LightRAG/lightrag/kg/nano_vector_db_impl.py

"""
NanoVectorDB Storage Module
=======================

This module provides a storage interface for graphs using NetworkX, a popular Python library for creating, manipulating, and studying the structure, dynamics, and functions of complex networks.

The `NetworkXStorage` class extends the `BaseGraphStorage` class from the LightRAG library, providing methods to load, save, manipulate, and query graphs using NetworkX.

Author: lightrag team
Created: 2024-01-25
License: MIT

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.

Version: 1.0.0

Dependencies:
    - NetworkX
    - NumPy
    - LightRAG
    - graspologic

Features:
    - Load and save graphs in various formats (e.g., GEXF, GraphML, JSON)
    - Query graph nodes and edges
    - Calculate node and edge degrees
    - Embed nodes using various algorithms (e.g., Node2Vec)
    - Remove nodes and edges from the graph

Usage:
    from lightrag.storage.networkx_storage import NetworkXStorage

"""

import asyncio
import os
from tqdm.asyncio import tqdm as tqdm_async
from dataclasses import dataclass
import numpy as np
import pipmaster as pm

if not pm.is_installed("nano-vectordb"):
    pm.install("nano-vectordb")

from nano_vectordb import NanoVectorDB
import time

from lightrag.utils import (
    logger,
    compute_mdhash_id,
)

from lightrag.base import (
    BaseVectorStorage,
)


@dataclass
class NanoVectorDBStorage(BaseVectorStorage):
    cosine_better_than_threshold: float = None

    def __post_init__(self):
        # Initialize lock only for file operations
        self._save_lock = asyncio.Lock()
        # Use global config value if specified, otherwise use default
        kwargs = self.global_config.get("vector_db_storage_cls_kwargs", {})
        cosine_threshold = kwargs.get("cosine_better_than_threshold")
        if cosine_threshold is None:
            raise ValueError(
                "cosine_better_than_threshold must be specified in vector_db_storage_cls_kwargs"
            )
        self.cosine_better_than_threshold = cosine_threshold

        self._client_file_name = os.path.join(
            self.global_config["working_dir"], f"vdb_{self.namespace}.json"
        )
        self._max_batch_size = self.global_config["embedding_batch_num"]
        self._client = NanoVectorDB(
            self.embedding_func.embedding_dim, storage_file=self._client_file_name
        )

    async def upsert(self, data: dict[str, dict]):
        logger.info(f"Inserting {len(data)} vectors to {self.namespace}")
        if not len(data):
            logger.warning("You insert an empty data to vector DB")
            return []

        current_time = time.time()
        list_data = [
            {
                "__id__": k,
                "__created_at__": current_time,
                **{k1: v1 for k1, v1 in v.items() if k1 in self.meta_fields},
            }
            for k, v in data.items()
        ]
        contents = [v["content"] for v in data.values()]
        batches = [
            contents[i : i + self._max_batch_size]
            for i in range(0, len(contents), self._max_batch_size)
        ]

        async def wrapped_task(batch):
            result = await self.embedding_func(batch)
            pbar.update(1)
            return result

        embedding_tasks = [wrapped_task(batch) for batch in batches]
        pbar = tqdm_async(
            total=len(embedding_tasks), desc="Generating embeddings", unit="batch"
        )
        embeddings_list = await asyncio.gather(*embedding_tasks)

        embeddings = np.concatenate(embeddings_list)
        if len(embeddings) == len(list_data):
            for i, d in enumerate(list_data):
                d["__vector__"] = embeddings[i]
            results = self._client.upsert(datas=list_data)
            return results
        else:
            # sometimes the embedding is not returned correctly. just log it.
            logger.error(
                f"embedding is not 1-1 with data, {len(embeddings)} != {len(list_data)}"
            )

    async def query(self, query: str, top_k=5):
        embedding = await self.embedding_func([query])
        embedding = embedding[0]
        results = self._client.query(
            query=embedding,
            top_k=top_k,
            better_than_threshold=self.cosine_better_than_threshold,
        )
        results = [
            {
                **dp,
                "id": dp["__id__"],
                "distance": dp["__metrics__"],
                "created_at": dp.get("__created_at__"),
            }
            for dp in results
        ]
        return results

    @property
    def client_storage(self):
        return getattr(self._client, "_NanoVectorDB__storage")

    async def delete(self, ids: list[str]):
        """Delete vectors with specified IDs

        Args:
            ids: List of vector IDs to be deleted
        """
        try:
            self._client.delete(ids)
            logger.info(
                f"Successfully deleted {len(ids)} vectors from {self.namespace}"
            )
        except Exception as e:
            logger.error(f"Error while deleting vectors from {self.namespace}: {e}")

    async def delete_entity(self, entity_name: str):
        try:
            entity_id = compute_mdhash_id(entity_name, prefix="ent-")
            logger.debug(
                f"Attempting to delete entity {entity_name} with ID {entity_id}"
            )
            # Check if the entity exists
            if self._client.get([entity_id]):
                await self.delete([entity_id])
                logger.debug(f"Successfully deleted entity {entity_name}")
            else:
                logger.debug(f"Entity {entity_name} not found in storage")
        except Exception as e:
            logger.error(f"Error deleting entity {entity_name}: {e}")

    async def delete_entity_relation(self, entity_name: str) -> None:
        try:
            relations = [
                dp
                for dp in self.client_storage["data"]
                if dp["src_id"] == entity_name or dp["tgt_id"] == entity_name
            ]
            logger.debug(f"Found {len(relations)} relations for entity {entity_name}")
            ids_to_delete = [relation["__id__"] for relation in relations]

            if ids_to_delete:
                await self.delete(ids_to_delete)
                logger.debug(
                    f"Deleted {len(ids_to_delete)} relations for {entity_name}"
                )
            else:
                logger.debug(f"No relations found for entity {entity_name}")
        except Exception as e:
            logger.error(f"Error deleting relations for {entity_name}: {e}")

    async def index_done_callback(self):
        # Protect file write operation
        async with self._save_lock:
            self._client.save()