    
See also the [Pipelines API documentation](/docs/latest/apipipelinesmd) for more details. 

We plan many more features around the new pipelines incl. parallelized execution, distributed execution, definition via YAML files, dry runs - so stay tuned ...