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 https://python.langchain.com/docs/security 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(el.target, 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','{node.id}').fold()"
+ f".coalesce(unfold(),addV('{node.type}')"
+ f".property('id','{node.id}')"
+ f".property('type','{node.type}')"
)
for key, value in node.properties.items():
base_query += f".property('{key}', '{value}')"
return base_query + ")"
[docs]
def build_edge_query(self, relationship: Relationship) -> str:
source_query = f".has('id','{relationship.source.id}')"
target_query = f".has('id','{relationship.target.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 relationship.properties.items():
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 node.properties:
node.properties["label"] = node.type
if node.id in node_cache:
return node_cache[node.id]
else:
query = self.build_vertex_query(node)
_ = self.client.submit(query).all().result()[0]
node_cache[node.id] = node
return node
[docs]
def add_edge(self, relationship: Relationship) -> Any:
query = self.build_edge_query(relationship)
return self.client.submit(query).all().result()
