---
title: "ArcadeDBEmbeddingRetriever"
id: arcadedbembeddingretriever
slug: "/arcadedbembeddingretriever"
description: "An embedding-based Retriever compatible with the ArcadeDB Document Store."
---

# ArcadeDBEmbeddingRetriever

An embedding-based Retriever compatible with the ArcadeDB Document Store. It uses ArcadeDB's LSM_VECTOR (HNSW) index for vector similarity search.

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

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

</div>

## Overview

The `ArcadeDBEmbeddingRetriever` retrieves documents from `ArcadeDBDocumentStore` by comparing the query embedding with document embeddings using the store's HNSW index. It accepts optional `filters` for metadata filtering and `top_k` to limit the number of results. Use a Document Embedder in your indexing pipeline and a Text Embedder in your query pipeline so embeddings are available.

## Installation

```shell
pip install arcadedb-haystack
```

Ensure ArcadeDB is running, for example via Docker, and credentials are set (`ARCADEDB_USERNAME`, `ARCADEDB_PASSWORD`).

## Usage

### On its own

```python
from haystack_integrations.document_stores.arcadedb import ArcadeDBDocumentStore
from haystack_integrations.components.retrievers.arcadedb import (
    ArcadeDBEmbeddingRetriever,
)

document_store = ArcadeDBDocumentStore(
    url="http://localhost:2480",
    database="haystack",
    embedding_dimension=768,
)
retriever = ArcadeDBEmbeddingRetriever(document_store=document_store, top_k=5)

# Example: run with a query embedding (e.g. from an embedder)
result = retriever.run(query_embedding=[0.1] * 768)
for doc in result["documents"]:
    print(doc.content)
```

### In a pipeline

```python
from haystack import Document, Pipeline
from haystack.document_stores.types import DuplicatePolicy
from haystack.components.embedders import (
    SentenceTransformersTextEmbedder,
    SentenceTransformersDocumentEmbedder,
)
from haystack_integrations.document_stores.arcadedb import ArcadeDBDocumentStore
from haystack_integrations.components.retrievers.arcadedb import (
    ArcadeDBEmbeddingRetriever,
)

document_store = ArcadeDBDocumentStore(
    url="http://localhost:2480",
    database="haystack",
    embedding_dimension=768,
    recreate_type=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()
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())
query_pipeline.add_component(
    "retriever",
    ArcadeDBEmbeddingRetriever(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])
```
