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When you perform embedding retrieval, you use this component to transform your query into a vector. Then, the embedding Retriever looks for similar or relevant documents.

Folder path/embedders/
Most common position in a pipelineBefore an embedding Retriever in a query/RAG pipeline
Mandatory input variables"text": A string
Output variables"embedding": A list of float numbers

"meta": A dictionary of metadata


AzureOpenAITextEmbedder transforms a string into a vector that captures its semantics using an OpenAI embedding model. It uses Azure cognitive services for text and document embedding with models deployed on Azure.

To see the list of compatible embedding models, head over to Azure documentation. The default model for AzureOpenAITextEmbedder is text-embedding-ada-002.

Use AzureOpenAITextEmbedder to embed a simple string (such as a query) into a vector. For embedding lists of documents, use the AzureOpenAIDocumentEmbedder, which enriches the documents with the computed embedding, also known as vector.

To work with Azure components, you will need an Azure OpenAI API key, as well as an Azure OpenAI Endpoint. You can learn more about them in Azure documentation.

The component uses AZURE_OPENAI_API_KEY and AZURE_OPENAI_AD_TOKEN environment variables by default. Otherwise, you can pass api_key and azure_ad_token at initialization:

client = AzureOpenAITextEmbedder(azure_endpoint="<Your Azure endpoint e.g. `>",
                        azure_deployment="<a model name>")


We recommend using environment variables instead of initialization parameters.


On its own

Here is how you can use the component on its own:

from haystack.components.embedders import AzureOpenAITextEmbedder

text_to_embed = "I love pizza!"

text_embedder = AzureOpenAITextEmbedder()


# {'embedding': [0.017020374536514282, -0.023255806416273117, ...],
# 'meta': {'model': 'text-embedding-ada-002-v2',
#          'usage': {'prompt_tokens': 4, 'total_tokens': 4}}}

In a pipeline

from haystack import Document
from haystack import Pipeline
from haystack.document_stores.in_memory import InMemoryDocumentStore
from haystack.components.embedders import AzureOpenAITextEmbedder, AzureOpenAIDocumentEmbedder
from haystack.components.retrievers.in_memory import InMemoryEmbeddingRetriever

document_store = InMemoryDocumentStore(embedding_similarity_function="cosine")

documents = [Document(content="My name is Wolfgang and I live in Berlin"),
             Document(content="I saw a black horse running"),
             Document(content="Germany has many big cities")]

document_embedder = AzureOpenAIDocumentEmbedder()
documents_with_embeddings =['documents']

query_pipeline = Pipeline()
query_pipeline.add_component("text_embedder", AzureOpenAITextEmbedder())
query_pipeline.add_component("retriever", InMemoryEmbeddingRetriever(document_store=document_store))
query_pipeline.connect("text_embedder.embedding", "retriever.query_embedding")

query = "Who lives in Berlin?"

result ={"text_embedder":{"text": query}})


# Document(id=..., mimetype: 'text/plain', 
#  text: 'My name is Wolfgang and I live in Berlin')

Related Links

See the parameters details in our API reference: