Add ArcadeDB document store integration (#411)

* Add ArcadeDB document store integration

ArcadeDB is a multi-model database with native HNSW vector search,
document storage, and SQL metadata filtering in a single backend.

* Update integrations/arcadedb.md

Co-authored-by: Bilge Yücel <bilge.yucel@deepset.ai>

---------

Co-authored-by: Bilge Yücel <bilge.yucel@deepset.ai>

Author: lvca

integrations/arcadedb.md

@@ -0,0 +1,178 @@
+---
+layout: integration
+name: ArcadeDB
+description: Use ArcadeDB as a document store with native HNSW vector search for Haystack
+authors:
+  - name: ArcadeData Ltd
+    socials:
+      github: ArcadeData
+      twitter: arcade_db
+pypi: https://pypi.org/project/arcadedb-haystack/
+repo: https://github.com/ArcadeData/arcadedb-haystack
+type: Document Store
+report_issue: https://github.com/ArcadeData/arcadedb-haystack/issues
+logo: /logos/arcadedb.png
+version: Haystack 2.0
+toc: true
+---
+
+### Table of Contents
+
+- [Overview](#overview)
+- [Installation](#installation)
+- [Usage](#usage)
+- [License](#license)
+
+## Overview
+
+An integration of [ArcadeDB](https://arcadedb.com) with [Haystack](https://docs.haystack.deepset.ai/docs/intro) by [ArcadeData](https://arcadedata.com).
+
+Most RAG setups need separate backends for documents, vectors, and metadata search. ArcadeDB replaces all three in a single multi-model database:
+
+- **Document storage** — vertex-based records with flexible MAP metadata
+- **HNSW vector search** — native approximate nearest neighbor index via `vectorNeighbors()` (cosine, euclidean, dot product)
+- **SQL filtering** — full SQL WHERE clauses on metadata fields
+- **No special drivers** — pure HTTP/JSON API, no binary protocol or custom driver required
+
+The library provides an `ArcadeDBDocumentStore` that implements the Haystack [DocumentStore protocol](https://docs.haystack.deepset.ai/docs/document-store#documentstore-protocol), plus pipeline-ready retriever components:
+
+- **ArcadeDBDocumentStore** — stores Documents as ArcadeDB vertices with embeddings indexed by a dedicated HNSW Vector Index for dense retrieval.
+- **ArcadeDBEmbeddingRetriever** — a [retriever component](https://docs.haystack.deepset.ai/docs/retrievers) that queries the vector index to find related Documents, with support for metadata filtering and runtime parameter overrides.
+
+```text
+                                   +-----------------------------+
+                                   |      ArcadeDB Database      |
+                                   +-----------------------------+
+                                   |                             |
+                                   |      +----------------+     |
+                                   |      |    Document    |     |
+                write_documents    |      +----------------+     |
+          +------------------------+----->|   properties   |     |
+          |                        |      |                |     |
++---------+----------+             |      |   embedding    |     |
+|                    |             |      +--------+-------+     |
+| ArcadeDBDocument   |             |               |             |
+|       Store        |             |               |index/query  |
++---------+----------+             |               |             |
+          |                        |     +---------+---------+   |
+          |                        |     | HNSW Vector Index |   |
+          +----------------------->|     |                   |   |
+              _embedding_retrieval |     | (for embedding)   |   |
+                                   |     +-------------------+   |
+                                   |                             |
+                                   +-----------------------------+
+```
+
+In the above diagram:
+
+- `Document` is an ArcadeDB vertex type
+- `properties` are Document [attributes](https://docs.haystack.deepset.ai/docs/data-classes#document) stored as vertex properties
+- `embedding` is a vector property of type `LIST[FLOAT]`, indexed by ArcadeDB's native HNSW index
+- `HNSW Vector Index` provides approximate nearest neighbor search via `vectorNeighbors()`
+
+## Installation
+
+`arcadedb-haystack` can be installed using pip:
+
+```bash
+pip install arcadedb-haystack
+```
+
+## Usage
+
+Once installed, you can start using `ArcadeDBDocumentStore` as any other document store that supports embeddings.
+
+```python
+from haystack_integrations.document_stores.arcadedb import ArcadeDBDocumentStore
+
+document_store = ArcadeDBDocumentStore(
+    url="http://localhost:2480",
+    database="haystack",
+    embedding_dimension=384,
+    similarity_function="cosine",
+)
+```
+
+You will need a running ArcadeDB instance. The simplest way is with Docker:
+
+```bash
+docker run -d -p 2480:2480 \
+    -e JAVA_OPTS="-Darcadedb.server.rootPassword=arcadedb" \
+    arcadedata/arcadedb:latest
+```
+
+Set credentials via environment variables:
+
+```bash
+export ARCADEDB_USERNAME=root
+export ARCADEDB_PASSWORD=arcadedb
+```
+
+### Writing documents
+
+```python
+from haystack import Document
+from haystack.document_stores.types import DuplicatePolicy
+
+documents = [
+    Document(
+        content="ArcadeDB supports graphs, documents, and vectors.",
+        meta={"source": "docs", "category": "database"},
+    )
+]
+document_store.write_documents(documents, policy=DuplicatePolicy.OVERWRITE)
+```
+
+### Retrieving documents
+
+`ArcadeDBEmbeddingRetriever` can be used in a pipeline to retrieve documents by querying the HNSW vector index with an embedded query, including metadata filtering:
+
+```python
+from haystack import Document, Pipeline
+from haystack.components.embedders import SentenceTransformersTextEmbedder, SentenceTransformersDocumentEmbedder
+from haystack_integrations.components.retrievers.arcadedb import ArcadeDBEmbeddingRetriever
+from haystack_integrations.document_stores.arcadedb import ArcadeDBDocumentStore
+
+document_store = ArcadeDBDocumentStore(
+    url="http://localhost:2480",
+    database="haystack",
+    embedding_dimension=384,
+)
+
+# Index documents with embeddings
+documents = [
+    Document(content="My name is Morgan and I live in Paris.", meta={"release_date": "2018-12-09"})
+]
+
+document_embedder = SentenceTransformersDocumentEmbedder(model="sentence-transformers/all-MiniLM-L6-v2")
+documents_with_embeddings = document_embedder.run(documents)
+document_store.write_documents(documents_with_embeddings.get("documents"))
+
+# Build retrieval pipeline
+pipeline = Pipeline()
+pipeline.add_component("text_embedder", SentenceTransformersTextEmbedder(model="sentence-transformers/all-MiniLM-L6-v2"))
+pipeline.add_component("retriever", ArcadeDBEmbeddingRetriever(document_store=document_store))
+pipeline.connect("text_embedder.embedding", "retriever.query_embedding")
+
+result = pipeline.run(
+    data={
+        "text_embedder": {"text": "What cities do people live in?"},
+        "retriever": {
+            "top_k": 5,
+            "filters": {"field": "release_date", "operator": "==", "value": "2018-12-09"},
+        },
+    }
+)
+
+documents = result["retriever"]["documents"]
+```
+
+### More examples
+
+You can find more examples in the [repository](https://github.com/ArcadeData/arcadedb-haystack/tree/main/examples):
+
+- [embedding_retrieval.py](https://github.com/ArcadeData/arcadedb-haystack/blob/main/examples/embedding_retrieval.py) — Full workflow demonstrating document indexing and vector similarity retrieval with ArcadeDB.
+
+## License
+
+`arcadedb-haystack` is distributed under the terms of the [Apache 2.0](https://spdx.org/licenses/Apache-2.0.html) license.