AzureOpenAIDocumentEmbedder
This component computes the embeddings of a list of documents and stores the obtained vectors in the embedding field of each document. It uses Azure cognitive services for text and document embedding with models deployed on Azure.
| Name | AzureOpenAIDocumentEmbedder |
| Folder path | /embedders/ |
| Most common position in a pipeline | Before a DocumentWriter |
| Mandatory input variables | "documents": A list of documents |
| Output variables | "documents": A list of documents (enriched with embeddings) "meta": A dictionary of metadata |
Overview
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.
To see the list of compatible embedding models, head over to Azure documentation. The default model for AzureOpenAITextEmbedder is text-embedding-ada-002.
This component should be used to embed a list of documents. To embed a string, you should use the AzureOpenAITextEmbedder.
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 = AzureOpenAIDocumentEmbedder(azure_endpoint="<Your Azure endpoint e.g. `https://your-company.azure.openai.com/>",
api_key=Secret.from_token("<your-api-key>"),
azure_deployment="<a model name>")
We recommend using environment variables instead of initialization parameters.
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 AzureOpenAIDocumentEmbedder
doc = Document(content="some text",meta={"title": "relevant title", "page number": 18})
embedder = AzureOpenAIDocumentEmbedder(meta_fields_to_embed=["title"])
docs_w_embeddings = embedder.run(documents=[doc])["documents"]
Usage
On its own
from haystack import Document
from haystack.components.embedders import AzureOpenAIDocumentEmbedder
doc = Document(content="I love pizza!")
document_embedder = AzureOpenAIDocumentEmbedder()
result = document_embedder.run([doc])
print(result['documents'][0].embedding)
# [0.017020374536514282, -0.023255806416273117, ...]
In a pipeline
from haystack import Pipeline
from haystack.document_stores.in_memory import InMemoryDocumentStore
from haystack.components.embedders import AzureOpenAITextEmbedder, AzureOpenAIDocumentEmbedder
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", AzureOpenAIDocumentEmbedder())
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", 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 = 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')
Updated 7 months ago