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74 lines
2.3 KiB
Plaintext
74 lines
2.3 KiB
Plaintext
---
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title: "ArcadeDBDocumentStore"
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id: arcadedbdocumentstore
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slug: "/arcadedbdocumentstore"
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---
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# ArcadeDBDocumentStore
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<div className="key-value-table">
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| --- | --- |
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| API reference | [ArcadeDB](/reference/integrations-arcadedb) |
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| GitHub link | https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/arcadedb |
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</div>
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ArcadeDB is a multi-model database that supports vector search via its LSM_VECTOR (HNSW) index. The `ArcadeDBDocumentStore` uses ArcadeDB's HTTP/JSON API for all operations—no special drivers required. It supports dense embedding retrieval and SQL-based metadata filtering.
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For more information, see the [ArcadeDB documentation](https://docs.arcadedb.com/).
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## Installation
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Run ArcadeDB with Docker and update the password according to your setup:
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```shell
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docker run -d -p 2480:2480 \
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-e JAVA_OPTS="-Darcadedb.server.rootPassword=arcadedb" \
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arcadedata/arcadedb:latest
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```
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Install the Haystack integration:
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```shell
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pip install arcadedb-haystack
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```
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## Usage
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Set credentials via environment variables (recommended) or pass them explicitly:
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```shell
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export ARCADEDB_USERNAME=root
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export ARCADEDB_PASSWORD=arcadedb
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```
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Initialize the document store and write documents:
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```python
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from haystack import Document
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from haystack_integrations.document_stores.arcadedb import ArcadeDBDocumentStore
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document_store = ArcadeDBDocumentStore(
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url="http://localhost:2480",
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database="haystack",
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embedding_dimension=768,
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recreate_type=True,
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)
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document_store.write_documents([
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Document(content="This is first", embedding=[0.0] * 768),
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Document(content="This is second", embedding=[0.1, 0.2, 0.3] + [0.0] * 765),
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])
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print(document_store.count_documents())
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```
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To learn more about the initialization parameters, see the [API docs](/reference/integrations-arcadedb#arcadedbdocumentstore).
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Documents without embeddings or with a different dimension are stored with a zero-padded vector so they can be written and filtered; use an [Embedder](../pipeline-components/embedders/sentencetransformersdocumentembedder.mdx) for real embeddings.
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### Supported Retrievers
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- [ArcadeDBEmbeddingRetriever](../pipeline-components/retrievers/arcadedbembeddingretriever.mdx): An embedding-based Retriever that fetches documents from the Document Store by vector similarity (HNSW).
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