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Neo4jGraph

class Neo4jGraph(BaseGraphStorage):
Provides a connection to a Neo4j database for various graph operations. The detailed information about Neo4j is available at: Neo4j https://neo4j.com/docs/getting-started This module referred to the work of Langchian and Llamaindex. Parameters:
  • url (str): The URL of the Neo4j database server.
  • username (str): The username for database authentication.
  • password (str): The password for database authentication.
  • database (str): The name of the database to connect to. Defaults to neo4j.
  • timeout (Optional[float]): The timeout for transactions in seconds. Useful for terminating long-running queries. Defaults to None.
  • truncate (bool): A flag to indicate whether to remove lists with more than LIST_LIMIT elements from results. Defaults to False.

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init

def __init__(
    self,
    url: str,
    username: str,
    password: str,
    database: str = 'neo4j',
    timeout: Optional[float] = None,
    truncate: bool = False
):
Create a new Neo4j graph instance.

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get_client

def get_client(self):
Get the underlying graph storage client.

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get_schema

def get_schema(self, refresh: bool = False):
Retrieve the schema of the Neo4jGraph store. Parameters:
  • refresh (bool): A flag indicating whether to forcibly refresh the schema from the Neo4jGraph store regardless of whether it is already cached. Defaults to False.
Returns: str: The schema of the Neo4jGraph store.

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get_structured_schema

def get_structured_schema(self):
Returns: Dict[str, Any]: The structured schema of the graph.

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_value_truncate

def _value_truncate(self, raw_value: Any):
Truncates the input raw value by removing entries that is dictionary or list with values resembling embeddings and containing more than LIST_LIMIT elements. This method aims to reduce unnecessary computational cost and noise in scenarios where such detailed data structures are not needed. If the input value is not dictionary or list then give the raw value back. Parameters:
  • raw_value (Any): The raw value to be truncated.
Returns: Any: The truncated value, with embedding-like dictionaries and oversized lists handled.

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query

def query(self, query: str, params: Optional[Dict[str, Any]] = None):
Executes a Neo4j Cypher declarative query in a database. Parameters:
  • query (str): The Cypher query to be executed.
  • params (Optional[Dict[str, Any]]): A dictionary of parameters to be used in the query. Defaults to None.
Returns: List[Dict[str, Any]]: A list of dictionaries, each dictionary represents a row of results from the Cypher query.

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refresh_schema

def refresh_schema(self):
Refreshes the Neo4j graph schema information by querying the database for node properties, relationship properties, and relationships.

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add_triplet

def add_triplet(
    self,
    subj: str,
    obj: str,
    rel: str,
    timestamp: Optional[str] = None
):
Adds a relationship (triplet) between two entities in the database with a timestamp. Parameters:
  • subj (str): The identifier for the subject entity.
  • obj (str): The identifier for the object entity.
  • rel (str): The relationship between the subject and object.
  • timestamp (Optional[str]): The timestamp of the relationship. Defaults to None.

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_delete_rel

def _delete_rel(
    self,
    subj: str,
    obj: str,
    rel: str
):
Deletes a specific relationship between two nodes in the Neo4j database. Parameters:
  • subj (str): The identifier for the subject entity.
  • obj (str): The identifier for the object entity.
  • rel (str): The relationship between the subject and object to delete.

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_delete_entity

def _delete_entity(self, entity: str):
Deletes an entity from the Neo4j database based on its unique identifier. Parameters:
  • entity (str): The unique identifier of the entity to be deleted.

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_check_edges

def _check_edges(self, entity: str):
Checks if the given entity has any relationships in the graph database. Parameters:
  • entity (str): The unique identifier of the entity to check.
Returns: bool: True if the entity has at least one edge (relationship), False otherwise.

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delete_triplet

def delete_triplet(
    self,
    subj: str,
    obj: str,
    rel: str
):
Deletes a specific triplet from the graph, comprising a subject, object and relationship. Parameters:
  • subj (str): The identifier for the subject entity.
  • obj (str): The identifier for the object entity.
  • rel (str): The relationship between the subject and object.

