---
title: "ArangoEmbeddingRetriever"
id: arangoembeddingretriever
slug: "/arangoembeddingretriever"
description: "An embedding-based Retriever compatible with the ArangoDB Document Store."
---

# ArangoEmbeddingRetriever

An embedding-based Retriever compatible with the ArangoDB Document Store.

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

|  |  |
| --- | --- |
| **Most common position in a pipeline** | 1. After a Text Embedder and before a [`PromptBuilder`](../builders/promptbuilder.mdx) in a RAG pipeline <br /><br /> 2. The last component in a semantic search pipeline |
| **Mandatory init variables** | `document_store`: An instance of an [ArangoDocumentStore](../../document-stores/arangodocumentstore.mdx) |
| **Mandatory run variables** | `query_embedding`: A vector representing the query (a list of floats) |
| **Output variables** | `documents`: A list of documents |
| **API reference** | [ArangoDB](/reference/integrations-arangodb) |
| **GitHub link** | https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/arangodb |
| **Package name** | `arangodb-haystack` |

</div>

## Overview

The `ArangoEmbeddingRetriever` retrieves documents from an `ArangoDocumentStore` using ArangoDB's AQL vector functions. It compares the query embedding with document embeddings and returns the most similar documents.

In addition to `query_embedding`, the retriever accepts optional `filters` to narrow the search space and `top_k` to limit the number of results. Both can be set at initialization and overridden per call to `run()`.

The embedding dimension and similarity function (`cosine`, `dot_product`, or `l2`) are configured on the `ArangoDocumentStore` at initialization time.

## Installation

```shell
pip install arangodb-haystack
```

Ensure ArangoDB 3.12+ is running with the vector index enabled, for example via Docker:

```shell
docker run -d -p 8529:8529 \
  -e ARANGO_ROOT_PASSWORD=test-password \
  arangodb:3.12 arangod --vector-index
```

## Usage

### On its own

```python
from haystack import Document
from haystack_integrations.document_stores.arangodb import ArangoDocumentStore
from haystack_integrations.components.retrievers.arangodb import (
    ArangoEmbeddingRetriever,
)

document_store = ArangoDocumentStore(
    host="http://localhost:8529",
    embedding_dimension=3,
    recreate_collection=True,
)
document_store.write_documents(
    [
        Document(
            content="There are over 7,000 languages spoken around the world today.",
            embedding=[0.1, 0.2, 0.3],
        ),
        Document(
            content="Elephants have been observed to recognize themselves in mirrors.",
            embedding=[0.8, 0.1, 0.5],
        ),
    ],
)

retriever = ArangoEmbeddingRetriever(document_store=document_store, top_k=1)
result = retriever.run(query_embedding=[0.1, 0.2, 0.3])
print(result["documents"][0].content)
```

### In a pipeline

```python
from haystack import Document, Pipeline
from haystack.document_stores.types import DuplicatePolicy
from haystack.components.embedders import (
    SentenceTransformersDocumentEmbedder,
    SentenceTransformersTextEmbedder,
)
from haystack_integrations.document_stores.arangodb import ArangoDocumentStore
from haystack_integrations.components.retrievers.arangodb import (
    ArangoEmbeddingRetriever,
)

document_store = ArangoDocumentStore(
    host="http://localhost:8529",
    embedding_dimension=384,
    recreate_collection=True,
)

documents = [
    Document(content="There are over 7,000 languages spoken around the world today."),
    Document(
        content="Elephants have been observed to recognize themselves in mirrors.",
    ),
    Document(
        content="Bioluminescent waves can be seen in the Maldives and Puerto Rico.",
    ),
]

document_embedder = SentenceTransformersDocumentEmbedder(
    model="sentence-transformers/all-MiniLM-L6-v2",
)
documents_with_embeddings = document_embedder.run(documents)

document_store.write_documents(
    documents_with_embeddings["documents"],
    policy=DuplicatePolicy.OVERWRITE,
)

query_pipeline = Pipeline()
query_pipeline.add_component(
    "text_embedder",
    SentenceTransformersTextEmbedder(model="sentence-transformers/all-MiniLM-L6-v2"),
)
query_pipeline.add_component(
    "retriever",
    ArangoEmbeddingRetriever(document_store=document_store, top_k=3),
)
query_pipeline.connect("text_embedder.embedding", "retriever.query_embedding")

result = query_pipeline.run(
    {"text_embedder": {"text": "How many languages are there?"}},
)
print(result["retriever"]["documents"][0].content)
```