LightRAG/lightrag/kg/vector_tenant_support.py
2025-12-05 14:31:13 +08:00

187 lines
6.1 KiB
Python

# Vector Database Multi-Tenant Support Module
# Supports: Qdrant, Milvus, FAISS, Nano Vector DB
from typing import Any, Dict, List
class VectorTenantHelper:
"""Helper class for vector DB multi-tenant operations"""
@staticmethod
def add_tenant_metadata(
payload: Dict[str, Any], tenant_id: str, kb_id: str
) -> Dict[str, Any]:
"""Add tenant_id and kb_id to vector payload/metadata"""
payload["tenant_id"] = tenant_id
payload["kb_id"] = kb_id
return payload
@staticmethod
def get_tenant_filter(tenant_id: str, kb_id: str) -> Dict[str, Any]:
"""Create a filter for tenant isolation in vector DB queries"""
return {
"must": [
{"key": "tenant_id", "match": {"value": tenant_id}},
{"key": "kb_id", "match": {"value": kb_id}},
]
}
@staticmethod
def get_tenant_filter_milvus(tenant_id: str, kb_id: str) -> str:
"""Create a Milvus WHERE clause for tenant isolation"""
return f'tenant_id == "{tenant_id}" && kb_id == "{kb_id}"'
@staticmethod
def make_tenant_id(tenant_id: str, kb_id: str, original_id: str) -> str:
"""Create a tenant-scoped vector ID"""
return f"{tenant_id}:{kb_id}:{original_id}"
@staticmethod
def parse_tenant_id(tenant_id_str: str) -> Dict[str, str]:
"""Parse a tenant-scoped vector ID"""
parts = tenant_id_str.split(":", 2)
if len(parts) == 3:
return {"tenant_id": parts[0], "kb_id": parts[1], "original_id": parts[2]}
return {"original_id": tenant_id_str}
@staticmethod
def create_tenant_collection_name(
base_name: str, tenant_id: str, kb_id: str
) -> str:
"""Create a tenant-scoped collection name"""
return f"{base_name}_{tenant_id}_{kb_id}".replace("-", "_")
@staticmethod
def get_tenant_collection_pattern(
base_name: str, tenant_id: str, kb_id: str
) -> str:
"""Get a pattern for finding tenant-specific collections"""
return f"{base_name}_{tenant_id}_{kb_id}*"
class QdrantTenantHelper(VectorTenantHelper):
"""Qdrant-specific tenant helper"""
@staticmethod
def build_qdrant_filter(
tenant_id: str, kb_id: str, additional_filter: Dict = None
) -> Dict[str, Any]:
"""Build a Qdrant filter for tenant isolation"""
must_conditions = [
{"key": "tenant_id", "match": {"value": tenant_id}},
{"key": "kb_id", "match": {"value": kb_id}},
]
if additional_filter:
if "must" in additional_filter:
must_conditions.extend(additional_filter["must"])
result = {"must": must_conditions}
# Copy over any other filter conditions
for key in ["should", "must_not"]:
if key in additional_filter:
result[key] = additional_filter[key]
return result
return {"must": must_conditions}
@staticmethod
def update_qdrant_payload(
payload: Dict[str, Any], tenant_id: str, kb_id: str
) -> Dict[str, Any]:
"""Ensure Qdrant payload includes tenant metadata"""
return VectorTenantHelper.add_tenant_metadata(payload, tenant_id, kb_id)
class MilvusTenantHelper(VectorTenantHelper):
"""Milvus-specific tenant helper"""
@staticmethod
def build_milvus_expr(
tenant_id: str, kb_id: str, additional_expr: str = None
) -> str:
"""Build a Milvus WHERE expression for tenant isolation"""
expr = f'tenant_id == "{tenant_id}" && kb_id == "{kb_id}"'
if additional_expr:
expr += f" && ({additional_expr})"
return expr
@staticmethod
def insert_with_tenant(
collection, data: List[Dict[str, Any]], tenant_id: str, kb_id: str
):
"""Insert data with tenant metadata into Milvus"""
for item in data:
item["tenant_id"] = tenant_id
item["kb_id"] = kb_id
return collection.insert(data)
class FAISSTenantHelper(VectorTenantHelper):
"""FAISS-specific tenant helper"""
@staticmethod
def create_tenant_index_name(base_name: str, tenant_id: str, kb_id: str) -> str:
"""Create a tenant-scoped FAISS index name"""
sanitized_tenant = tenant_id.replace("-", "_").replace(":", "_")
sanitized_kb = kb_id.replace("-", "_").replace(":", "_")
return f"{base_name}_{sanitized_tenant}_{sanitized_kb}"
@staticmethod
def create_tenant_metadata_list(
num_vectors: int, tenant_id: str, kb_id: str, base_metadata: List[Dict] = None
) -> List[Dict[str, Any]]:
"""Create metadata list for FAISS vectors with tenant info"""
metadata_list = []
for i in range(num_vectors):
metadata = {"tenant_id": tenant_id, "kb_id": kb_id, "index": i}
if base_metadata and i < len(base_metadata):
metadata.update(base_metadata[i])
metadata_list.append(metadata)
return metadata_list
@staticmethod
def filter_metadata_by_tenant(
metadata_list: List[Dict[str, Any]], tenant_id: str, kb_id: str
) -> List[int]:
"""Filter metadata list and return matching indices"""
matching_indices = []
for i, metadata in enumerate(metadata_list):
if (
metadata.get("tenant_id") == tenant_id
and metadata.get("kb_id") == kb_id
):
matching_indices.append(i)
return matching_indices
class NanoVectorTenantHelper(VectorTenantHelper):
"""Nano Vector DB-specific tenant helper"""
@staticmethod
def build_nano_filter(tenant_id: str, kb_id: str) -> Dict[str, Any]:
"""Build a Nano Vector DB filter for tenant isolation"""
return {"tenant_id": tenant_id, "kb_id": kb_id}
@staticmethod
def update_nano_document(
doc: Dict[str, Any], tenant_id: str, kb_id: str
) -> Dict[str, Any]:
"""Update document to include tenant metadata"""
if "metadata" not in doc:
doc["metadata"] = {}
doc["metadata"]["tenant_id"] = tenant_id
doc["metadata"]["kb_id"] = kb_id
return doc