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.


Name	OpenAITextEmbedder
Folder path	/embedders/
Most common position in a pipeline	Before 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

Overview

To see the list of compatible OpenAI embedding models, head over to OpenAI documentation. The default model for OpenAITextEmbedder is text-embedding-ada-002. You can specify another model with the model parameter when initializing this component.

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>"))

Usage

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>"))

print(text_embedder.run(text_to_embed))

# {'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 = document_embedder.run(documents)['documents']
document_store.write_documents(documents_with_embeddings)

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 = query_pipeline.run({"text_embedder":{"text": query}})

print(result['retriever']['documents'][0])

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