chore: import upstream snapshot with attribution

docs-website/reference_versioned_docs/version-2.22/integrations-api/falkordb.md

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+---
+title: "FalkorDB"
+id: integrations-falkordb
+description: "FalkorDB integration for Haystack"
+slug: "/integrations-falkordb"
+---
+
+
+## haystack_integrations.components.retrievers.falkordb.cypher_retriever
+
+### FalkorDBCypherRetriever
+
+A power-user retriever for executing arbitrary OpenCypher queries against FalkorDB.
+
+This retriever allows you to leverage graph traversal and multi-hop queries in
+GraphRAG pipelines. The query must return nodes or dictionaries that can be
+mapped exactly to a Haystack `Document`.
+
+**Security Warning:** Raw Cypher queries must only come from trusted sources. Do
+not use un-sanitised user input directly in query strings. Use `parameters` instead.
+
+Usage example:
+
+```python
+from haystack_integrations.document_stores.falkordb import FalkorDBDocumentStore
+from haystack_integrations.components.retrievers.falkordb import FalkorDBCypherRetriever
+
+store = FalkorDBDocumentStore(host="localhost", port=6379)
+retriever = FalkorDBCypherRetriever(
+    document_store=store,
+    custom_cypher_query="MATCH (d:Document)-[:RELATES_TO]->(:Concept {name: $concept}) RETURN d"
+)
+
+res = retriever.run(parameters={"concept": "GraphRAG"})
+print(res["documents"])
+```
+
+#### __init__
+
+```python
+__init__(
+    document_store: FalkorDBDocumentStore,
+    custom_cypher_query: str | None = None,
+) -> None
+```
+
+Create a new FalkorDBCypherRetriever.
+
+**Parameters:**
+
+- **document_store** (<code>FalkorDBDocumentStore</code>) – The FalkorDBDocumentStore instance.
+- **custom_cypher_query** (<code>str | None</code>) – A static OpenCypher query to execute. Can be
+  overridden at runtime by passing `query` to `run()`.
+
+**Raises:**
+
+- <code>ValueError</code> – If the provided `document_store` is not a `FalkorDBDocumentStore`.
+
+#### to_dict
+
+```python
+to_dict() -> dict[str, Any]
+```
+
+Serialise the retriever to a dictionary.
+
+**Returns:**
+
+- <code>dict\[str, Any\]</code> – Dictionary representation of the retriever.
+
+#### from_dict
+
+```python
+from_dict(data: dict[str, Any]) -> FalkorDBCypherRetriever
+```
+
+Deserialise a `FalkorDBCypherRetriever` produced by `to_dict`.
+
+**Parameters:**
+
+- **data** (<code>dict\[str, Any\]</code>) – Serialised retriever dictionary.
+
+**Returns:**
+
+- <code>FalkorDBCypherRetriever</code> – Reconstructed `FalkorDBCypherRetriever` instance.
+
+#### run
+
+```python
+run(
+    query: str | None = None, parameters: dict[str, Any] | None = None
+) -> dict[str, list[Document]]
+```
+
+Retrieve documents by executing an OpenCypher query.
+
+If a `query` is provided here, it overrides the `custom_cypher_query`
+set during initialisation.
+
+**Parameters:**
+
+- **query** (<code>str | None</code>) – Optional OpenCypher query string.
+- **parameters** (<code>dict\[str, Any\] | None</code>) – Optional dictionary of query parameters (referenced as
+  `$param_name` in the Cypher string).
+
+**Returns:**
+
+- <code>dict\[str, list\[Document\]\]</code> – Dictionary containing a `"documents"` key with the retrieved documents.
+
+**Raises:**
+
+- <code>ValueError</code> – If no query string is provided (both here and at init).
+
+## haystack_integrations.components.retrievers.falkordb.embedding_retriever
+
+### FalkorDBEmbeddingRetriever
+
+A component for retrieving documents from a FalkorDBDocumentStore using vector similarity.
+
+The retriever uses FalkorDB's native vector search index to find documents whose embeddings
+are most similar to the provided query embedding.
+
+Usage example:
+
+```python
+from haystack.dataclasses import Document
+from haystack_integrations.document_stores.falkordb import FalkorDBDocumentStore
+from haystack_integrations.components.retrievers.falkordb import FalkorDBEmbeddingRetriever
+
+store = FalkorDBDocumentStore(host="localhost", port=6379)
+store.write_documents([
+    Document(content="GraphRAG is powerful.", embedding=[0.1, 0.2, 0.3]),
+    Document(content="FalkorDB is fast.", embedding=[0.8, 0.9, 0.1]),
+])
+
+retriever = FalkorDBEmbeddingRetriever(document_store=store)
+res = retriever.run(query_embedding=[0.1, 0.2, 0.3])
+print(res["documents"][0].content)  # "GraphRAG is powerful."
