---
title: "TextEmbeddingRetriever"
id: textembeddingretriever
slug: "/textembeddingretriever"
description: "Wraps an embedding-based retriever with a text embedder into a single component that accepts a text query."
---

# TextEmbeddingRetriever

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

<div className="key-value-table">

|  |  |
| --- | --- |
| **Most common position in a pipeline** | In query pipelines:  <br />In a RAG pipeline, before a [`ChatPromptBuilder`](../builders/chatpromptbuilder.mdx)  <br />In a semantic search pipeline, as the last component  <br />As a retriever inside [`MultiRetriever`](multiretriever.mdx) |
| **Mandatory init variables** | `retriever`: An embedding-based Retriever<br />`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](/reference/retrievers-api) |
| **GitHub link** | https://github.com/deepset-ai/haystack/blob/main/haystack/components/retrievers/text_embedding_retriever.py |
| **Package name** | `haystack-ai` |

</div>

## 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`](multiretriever.mdx).

## Usage

### On its own

The examples on this page use Sentence Transformers embedders that have moved to the `sentence-transformers-haystack` package. Install it to run the examples:

```shell
pip install sentence-transformers-haystack
```

```python
from haystack import Document
from haystack.document_stores.in_memory import InMemoryDocumentStore
from haystack.document_stores.types import DuplicatePolicy
from haystack_integrations.components.embedders.sentence_transformers 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`](multiretriever.mdx):

```python
from haystack_integrations.components.embedders.sentence_transformers 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",
            ),
        ),
    },
)
```