# Pipelines Tutorial [](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/11_Pipelines.ipynb) In this tutorial, you will learn how the `Pipeline` class acts as a connector between all the different building blocks that are found in FARM. Whether you are using a Reader, Generator, Summarizer or Retriever (or 2), the `Pipeline` class will help you build a Directed Acyclic Graph (DAG) that determines how to route the output of one component into the input of another. ## Setting Up the Environment Let's start by ensuring we have a GPU running to ensure decent speed in this tutorial. In Google colab, you can change to a GPU runtime in the menu: - **Runtime -> Change Runtime type -> Hardware accelerator -> GPU** ```python # Make sure you have a GPU running !nvidia-smi ``` These lines are to install Haystack through pip ```python # Install the latest release of Haystack in your own environment #! pip install farm-haystack # Install the latest main of Haystack !pip install --upgrade pip !pip install git+https://github.com/deepset-ai/haystack.git#egg=farm-haystack[colab] # Install pygraphviz !apt install libgraphviz-dev !pip install pygraphviz ``` If running from Colab or a no Docker environment, you will want to start Elasticsearch from source ```python # In Colab / No Docker environments: Start Elasticsearch from source ! wget https://artifacts.elastic.co/downloads/elasticsearch/elasticsearch-7.9.2-linux-x86_64.tar.gz -q ! tar -xzf elasticsearch-7.9.2-linux-x86_64.tar.gz ! chown -R daemon:daemon elasticsearch-7.9.2 import os from subprocess import Popen, PIPE, STDOUT es_server = Popen( ["elasticsearch-7.9.2/bin/elasticsearch"], stdout=PIPE, stderr=STDOUT, preexec_fn=lambda: os.setuid(1) # as daemon ) # wait until ES has started ! sleep 30 ``` ## Initialization Then let's fetch some data (in this case, pages from the Game of Thrones wiki) and prepare it so that it can be used indexed into our `DocumentStore` ```python from haystack.utils import ( print_answers, print_documents, fetch_archive_from_http, convert_files_to_dicts, clean_wiki_text, ) # Download and prepare data - 517 Wikipedia articles for Game of Thrones doc_dir = "data/tutorial11" s3_url = "https://s3.eu-central-1.amazonaws.com/deepset.ai-farm-qa/datasets/documents/wiki_gameofthrones_txt11.zip" fetch_archive_from_http(url=s3_url, output_dir=doc_dir) # convert files to dicts containing documents that can be indexed to our datastore got_dicts = convert_files_to_dicts(dir_path=doc_dir, clean_func=clean_wiki_text, split_paragraphs=True) ``` Here we initialize the core components that we will be gluing together using the `Pipeline` class. We have a `DocumentStore`, an `ElasticsearchRetriever` and a `FARMReader`. These can be combined to create a classic Retriever-Reader pipeline that is designed to perform Open Domain Question Answering. ```python from haystack import Pipeline from haystack.utils import launch_es from haystack.document_stores import ElasticsearchDocumentStore from haystack.nodes import ElasticsearchRetriever, EmbeddingRetriever, FARMReader # Initialize DocumentStore and index documents launch_es() document_store = ElasticsearchDocumentStore() document_store.delete_documents() document_store.write_documents(got_dicts) # Initialize Sparse retriever es_retriever = ElasticsearchRetriever(document_store=document_store) # Initialize dense retriever embedding_retriever = EmbeddingRetriever( document_store, model_format="sentence_transformers", embedding_model="sentence-transformers/multi-qa-mpnet-base-dot-v1", ) document_store.update_embeddings(embedding_retriever, update_existing_embeddings=False) # Initialize reader reader = FARMReader(model_name_or_path="deepset/roberta-base-squad2") ``` ## Prebuilt Pipelines Haystack features many prebuilt pipelines that cover common tasks. Here we have an `ExtractiveQAPipeline` (the successor to the now deprecated `Finder` class). ```python from haystack.pipelines import ExtractiveQAPipeline # Prebuilt pipeline p_extractive_premade = ExtractiveQAPipeline(reader=reader, retriever=es_retriever) res = p_extractive_premade.run( query="Who is the father of Arya Stark?", params={"Retriever": {"top_k": 10}, "Reader": {"top_k": 5}} ) print_answers(res, details="minimum") ``` If you want to just do the retrieval step, you can use a `DocumentSearchPipeline` ```python from haystack.pipelines import DocumentSearchPipeline p_retrieval = DocumentSearchPipeline(es_retriever) res = p_retrieval.run(query="Who is the father of Arya Stark?", params={"Retriever": {"top_k": 10}}) print_documents(res, max_text_len=200) ``` Or if you want to use a `Generator` instead