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97 lines
4.0 KiB
Python
97 lines
4.0 KiB
Python
# -*- coding: utf-8 -*-
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# Copyright 2023 OpenSPG Authors
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#
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# Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except
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# in compliance with the License. You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software distributed under the License
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# is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express
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# or implied.
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import os
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from collections import defaultdict
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from typing import List
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from kag.builder.model.sub_graph import SubGraph
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from knext.common.base.runnable import Input, Output
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from kag.common.vectorizer import Vectorizer, Neo4jBatchVectorizer
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from kag.interface.builder.vectorizer_abc import VectorizerABC
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from knext.schema.client import SchemaClient
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from knext.project.client import ProjectClient
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from knext.schema.model.base import IndexTypeEnum
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class BatchVectorizer(VectorizerABC):
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def __init__(self, project_id: str = None, **kwargs):
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super().__init__(**kwargs)
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self.project_id = project_id or os.getenv("KAG_PROJECT_ID")
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self._init_graph_store()
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self.vec_meta = self._init_vec_meta()
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self.vectorizer = Vectorizer.from_config(self.vectorizer_config)
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def _init_graph_store(self):
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"""
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Initializes the Graph Store client.
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This method retrieves the graph store configuration from environment variables and the project ID.
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It then fetches the project configuration using the project ID and updates the graph store configuration
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with any additional settings from the project. Finally, it creates and initializes the graph store client
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using the updated configuration.
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Args:
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project_id (str): The id of project.
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Returns:
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GraphStore
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"""
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graph_store_config = eval(os.getenv("KAG_GRAPH_STORE", "{}"))
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vectorizer_config = eval(os.getenv("KAG_VECTORIZER", "{}"))
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config = ProjectClient().get_config(self.project_id)
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graph_store_config.update(config.get("graph_store", {}))
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vectorizer_config.update(config.get("vectorizer", {}))
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self.vectorizer_config = vectorizer_config
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def _init_vec_meta(self):
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vec_meta = defaultdict(list)
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schema_client = SchemaClient(project_id=self.project_id)
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spg_types = schema_client.load()
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for type_name, spg_type in spg_types.items():
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for prop_name, prop in spg_type.properties.items():
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if prop_name == "name" or prop.index_type in [IndexTypeEnum.Vector, IndexTypeEnum.TextAndVector]:
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vec_meta[type_name].append(self._create_vector_field_name(prop_name))
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return vec_meta
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def _create_vector_field_name(self, property_key):
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from kag.common.utils import to_snake_case
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name = f"{property_key}_vector"
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name = to_snake_case(name)
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return "_" + name
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def _neo4j_batch_vectorize(self, vectorizer: Vectorizer, input: SubGraph) -> SubGraph:
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node_list = []
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node_batch = []
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for node in input.nodes:
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if not node.id or not node.name:
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continue
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properties = {"id": node.id, "name": node.name}
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properties.update(node.properties)
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node_list.append((node, properties))
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node_batch.append((node.label, properties.copy()))
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batch_vectorizer = Neo4jBatchVectorizer(vectorizer, self.vec_meta)
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batch_vectorizer.batch_vectorize(node_batch)
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for (node, properties), (_node_label, new_properties) in zip(
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node_list, node_batch
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):
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for key, value in properties.items():
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if key in new_properties and new_properties[key] == value:
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del new_properties[key]
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node.properties.update(new_properties)
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return input
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def invoke(self, input: Input, **kwargs) -> List[Output]:
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modified_input = self._neo4j_batch_vectorize(self.vectorizer, input)
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return [modified_input]
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