302 lines
10 KiB
Python
302 lines
10 KiB
Python
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import asyncio
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import html
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import os
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from dataclasses import dataclass
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from typing import Any, Union, cast
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import networkx as nx
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import numpy as np
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from nano_vectordb import NanoVectorDB
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from .utils import (
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logger,
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load_json,
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write_json,
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compute_mdhash_id,
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)
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from .base import (
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BaseGraphStorage,
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BaseKVStorage,
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BaseVectorStorage,
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)
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@dataclass
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class JsonKVStorage(BaseKVStorage):
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def __post_init__(self):
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working_dir = self.global_config["working_dir"]
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self._file_name = os.path.join(working_dir, f"kv_store_{self.namespace}.json")
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self._data = load_json(self._file_name) or {}
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logger.info(f"Load KV {self.namespace} with {len(self._data)} data")
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async def all_keys(self) -> list[str]:
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return list(self._data.keys())
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async def index_done_callback(self):
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write_json(self._data, self._file_name)
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async def get_by_id(self, id):
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return self._data.get(id, None)
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async def get_by_ids(self, ids, fields=None):
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if fields is None:
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return [self._data.get(id, None) for id in ids]
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return [
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(
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{k: v for k, v in self._data[id].items() if k in fields}
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if self._data.get(id, None)
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else None
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)
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for id in ids
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]
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async def filter_keys(self, data: list[str]) -> set[str]:
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return set([s for s in data if s not in self._data])
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async def upsert(self, data: dict[str, dict]):
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left_data = {k: v for k, v in data.items() if k not in self._data}
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self._data.update(left_data)
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return left_data
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async def drop(self):
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self._data = {}
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@dataclass
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class NanoVectorDBStorage(BaseVectorStorage):
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cosine_better_than_threshold: float = 0.2
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def __post_init__(self):
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self._client_file_name = os.path.join(
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self.global_config["working_dir"], f"vdb_{self.namespace}.json"
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)
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self._max_batch_size = self.global_config["embedding_batch_num"]
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self._client = NanoVectorDB(
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self.embedding_func.embedding_dim, storage_file=self._client_file_name
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)
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self.cosine_better_than_threshold = self.global_config.get(
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"cosine_better_than_threshold", self.cosine_better_than_threshold
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)
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async def upsert(self, data: dict[str, dict]):
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logger.info(f"Inserting {len(data)} vectors to {self.namespace}")
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if not len(data):
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logger.warning("You insert an empty data to vector DB")
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return []
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list_data = [
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{
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"__id__": k,
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**{k1: v1 for k1, v1 in v.items() if k1 in self.meta_fields},
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}
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for k, v in data.items()
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]
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contents = [v["content"] for v in data.values()]
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batches = [
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contents[i : i + self._max_batch_size]
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for i in range(0, len(contents), self._max_batch_size)
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]
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embeddings_list = await asyncio.gather(
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*[self.embedding_func(batch) for batch in batches]
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)
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embeddings = np.concatenate(embeddings_list)
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for i, d in enumerate(list_data):
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d["__vector__"] = embeddings[i]
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results = self._client.upsert(datas=list_data)
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return results
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async def query(self, query: str, top_k=5):
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embedding = await self.embedding_func([query])
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embedding = embedding[0]
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results = self._client.query(
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query=embedding,
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top_k=top_k,
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better_than_threshold=self.cosine_better_than_threshold,
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)
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results = [
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{**dp, "id": dp["__id__"], "distance": dp["__metrics__"]} for dp in results
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]
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return results
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@property
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def client_storage(self):
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return getattr(self._client, "_NanoVectorDB__storage")
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async def delete_entity(self, entity_name: str):
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try:
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entity_id = [compute_mdhash_id(entity_name, prefix="ent-")]
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if self._client.get(entity_id):
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self._client.delete(entity_id)
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logger.info(f"Entity {entity_name} have been deleted.")
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else:
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logger.info(f"No entity found with name {entity_name}.")
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except Exception as e:
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logger.error(f"Error while deleting entity {entity_name}: {e}")
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async def delete_relation(self, entity_name: str):
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try:
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relations = [
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dp
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for dp in self.client_storage["data"]
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if dp["src_id"] == entity_name or dp["tgt_id"] == entity_name
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]
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ids_to_delete = [relation["__id__"] for relation in relations]
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if ids_to_delete:
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self._client.delete(ids_to_delete)
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logger.info(
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f"All relations related to entity {entity_name} have been deleted."
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)
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else:
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logger.info(f"No relations found for entity {entity_name}.")