of a `Reader`, you can initialize a `GenerativeQAPipeline` like this: ```python from haystack.pipelines import GenerativeQAPipeline, FAQPipeline from haystack.nodes import RAGenerator # We set this to True so that the document store returns document embeddings with each document # This is needed by the Generator document_store.return_embedding = True # Initialize generator rag_generator = RAGenerator() # Generative QA p_generator = GenerativeQAPipeline(generator=rag_generator, retriever=embedding_retriever) res = p_generator.run(query="Who is the father of Arya Stark?", params={"Retriever": {"top_k": 10}}) print_answers(res, details="minimum") # We are setting this to False so that in later pipelines, # we get a cleaner printout document_store.return_embedding = False ``` Haystack features prebuilt pipelines to do: - just document search (DocumentSearchPipeline), - document search with summarization (SearchSummarizationPipeline) - generative QA (GenerativeQAPipeline) - FAQ style QA (FAQPipeline) - translated search (TranslationWrapperPipeline) To find out more about these pipelines, have a look at our [documentation](https://haystack.deepset.ai/docs/latest/pipelinesmd) With any Pipeline, whether prebuilt or custom constructed, you can save a diagram showing how all the components are connected.  ```python p_extractive_premade.draw("pipeline_extractive_premade.png") p_retrieval.draw("pipeline_retrieval.png") p_generator.draw("pipeline_generator.png") ``` ## Custom Pipelines Now we are going to rebuild the `ExtractiveQAPipelines` using the generic Pipeline class. We do this by adding the building blocks that we initialized as nodes in the graph. ```python # Custom built extractive QA pipeline p_extractive = Pipeline() p_extractive.add_node(component=es_retriever, name="Retriever", inputs=["Query"]) p_extractive.add_node(component=reader, name="Reader", inputs=["Retriever"]) # Now we can run it res = p_extractive.run( query="Who is the father of Arya Stark?", params={"Retriever": {"top_k": 10}, "Reader": {"top_k": 5}} ) print_answers(res, details="minimum") p_extractive.draw("pipeline_extractive.png") ``` Pipelines offer a very simple way to ensemble together different components. In this example, we are going to combine the power of an `EmbeddingRetriever` with the keyword based `ElasticsearchRetriever`. See our [documentation](https://haystack.deepset.ai/docs/latest/retrievermd) to understand why we might want to combine a dense and sparse retriever.  Here we use a `JoinDocuments` node so that the predictions from each retriever can be merged together. ```python from haystack.pipelines import JoinDocuments # Create ensembled pipeline p_ensemble = Pipeline() p_ensemble.add_node(component=es_retriever, name="ESRetriever", inputs=["Query"]) p_ensemble.add_node(component=embedding_retriever, name="EmbeddingRetriever", inputs=["Query"]) p_ensemble.add_node( component=JoinDocuments(join_mode="concatenate"), name="JoinResults", inputs=["ESRetriever", "EmbeddingRetriever"] ) p_ensemble.add_node(component=reader, name="Reader", inputs=["JoinResults"]) p_ensemble.draw("pipeline_ensemble.png") # Run pipeline res = p_ensemble.run( query="Who is the father of Arya Stark?", params={"EmbeddingRetriever": {"top_k": 5}, "ESRetriever": {"top_k": 5}} ) print_answers(res, details="minimum") ``` ## Custom Nodes Nodes are relatively simple objects and we encourage our users to design their own if they don't see on that fits their use case The only requirements are: - Create a class that inherits `BaseComponent`. - Add a method run() to your class. Add the mandatory and optional arguments it needs to process. These arguments must be passed as input to the pipeline, inside `params`, or output by preceding nodes. - Add processing logic inside the run() (e.g. reformatting the query). - Return a tuple that contains your output data (for the next node) and the name of the outgoing edge (by default "output_1" for nodes that have one output) - 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). Here we have a template for a Node: ```python from haystack import BaseComponent from typing import Optional class CustomNode(BaseComponent): outgoing_edges = 1 def run(self, query: str, my_optional_param: Optional[int]): # process the inputs output = {"my_output": ...} return output, "output_1" ``` ## Decision Nodes Decision Nodes help you route your data so that only certain branches of your `Pipeline` are run. One popular use case for such query classifiers is routing keyword queries to Elasticsearch and questions to EmbeddingRetriever + Reader. With this approach you keep optimal speed and simplicity for keywords while going deep with transformers when it's most helpful.  