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_get_node_import_query

def _get_node_import_query(self, base_entity_label: bool, include_source: bool):
Constructs a Cypher query string for importing nodes into a Neo4j database. Parameters:
  • base_entity_label (bool): Flag indicating whether to use a base entity label in the MERGE operation.
  • include_source (bool): Flag indicating whether to include source element information in the query.
Returns: str: A Cypher query string tailored based on the provided flags.

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_get_rel_import_query

def _get_rel_import_query(self, base_entity_label: bool):
Constructs a Cypher query string for importing relationship into a Neo4j database. Parameters:
  • base_entity_label (bool): Flag indicating whether to use a base entity label in the MERGE operation.
Returns: str: A Cypher query string tailored based on the provided flags.

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add_graph_elements

def add_graph_elements(
    self,
    graph_elements: List[GraphElement],
    include_source: bool = False,
    base_entity_label: bool = False
):
Adds nodes and relationships from a list of GraphElement objects to the graph storage. Parameters:
  • graph_elements (List[GraphElement]): A list of GraphElement objects that contain the nodes and relationships to be added to the graph. Each GraphElement should encapsulate the structure of part of the graph, including nodes, relationships, and the source element information.
  • include_source (bool, optional): If True, stores the source element and links it to nodes in the graph using the MENTIONS relationship. This is useful for tracing back the origin of data. Merges source elements based on the id property from the source element metadata if available; otherwise it calculates the MD5 hash of page_content for merging process. Defaults to False.
  • base_entity_label (bool, optional): If True, each newly created node gets a secondary BASE_ENTITY_LABEL label, which is indexed and improves import speed and performance. Defaults to False.

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random_walk_with_restarts

def random_walk_with_restarts(
    self,
    graph_name: str,
    sampling_ratio: float,
    start_node_ids: List[int],
    restart_probability: float = 0.1,
    node_label_stratification: bool = False,
    relationship_weight_property: Optional[str] = None
):
Runs the Random Walk with Restarts (RWR) sampling algorithm. Parameters:
  • graph_name (str): The name of the original graph in the graph catalog.
  • sampling_ratio (float): The fraction of nodes in the original graph to be sampled.
  • start_node_ids (List[int]): IDs of the initial set of nodes of the original graph from which the sampling random walks will start.
  • restart_probability (float, optional): The probability that a sampling random walk restarts from one of the start nodes. Defaults to 0.1.
  • node_label_stratification (bool, optional): If true, preserves the node label distribution of the original graph. Defaults to False.
  • relationship_weight_property (Optional[str], optional): Name of the relationship property to use as weights. If unspecified, the algorithm runs unweighted. Defaults to None.
Returns: Dict[str, Any]: A dictionary with the results of the RWR sampling.

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common_neighbour_aware_random_walk

def common_neighbour_aware_random_walk(
    self,
    graph_name: str,
    sampling_ratio: float,
    start_node_ids: List[int],
    node_label_stratification: bool = False,
    relationship_weight_property: Optional[str] = None
):
Runs the Common Neighbour Aware Random Walk (CNARW) sampling algorithm. Parameters:
  • graph_name (str): The name of the original graph in the graph catalog.
  • sampling_ratio (float): The fraction of nodes in the original graph to be sampled.
  • start_node_ids (List[int]): IDs of the initial set of nodes of the original graph from which the sampling random walks will start.
  • node_label_stratification (bool, optional): If true, preserves the node label distribution of the original graph. Defaults to False.
  • relationship_weight_property (Optional[str], optional): Name of the relationship property to use as weights. If unspecified, the algorithm runs unweighted. Defaults to None.
Returns: Dict[str, Any]: A dictionary with the results of the CNARW sampling.

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get_triplet

def get_triplet(
    self,
    subj: Optional[str] = None,
    obj: Optional[str] = None,
    rel: Optional[str] = None
):
Query triplet information. If subj, obj, or rel is not specified, returns all matching triplets. Parameters:
  • subj (Optional[str]): The ID of the subject node. If None, matches any subject node. (default: :obj:None)
  • obj (Optional[str]): The ID of the object node. If None, matches any object node. (default: :obj:None)
  • rel (Optional[str]): The type of relationship. If None, matches any relationship type. (default: :obj:None)
Returns: List[Dict[str, Any]]: A list of matching triplets, each containing subj, obj, rel, and timestamp.