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1245 lines
46 KiB
1245 lines
46 KiB
import os
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from dataclasses import dataclass, field
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import numpy as np
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import configparser
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import asyncio
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from typing import Any, List, Union, final
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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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DocProcessingStatus,
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DocStatus,
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DocStatusStorage,
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)
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from ..namespace import NameSpace, is_namespace
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from ..utils import logger, compute_mdhash_id
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from ..types import KnowledgeGraph, KnowledgeGraphNode, KnowledgeGraphEdge
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import pipmaster as pm
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if not pm.is_installed("pymongo"):
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pm.install("pymongo")
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if not pm.is_installed("motor"):
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pm.install("motor")
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from motor.motor_asyncio import ( # type: ignore
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AsyncIOMotorClient,
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AsyncIOMotorDatabase,
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AsyncIOMotorCollection,
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)
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from pymongo.operations import SearchIndexModel # type: ignore
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from pymongo.errors import PyMongoError # type: ignore
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config = configparser.ConfigParser()
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config.read("config.ini", "utf-8")
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class ClientManager:
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_instances = {"db": None, "ref_count": 0}
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_lock = asyncio.Lock()
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@classmethod
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async def get_client(cls) -> AsyncIOMotorDatabase:
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async with cls._lock:
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if cls._instances["db"] is None:
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uri = os.environ.get(
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"MONGO_URI",
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config.get(
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"mongodb",
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"uri",
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fallback="mongodb://root:root@localhost:27017/",
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),
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)
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database_name = os.environ.get(
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"MONGO_DATABASE",
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config.get("mongodb", "database", fallback="LightRAG"),
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)
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client = AsyncIOMotorClient(uri)
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db = client.get_database(database_name)
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cls._instances["db"] = db
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cls._instances["ref_count"] = 0
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cls._instances["ref_count"] += 1
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return cls._instances["db"]
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@classmethod
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async def release_client(cls, db: AsyncIOMotorDatabase):
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async with cls._lock:
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if db is not None:
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if db is cls._instances["db"]:
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cls._instances["ref_count"] -= 1
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if cls._instances["ref_count"] == 0:
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cls._instances["db"] = None
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@final
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@dataclass
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class MongoKVStorage(BaseKVStorage):
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db: AsyncIOMotorDatabase = field(default=None)
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_data: AsyncIOMotorCollection = field(default=None)
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def __post_init__(self):
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self._collection_name = self.namespace
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async def initialize(self):
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if self.db is None:
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self.db = await ClientManager.get_client()
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self._data = await get_or_create_collection(self.db, self._collection_name)
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logger.debug(f"Use MongoDB as KV {self._collection_name}")
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async def finalize(self):
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if self.db is not None:
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await ClientManager.release_client(self.db)
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self.db = None
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self._data = None
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async def get_by_id(self, id: str) -> dict[str, Any] | None:
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return await self._data.find_one({"_id": id})
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async def get_by_ids(self, ids: list[str]) -> list[dict[str, Any]]:
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cursor = self._data.find({"_id": {"$in": ids}})
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return await cursor.to_list()
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async def filter_keys(self, keys: set[str]) -> set[str]:
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cursor = self._data.find({"_id": {"$in": list(keys)}}, {"_id": 1})
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existing_ids = {str(x["_id"]) async for x in cursor}
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return keys - existing_ids
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async def upsert(self, data: dict[str, dict[str, Any]]) -> None:
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logger.info(f"Inserting {len(data)} to {self.namespace}")
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if not data:
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return
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if is_namespace(self.namespace, NameSpace.KV_STORE_LLM_RESPONSE_CACHE):
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update_tasks: list[Any] = []
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for mode, items in data.items():
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for k, v in items.items():
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key = f"{mode}_{k}"
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data[mode][k]["_id"] = f"{mode}_{k}"
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update_tasks.append(
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self._data.update_one(
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{"_id": key}, {"$setOnInsert": v}, upsert=True
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)
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)
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await asyncio.gather(*update_tasks)
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else:
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update_tasks = []
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for k, v in data.items():
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data[k]["_id"] = k
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update_tasks.append(
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self._data.update_one({"_id": k}, {"$set": v}, upsert=True)
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)
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await asyncio.gather(*update_tasks)
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async def get_by_mode_and_id(self, mode: str, id: str) -> Union[dict, None]:
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if is_namespace(self.namespace, NameSpace.KV_STORE_LLM_RESPONSE_CACHE):
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res = {}
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v = await self._data.find_one({"_id": mode + "_" + id})
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if v:
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res[id] = v
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logger.debug(f"llm_response_cache find one by:{id}")
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return res
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else:
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return None
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else:
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return None
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async def index_done_callback(self) -> None:
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# Mongo handles persistence automatically
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pass
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async def delete(self, ids: list[str]) -> None:
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"""Delete documents with specified IDs
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Args:
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ids: List of document IDs to be deleted
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"""
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if not ids:
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return
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try:
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result = await self._data.delete_many({"_id": {"$in": ids}})
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logger.info(
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f"Deleted {result.deleted_count} documents from {self.namespace}"
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)
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except PyMongoError as e:
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logger.error(f"Error deleting documents from {self.namespace}: {e}")
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async def drop_cache_by_modes(self, modes: list[str] | None = None) -> bool:
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"""Delete specific records from storage by cache mode
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Args:
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modes (list[str]): List of cache modes to be dropped from storage
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Returns:
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bool: True if successful, False otherwise
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"""
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if not modes:
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return False
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try:
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# Build regex pattern to match documents with the specified modes
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pattern = f"^({'|'.join(modes)})_"
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result = await self._data.delete_many({"_id": {"$regex": pattern}})
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logger.info(f"Deleted {result.deleted_count} documents by modes: {modes}")
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return True
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except Exception as e:
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logger.error(f"Error deleting cache by modes {modes}: {e}")
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return False
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async def drop(self) -> dict[str, str]:
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"""Drop the storage by removing all documents in the collection.
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Returns:
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dict[str, str]: Status of the operation with keys 'status' and 'message'
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"""
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try:
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result = await self._data.delete_many({})
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deleted_count = result.deleted_count
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logger.info(
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f"Dropped {deleted_count} documents from doc status {self._collection_name}"
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)
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return {
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"status": "success",
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"message": f"{deleted_count} documents dropped",
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}
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except PyMongoError as e:
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logger.error(f"Error dropping doc status {self._collection_name}: {e}")
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return {"status": "error", "message": str(e)}
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@final
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@dataclass
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class MongoDocStatusStorage(DocStatusStorage):
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db: AsyncIOMotorDatabase = field(default=None)
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_data: AsyncIOMotorCollection = field(default=None)
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def __post_init__(self):
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self._collection_name = self.namespace
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async def initialize(self):
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if self.db is None:
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self.db = await ClientManager.get_client()
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self._data = await get_or_create_collection(self.db, self._collection_name)
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logger.debug(f"Use MongoDB as DocStatus {self._collection_name}")
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async def finalize(self):
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if self.db is not None:
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await ClientManager.release_client(self.db)
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self.db = None
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self._data = None
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async def get_by_id(self, id: str) -> Union[dict[str, Any], None]:
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return await self._data.find_one({"_id": id})
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async def get_by_ids(self, ids: list[str]) -> list[dict[str, Any]]:
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cursor = self._data.find({"_id": {"$in": ids}})
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return await cursor.to_list()
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async def filter_keys(self, data: set[str]) -> set[str]:
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cursor = self._data.find({"_id": {"$in": list(data)}}, {"_id": 1})
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existing_ids = {str(x["_id"]) async for x in cursor}
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return data - existing_ids
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async def upsert(self, data: dict[str, dict[str, Any]]) -> None:
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logger.info(f"Inserting {len(data)} to {self.namespace}")
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if not data:
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return
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update_tasks: list[Any] = []
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for k, v in data.items():
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data[k]["_id"] = k
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update_tasks.append(
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self._data.update_one({"_id": k}, {"$set": v}, upsert=True)
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)
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await asyncio.gather(*update_tasks)
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async def get_status_counts(self) -> dict[str, int]:
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"""Get counts of documents in each status"""
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pipeline = [{"$group": {"_id": "$status", "count": {"$sum": 1}}}]
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cursor = self._data.aggregate(pipeline)
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result = await cursor.to_list()
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counts = {}
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for doc in result:
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counts[doc["_id"]] = doc["count"]
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return counts
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async def get_docs_by_status(
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self, status: DocStatus
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) -> dict[str, DocProcessingStatus]:
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"""Get all documents with a specific status"""
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cursor = self._data.find({"status": status.value})
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result = await cursor.to_list()
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return {
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doc["_id"]: DocProcessingStatus(
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content=doc["content"],
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content_summary=doc.get("content_summary"),
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content_length=doc["content_length"],
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status=doc["status"],
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created_at=doc.get("created_at"),
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updated_at=doc.get("updated_at"),
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chunks_count=doc.get("chunks_count", -1),
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file_path=doc.get("file_path", doc["_id"]),
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)
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for doc in result
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}
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async def index_done_callback(self) -> None:
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# Mongo handles persistence automatically
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pass
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async def drop(self) -> dict[str, str]:
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"""Drop the storage by removing all documents in the collection.
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|
Returns:
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dict[str, str]: Status of the operation with keys 'status' and 'message'
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"""
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try:
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result = await self._data.delete_many({})
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deleted_count = result.deleted_count
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logger.info(
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f"Dropped {deleted_count} documents from doc status {self._collection_name}"
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)
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return {
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"status": "success",
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"message": f"{deleted_count} documents dropped",
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}
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except PyMongoError as e:
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logger.error(f"Error dropping doc status {self._collection_name}: {e}")
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return {"status": "error", "message": str(e)}
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|
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@final
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@dataclass
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class MongoGraphStorage(BaseGraphStorage):
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"""
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A concrete implementation using MongoDB's $graphLookup to demonstrate multi-hop queries.
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"""
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db: AsyncIOMotorDatabase = field(default=None)
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collection: AsyncIOMotorCollection = field(default=None)
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def __init__(self, namespace, global_config, embedding_func):
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super().__init__(
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namespace=namespace,
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global_config=global_config,
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embedding_func=embedding_func,
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)
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self._collection_name = self.namespace
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async def initialize(self):
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if self.db is None:
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self.db = await ClientManager.get_client()
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self.collection = await get_or_create_collection(
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self.db, self._collection_name
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)
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logger.debug(f"Use MongoDB as KG {self._collection_name}")
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async def finalize(self):
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if self.db is not None:
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await ClientManager.release_client(self.db)
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self.db = None
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self.collection = None
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|
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#
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# -------------------------------------------------------------------------
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# HELPER: $graphLookup pipeline
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# -------------------------------------------------------------------------
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#
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async def _graph_lookup(
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self, start_node_id: str, max_depth: int = None
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) -> List[dict]:
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"""
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Performs a $graphLookup starting from 'start_node_id' and returns
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all reachable documents (including the start node itself).
