---
title: "InMemoryEmbeddingRetriever"
id: inmemoryembeddingretriever
slug: "/inmemoryembeddingretriever"
description: "Use this Retriever with the InMemoryDocumentStore if you're looking for embedding-based retrieval."
---

# InMemoryEmbeddingRetriever

Use this Retriever with the InMemoryDocumentStore if you're looking for embedding-based retrieval.

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

|  |  |
| --- | --- |
| **Most common position in a pipeline** | In query pipelines:  <br />In a RAG pipeline, before a [`PromptBuilder`](../builders/promptbuilder.mdx)  <br />In a semantic search pipeline, as the last component  <br />In an extractive QA pipeline, after a Tex tEmbedder and before an [`ExtractiveReader`](../readers/extractivereader.mdx) |
| **Mandatory init variables** | `document_store`: An instance of [InMemoryDocumentStore](../../document-stores/inmemorydocumentstore.mdx) |
| **Mandatory run variables** | `query_embedding`: A list of floating point numbers |
| **Output variables** | `documents`: A list of documents |
| **API reference** | [Retrievers](/reference/retrievers-api) |
| **GitHub link** | https://github.com/deepset-ai/haystack/blob/main/haystack/components/retrievers/in_memory/embedding_retriever.py |
| **Package name** | `haystack-ai` |

</div>

## Overview

The `InMemoryEmbeddingRetriever` is an embedding-based Retriever compatible with the `InMemoryDocumentStore`. It compares the query and Document embeddings and fetches the Documents most relevant to the query from the `InMemoryDocumentStore` based on the outcome.

When using the `InMemoryEmbeddingRetriever` in your NLP system, make sure it has the query and Document embeddings available. You can do so by adding a DocumentEmbedder to your indexing pipeline and a Text Embedder to your query pipeline. For details, see [Embedders](../embedders.mdx).

In addition to the `query_embedding`, the `InMemoryEmbeddingRetriever` accepts other optional parameters, including `top_k` (the maximum number of Documents to retrieve) and `filters` to narrow down the search space.

The `embedding_similarity_function`  to use for embedding retrieval must be defined when the corresponding`InMemoryDocumentStore` is initialized.

## Usage

### In a pipeline

Use this Retriever in a query pipeline like this:

```python
from haystack import Document, Pipeline
from haystack.document_stores.in_memory import InMemoryDocumentStore
from haystack.components.embedders import (
    SentenceTransformersTextEmbedder,
    SentenceTransformersDocumentEmbedder,
)
from haystack.components.retrievers import InMemoryEmbeddingRetriever

document_store = InMemoryDocumentStore(embedding_similarity_function="cosine")

documents = [
    Document(content="There are over 7,000 languages spoken around the world today."),
    Document(
        content="Elephants have been observed to behave in a way that indicates a high level of self-awareness, such as recognizing themselves in mirrors.",
    ),
    Document(
        content="In certain parts of the world, like the Maldives, Puerto Rico, and San Diego, you can witness the phenomenon of bioluminescent waves.",
    ),
]

document_embedder = SentenceTransformersDocumentEmbedder()

documents_with_embeddings = document_embedder.run(documents)["documents"]
document_store.write_documents(documents_with_embeddings)

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 = "How many languages are there?"

result = query_pipeline.run({"text_embedder": {"text": query}})

print(result["retriever"]["documents"][0])
```