+```
+
+#### __init__
+
+```python
+__init__(
+    document_store: FalkorDBDocumentStore,
+    filters: dict[str, Any] | None = None,
+    top_k: int = 10,
+    filter_policy: FilterPolicy = FilterPolicy.REPLACE,
+) -> None
+```
+
+Create a new FalkorDBEmbeddingRetriever.
+
+**Parameters:**
+
+- **document_store** (<code>FalkorDBDocumentStore</code>) – The FalkorDBDocumentStore instance.
+- **filters** (<code>dict\[str, Any\] | None</code>) – Optional Haystack filters to narrow down the search space.
+- **top_k** (<code>int</code>) – Maximum number of documents to retrieve.
+- **filter_policy** (<code>FilterPolicy</code>) – Policy to determine how runtime filters are combined with
+  initialization filters.
+
+**Raises:**
+
+- <code>ValueError</code> – If the provided `document_store` is not a `FalkorDBDocumentStore`.
+
+#### to_dict
+
+```python
+to_dict() -> dict[str, Any]
+```
+
+Serialise the retriever to a dictionary.
+
+**Returns:**
+
+- <code>dict\[str, Any\]</code> – Dictionary representation of the retriever.
+
+#### from_dict
+
+```python
+from_dict(data: dict[str, Any]) -> FalkorDBEmbeddingRetriever
+```
+
+Deserialise a `FalkorDBEmbeddingRetriever` produced by `to_dict`.
+
+**Parameters:**
+
+- **data** (<code>dict\[str, Any\]</code>) – Serialised retriever dictionary.
+
+**Returns:**
+
+- <code>FalkorDBEmbeddingRetriever</code> – Reconstructed `FalkorDBEmbeddingRetriever` instance.
+
+#### run
+
+```python
+run(
+    query_embedding: list[float],
+    filters: dict[str, Any] | None = None,
+    top_k: int | None = None,
+) -> dict[str, list[Document]]
+```
+
+Retrieve documents by vector similarity.
+
+**Parameters:**
+
+- **query_embedding** (<code>list\[float\]</code>) – Query embedding vector.
+- **filters** (<code>dict\[str, Any\] | None</code>) – Optional Haystack filters to be combined with the init filters based
+  on the configured filter policy.
+- **top_k** (<code>int | None</code>) – Maximum number of documents to return. If not provided, the default
+  top_k from initialization is used.
+
+**Returns:**
+
+- <code>dict\[str, list\[Document\]\]</code> – Dictionary containing a `"documents"` key with the retrieved documents.
+
+## haystack_integrations.document_stores.falkordb.document_store
+
+### FalkorDBDocumentStore
+
+Bases: <code>DocumentStore</code>
+
+A Haystack DocumentStore backed by FalkorDB — a high-performance graph database.
+
+Optimised for GraphRAG workloads.
+
+Documents are stored as graph nodes (labelled `Document` by default) in a named
+FalkorDB graph. Document properties, including `meta` fields, are stored
+**flat** at the same level as `id` and `content` — exactly the same layout as
+the `neo4j-haystack` reference integration.
+
+Vector search is performed via FalkorDB's native vector index —
+**no APOC is required**. All bulk writes use `UNWIND` + `MERGE` for safe,
+idiomatic OpenCypher upserts.
+
+Usage example:
+
+```python
+from haystack_integrations.document_stores.falkordb import FalkorDBDocumentStore
+from haystack.dataclasses import Document
+
+store = FalkorDBDocumentStore(host="localhost", port=6379)
+store.write_documents([
+    Document(content="Hello, GraphRAG!", meta={"year": 2024}),
+])
+print(store.count_documents())  # 1
+```
+
+#### __init__
+
+```python
+__init__(
+    *,
+    host: str = "localhost",
+    port: int = 6379,
+    graph_name: str = "haystack",
+    username: str | None = None,
+    password: Secret | None = None,
+    node_label: str = "Document",
+    embedding_dim: int = 768,
+    embedding_field: str = "embedding",
+    similarity: SimilarityFunction = "cosine",
+    write_batch_size: int = 100,
+    recreate_graph: bool = False,
+    verify_connectivity: bool = False
+) -> None
+```
+
+Create a new FalkorDBDocumentStore.