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Pipeline Explanation:
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- 1) $match: We match the start node document by _id = start_node_id.
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- 2) $graphLookup:
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"from": same collection,
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"startWith": "$edges.target" (the immediate neighbors in 'edges'),
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"connectFromField": "edges.target",
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"connectToField": "_id",
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"as": "reachableNodes",
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"maxDepth": max_depth (if provided),
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"depthField": "depth" (used for debugging or filtering).
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- 3) We add an $project or $unwind as needed to extract data.
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"""
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pipeline = [
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{"$match": {"_id": start_node_id}},
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{
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"$graphLookup": {
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"from": self.collection.name,
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"startWith": "$edges.target",
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"connectFromField": "edges.target",
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"connectToField": "_id",
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"as": "reachableNodes",
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"depthField": "depth",
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}
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},
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]
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# If you want a limited depth (e.g., only 1 or 2 hops), set maxDepth
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if max_depth is not None:
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pipeline[1]["$graphLookup"]["maxDepth"] = max_depth
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# Return the matching doc plus a field "reachableNodes"
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cursor = self.collection.aggregate(pipeline)
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results = await cursor.to_list(None)
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# If there's no matching node, results = [].
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# Otherwise, results[0] is the start node doc,
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# plus results[0]["reachableNodes"] is the array of connected docs.
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return results
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|
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#
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# -------------------------------------------------------------------------
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# BASIC QUERIES
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# -------------------------------------------------------------------------
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#
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async def has_node(self, node_id: str) -> bool:
|
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"""
|
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Check if node_id is present in the collection by looking up its doc.
|
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No real need for $graphLookup here, but let's keep it direct.
|
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"""
|
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doc = await self.collection.find_one({"_id": node_id}, {"_id": 1})
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return doc is not None
|
|
|
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async def has_edge(self, source_node_id: str, target_node_id: str) -> bool:
|
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"""
|
|
Check if there's a direct single-hop edge from source_node_id to target_node_id.
|
|
|
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We'll do a $graphLookup with maxDepth=0 from the source node—meaning
|
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"Look up zero expansions." Actually, for a direct edge check, we can do maxDepth=1
|
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and then see if the target node is in the "reachableNodes" at depth=0.
|
|
|
|
But typically for a direct edge, we might just do a find_one.
|
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Below is a demonstration approach.
|
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"""
|
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# We can do a single-hop graphLookup (maxDepth=0 or 1).
|
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# Then check if the target_node appears among the edges array.
|
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pipeline = [
|
|
{"$match": {"_id": source_node_id}},
|
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{
|
|
"$graphLookup": {
|
|
"from": self.collection.name,
|
|
"startWith": "$edges.target",
|
|
"connectFromField": "edges.target",
|
|
"connectToField": "_id",
|
|
"as": "reachableNodes",
|
|
"depthField": "depth",
|
|
"maxDepth": 0, # means: do not follow beyond immediate edges
|
|
}
|
|
},
|
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{
|
|
"$project": {
|
|
"_id": 0,
|
|
"reachableNodes._id": 1, # only keep the _id from the subdocs
|
|
}
|
|
},
|
|
]
|
|
cursor = self.collection.aggregate(pipeline)
|
|
results = await cursor.to_list(None)
|
|
if not results:
|
|
return False
|
|
|
|
# results[0]["reachableNodes"] are the immediate neighbors
|
|
reachable_ids = [d["_id"] for d in results[0].get("reachableNodes", [])]
|
|
return target_node_id in reachable_ids
|
|
|
|
#
|
|
# -------------------------------------------------------------------------
|
|
# DEGREES
|
|
# -------------------------------------------------------------------------
|
|
#
|
|
|
|
async def node_degree(self, node_id: str) -> int:
|
|
"""
|
|
Returns the total number of edges connected to node_id (both inbound and outbound).
|
|
The easiest approach is typically two queries:
|
|
- count of edges array in node_id's doc
|
|
- count of how many other docs have node_id in their edges.target.
|
|
|
|
But we'll do a $graphLookup demonstration for inbound edges:
|
|
1) Outbound edges: direct from node's edges array
|
|
2) Inbound edges: we can do a special $graphLookup from all docs
|
|
or do an explicit match.
|
|
|
|
For demonstration, let's do this in two steps (with second step $graphLookup).
|
|
"""
|
|
# --- 1) Outbound edges (direct from doc) ---
|
|
doc = await self.collection.find_one({"_id": node_id}, {"edges": 1})
|
|
if not doc:
|
|
return 0
|
|
outbound_count = len(doc.get("edges", []))
