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317 lines
11 KiB
317 lines
11 KiB
import asyncio
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import os
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from typing import Any, final
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from dataclasses import dataclass
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import numpy as np
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from lightrag.utils import logger, compute_mdhash_id
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from ..base import BaseVectorStorage
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import pipmaster as pm
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if not pm.is_installed("configparser"):
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pm.install("configparser")
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if not pm.is_installed("pymilvus"):
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pm.install("pymilvus")
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import configparser
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from pymilvus import MilvusClient # type: ignore
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config = configparser.ConfigParser()
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config.read("config.ini", "utf-8")
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@final
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@dataclass
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class MilvusVectorDBStorage(BaseVectorStorage):
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@staticmethod
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def create_collection_if_not_exist(
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client: MilvusClient, collection_name: str, **kwargs
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):
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if client.has_collection(collection_name):
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return
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client.create_collection(
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collection_name, max_length=64, id_type="string", **kwargs
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)
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def __post_init__(self):
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kwargs = self.global_config.get("vector_db_storage_cls_kwargs", {})
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cosine_threshold = kwargs.get("cosine_better_than_threshold")
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if cosine_threshold is None:
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raise ValueError(
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"cosine_better_than_threshold must be specified in vector_db_storage_cls_kwargs"
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)
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self.cosine_better_than_threshold = cosine_threshold
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self._client = MilvusClient(
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uri=os.environ.get(
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"MILVUS_URI",
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config.get(
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"milvus",
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"uri",
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fallback=os.path.join(
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self.global_config["working_dir"], "milvus_lite.db"
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),
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),
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),
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user=os.environ.get(
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"MILVUS_USER", config.get("milvus", "user", fallback=None)
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),
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password=os.environ.get(
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"MILVUS_PASSWORD", config.get("milvus", "password", fallback=None)
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),
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token=os.environ.get(
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"MILVUS_TOKEN", config.get("milvus", "token", fallback=None)
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),
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db_name=os.environ.get(
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"MILVUS_DB_NAME", config.get("milvus", "db_name", fallback=None)
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),
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)
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self._max_batch_size = self.global_config["embedding_batch_num"]
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MilvusVectorDBStorage.create_collection_if_not_exist(
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self._client,
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self.namespace,
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dimension=self.embedding_func.embedding_dim,
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)
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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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import time
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current_time = int(time.time())
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list_data: list[dict[str, Any]] = [
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{
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"id": k,
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"created_at": current_time,
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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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embedding_tasks = [self.embedding_func(batch) for batch in batches]
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embeddings_list = await asyncio.gather(*embedding_tasks)
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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(collection_name=self.namespace, data=list_data)
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return results
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async def query(
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self, query: str, top_k: int, ids: list[str] | None = None
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) -> list[dict[str, Any]]:
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embedding = await self.embedding_func(
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[query], _priority=5
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) # higher priority for query
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results = self._client.search(
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collection_name=self.namespace,
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data=embedding,
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limit=top_k,
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output_fields=list(self.meta_fields) + ["created_at"],
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search_params={
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"metric_type": "COSINE",
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"params": {"radius": self.cosine_better_than_threshold},
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},
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)
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print(results)
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return [
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{
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**dp["entity"],
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"id": dp["id"],
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"distance": dp["distance"],
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# created_at is requested in output_fields, so it should be a top-level key in the result dict (dp)
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"created_at": dp.get("created_at"),
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}
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for dp in results[0]
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]
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async def index_done_callback(self) -> None:
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# Milvus handles persistence automatically
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pass
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async def delete_entity(self, entity_name: str) -> None:
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"""Delete an entity from the vector database
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Args:
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entity_name: The name of the entity to delete
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"""
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try:
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# Compute entity ID from name
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entity_id = compute_mdhash_id(entity_name, prefix="ent-")
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logger.debug(
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f"Attempting to delete entity {entity_name} with ID {entity_id}"
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)
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# Delete the entity from Milvus collection
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result = self._client.delete(
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collection_name=self.namespace, pks=[entity_id]
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)
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if result and result.get("delete_count", 0) > 0:
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logger.debug(f"Successfully deleted entity {entity_name}")
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else:
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logger.debug(f"Entity {entity_name} not found in storage")