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except Exception as e:
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logger.error(
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f"Error while deleting relations for entity {entity_name}: {e}"
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)
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async def index_done_callback(self):
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self._client.save()
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@dataclass
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class NetworkXStorage(BaseGraphStorage):
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@staticmethod
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def load_nx_graph(file_name) -> nx.Graph:
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if os.path.exists(file_name):
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return nx.read_graphml(file_name)
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return None
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@staticmethod
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def write_nx_graph(graph: nx.Graph, file_name):
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logger.info(
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f"Writing graph with {graph.number_of_nodes()} nodes, {graph.number_of_edges()} edges"
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)
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nx.write_graphml(graph, file_name)
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@staticmethod
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def stable_largest_connected_component(graph: nx.Graph) -> nx.Graph:
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"""Refer to https://github.com/microsoft/graphrag/index/graph/utils/stable_lcc.py
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Return the largest connected component of the graph, with nodes and edges sorted in a stable way.
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"""
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from graspologic.utils import largest_connected_component
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graph = graph.copy()
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graph = cast(nx.Graph, largest_connected_component(graph))
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node_mapping = {
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node: html.unescape(node.upper().strip()) for node in graph.nodes()
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} # type: ignore
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graph = nx.relabel_nodes(graph, node_mapping)
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return NetworkXStorage._stabilize_graph(graph)
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@staticmethod
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def _stabilize_graph(graph: nx.Graph) -> nx.Graph:
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"""Refer to https://github.com/microsoft/graphrag/index/graph/utils/stable_lcc.py
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Ensure an undirected graph with the same relationships will always be read the same way.
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"""
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fixed_graph = nx.DiGraph() if graph.is_directed() else nx.Graph()
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sorted_nodes = graph.nodes(data=True)
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sorted_nodes = sorted(sorted_nodes, key=lambda x: x[0])
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fixed_graph.add_nodes_from(sorted_nodes)
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edges = list(graph.edges(data=True))
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if not graph.is_directed():
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def _sort_source_target(edge):
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source, target, edge_data = edge
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if source > target:
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temp = source
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source = target
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target = temp
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return source, target, edge_data
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edges = [_sort_source_target(edge) for edge in edges]
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def _get_edge_key(source: Any, target: Any) -> str:
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return f"{source} -> {target}"
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edges = sorted(edges, key=lambda x: _get_edge_key(x[0], x[1]))
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fixed_graph.add_edges_from(edges)
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return fixed_graph
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def __post_init__(self):
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self._graphml_xml_file = os.path.join(
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self.global_config["working_dir"], f"graph_{self.namespace}.graphml"
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)
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preloaded_graph = NetworkXStorage.load_nx_graph(self._graphml_xml_file)
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if preloaded_graph is not None:
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logger.info(
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f"Loaded graph from {self._graphml_xml_file} with {preloaded_graph.number_of_nodes()} nodes, {preloaded_graph.number_of_edges()} edges"
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)
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self._graph = preloaded_graph or nx.Graph()
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self._node_embed_algorithms = {
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"node2vec": self._node2vec_embed,
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}
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async def index_done_callback(self):
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NetworkXStorage.write_nx_graph(self._graph, self._graphml_xml_file)
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async def has_node(self, node_id: str) -> bool:
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return self._graph.has_node(node_id)
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async def has_edge(self, source_node_id: str, target_node_id: str) -> bool:
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return self._graph.has_edge(source_node_id, target_node_id)
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async def get_node(self, node_id: str) -> Union[dict, None]:
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return self._graph.nodes.get(node_id)
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async def node_degree(self, node_id: str) -> int:
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return self._graph.degree(node_id)
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async def edge_degree(self, src_id: str, tgt_id: str) -> int:
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return self._graph.degree(src_id) + self._graph.degree(tgt_id)
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async def get_edge(
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self, source_node_id: str, target_node_id: str
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) -> Union[dict, None]:
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return self._graph.edges.get((source_node_id, target_node_id))
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async def get_node_edges(self, source_node_id: str):
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if self._graph.has_node(source_node_id):
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return list(self._graph.edges(source_node_id))
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return None
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async def upsert_node(self, node_id: str, node_data: dict[str, str]):
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self._graph.add_node(node_id, **node_data)
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async def upsert_edge(
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self, source_node_id: str, target_node_id: str, edge_data: dict[str, str]
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):
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self._graph.add_edge(source_node_id, target_node_id, **edge_data)
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async def delete_node(self, node_id: str):
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"""
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Delete a node from the graph based on the specified node_id.
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:param node_id: The node_id to delete
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"""
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if self._graph.has_node(node_id):
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self._graph.remove_node(node_id)
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logger.info(f"Node {node_id} deleted from the graph.")
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else:
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logger.warning(f"Node {node_id} not found in the graph for deletion.")
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async def embed_nodes(self, algorithm: str) -> tuple[np.ndarray, list[str]]:
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if algorithm not in self._node_embed_algorithms:
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raise ValueError(f"Node embedding algorithm {algorithm} not supported")
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return await self._node_embed_algorithms[algorithm]()
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# @TODO: NOT USED
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async def _node2vec_embed(self):
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from graspologic import embed
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embeddings, nodes = embed.node2vec_embed(
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self._graph,
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**self.global_config["node2vec_params"],
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)
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nodes_ids = [self._graph.nodes[node_id]["id"] for node_id in nodes]
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return embeddings, nodes_ids
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