|
|
|
|
# --- 2) Inbound edges:
|
|
# A simple way is: find all docs where "edges.target" == node_id.
|
|
# But let's do a $graphLookup from `node_id` in REVERSE.
|
|
# There's a trick to do "reverse" graphLookups: you'd store
|
|
# reversed edges or do a more advanced pipeline. Typically you'd do
|
|
# a direct match. We'll just do a direct match for inbound.
|
|
inbound_count_pipeline = [
|
|
{"$match": {"edges.target": node_id}},
|
|
{
|
|
"$project": {
|
|
"matchingEdgesCount": {
|
|
"$size": {
|
|
"$filter": {
|
|
"input": "$edges",
|
|
"as": "edge",
|
|
"cond": {"$eq": ["$$edge.target", node_id]},
|
|
}
|
|
}
|
|
}
|
|
}
|
|
},
|
|
{"$group": {"_id": None, "totalInbound": {"$sum": "$matchingEdgesCount"}}},
|
|
]
|
|
inbound_cursor = self.collection.aggregate(inbound_count_pipeline)
|
|
inbound_result = await inbound_cursor.to_list(None)
|
|
inbound_count = inbound_result[0]["totalInbound"] if inbound_result else 0
|
|
|
|
return outbound_count + inbound_count
|
|
|
|
async def edge_degree(self, src_id: str, tgt_id: str) -> int:
|
|
"""
|
|
If your graph can hold multiple edges from the same src to the same tgt
|
|
(e.g. different 'relation' values), you can sum them. If it's always
|
|
one edge, this is typically 1 or 0.
|
|
|
|
We'll do a single-hop $graphLookup from src_id,
|
|
then count how many edges reference tgt_id at depth=0.
|
|
"""
|
|
pipeline = [
|
|
{"$match": {"_id": src_id}},
|
|
{
|
|
"$graphLookup": {
|
|
"from": self.collection.name,
|
|
"startWith": "$edges.target",
|
|
"connectFromField": "edges.target",
|
|
"connectToField": "_id",
|
|
"as": "neighbors",
|
|
"depthField": "depth",
|
|
"maxDepth": 0,
|
|
}
|
|
},
|
|
{"$project": {"edges": 1, "neighbors._id": 1, "neighbors.type": 1}},
|
|
]
|
|
cursor = self.collection.aggregate(pipeline)
|
|
results = await cursor.to_list(None)
|
|
if not results:
|
|
return 0
|
|
|
|
# We can simply count how many edges in `results[0].edges` have target == tgt_id.
|
|
edges = results[0].get("edges", [])
|
|
count = sum(1 for e in edges if e.get("target") == tgt_id)
|
|
return count
|
|
|
|
#
|
|
# -------------------------------------------------------------------------
|
|
# GETTERS
|
|
# -------------------------------------------------------------------------
|
|
#
|
|
|
|
async def get_node(self, node_id: str) -> dict[str, str] | None:
|
|
"""
|
|
Return the full node document (including "edges"), or None if missing.
|
|
"""
|
|
return await self.collection.find_one({"_id": node_id})
|
|
|
|
async def get_edge(
|
|
self, source_node_id: str, target_node_id: str
|
|
) -> dict[str, str] | None:
|
|
pipeline = [
|
|
{"$match": {"_id": source_node_id}},
|
|
{
|
|
"$graphLookup": {
|
|
"from": self.collection.name,
|
|
"startWith": "$edges.target",
|
|
"connectFromField": "edges.target",
|
|
"connectToField": "_id",
|
|
"as": "neighbors",
|
|
"depthField": "depth",
|
|
"maxDepth": 0,
|
|
}
|
|
},
|
|
{"$project": {"edges": 1}},
|
|
]
|
|
cursor = self.collection.aggregate(pipeline)
|
|
docs = await cursor.to_list(None)
|
|
if not docs:
|
|
return None
|
|
|
|
for e in docs[0].get("edges", []):
|
|
if e.get("target") == target_node_id:
|
|
return e
|
|
return None
|
|
|
|
async def get_node_edges(self, source_node_id: str) -> list[tuple[str, str]] | None:
|
|
"""
|
|
Return a list of (source_id, target_id) for direct edges from source_node_id.
|
|
Demonstrates $graphLookup at maxDepth=0, though direct doc retrieval is simpler.
|
|
"""
|
|
pipeline = [
|
|
{"$match": {"_id": source_node_id}},
|
|
{
|
|
"$graphLookup": {
|
|
"from": self.collection.name,
|
|
"startWith": "$edges.target",
|
|
"connectFromField": "edges.target",
|
|
"connectToField": "_id",
|
|
"as": "neighbors",
|
|
"depthField": "depth",
|
|
"maxDepth": 0,
|
|
}
|
|
},
|
|
{"$project": {"_id": 0, "edges": 1}},
|
|
]
|
|
cursor = self.collection.aggregate(pipeline)
|
|
result = await cursor.to_list(None)
|
|
if not result:
|
|
return None
|
|
|
|
edges = result[0].get("edges", [])
|
|
return [(source_node_id, e["target"]) for e in edges]
|
|
|
|
#
|
|
# -------------------------------------------------------------------------
|
|
# UPSERTS
|
|
# -------------------------------------------------------------------------
|
|
#
|
|
|
|
async def upsert_node(self, node_id: str, node_data: dict[str, str]) -> None:
|
|
"""
|
|
Insert or update a node document. If new, create an empty edges array.
|
|
"""
|
|
# By default, preserve existing 'edges'.
|
|
# We'll only set 'edges' to [] on insert (no overwrite).
|
|
update_doc = {"$set": {**node_data}, "$setOnInsert": {"edges": []}}
|
|
await self.collection.update_one({"_id": node_id}, update_doc, upsert=True)
|
|
|
|
async def upsert_edge(
|
|
self, source_node_id: str, target_node_id: str, edge_data: dict[str, str]
|
|
) -> None:
|
|
"""
|
|
Upsert an edge from source_node_id -> target_node_id with optional 'relation'.
