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


Name	SentenceTransformersDocumentEmbedder
Folder path	/embedders/
Most common position in a pipeline	Before a `DocumentWriter` in an indexing pipeline
Mandatory input variables	"documents": A list of documents
Output variables	"documents": A list of documents (enriched with embeddings)

Overview

SentenceTransformersDocumentEmbedder should be used to embed a list of documents. To embed a string, use the SentenceTransformersTextEmbedder.

The component uses a HF_API_TOKEN environment variable by default. Otherwise, you can pass a Hugging Face API token at initialization with token:

document_embedder = SentenceTransformersDocumentEmbedder(token=Secret.from_token("<your-api-key>"))

Compatible Models

The default embedding model is `sentence-transformers/all-mpnet-base-v2`. You can specify another model with the model parameter when initializing this component.

See the original models in the Sentence Transformers documentation.

Nowadays, most of the models in the Massive Text Embedding Benchmark (MTEB) Leaderboard are compatible with Sentence Transformers.
You can look for compatibility in the model card: an example related to BGE models.

Instructions

Some recent models that you can find in MTEB require prepending the text with an instruction to work better for retrieval.
For example, if you use intfloat/e5-large-v2, you should prefix your document with the following instruction: “passage:”

This is how it works with SentenceTransformersDocumentEmbedder:

embedder = SentenceTransformersDocumentEmbedder(model="intfloat/e5-large-v2",
                                                prefix="passage")

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 SentenceTransformersDocumentEmbedder

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

embedder = SentenceTransformersDocumentEmbedder(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 SentenceTransformersDocumentEmbedder
doc = Document(content="I love pizza!")
doc_embedder = SentenceTransformersDocumentEmbedder()
doc_embedder.warm_up()

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

# [-0.07804739475250244, 0.1498992145061493, ...]

In a pipeline

from haystack import Document
from haystack import Pipeline
from haystack.document_stores.in_memory import InMemoryDocumentStore
from haystack.components.embedders import SentenceTransformersTextEmbedder, SentenceTransformersDocumentEmbedder
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", SentenceTransformersDocumentEmbedder())
indexing_pipeline.add_component("writer", DocumentWriter(document_store=document_store))
indexing_pipeline.connect("embedder", "writer")

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

query = "Who lives in Berlin?"

indexing_pipeline.run({"documents": documents})
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')