About Pipelines

Pipelines contain the processing stages needed to execute a query and index your files. These stages are pipeline nodes, that are connected in series so that the output of one node is used by the next node in the pipeline.

How Do Pipelines Work?

An icon showing nodes joined together to form a pipeline. Nodes are depicted as squares with rounded edges joined together with a dotted line.

Pipelines define how data flows through its nodes to achieve the best search results. For example, a basic pipeline can be made up of a Retriever and a Reader. When given a query, the retriever goes through all the documents in your deepset Cloud workspace and selects the most relevant ones to the query. Then, the reader uses the documents selected by the retriever and highlights the word, phrase, sentence, or paragraph that answers your query.

Nodes are like building blocks that you can mix and match or replace. They can be connected as a Directed Acyclic Graph (DAG), thus allowing for more complex workflows, such as decision nodes or having the output of multiple nodes combined.

Pipelines run on the files you uploaded to deepset Cloud. You can't pass files or text in a query. When you deploy your pipeline, it indexes the files, turns them into Documents, and stores them in the DocumentStore from where they're retrieved at the time of the search. The exact steps involved in indexing are defined in your indexing pipeline. Documents inherit metadata from files. Multiple documents may come from one file.

Your files are indexed once; they aren't indexed every time a pipeline runs. If you add a new file after you deploy your pipeline, only this file is indexed. The same is true for conversion. If you're using a Converter node in your pipeline, it converts the files only once; it doesn't convert them every time you run your search.

deepset Cloud currently supports question answering, information retrieval, and generative pipelines using large language models.

Indexing and Query Pipelines

To run a search in deepset Cloud, you must define two pipelines in your pipeline file:

  • A query pipeline that contains a recipe for how to execute a query. It runs on the Documents created from files you uploaded to deepset Cloud. These documents are stored in the DocumentStore.
    The input of a query pipeline is always Query and the output is the Answer.
An image depicting the query pipeline. It starts with the query "what is the capital of sudan", then there's an arrow pointing from the query to the pipeline which is a set of connected nodes that communicate with the document store. At the end, there's the answer Khartoum.
  • An indexing pipeline that defines how you want to preprocess your files before running a search on them.
    The input of the indexing pipeline is always File and the output are Documents.
An image of an indexing pipeline. At the beginning, there's an icon representing a file, the file goes into the indexing pipeline which is a set of connected nodes, and then at the end, it returns documents in the document store as the output. The document store icon is a tray of documents.

In deepset Cloud, you define the indexing and query pipelines in one file, which you later deploy to use for search.

Example of an indexing and a query pipeline
version: '1.21.0'
name: "my_sample_pipeline"

components:    # define all the nodes that make up your pipeline:
  - name: DocumentStore
    type: DeepsetCloudDocumentStore
  - name: Retriever
    type: ElasticsearchRetriever
      document_store: DocumentStore    # params can reference other Components defined in the YAML
      top_k: 20
  - name: Reader       # custom-name for the component; helpful for visualization & debugging (coming soon)
    type: FARMReader    # Haystack class name for the Component
      model_name_or_path: deepset/roberta-base-squad2-distilled
      context_window_size: 500
      return_no_answer: true
  - name: TextFileConverter
    type: TextConverter
  - name: Preprocessor
    type: PreProcessor
      split_by: word
      split_length: 250
      language: en # Specify the language of your documents

# this is the query pipeline:
  - name: query    # a sample extractive-qa Pipeline
    type: Query
      - name: Retriever
        inputs: [Query]
      - name: Reader
        inputs: [Retriever]
# this is the indexing pipeline:
  - name: indexing
    type: Indexing
      - name: TextFileConverter
        inputs: [File]
      - name: Preprocessor
        inputs: [ TextFileConverter]
      - name: Retriever
        inputs: [Preprocessor]
      - name: DocumentStore #an indexing pipeline must always use a DocumentStore
        inputs: [Retriever]

For more pipeline examples, see Pipeline Examples.

The Pipelines Page

All the pipelines created by your organization are listed on the Pipelines page. The pipelines listed under Deployed are the ones that you can run your search with. The pipelines under In Development are drafts you must deploy before you can use them for your search.

Clicking a pipeline opens Pipeline Details, where you can check all the information about your pipeline, including pipeline logs.

Pipeline Status

When you deploy a pipeline, it changes its status as follows:

  • Not indexed: The pipeline is being deployed, but the files have not yet been indexed
  • Indexing: Your files are being indexed. You can see how many files have already been indexed if you hover your mouse over the Indexing label.
  • Indexed: Your pipeline is deployed, all the files are indexed, and you can use your pipeline for search.
  • Partially indexed: At least one of the files wasn't indexed. This may be an NLP-related problem, a problem with your file, or a Node in the pipeline. You can still run a search if at least some files were indexed.
  • Failed to deploy: It's a fatal state. Your pipeline was not deployed, and your files are not indexed. For ideas on how to fix it, see Troubleshoot Pipeline Deployment.