Create a Pipeline with REST API

Upload a pipeline to deepset AI Platform using an API endpoint. Use this method if you have a pipeline YAML ready.

Prerequisites

To learn about how pipelines and components work in deepset AI Platform, see Pipeline Components and Pipelines.
To use a hosted model, Connect to Model Providers first so that you don't have to pass the API key within the pipeline. For Hugging Face, this is only required for private models. Once deepset is connected to a model provider, just pass the model name in the model parameter of the component that uses it in the pipeline. deepset AI Platform will download and load the model. For more information, see Language Models in deepset AI Platform.
An API Key to connect to deepset AI Platform.
A ready pipeline to upload to deepset AI Platform in the YAML format.

YAML Export
You can first build your pipeline in Pipeline Builder and then export it to YAML from there.

Pipeline YAML Explained

Your pipeline definition file is in the YAML format. Make sure that you follow the same indentation structure as in this example:

components: # This section defines your pipeline components and their settings
  component_1: # Give your component a friendly name, you'll use it in the sections below
    type: # You can find the component type in documentation on a component's page (here maybe a link to components' docs)
    init_parameters: # Customize the component's settings, to use default values, skip this
    
  component_2:
    type: 
    init_parameters:
      parameter1: value1
      parameter2: value2
 
connections:  # Define how the components are connected
              # You must explicitly indicate the intpus and outputs you want to connect
              # Input and output types must be the same to be connected
              # You can check components outputs and inputs in documentation
- sender: component_1.output_name # Here you define the output name this component sends to the receiver component
  receiver: component_2.input_name # Here you define the input name that receives the output of the sender component

inputs:  # List all components that need query and filters as inputs but aren't getting them from any other component connected to them
  query:  # These components will receive the query as input
  - "component_1.question"
  filters:  # These components will receive a potential query filter as input
  - "component_1.filters"
outputs:  # Defines the output of your pipeline, usually the output of the last component
  documents: "component_2.documents"  # The output of the pipeline is the retrieved documents

An example pipeline:

components:
  chat_summary_prompt_builder:
    type: haystack.components.builders.prompt_builder.PromptBuilder
    init_parameters:
      template: |-
        Rewrite the current question so that it is suitable for web search ONLY if chat history is provided.
        If the chat history is empty, DO NOT reformulate the question. 
        Be cautious when reformulating the current question. Substantial changes to the current question that distort the meaning of the current question are undesirable. 
        It is possible that the current question does not need any changes. 
        The chat history can help to incorporate context into the reformulated question. 
        Make sure to incorporate that chat history into the reformulated question ONLY if needed. 
        The overall meaning of the reformulated question must remain the same as the current question. 
        You cannot change or dismiss keywords in the current question. 
        If you do not want to make changes, just output the current question. 
        Chat History: {{question}}
        Reformulated Question:

  chat_summary_llm:
    type: haystack.components.generators.openai.OpenAIGenerator
    init_parameters:
      api_key: {"type": "env_var", "env_vars": ["OPENAI_API_KEY"], "strict": False}
      model: "gpt-3.5-turbo"
      generation_kwargs:
        max_tokens: 650
        temperature: 0.0
        seed: 0

  replies_to_query:
    type: haystack.components.converters.output_adapter.OutputAdapter
    init_parameters:
      template: "{{ replies[0] }}"
      output_type: str

  bm25_retriever: # Selects the most similar documents from the document store
    type: haystack_integrations.components.retrievers.opensearch.bm25_retriever.OpenSearchBM25Retriever
    init_parameters:
      document_store:
        init_parameters:
          use_ssl: True
          verify_certs: False
          hosts:
            - ${OPENSEARCH_HOST}
          http_auth:
            - "${OPENSEARCH_USER}"
            - "${OPENSEARCH_PASSWORD}"
        type: haystack_integrations.document_stores.opensearch.document_store.OpenSearchDocumentStore
      top_k: 20 # The number of results to return

  query_embedder:
    type: haystack.components.embedders.sentence_transformers_text_embedder.SentenceTransformersTextEmbedder
    init_parameters:
      model: "intfloat/e5-base-v2"
      device: null