+
+**Parameters:**
+
+- **host** (<code>str</code>) – Hostname of the FalkorDB server.
+- **port** (<code>int</code>) – Port the FalkorDB server listens on.
+- **graph_name** (<code>str</code>) – Name of the FalkorDB graph to use. Each graph is an isolated
+  namespace.
+- **username** (<code>str | None</code>) – Optional username for FalkorDB authentication.
+- **password** (<code>Secret | None</code>) – Optional :class:`haystack.utils.Secret` holding the FalkorDB
+  password. The secret value is resolved lazily on first connection.
+- **node_label** (<code>str</code>) – Label used for document nodes in the graph.
+- **embedding_dim** (<code>int</code>) – Dimensionality of the vector embeddings. Used when
+  creating the vector index.
+- **embedding_field** (<code>str</code>) – Name of the node property that stores the embedding
+  vector.
+- **similarity** (<code>SimilarityFunction</code>) – Similarity function for the vector index. Accepted values
+  are `"cosine"` and `"euclidean"`.
+- **write_batch_size** (<code>int</code>) – Number of documents written per `UNWIND` batch.
+- **recreate_graph** (<code>bool</code>) – When `True` the existing graph (and all its data) is
+  dropped and recreated on initialisation. Useful for tests.
+- **verify_connectivity** (<code>bool</code>) – When `True` a connectivity probe is run
+  immediately in `__init__` — raises if the server is unreachable.
+
+**Raises:**
+
+- <code>ValueError</code> – If `similarity` is not `"cosine"` or `"euclidean"`.
+
+#### to_dict
+
+```python
+to_dict() -> dict[str, Any]
+```
+
+Serialise the store to a dictionary suitable for `from_dict`.
+
+**Returns:**
+
+- <code>dict\[str, Any\]</code> – Dictionary representation of the store.
+
+#### from_dict
+
+```python
+from_dict(data: dict[str, Any]) -> FalkorDBDocumentStore
+```
+
+Deserialise a `FalkorDBDocumentStore` produced by `to_dict`.
+
+**Parameters:**
+
+- **data** (<code>dict\[str, Any\]</code>) – Serialised store dictionary.
+
+**Returns:**
+
+- <code>FalkorDBDocumentStore</code> – Reconstructed `FalkorDBDocumentStore` instance.
+
+#### count_documents
+
+```python
+count_documents() -> int
+```
+
+Return the number of documents currently stored in the graph.
+
+**Returns:**
+
+- <code>int</code> – Integer count of document nodes.
+
+#### filter_documents
+
+```python
+filter_documents(filters: dict[str, Any] | None = None) -> list[Document]
+```
+
+Retrieve all documents that match the provided Haystack filters.
+
+**Parameters:**
+
+- **filters** (<code>dict\[str, Any\] | None</code>) – Optional Haystack filter dict. When `None` all documents are
+  returned. For filter syntax see
+  [Metadata filtering](https://docs.haystack.deepset.ai/docs/metadata-filtering)
+
+**Returns:**
+
+- <code>list\[Document\]</code> – List of matching :class:`haystack.dataclasses.Document` objects.
+
+**Raises:**
+
+- <code>ValueError</code> – If the filter dict is malformed.
+
+#### write_documents
+
+```python
+write_documents(
+    documents: list[Document], policy: DuplicatePolicy = DuplicatePolicy.NONE
+) -> int
+```
+
+Write documents to the FalkorDB graph using `UNWIND` + `MERGE` for batching.
+
+Document `meta` fields are stored **flat** at the same level as `id` and
+`content` — no prefix is added. This matches the layout used by the
+`neo4j-haystack` reference integration.
+
+**Parameters:**
+
+- **documents** (<code>list\[Document\]</code>) – List of :class:`haystack.dataclasses.Document` objects.
+- **policy** (<code>DuplicatePolicy</code>) – How to handle documents whose `id` already exists.
+  Defaults to :attr:`DuplicatePolicy.NONE` (treated as FAIL).
+
+**Returns:**
+
+- <code>int</code> – Number of documents written or updated.
+
+**Raises:**
+
+- <code>ValueError</code> – If `documents` contains non-Document elements.
+- <code>DuplicateDocumentError</code> – If `policy` is FAIL / NONE and a duplicate
+  ID is encountered.
