Source code for langchain_community.graphs.gremlin_graph

import hashlib
import sys
from typing import Any, Dict, List, Optional, Union

from langchain_core.utils import get_from_env

from langchain_community.graphs.graph_document import GraphDocument, Node, Relationship
from langchain_community.graphs.graph_store import GraphStore

[docs]class GremlinGraph(GraphStore): """Gremlin wrapper for graph operations. Parameters: url (Optional[str]): The URL of the Gremlin database server or env GREMLIN_URI username (Optional[str]): The collection-identifier like '/dbs/database/colls/graph' or env GREMLIN_USERNAME if none provided password (Optional[str]): The connection-key for database authentication or env GREMLIN_PASSWORD if none provided traversal_source (str): The traversal source to use for queries. Defaults to 'g'. message_serializer (Optional[Any]): The message serializer to use for requests. Defaults to serializer.GraphSONSerializersV2d0() *Security note*: Make sure that the database connection uses credentials that are narrowly-scoped to only include necessary permissions. Failure to do so may result in data corruption or loss, since the calling code may attempt commands that would result in deletion, mutation of data if appropriately prompted or reading sensitive data if such data is present in the database. The best way to guard against such negative outcomes is to (as appropriate) limit the permissions granted to the credentials used with this tool. See for more information. *Implementation details*: The Gremlin queries are designed to work with Azure CosmosDB limitations """ @property def get_structured_schema(self) -> Dict[str, Any]: return self.structured_schema
[docs] def __init__( self, url: Optional[str] = None, username: Optional[str] = None, password: Optional[str] = None, traversal_source: str = "g", message_serializer: Optional[Any] = None, ) -> None: """Create a new Gremlin graph wrapper instance.""" try: import asyncio from gremlin_python.driver import client, serializer if sys.platform == "win32": asyncio.set_event_loop_policy(asyncio.WindowsSelectorEventLoopPolicy()) except ImportError: raise ImportError( "Please install gremlin-python first: " "`pip3 install gremlinpython" ) self.client = client.Client( url=get_from_env("url", "GREMLIN_URI", url), traversal_source=traversal_source, username=get_from_env("username", "GREMLIN_USERNAME", username), password=get_from_env("password", "GREMLIN_PASSWORD", password), message_serializer=message_serializer if message_serializer else serializer.GraphSONSerializersV2d0(), ) self.schema: str = ""
@property def get_schema(self) -> str: """Returns the schema of the Gremlin database""" if len(self.schema) == 0: self.refresh_schema() return self.schema
[docs] def refresh_schema(self) -> None: """ Refreshes the Gremlin graph schema information. """ vertex_schema = self.client.submit("g.V().label().dedup()").all().result() edge_schema = self.client.submit("g.E().label().dedup()").all().result() vertex_properties = ( self.client.submit( "g.V().group().by(label).by(properties().label().dedup().fold())" ) .all() .result()[0] ) self.structured_schema = { "vertex_labels": vertex_schema, "edge_labels": edge_schema, "vertice_props": vertex_properties, } self.schema = "\n".join( [ "Vertex labels are the following:", ",".join(vertex_schema), "Edge labes are the following:", ",".join(edge_schema), f"Vertices have following properties:\n{vertex_properties}", ] )
[docs] def query(self, query: str, params: dict = {}) -> List[Dict[str, Any]]: q = self.client.submit(query) return q.all().result()
[docs] def add_graph_documents( self, graph_documents: List[GraphDocument], include_source: bool = False ) -> None: """ Take GraphDocument as input as uses it to construct a graph. """ node_cache: Dict[Union[str, int], Node] = {} for document in graph_documents: if include_source: # Create document vertex doc_props = { "page_content": document.source.page_content, "metadata": document.source.metadata, } doc_id = hashlib.md5(document.source.page_content.encode()).hexdigest() doc_node = self.add_node( Node(id=doc_id, type="Document", properties=doc_props), node_cache ) # Import nodes to vertices for n in document.nodes: node = self.add_node(n) if include_source: # Add Edge to document for each node self.add_edge( Relationship( type="contains information about", source=doc_node, target=node, properties={}, ) ) self.add_edge( Relationship( type="is extracted from", source=node, target=doc_node, properties={}, ) ) # Edges for el in document.relationships: # Find or create the source vertex self.add_node(el.source, node_cache) # Find or create the target vertex self.add_node(, node_cache) # Find or create the edge self.add_edge(el)
[docs] def build_vertex_query(self, node: Node) -> str: base_query = ( f"g.V().has('id','{}').fold()" + f".coalesce(unfold(),addV('{node.type}')" + f".property('id','{}')" + f".property('type','{node.type}')" ) for key, value in base_query += f".property('{key}', '{value}')" return base_query + ")"
[docs] def build_edge_query(self, relationship: Relationship) -> str: source_query = f".has('id','{}')" target_query = f".has('id','{}')" base_query = f""""g.V(){source_query}.as('a') .V(){target_query}.as('b') .choose( __.inE('{relationship.type}').where(outV().as('a')), __.identity(), __.addE('{relationship.type}').from('a').to('b') ) """.replace("\n", "").replace("\t", "") for key, value in base_query += f".property('{key}', '{value}')" return base_query
[docs] def add_node(self, node: Node, node_cache: dict = {}) -> Node: # if properties does not have label, add type as label if "label" not in["label"] = node.type if in node_cache: return node_cache[] else: query = self.build_vertex_query(node) _ = self.client.submit(query).all().result()[0] node_cache[] = node return node
[docs] def add_edge(self, relationship: Relationship) -> Any: query = self.build_edge_query(relationship) return self.client.submit(query).all().result()