|
|
If an edge with the same target exists, we remove it and re-insert with updated data.
|
|
"""
|
|
# Ensure source node exists
|
|
await self.upsert_node(source_node_id, {})
|
|
|
|
# Remove existing edge (if any)
|
|
await self.collection.update_one(
|
|
{"_id": source_node_id}, {"$pull": {"edges": {"target": target_node_id}}}
|
|
)
|
|
|
|
# Insert new edge
|
|
new_edge = {"target": target_node_id}
|
|
new_edge.update(edge_data)
|
|
await self.collection.update_one(
|
|
{"_id": source_node_id}, {"$push": {"edges": new_edge}}
|
|
)
|
|
|
|
#
|
|
# -------------------------------------------------------------------------
|
|
# DELETION
|
|
# -------------------------------------------------------------------------
|
|
#
|
|
|
|
async def delete_node(self, node_id: str) -> None:
|
|
"""
|
|
1) Remove node's doc entirely.
|
|
2) Remove inbound edges from any doc that references node_id.
|
|
"""
|
|
# Remove inbound edges from all other docs
|
|
await self.collection.update_many({}, {"$pull": {"edges": {"target": node_id}}})
|
|
|
|
# Remove the node doc
|
|
await self.collection.delete_one({"_id": node_id})
|
|
|
|
#
|
|
# -------------------------------------------------------------------------
|
|
# QUERY
|
|
# -------------------------------------------------------------------------
|
|
#
|
|
|
|
async def get_all_labels(self) -> list[str]:
|
|
"""
|
|
Get all existing node _id in the database
|
|
Returns:
|
|
[id1, id2, ...] # Alphabetically sorted id list
|
|
"""
|
|
# Use MongoDB's distinct and aggregation to get all unique labels
|
|
pipeline = [
|
|
{"$group": {"_id": "$_id"}}, # Group by _id
|
|
{"$sort": {"_id": 1}}, # Sort alphabetically
|
|
]
|
|
|
|
cursor = self.collection.aggregate(pipeline)
|
|
labels = []
|
|
async for doc in cursor:
|
|
labels.append(doc["_id"])
|
|
return labels
|
|
|
|
async def get_knowledge_graph(
|
|
self, node_label: str, max_depth: int = 5
|
|
) -> KnowledgeGraph:
|
|
"""
|
|
Get complete connected subgraph for specified node (including the starting node itself)
|
|
|
|
Args:
|
|
node_label: Label of the nodes to start from
|
|
max_depth: Maximum depth of traversal (default: 5)
|
|
|
|
Returns:
|
|
KnowledgeGraph object containing nodes and edges of the subgraph
|
|
"""
|
|
label = node_label
|
|
result = KnowledgeGraph()
|
|
seen_nodes = set()
|
|
seen_edges = set()
|
|
|
|
try:
|
|
if label == "*":
|
|
# Get all nodes and edges
|
|
async for node_doc in self.collection.find({}):
|
|
node_id = str(node_doc["_id"])
|
|
if node_id not in seen_nodes:
|
|
result.nodes.append(
|
|
KnowledgeGraphNode(
|
|
id=node_id,
|
|
labels=[node_doc.get("_id")],
|
|
properties={
|
|
k: v
|
|
for k, v in node_doc.items()
|
|
if k not in ["_id", "edges"]
|
|
},
|
|
)
|
|
)
|
|
seen_nodes.add(node_id)
|
|
|
|
# Process edges
|
|
for edge in node_doc.get("edges", []):
|
|
edge_id = f"{node_id}-{edge['target']}"
|
|
if edge_id not in seen_edges:
|
|
result.edges.append(
|
|
KnowledgeGraphEdge(
|
|
id=edge_id,
|
|
type=edge.get("relation", ""),
|
|
source=node_id,
|
|
target=edge["target"],
|
|
properties={
|
|
k: v
|
|
for k, v in edge.items()
|
|
if k not in ["target", "relation"]
|
|
},
|
|
)
|
|
)
|
|
seen_edges.add(edge_id)
|
|
else:
|
|
# Verify if starting node exists
|
|
start_nodes = self.collection.find({"_id": label})
|
|
start_nodes_exist = await start_nodes.to_list(length=1)
|
|
if not start_nodes_exist:
|
|
logger.warning(f"Starting node with label {label} does not exist!")
