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OpenAITextEmbedder transforms a string into a vector that captures its semantics using an OpenAI embedding model.

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.

TypeText Embedder
Position in a pipelineBefore an embedding Retriever in a Query/RAG pipeline
Inputs"text": a string
Outputs"embedding": a List of float numbers

"meta": a Dictionary of metadata


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

Use OpenAITextEmbedder to embed a simple string (such as a query) into a vector. For embedding lists of Documents, use the OpenAIDocumentEmbedder, which enriches the Document with the computed embedding, also known as vector.

The component uses an OPENAI_API_KEY environment variable by default. Otherwise, you can pass an API key at initialization with api_key:

embedder = OpenAITextEmbedder(api_key=Secret.from_token("<your-api-key>"))


On its own

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

from haystack.components.embedders import OpenAITextEmbedder

text_to_embed = "I love pizza!"

text_embedder = OpenAITextEmbedder(api_key=Secret.from_token("<your-api-key>"))


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


We recommend setting OPENAI_API_KEY as an environment variable instead of setting it as a parameter.

In a pipeline

from haystack import Document
from haystack import Pipeline
from haystack.document_stores.in_memory import InMemoryDocumentStore
from haystack.components.embedders import OpenAITextEmbedder, OpenAIDocumentEmbedder
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 = OpenAIDocumentEmbedder()
documents_with_embeddings =['documents']

query_pipeline = Pipeline()
query_pipeline.add_component("text_embedder", OpenAITextEmbedder())
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: