TextEmbeddingRetriever

Wraps an embedding-based retriever with a text embedder into a single component that accepts a text query.

Most common position in a pipeline	In query pipelines: In a RAG pipeline, before a ChatPromptBuilder In a semantic search pipeline, as the last component As a retriever inside MultiRetriever
Mandatory init variables	retriever: An embedding-based Retriever text_embedder: A Text Embedder component
Mandatory run variables	query: A query string
Output variables	documents: A list of retrieved documents sorted by relevance score
API reference	Retrievers
GitHub link	https://github.com/deepset-ai/haystack/blob/main/haystack/components/retrievers/text_embedding_retriever.py
Package name	haystack-ai

Overview

TextEmbeddingRetriever bundles a text embedder and an embedding-based retriever into a single component. It accepts a plain text query, converts it to an embedding internally, and returns documents sorted by relevance score.

You can use it anywhere an embedding-based retriever fits: in RAG pipelines before a prompt builder, as the final component in a semantic search pipeline, or as a drop-in retriever inside MultiRetriever.

Usage

On its own

python

from haystack import Document
from haystack.document_stores.in_memory import InMemoryDocumentStore
from haystack.document_stores.types import DuplicatePolicy
from haystack.components.embedders import (
    SentenceTransformersDocumentEmbedder,
    SentenceTransformersTextEmbedder,
)
from haystack.components.retrievers import (
    InMemoryEmbeddingRetriever,
    TextEmbeddingRetriever,
)
from haystack.components.writers import DocumentWriter

documents = [
    Document(
        content="Renewable energy is energy that is collected from renewable resources.",
    ),
    Document(
        content="Solar energy is a type of green energy that is harnessed from the sun.",
    ),
    Document(
        content="Wind energy is another type of green energy that is generated by wind turbines.",
    ),
    Document(
        content="Geothermal energy is heat that comes from the sub-surface of the earth.",
    ),
]

doc_store = InMemoryDocumentStore()
doc_embedder = SentenceTransformersDocumentEmbedder(
    model="sentence-transformers/all-MiniLM-L6-v2",
)
doc_writer = DocumentWriter(document_store=doc_store, policy=DuplicatePolicy.SKIP)
doc_writer.run(documents=doc_embedder.run(documents)["documents"])

retriever = TextEmbeddingRetriever(
    retriever=InMemoryEmbeddingRetriever(document_store=doc_store, top_k=2),
    text_embedder=SentenceTransformersTextEmbedder(
        model="sentence-transformers/all-MiniLM-L6-v2",
    ),
)

result = retriever.run(query="Geothermal energy")
for doc in result["documents"]:
    print(f"Content: {doc.content}, Score: {doc.score}")

As part of MultiRetriever

TextEmbeddingRetriever is most commonly used as one of the retrievers inside a MultiRetriever:

python

from haystack.components.embedders import SentenceTransformersTextEmbedder
from haystack.components.retrievers import (
    InMemoryBM25Retriever,
    InMemoryEmbeddingRetriever,
)
from haystack.components.retrievers import MultiRetriever, TextEmbeddingRetriever

retriever = MultiRetriever(
    retrievers={
        "bm25": InMemoryBM25Retriever(document_store=doc_store),
        "embedding": TextEmbeddingRetriever(
            retriever=InMemoryEmbeddingRetriever(document_store=doc_store),
            text_embedder=SentenceTransformersTextEmbedder(
                model="sentence-transformers/all-MiniLM-L6-v2",
            ),
        ),
    },
)