+- <code>DocumentStoreError</code> – If any other DB error occurs.
+
+#### delete_documents
+
+```python
+delete_documents(document_ids: list[str]) -> None
+```
+
+Delete documents by their IDs using a single `UNWIND`-based query.
+
+**Parameters:**
+
+- **document_ids** (<code>list\[str\]</code>) – List of document IDs to remove from the graph.
+
+#### delete_all_documents
+
+```python
+delete_all_documents() -> None
+```
+
+Delete all documents from the graph.
+
+#### delete_by_filter
+
+```python
+delete_by_filter(filters: dict[str, Any]) -> int
+```
+
+Delete all documents that match the provided filters.
+
+**Parameters:**
+
+- **filters** (<code>dict\[str, Any\]</code>) – Haystack filter dict.
+
+**Returns:**
+
+- <code>int</code> – Number of documents deleted.
+
+#### update_by_filter
+
+```python
+update_by_filter(filters: dict[str, Any], meta: dict[str, Any]) -> int
+```
+
+Update metadata fields on all documents that match the provided filters.
+
+**Parameters:**
+
+- **filters** (<code>dict\[str, Any\]</code>) – Haystack filter dict selecting which documents to update.
+- **meta** (<code>dict\[str, Any\]</code>) – Metadata fields to set. Keys may include or omit the `meta.` prefix.
+
+**Returns:**
+
+- <code>int</code> – Number of documents updated.
+
+#### count_documents_by_filter
+
+```python
+count_documents_by_filter(filters: dict[str, Any]) -> int
+```
+
+Return the number of documents that match the provided filters.
+
+**Parameters:**
+
+- **filters** (<code>dict\[str, Any\]</code>) – Haystack filter dict.
+
+**Returns:**
+
+- <code>int</code> – Integer count of matching document nodes.
+
+#### count_unique_metadata_by_filter
+
+```python
+count_unique_metadata_by_filter(
+    filters: dict[str, Any], metadata_fields: list[str]
+) -> dict[str, int]
+```
+
+Return the number of unique values for each metadata field among matching documents.
+
+**Parameters:**
+
+- **filters** (<code>dict\[str, Any\]</code>) – Haystack filter dict. Pass an empty dict to count across all documents.
+- **metadata_fields** (<code>list\[str\]</code>) – List of metadata field names. May include or omit the `meta.` prefix.
+
+**Returns:**
+
+- <code>dict\[str, int\]</code> – Dict mapping each field name (without `meta.` prefix) to its unique value count.
+
+#### get_metadata_fields_info
+
+```python
+get_metadata_fields_info() -> dict[str, dict[str, str]]
+```
+
+Return type information for each metadata field present on document nodes.
+
+**Returns:**
+
+- <code>dict\[str, dict\[str, str\]\]</code> – Dict mapping field names to a `{"type": <typename>}` dict.
+  Type names are `"str"`, `"int"`, `"float"`, or `"bool"`.
+
+#### get_metadata_field_min_max
+
+```python
+get_metadata_field_min_max(metadata_field: str) -> dict[str, Any]
+```
+
+Return the minimum and maximum values for the given metadata field.
+
+**Parameters:**
+
+- **metadata_field** (<code>str</code>) – Metadata field name. May include or omit the `meta.` prefix.
+
+**Returns:**
+
+- <code>dict\[str, Any\]</code> – Dict with keys `"min"` and `"max"`. Values are `None` when no documents
+  have a non-null value for the field.
+
+#### get_metadata_field_unique_values
+
+```python
+get_metadata_field_unique_values(
+    metadata_field: str,
+    search_term: str | None = None,
+    size: int | None = 10000,
+    after: dict[str, Any] | None = None,
+) -> tuple[list[Any], dict[str, Any] | None]
+```
+
+Return distinct values for the given metadata field with optional filtering and pagination.
+
+**Parameters:**
+
+- **metadata_field** (<code>str</code>) – Metadata field name. May include or omit the `meta.` prefix.
+- **search_term** (<code>str | None</code>) – Optional substring filter applied to string field values.
+- **size** (<code>int | None</code>) – Maximum number of values to return per page. Defaults to 10 000.
+- **after** (<code>dict\[str, Any\] | None</code>) – Pagination cursor returned by a previous call. Pass `None` for the first page.
+
+**Returns:**
+
+- <code>tuple\[list\[Any\], dict\[str, Any\] | None\]</code> – Tuple of `(values, next_cursor)`. `next_cursor` is `None` on the last page.