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except Exception as e:
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logger.error(f"Error deleting entity {entity_name}: {e}")
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async def delete_entity_relation(self, entity_name: str) -> None:
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"""Delete all relations associated with an entity
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Args:
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entity_name: The name of the entity whose relations should be deleted
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"""
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try:
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# Search for relations where entity is either source or target
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expr = f'src_id == "{entity_name}" or tgt_id == "{entity_name}"'
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# Find all relations involving this entity
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results = self._client.query(
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collection_name=self.namespace, filter=expr, output_fields=["id"]
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)
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if not results or len(results) == 0:
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logger.debug(f"No relations found for entity {entity_name}")
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return
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# Extract IDs of relations to delete
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relation_ids = [item["id"] for item in results]
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logger.debug(
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f"Found {len(relation_ids)} relations for entity {entity_name}"
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)
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# Delete the relations
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if relation_ids:
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delete_result = self._client.delete(
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collection_name=self.namespace, pks=relation_ids
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)
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logger.debug(
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f"Deleted {delete_result.get('delete_count', 0)} relations for {entity_name}"
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)
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except Exception as e:
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logger.error(f"Error deleting relations for {entity_name}: {e}")
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async def delete(self, ids: list[str]) -> None:
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"""Delete vectors with specified IDs
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Args:
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ids: List of vector IDs to be deleted
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"""
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try:
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# Delete vectors by IDs
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result = self._client.delete(collection_name=self.namespace, pks=ids)
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if result and result.get("delete_count", 0) > 0:
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logger.debug(
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f"Successfully deleted {result.get('delete_count', 0)} vectors from {self.namespace}"
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)
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else:
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logger.debug(f"No vectors were deleted from {self.namespace}")
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except Exception as e:
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logger.error(f"Error while deleting vectors from {self.namespace}: {e}")
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async def get_by_id(self, id: str) -> dict[str, Any] | None:
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"""Get vector data by its ID
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Args:
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id: The unique identifier of the vector
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Returns:
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The vector data if found, or None if not found
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"""
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try:
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# Query Milvus for a specific ID
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result = self._client.query(
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collection_name=self.namespace,
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filter=f'id == "{id}"',
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output_fields=list(self.meta_fields) + ["id", "created_at"],
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)
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if not result or len(result) == 0:
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return None
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# Ensure the result contains created_at field
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if "created_at" not in result[0]:
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result[0]["created_at"] = None
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return result[0]
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except Exception as e:
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logger.error(f"Error retrieving vector data for ID {id}: {e}")
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return None
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async def get_by_ids(self, ids: list[str]) -> list[dict[str, Any]]:
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"""Get multiple vector data by their IDs
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Args:
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ids: List of unique identifiers
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Returns:
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List of vector data objects that were found
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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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# Prepare the ID filter expression
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id_list = '", "'.join(ids)
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filter_expr = f'id in ["{id_list}"]'
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# Query Milvus with the filter
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result = self._client.query(
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collection_name=self.namespace,
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filter=filter_expr,
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output_fields=list(self.meta_fields) + ["id", "created_at"],
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)
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# Ensure each result contains created_at field
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for item in result:
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if "created_at" not in item:
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item["created_at"] = None
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return result or []
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except Exception as e:
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logger.error(f"Error retrieving vector data for IDs {ids}: {e}")
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return []
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async def drop(self) -> dict[str, str]:
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"""Drop all vector data from storage and clean up resources
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This method will delete all data from the Milvus collection.
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Returns:
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dict[str, str]: Operation status and message
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- On success: {"status": "success", "message": "data dropped"}
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- On failure: {"status": "error", "message": "<error details>"}
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"""
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try:
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# Drop the collection and recreate it
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if self._client.has_collection(self.namespace):
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self._client.drop_collection(self.namespace)
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# Recreate the collection
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MilvusVectorDBStorage.create_collection_if_not_exist(
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self._client,
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self.namespace,
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dimension=self.embedding_func.embedding_dim,
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)
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logger.info(
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f"Process {os.getpid()} drop Milvus collection {self.namespace}"
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)
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return {"status": "success", "message": "data dropped"}
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except Exception as e:
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logger.error(f"Error dropping Milvus collection {self.namespace}: {e}")
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return {"status": "error", "message": str(e)}
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