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Use OpenAI Models

Use OpenAI models in your pipelines.

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You can use OpenAI's embedding models and LLMs:

Prerequisites

You need an API key from an active OpenAI account. For details on obtaining it, see Secret keys in OpenAI.

Use OpenAI Models

First, connect deepset Cloud to OpenAI through the Connections page:

  1. Click your initials in the top right corner and select Connections.

  2. Click Connect next to the provider.

  3. Enter your user access token and submit it.

Then, add a component that uses an OpenAI model to your pipeline. Here are the components by the model type they use:

  • Embedding models:
    • OpenAITextEmbedder: Calculates embeddings for text, like query. Often used in query pipelines to embed a query and pass the embedding to an embedding retriever.
    • OpenAIDocumentEmbedder: Calculates embeddings for documents. Often used in indexing pipeline to embed documents and pass them to DocumentWriter.

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Embedding Models in Query and Indexing Pipelines

The embedding model you use to embed documents in your indexing pipeline must be the same as the embedding model you use to embed the query in your query pipeline.


  • LLMs:
    • OpenAIGenerator: Generates text using OpenAI models, often used in RAG pipelines.

Usage Examples

This is an example of how to use OpenAI's embedding models and an LLM in indexing and query pipelines (each in a separate tab):

components:
  ...
    splitter:
      type: haystack.components.preprocessors.document_splitter.DocumentSplitter
      init_parameters:
        split_by: word
        split_length: 250
        split_overlap: 30

    document_embedder:
      type: haystack.components.embedders.openai_document_embedder.OpenAIDocumentEmbedder
      init_parameters:
        model: text-embedding-ada-002 # the model to use

    writer:
      type: haystack.components.writers.document_writer.DocumentWriter
      init_parameters:
        document_store:
          type: haystack_integrations.document_stores.opensearch.document_store.OpenSearchDocumentStore
          init_parameters:
            embedding_dim: 768
            similarity: cosine
        policy: OVERWRITE
        
connections:  # Defines how the components are connected
  ...
  - sender: splitter.documents
    receiver: document_embedder.documents
  - sender: document_embedder.documents
    receiver: writer.documents

components:
  ...
    query_embedder:
      type: haystack.components.embedders.openai_text_embedder.OpenAITextEmbedder
      init_parameters:
        model: "text-embedding-ada-002"
        
    retriever:
      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 
        
    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:

    generator:
      type: haystack.components.generators.openai.OpenAIGenerator
      init_parameters:
        model: "gpt-3.5-turbo" # the model to use
        generation_kwargs: # additional parameters for the model
          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: query_embedder.embedding # AmazonBedrockTextEmbedder sends the embedded query to the retriever
    receiver: retriever.query_embedding 
  - sender: retriever.documents
    receiver: prompt_builder.documents
  - sender: prompt_builder.prompt
    receiver: generator.prompt
  - sender: generator.replies
    receiver: answer_builder.replies
    ...
    
  inputs:
   query:
   ..
   - "query_embedder.text" # TextEmbedder needs query as input and it's not getting it
   - "retriever.query"     # from any component it's connected to, so it needs to receive it from the pipeline.
   - "prompt_builder.question"
   - "answer_builder.query"                       
	
    ...													 
   ...

Updated about 1 month ago