|
|
return result
|
|
|
|
# Use $graphLookup for traversal
|
|
pipeline = [
|
|
{
|
|
"$match": {"_id": label}
|
|
}, # Start with nodes having the specified label
|
|
{
|
|
"$graphLookup": {
|
|
"from": self._collection_name,
|
|
"startWith": "$edges.target",
|
|
"connectFromField": "edges.target",
|
|
"connectToField": "_id",
|
|
"maxDepth": max_depth,
|
|
"depthField": "depth",
|
|
"as": "connected_nodes",
|
|
}
|
|
},
|
|
]
|
|
|
|
async for doc in self.collection.aggregate(pipeline):
|
|
# Add the start node
|
|
node_id = str(doc["_id"])
|
|
if node_id not in seen_nodes:
|
|
result.nodes.append(
|
|
KnowledgeGraphNode(
|
|
id=node_id,
|
|
labels=[
|
|
doc.get(
|
|
"_id",
|
|
)
|
|
],
|
|
properties={
|
|
k: v
|
|
for k, v in doc.items()
|
|
if k
|
|
not in [
|
|
"_id",
|
|
"edges",
|
|
"connected_nodes",
|
|
"depth",
|
|
]
|
|
},
|
|
)
|
|
)
|
|
seen_nodes.add(node_id)
|
|
|
|
# Add edges from start node
|
|
for edge in doc.get("edges", []):
|
|
edge_id = f"{node_id}-{edge['target']}"
|
|
if edge_id not in seen_edges:
|
|
result.edges.append(
|
|
KnowledgeGraphEdge(
|
|
id=edge_id,
|
|
type=edge.get("relation", ""),
|
|
source=node_id,
|
|
target=edge["target"],
|
|
properties={
|
|
k: v
|
|
for k, v in edge.items()
|
|
if k not in ["target", "relation"]
|
|
},
|
|
)
|
|
)
|
|
seen_edges.add(edge_id)
|
|
|
|
# Add connected nodes and their edges
|
|
for connected in doc.get("connected_nodes", []):
|
|
node_id = str(connected["_id"])
|
|
if node_id not in seen_nodes:
|
|
result.nodes.append(
|
|
KnowledgeGraphNode(
|
|
id=node_id,
|
|
labels=[connected.get("_id")],
|
|
properties={
|
|
k: v
|
|
for k, v in connected.items()
|
|
if k not in ["_id", "edges", "depth"]
|
|
},
|
|
)
|
|
)
|
|
seen_nodes.add(node_id)
|
|
|
|
# Add edges from connected nodes
|
|
for edge in connected.get("edges", []):
|
|
edge_id = f"{node_id}-{edge['target']}"
|
|
if edge_id not in seen_edges:
|
|
result.edges.append(
|
|
KnowledgeGraphEdge(
|
|
id=edge_id,
|
|
type=edge.get("relation", ""),
|
|
source=node_id,
|
|
target=edge["target"],
|
|
properties={
|
|
k: v
|
|
for k, v in edge.items()
|
|
if k not in ["target", "relation"]
|
|
},
|
|
)
|
|
)
|
|
seen_edges.add(edge_id)
|
|
|
|
logger.info(
|
|
f"Subgraph query successful | Node count: {len(result.nodes)} | Edge count: {len(result.edges)}"
|
|
)
|
|
|
|
except PyMongoError as e:
|
|
logger.error(f"MongoDB query failed: {str(e)}")
|
|
|
|
return result
|
|
|
|
async def index_done_callback(self) -> None:
|
|
# Mongo handles persistence automatically
|
|
pass
|
|
|
|
async def remove_nodes(self, nodes: list[str]) -> None:
|
|
"""Delete multiple nodes
|
|
|
|
Args:
|
|
nodes: List of node IDs to be deleted
|
|
"""
|
|
logger.info(f"Deleting {len(nodes)} nodes")
|
|
if not nodes:
|
|
return
|
|
|
|
# 1. Remove all edges referencing these nodes (remove from edges array of other nodes)
|
|
await self.collection.update_many(
|
|
{}, {"$pull": {"edges": {"target": {"$in": nodes}}}}
|
|
)
|
|
|
|
# 2. Delete the node documents
|
|
await self.collection.delete_many({"_id": {"$in": nodes}})
|
|
|
|
logger.debug(f"Successfully deleted nodes: {nodes}")
|
|
|
|
async def remove_edges(self, edges: list[tuple[str, str]]) -> None:
|
|
"""Delete multiple edges
|
|
|
|
Args:
|
|
edges: List of edges to be deleted, each edge is a (source, target) tuple
|
|
"""
|
|
logger.info(f"Deleting {len(edges)} edges")
|
|
if not edges:
|
|
return
|
|
|
|
update_tasks = []
|
|
for source, target in edges:
|
|
# Remove edge pointing to target from source node's edges array
|
|
update_tasks.append(
|
|
self.collection.update_one(
|
|
{"_id": source}, {"$pull": {"edges": {"target": target}}}
|
|
)
|
|
)
|
|
|
|
if update_tasks:
|
|
await asyncio.gather(*update_tasks)
|
|
|
|
logger.debug(f"Successfully deleted edges: {edges}")
|
|
|
|
async def drop(self) -> dict[str, str]:
|
|
"""Drop the storage by removing all documents in the collection.
|
|
|
|
Returns:
|
|
dict[str, str]: Status of the operation with keys 'status' and 'message'
|
|
"""
|
|
try:
|
|
result = await self.collection.delete_many({})
|
|
deleted_count = result.deleted_count
|
|
|
|
logger.info(
|
|
f"Dropped {deleted_count} documents from graph {self._collection_name}"
|
|
)
|
|
return {
|
|
"status": "success",
|
|
"message": f"{deleted_count} documents dropped",
|
|
}
|
|
except PyMongoError as e:
|
|
logger.error(f"Error dropping graph {self._collection_name}: {e}")
|
|
return {"status": "error", "message": str(e)}
|
|
|
|
|
|
@final
|
|
@dataclass
|
|
class MongoVectorDBStorage(BaseVectorStorage):
|
|
db: AsyncIOMotorDatabase | None = field(default=None)
|
|
_data: AsyncIOMotorCollection | None = field(default=None)
|
|
|
|
def __post_init__(self):
|
|
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._collection_name = self.namespace
|
|
self._max_batch_size = self.global_config["embedding_batch_num"]
|
|
|
|
async def initialize(self):
|
|
if self.db is None:
|
|
self.db = await ClientManager.get_client()
|
|
self._data = await get_or_create_collection(self.db, self._collection_name)
|
|
|
|
# Ensure vector index exists
|
|
await self.create_vector_index_if_not_exists()
|
|
|
|
logger.debug(f"Use MongoDB as VDB {self._collection_name}")
|
|
|
|
async def finalize(self):
|
|
if self.db is not None:
|
|
await ClientManager.release_client(self.db)
|
|
self.db = None
|
|
self._data = None
|
|
|
|
async def create_vector_index_if_not_exists(self):
|
|
"""Creates an Atlas Vector Search index."""