Though this looks very similar to the ensembled pipeline shown above, the key difference is that only one of the retrievers is run for each request. By contrast both retrievers are always run in the ensembled approach. Below, we define a very naive `QueryClassifier` and show how to use it: ```python class CustomQueryClassifier(BaseComponent): outgoing_edges = 2 def run(self, query: str): if "?" in query: return {}, "output_2" else: return {}, "output_1" # Here we build the pipeline p_classifier = Pipeline() p_classifier.add_node(component=CustomQueryClassifier(), name="QueryClassifier", inputs=["Query"]) p_classifier.add_node(component=es_retriever, name="ESRetriever", inputs=["QueryClassifier.output_1"]) p_classifier.add_node(component=embedding_retriever, name="EmbeddingRetriever", inputs=["QueryClassifier.output_2"]) p_classifier.add_node(component=reader, name="QAReader", inputs=["ESRetriever", "EmbeddingRetriever"]) p_classifier.draw("pipeline_classifier.png") # Run only the dense retriever on the full sentence query res_1 = p_classifier.run(query="Who is the father of Arya Stark?") print("Embedding Retriever Results" + "\n" + "=" * 15) print_answers(res_1) # Run only the sparse retriever on a keyword based query res_2 = p_classifier.run(query="Arya Stark father") print("ES Results" + "\n" + "=" * 15) print_answers(res_2) ``` ## Evaluation Nodes We have also designed a set of nodes that can be used to evaluate the performance of a system. Have a look at our [tutorial](https://haystack.deepset.ai/docs/latest/tutorial5md) to get hands on with the code and learn more about Evaluation Nodes! ## Debugging Pipelines You can print out debug information from nodes in your pipelines in a few different ways. ```python # 1) You can set the `debug` attribute of a given node. es_retriever.debug = True # 2) You can provide `debug` as a parameter when running your pipeline result = p_classifier.run(query="Who is the father of Arya Stark?", params={"ESRetriever": {"debug": True}}) # 3) You can provide the `debug` paramter to all nodes in your pipeline result = p_classifier.run(query="Who is the father of Arya Stark?", params={"debug": True}) result["_debug"] ``` ## YAML Configs A full `Pipeline` can be defined in a YAML file and simply loaded. Having your pipeline available in a YAML is particularly useful when you move between experimentation and production environments. Just export the YAML from your notebook / IDE and import it into your production environment. It also helps with version control of pipelines, allows you to share your pipeline easily with colleagues, and simplifies the configuration of pipeline parameters in production. It consists of two main sections: you define all objects (e.g. a reader) in components and then stick them together to a pipeline in pipelines. You can also set one component to be multiple nodes of a pipeline or to be a node across multiple pipelines. It will be loaded just once in memory and therefore doesn't hurt your resources more than actually needed. The contents of a YAML file should look something like this: ```yaml version: '0.7' components: # define all the building-blocks for Pipeline - name: MyReader # custom-name for the component; helpful for visualization & debugging type: FARMReader # Haystack Class name for the component params: no_ans_boost: -10 model_name_or_path: deepset/roberta-base-squad2 - 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: haystack_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] ``` To load, simply call: ``` python pipeline.load_from_yaml(Path("sample.yaml")) ``` ## Conclusion The possibilities are endless with the `Pipeline` class and we hope that this tutorial will inspire you to build custom pipeplines that really work for your use case! ## About us This [Haystack](https://github.com/deepset-ai/haystack/) notebook was made with love by [deepset](https://deepset.ai/) in Berlin, Germany We bring NLP to the industry via open source! Our focus: Industry specific language models & large scale QA systems. Some of our other work: - [German BERT](https://deepset.ai/german-bert) - [GermanQuAD and GermanDPR](https://deepset.ai/germanquad) - [FARM](https://github.com/deepset-ai/FARM) Get in touch: [Twitter](https://twitter.com/deepset_ai) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Slack](https://haystack.deepset.ai/community/join) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Website](https://deepset.ai) By the way: [we're hiring!](https://www.deepset.ai/jobs)