The vectors computed by this component are necessary to perform embedding retrieval on a collection of documents. At retrieval time, the vector representing the query is compared with those of the documents to find the most similar or relevant documents.


Most common position in a pipeline	Before a `DocumentWriter` in an indexing pipeline
Mandatory init variables	"api_key": An OpenAI API key. Can be set with `OPENAI_API_KEY` env var.
Mandatory run variables	"documents": A list of documents
Output variables	"documents": A list of documents (enriched with embeddings) "meta": A dictionary of metadata
API reference	Embedders
GitHub link	https://github.com/deepset-ai/haystack/blob/main/haystack/components/embedders/openai_document_embedder.py

Overview

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

This component should be used to embed a list of documents. To embed a string, use the OpenAITextEmbedder.

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

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

Embedding Metadata

Text documents often come with a set of metadata. If they are distinctive and semantically meaningful, you can embed them along with the text of the document to improve retrieval.

You can do this easily by using the Document Embedder:

from haystack import Document
from haystack.components.embedders import OpenAIDocumentEmbedder

doc = Document(content="some text",meta={"title": "relevant title", "page number": 18})

embedder = OpenAIDocumentEmbedder(meta_fields_to_embed=["title"])

docs_w_embeddings = embedder.run(documents=[doc])["documents"]

Usage

On its own

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

from haystack.components.embedders import OpenAIDocumentEmbedder

doc = Document(content="I love pizza!")

document_embedder = OpenAIDocumentEmbedder(api_key=Secret.from_token("<your-api-key>"))

result = document_embedder.run([doc])
print(result['documents'][0].embedding)

# [0.017020374536514282, -0.023255806416273117, ...]

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

In a pipeline

from haystack import Pipeline
from haystack.document_stores.in_memory import InMemoryDocumentStore
from haystack.components.embedders import OpenAITextEmbedder, OpenAIDocumentEmbedder
from haystack.components.writers import DocumentWriter
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")]

indexing_pipeline = Pipeline()
indexing_pipeline.add_component("embedder", OpenAIDocumentEmbedder())
indexing_pipeline.add_component("writer", DocumentWriter(document_store=document_store))
indexing_pipeline.connect("embedder", "writer")

indexing_pipeline.run({"embedder": {"documents": 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 = 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')