|
|
try:
|
|
index_name = "vector_knn_index"
|
|
|
|
indexes = await self._data.list_search_indexes().to_list(length=None)
|
|
for index in indexes:
|
|
if index["name"] == index_name:
|
|
logger.debug("vector index already exist")
|
|
return
|
|
|
|
search_index_model = SearchIndexModel(
|
|
definition={
|
|
"fields": [
|
|
{
|
|
"type": "vector",
|
|
"numDimensions": self.embedding_func.embedding_dim, # Ensure correct dimensions
|
|
"path": "vector",
|
|
"similarity": "cosine", # Options: euclidean, cosine, dotProduct
|
|
}
|
|
]
|
|
},
|
|
name=index_name,
|
|
type="vectorSearch",
|
|
)
|
|
|
|
await self._data.create_search_index(search_index_model)
|
|
logger.info("Vector index created successfully.")
|
|
|
|
except PyMongoError as _:
|
|
logger.debug("vector index already exist")
|
|
|
|
async def upsert(self, data: dict[str, dict[str, Any]]) -> None:
|
|
logger.info(f"Inserting {len(data)} to {self.namespace}")
|
|
if not data:
|
|
return
|
|
|
|
# Add current time as Unix timestamp
|
|
import time
|
|
|
|
current_time = int(time.time())
|
|
|
|
list_data = [
|
|
{
|
|
"_id": k,
|
|
"created_at": current_time, # Add created_at field as Unix timestamp
|
|
**{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)
|
|
]
|
|
|
|
embedding_tasks = [self.embedding_func(batch) for batch in batches]
|
|
embeddings_list = await asyncio.gather(*embedding_tasks)
|
|
embeddings = np.concatenate(embeddings_list)
|
|
for i, d in enumerate(list_data):
|
|
d["vector"] = np.array(embeddings[i], dtype=np.float32).tolist()
|
|
|
|
update_tasks = []
|
|
for doc in list_data:
|
|
update_tasks.append(
|
|
self._data.update_one({"_id": doc["_id"]}, {"$set": doc}, upsert=True)
|
|
)
|
|
await asyncio.gather(*update_tasks)
|
|
|
|
return list_data
|
|
|
|
async def query(
|
|
self, query: str, top_k: int, ids: list[str] | None = None
|
|
) -> list[dict[str, Any]]:
|
|
"""Queries the vector database using Atlas Vector Search."""
|
|
# Generate the embedding
|
|
embedding = await self.embedding_func(
|
|
[query], _priority=5
|
|
) # higher priority for query
|
|
|
|
# Convert numpy array to a list to ensure compatibility with MongoDB
|
|
query_vector = embedding[0].tolist()
|
|
|
|
# Define the aggregation pipeline with the converted query vector
|
|
pipeline = [
|
|
{
|
|
"$vectorSearch": {
|
|
"index": "vector_knn_index", # Ensure this matches the created index name
|
|
"path": "vector",
|
|
"queryVector": query_vector,
|
|
"numCandidates": 100, # Adjust for performance
|
|
"limit": top_k,
|
|
}
|
|
},
|
|
{"$addFields": {"score": {"$meta": "vectorSearchScore"}}},
|
|
{"$match": {"score": {"$gte": self.cosine_better_than_threshold}}},
|
|
{"$project": {"vector": 0}},
|
|
]
|
|
|
|
# Execute the aggregation pipeline
|
|
cursor = self._data.aggregate(pipeline)
|
|
results = await cursor.to_list()
|
|
|
|
# Format and return the results with created_at field
|
|
return [
|
|
{
|
|
**doc,
|
|
"id": doc["_id"],
|
|
"distance": doc.get("score", None),
|
|
"created_at": doc.get("created_at"), # Include created_at field
|
|
}
|
|
for doc in results
|
|
]
|
|
|
|
async def index_done_callback(self) -> None:
|
|
# Mongo handles persistence automatically
|
|
pass
|
|
|
|
async def delete(self, ids: list[str]) -> None:
|
|
"""Delete vectors with specified IDs
|
|
|
|
Args:
|
|
ids: List of vector IDs to be deleted
|
|
"""
|
|
logger.info(f"Deleting {len(ids)} vectors from {self.namespace}")
|
|
if not ids:
|
|
return
|
|
|
|
try:
|
|
result = await self._data.delete_many({"_id": {"$in": ids}})
|
|
logger.debug(
|
|
f"Successfully deleted {result.deleted_count} vectors from {self.namespace}"
|
|
)
|
|
except PyMongoError as e:
|
|
logger.error(
|
|
f"Error while deleting vectors from {self.namespace}: {str(e)}"
|
|
)
|
|
|
|
async def delete_entity(self, entity_name: str) -> None:
|
|
"""Delete an entity by its name
|
|
|
|
Args:
|
|
entity_name: Name of the entity to delete
|
|
"""
|
|
try:
|
|
entity_id = compute_mdhash_id(entity_name, prefix="ent-")
|
|
logger.debug(
|
|
f"Attempting to delete entity {entity_name} with ID {entity_id}"
|
|
)
|
|
|
|
result = await self._data.delete_one({"_id": entity_id})
|
|
if result.deleted_count > 0:
|
|
logger.debug(f"Successfully deleted entity {entity_name}")
