Add ArangoDB integration page (#511)

* Add ArangoDB integration page

* Add ArangoDB integration page
* Add logo for ArangoDB integration

* Update ArangoDB integration logo

Author: bogdankostic

integrations/arangodb-document-store.md

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+---
+layout: integration
+name: ArangoDB
+description: Use the ArangoDB database as a Document Store with Haystack
+authors:
+    - name: deepset
+      socials:
+        github: deepset-ai
+        twitter: Haystack_AI
+        linkedin: https://www.linkedin.com/company/deepset-ai/
+pypi: https://pypi.org/project/arangodb-haystack/
+repo: https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/arangodb
+type: Document Store
+report_issue: https://github.com/deepset-ai/haystack-core-integrations/issues
+logo: /logos/arangodb.png
+version: Haystack 2.0
+toc: true
+---
+
+[![PyPI - Version](https://img.shields.io/pypi/v/arangodb-haystack.svg)](https://pypi.org/project/arangodb-haystack/)
+[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/arangodb-haystack.svg)](https://pypi.org/project/arangodb-haystack/)
+[![test](https://github.com/deepset-ai/haystack-core-integrations/actions/workflows/arangodb.yml/badge.svg)](https://github.com/deepset-ai/haystack-core-integrations/actions/workflows/arangodb.yml)
+
+-----
+
+**Table of Contents**
+
+- [Overview](#overview)
+- [Installation](#installation)
+- [Usage](#usage)
+- [License](#license)
+
+## Overview
+
+[ArangoDB](https://arango.ai/) is an open-source, multi-model database that combines documents, graphs, and key/values with native vector search. This integration lets you use ArangoDB as a [Document Store](https://docs.haystack.deepset.ai/docs/document-store) in Haystack and retrieve documents with vector similarity search, which makes it a good fit for RAG and GraphRAG pipelines.
+
+The integration provides two components:
+
+- `ArangoDocumentStore`: a Document Store that stores documents (including their embeddings) in an ArangoDB collection and implements the [DocumentStore protocol](https://docs.haystack.deepset.ai/docs/document-store#documentstore-protocol).
+- `ArangoEmbeddingRetriever`: a [retriever](https://docs.haystack.deepset.ai/docs/retrievers) that fetches the most relevant documents from an `ArangoDocumentStore` using vector similarity on embeddings.
+
+## Installation
+
+Vector search requires ArangoDB 3.12 or later with the vector index enabled. You can quickly start a local instance with Docker:
+
+```bash
+docker run -e ARANGO_ROOT_PASSWORD=test-password -p 8529:8529 arangodb:3.12 arangod --vector-index
+```
+
+Install the integration with `pip`:
+
+```bash
+pip install arangodb-haystack
+```
+
+## Usage
+
+By default, the `ArangoDocumentStore` reads its credentials from the `ARANGO_USERNAME` (optional, falls back to the `root` user) and `ARANGO_PASSWORD` environment variables:
+
+```bash
+export ARANGO_PASSWORD="test-password"
+```
+
+Then initialize the Document Store:
+
+```python
+from haystack_integrations.document_stores.arangodb import ArangoDocumentStore
+
+document_store = ArangoDocumentStore(
+    host="http://localhost:8529",
+    database="haystack",
+    collection_name="haystack_documents",
+    embedding_dimension=768,
+    similarity_function="cosine",
+    recreate_collection=True,
+)
+```
+
+### Writing Documents to ArangoDocumentStore
+
+To write documents to the `ArangoDocumentStore`, create an indexing pipeline that embeds and writes documents:
+
+```python
+from haystack import Pipeline
+from haystack.components.converters import TextFileToDocument
+from haystack.components.writers import DocumentWriter
+from haystack.components.embedders import SentenceTransformersDocumentEmbedder
+
+indexing = Pipeline()
+indexing.add_component("converter", TextFileToDocument())
+indexing.add_component("embedder", SentenceTransformersDocumentEmbedder())
+indexing.add_component("writer", DocumentWriter(document_store))
+indexing.connect("converter", "embedder")
+indexing.connect("embedder", "writer")
+
+indexing.run({"converter": {"sources": file_paths}})
+```
+
+### Retrieval from ArangoDocumentStore
+
+You can retrieve documents that are semantically similar to a query with a pipeline that uses the `ArangoEmbeddingRetriever`:
+
+```python
+from haystack import Pipeline
+from haystack.components.embedders import SentenceTransformersTextEmbedder
+from haystack_integrations.components.retrievers.arangodb import ArangoEmbeddingRetriever
+
+querying = Pipeline()
+querying.add_component("embedder", SentenceTransformersTextEmbedder())
+querying.add_component("retriever", ArangoEmbeddingRetriever(document_store=document_store, top_k=3))
+querying.connect("embedder", "retriever")
+
+results = querying.run({"embedder": {"text": "my query"}})
+```
+
+The retriever also supports [metadata filtering](https://docs.haystack.deepset.ai/docs/metadata-filtering), which you can pass either at initialization or at query time.
+
+## License
+
+`arangodb-haystack` is distributed under the terms of the [Apache-2.0](https://spdx.org/licenses/Apache-2.0.html) license.