hugegraph-llm/src/hugegraph_llm/indices/vector_index.py

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# to you under the Apache License, Version 2.0 (the
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#
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import logging
import os
import pickle as pkl
from copy import deepcopy
from typing import List, Dict, Any, Set, Union

import faiss
import numpy as np

from hugegraph_llm.utils.log import log

INDEX_FILE_NAME = "index.faiss"
PROPERTIES_FILE_NAME = "properties.pkl"


class VectorIndex:
    """Comment"""
    def __init__(self, embed_dim: int = 1024):
        self.index = faiss.IndexFlatL2(embed_dim)
        self.properties = []

    @staticmethod
    def from_index_file(dir_path: str) -> "VectorIndex":
        index_file = os.path.join(dir_path, INDEX_FILE_NAME)
        properties_file = os.path.join(dir_path, PROPERTIES_FILE_NAME)
        if not os.path.exists(index_file) or not os.path.exists(properties_file):
            log.warning("No index file found, create a new one.")
            return VectorIndex()
        faiss_index = faiss.read_index(index_file)
        embed_dim = faiss_index.d
        with open(properties_file, "rb") as f:
            properties = pkl.load(f)
        vector_index = VectorIndex(embed_dim)
        vector_index.index = faiss_index
        vector_index.properties = properties
        return vector_index

    def to_index_file(self, dir_path: str):
        if not os.path.exists(dir_path):
            os.makedirs(dir_path)
        index_file = os.path.join(dir_path, INDEX_FILE_NAME)
        properties_file = os.path.join(dir_path, PROPERTIES_FILE_NAME)
        faiss.write_index(self.index, index_file)
        with open(properties_file, "wb") as f:
            pkl.dump(self.properties, f)

    def add(self, vectors: List[List[float]], props: List[Any]):
        if len(vectors) == 0:
            return
        if self.index.ntotal == 0 and len(vectors[0]) != self.index.d:
            self.index = faiss.IndexFlatL2(len(vectors[0]))
        self.index.add(np.array(vectors))
        self.properties.extend(props)

    def remove(self, props: Union[Set[Any], List[Any]]) -> int:
        if isinstance(props, list):
            props = set(props)
        indices = []
        remove_num = 0
        for i, p in enumerate(self.properties):
            if p in props:
                indices.append(i)
                remove_num += 1
        self.index.remove_ids(np.array(indices))
        self.properties = [p for i, p in enumerate(self.properties) if i not in indices]
        return remove_num

    def search(self, query_vector: List[float], top_k: int) -> List[Dict[str, Any]]:
        if self.index.ntotal == 0:
            return []
        if len(query_vector) != self.index.d:
            raise ValueError("Query vector dimension does not match index dimension!")
        _, indices = self.index.search(np.array([query_vector]), top_k)
        results = []
        for i in indices[0]:
            results.append(deepcopy(self.properties[i]))
        return results

    @staticmethod
    def clean(dir_path: str):
        index_file = os.path.join(dir_path, INDEX_FILE_NAME)
        properties_file = os.path.join(dir_path, PROPERTIES_FILE_NAME)
        if os.path.exists(index_file):
            os.remove(index_file)
        if os.path.exists(properties_file):
            os.remove(properties_file)