AzureOpenAITextEmbedder

Embed strings, like user queries, using OpenAI models deployed on Azure.

Basic Information

• Type: haystack.components.embedders.azure_text_embedder.AzureOpenAITextEmbedder
• Components it can connect with:
  • Query: AzureOpenAITextEmbedder can receive a string to embed from the Query input.
  • Retrievers: AzureOpenAITextEmbedder can send the embedded text to Retrievers that use the embeddings to retrieve documents from a document store.

Inputs

Parameter	Type	Default	Description
text	str		Text to embed.

Outputs

Parameter	Type	Default	Description
embedding	List[float]		A list of floats representing the embedding of the input text.
meta	Dict[str, Any]		Information about the usage of the model, including model name and token usage.

Overview

You can use AzureOpenAITextEmbedder in your query pipelines to turn user queries into vector representations (embreddings). You need this to perform semantic-based retrieval, where you can search for documents that are similar to the user query. The retriever then compares the documents and query embeddings to find the most relevant documents.

For a list of supported models, see Azure documentation.

Embedding Models in Query Pipelines and Indexes

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.

This means the embedders for your indexing and query pipelines must match. For example, if you use CohereDocumentEmbedder to embed your documents, you should use CohereTextEmbedder with the same model to embed your queries.

Authentication

You need an Azure OpenAI API key to use this component. Connect deepset AI Platform to your Azure OpenAI account. For more information, see Using Azure OpenAI Models.

Usage Example

This is an example of a query pipeline that uses AzureOpenAITextEmbedder to embed the user query and send it to the retriever.

components:
  ...
    query_embedder:
      type: haystack.components.embedders.azure_text_embedder.AzureOpenAITextEmbedder
      init_parameters:
        azure_endpoint: "https://your-company.azure.openai.com/"
        azure_deployment: "text-embedding-ada-002" #this is the name of the model you want to use
        
    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.azure.AzureOpenAIGenerator
      init_parameters:
         generation_kwargs:
         temperature: 0.0
         azure_deployment: gpt-35-turbo #this is the model you want to use
       
    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"                       
	
    ...													 
   ...

Parameters

Init Parameters

These are the parameters you can configure in Pipeline Builder:

Parameter	Type	Default	Description
azure_endpoint	Optional[str]	None	The endpoint of the model deployed on Azure.
api_version	Optional[str]	2023-05-15	The version of the API to use.
azure_deployment	str	text-embedding-ada-002	The name of the model deployed on Azure. The default model is text-embedding-ada-002.
dimensions	Optional[int]	None	The number of dimensions the resulting output embeddings should have. Only supported in text-embedding-3 and later models.
api_key	Optional[Secret]	Secret.from_env_var('AZURE_OPENAI_API_KEY', strict=False)	The Azure OpenAI API key. You can set it with an environment variable AZURE_OPENAI_API_KEY, or pass with this parameter during initialization.
azure_ad_token	Optional[Secret]	Secret.from_env_var('AZURE_OPENAI_AD_TOKEN', strict=False)	Microsoft Entra ID token, see Microsoft's Entra ID documentation for more information. You can set it with an environment variable AZURE_OPENAI_AD_TOKEN, or pass with this parameter during initialization. Previously called Azure Active Directory.
organization	Optional[str]	None	Your organization ID. See OpenAI's Setting Up Your Organization for more information.
timeout	Optional[float]	None	The timeout for AzureOpenAI client calls, in seconds. If not set, defaults to either the OPENAI_TIMEOUT environment variable, or 30 seconds.
max_retries	Optional[int]	None	Maximum number of retries to contact AzureOpenAI after an internal error. If not set, defaults to either the OPENAI_MAX_RETRIES environment variable, or to 5 retries.
prefix	str		A string to add at the beginning of each text.
suffix	str		A string to add at the end of each text.
default_headers	Optional[Dict[str, str]]	None	Default headers to send to the AzureOpenAI client.
azure_ad_token_provider	Optional[AzureADTokenProvider]	None	A function that returns an Azure Active Directory token, will be invoked on every request.
http_client_kwargs	Optional[Dict[str, Any]]	None	A dictionary of keyword arguments to configure a custom httpx.Clientor httpx.AsyncClient. For more information, see the HTTPX documentation.

Run Method Parameters

These are the parameters you can configure for the component's run() method. This means you can pass these parameters at query time through the API, in Playground, or when running a job. For details, see Modify Pipeline Parameters at Query Time.

Parameter	Type	Default	Description
text	str		Text to embed.