Pipelines

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.

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:

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:

p.draw(path="custom_pipe.png")

image

Multiple retrievers

You can now also use multiple Retrievers and join their results:

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

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

    class QueryClassifier():
        outgoing_edges = 2

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

            else:
                return (kwargs, "output_2")

    pipe = Pipeline()
    pipe.add_node(component=QueryClassifier(), name="QueryClassifier", inputs=["Query"])
    pipe.add_node(component=es_retriever, name="ESRetriever", inputs=["QueryClassifier.output_1"])
    pipe.add_node(component=dpr_retriever, name="DPRRetriever", inputs=["QueryClassifier.output_2"])
    pipe.add_node(component=JoinDocuments(join_mode="concatenate"), name="JoinResults",
                  inputs=["ESRetriever", "DPRRetriever"])
    pipe.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 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 ...

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