|
|
else:
|
|
logger.debug(f"Entity {entity_name} not found in storage")
|
|
except PyMongoError as e:
|
|
logger.error(f"Error deleting entity {entity_name}: {str(e)}")
|
|
|
|
async def delete_entity_relation(self, entity_name: str) -> None:
|
|
"""Delete all relations associated with an entity
|
|
|
|
Args:
|
|
entity_name: Name of the entity whose relations should be deleted
|
|
"""
|
|
try:
|
|
# Find relations where entity appears as source or target
|
|
relations_cursor = self._data.find(
|
|
{"$or": [{"src_id": entity_name}, {"tgt_id": entity_name}]}
|
|
)
|
|
relations = await relations_cursor.to_list(length=None)
|
|
|
|
if not relations:
|
|
logger.debug(f"No relations found for entity {entity_name}")
|
|
return
|
|
|
|
# Extract IDs of relations to delete
|
|
relation_ids = [relation["_id"] for relation in relations]
|
|
logger.debug(
|
|
f"Found {len(relation_ids)} relations for entity {entity_name}"
|
|
)
|
|
|
|
# Delete the relations
|
|
result = await self._data.delete_many({"_id": {"$in": relation_ids}})
|
|
logger.debug(f"Deleted {result.deleted_count} relations for {entity_name}")
|
|
except PyMongoError as e:
|
|
logger.error(f"Error deleting relations for {entity_name}: {str(e)}")
|
|
|
|
except PyMongoError as e:
|
|
logger.error(f"Error searching by prefix in {self.namespace}: {str(e)}")
|
|
return []
|
|
|
|
async def get_by_id(self, id: str) -> dict[str, Any] | None:
|
|
"""Get vector data by its ID
|
|
|
|
Args:
|
|
id: The unique identifier of the vector
|
|
|
|
Returns:
|
|
The vector data if found, or None if not found
|
|
"""
|
|
try:
|
|
# Search for the specific ID in MongoDB
|
|
result = await self._data.find_one({"_id": id})
|
|
if result:
|
|
# Format the result to include id field expected by API
|
|
result_dict = dict(result)
|
|
if "_id" in result_dict and "id" not in result_dict:
|
|
result_dict["id"] = result_dict["_id"]
|
|
return result_dict
|
|
return None
|
|
except Exception as e:
|
|
logger.error(f"Error retrieving vector data for ID {id}: {e}")
|
|
return None
|
|
|
|
async def get_by_ids(self, ids: list[str]) -> list[dict[str, Any]]:
|
|
"""Get multiple vector data by their IDs
|
|
|
|
Args:
|
|
ids: List of unique identifiers
|
|
|
|
Returns:
|
|
List of vector data objects that were found
|
|
"""
|
|
if not ids:
|
|
return []
|
|
|
|
try:
|
|
# Query MongoDB for multiple IDs
|
|
cursor = self._data.find({"_id": {"$in": ids}})
|
|
results = await cursor.to_list(length=None)
|
|
|
|
# Format results to include id field expected by API
|
|
formatted_results = []
|
|
for result in results:
|
|
result_dict = dict(result)
|
|
if "_id" in result_dict and "id" not in result_dict:
|
|
result_dict["id"] = result_dict["_id"]
|
|
formatted_results.append(result_dict)
|
|
|
|
return formatted_results
|
|
except Exception as e:
|
|
logger.error(f"Error retrieving vector data for IDs {ids}: {e}")
|
|
return []
|
|
|
|
async def drop(self) -> dict[str, str]:
|
|
"""Drop the storage by removing all documents in the collection and recreating vector index.
|
|
|
|
Returns:
|
|
dict[str, str]: Status of the operation with keys 'status' and 'message'
|
|
"""
|
|
try:
|
|
# Delete all documents
|
|
result = await self._data.delete_many({})
|
|
deleted_count = result.deleted_count
|
|
|
|
# Recreate vector index
|
|
await self.create_vector_index_if_not_exists()
|
|
|
|
logger.info(
|
|
f"Dropped {deleted_count} documents from vector storage {self._collection_name} and recreated vector index"
|
|
)
|
|
return {
|
|
"status": "success",
|
|
"message": f"{deleted_count} documents dropped and vector index recreated",
|
|
}
|
|
except PyMongoError as e:
|
|
logger.error(f"Error dropping vector storage {self._collection_name}: {e}")
|
|
return {"status": "error", "message": str(e)}
|
|
|
|
|
|
async def get_or_create_collection(db: AsyncIOMotorDatabase, collection_name: str):
|
|
collection_names = await db.list_collection_names()
|
|
|
|
if collection_name not in collection_names:
|
|
collection = await db.create_collection(collection_name)
|
|
logger.info(f"Created collection: {collection_name}")
|
|
return collection
|
|
else:
|
|
logger.debug(f"Collection '{collection_name}' already exists.")
|
|
return db.get_collection(collection_name)
|