  embedding_retriever: # Selects the most similar documents from the document store
    type: haystack_integrations.components.retrievers.opensearch.embedding_retriever.OpenSearchEmbeddingRetriever
    init_parameters:
      document_store:
        init_parameters:
          use_ssl: True
          verify_certs: False
          http_auth:
            - "${OPENSEARCH_USER}"
            - "${OPENSEARCH_PASSWORD}"
        type: haystack_integrations.document_stores.opensearch.document_store.OpenSearchDocumentStore
      top_k: 20 # The number of results to return

  document_joiner:
    type: haystack.components.joiners.document_joiner.DocumentJoiner
    init_parameters:
      join_mode: concatenate

  ranker:
    type: haystack.components.rankers.transformers_similarity.TransformersSimilarityRanker
    init_parameters:
      model: "intfloat/simlm-msmarco-reranker"
      top_k: 8
      device: null
      model_kwargs:
        torch_dtype: "torch.float16"

  qa_prompt_builder:
    type: haystack.components.builders.prompt_builder.PromptBuilder
    init_parameters:
      template: |-
        You are a technical expert.
        You answer questions truthfully based on provided documents.
        For each document check whether it is related to the question.
        Only use documents that are related to the question to answer it.
        Ignore documents that are not related to the question.
        If the answer exists in several documents, summarize them.
        Only answer based on the documents provided. Don't make things up.
        If the documents can't answer the question or you are unsure say: 'The answer can't be found in the text'.
        These are the documents:
        {% for document in documents %}
        Document[{{ loop.index }}]:
        {{ document.content }}
        {% endfor %}
        Question: {{question}}
        Answer:

  qa_llm:
    type: haystack.components.generators.openai.OpenAIGenerator
    init_parameters:
      api_key: {"type": "env_var", "env_vars": ["OPENAI_API_KEY"], "strict": False}
      model: "gpt-3.5-turbo"
      generation_kwargs:
        max_tokens: 400
        temperature: 0.0
        seed: 0

  answer_builder:
    init_parameters: {}
    type: haystack.components.builders.answer_builder.AnswerBuilder

connections:  # Defines how the components are connected
- sender: chat_summary_prompt_builder.prompt
  receiver: chat_summary_llm.prompt
- sender: chat_summary_llm.replies
  receiver: replies_to_query.replies
- sender: replies_to_query.output
  receiver: bm25_retriever.query
- sender: replies_to_query.output
  receiver: query_embedder.text
- sender: replies_to_query.output
  receiver: ranker.query
- sender: replies_to_query.output
  receiver: qa_prompt_builder.question
- sender: replies_to_query.output
  receiver: answer_builder.query
- sender: bm25_retriever.documents
  receiver: document_joiner.documents
- sender: query_embedder.embedding
  receiver: embedding_retriever.query_embedding
- sender: embedding_retriever.documents
  receiver: document_joiner.documents
- sender: document_joiner.documents
  receiver: ranker.documents
- sender: ranker.documents
  receiver: qa_prompt_builder.documents
- sender: ranker.documents
  receiver: answer_builder.documents
- sender: qa_prompt_builder.prompt
  receiver: qa_llm.prompt
- sender: qa_llm.replies
  receiver: answer_builder.replies

max_loops_allowed: 100

inputs:  # Define the inputs for your pipeline
  query:  # These components will receive the query as input
  - "chat_summary_prompt_builder.question"

  filters:  # These components will receive a potential query filter as input
  - "bm25_retriever.filters"
  - "embedding_retriever.filters"

outputs:  # Defines the output of your pipeline
  documents: "ranker.documents"  # The output of the pipeline is the retrieved documents
  answers: "answer_builder.answers"  # The output of the pipeline is the retrieved documents

Create a Pipeline

Use the following code as a starting point for your API request:

curl --request POST \
     --url https://api.cloud.deepset.ai/api/v1/workspaces/<YOUR_WORKSPACE>/pipelines \
     --header 'Accept: application/json' \
     --header 'Authorization: Bearer <YOUR_API_KEY>'\
     --data-binary "@path/to/pipeline.yaml"

In line 2, replace <YOUR_WORKSPACE> with the name of the workspace where you want to create the pipeline.
In line 4, replace <YOUR_API_KEY> with the deepset API key.
In line 5, replace "@path/to/pipeline.yaml" with the path to your pipeline YAML file.

See the REST API endpoint documentation.

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Prerequisites​

Create a Pipeline​

Prerequisites

Create a Pipeline