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from typing import Optional, Tuple, List, Dict, Any
from open_webui.config import (
MILVUS_URI,
MILVUS_TOKEN,
MILVUS_DB,
MILVUS_COLLECTION_PREFIX,
MILVUS_INDEX_TYPE,
MILVUS_METRIC_TYPE,
MILVUS_HNSW_M,
MILVUS_HNSW_EFCONSTRUCTION,
MILVUS_IVF_FLAT_NLIST,
)
from open_webui.retrieval.vector.main import (
GetResult,
SearchResult,
VectorDBBase,
VectorItem,
)
from pymilvus import (
connections,
utility,
Collection,
CollectionSchema,
FieldSchema,
DataType,
)
log = logging.getLogger(__name__)
RESOURCE_ID_FIELD = "resource_id"
class MilvusClient(VectorDBBase):
def __init__(self):
# Milvus collection names can only contain numbers, letters, and underscores.
self.collection_prefix = MILVUS_COLLECTION_PREFIX.replace("-", "_")
connections.connect(
alias="default",
uri=MILVUS_URI,
token=MILVUS_TOKEN,
db_name=MILVUS_DB,
)
# Main collection types for multi-tenancy
self.MEMORY_COLLECTION = f"{self.collection_prefix}_memories"
self.KNOWLEDGE_COLLECTION = f"{self.collection_prefix}_knowledge"
self.FILE_COLLECTION = f"{self.collection_prefix}_files"
self.WEB_SEARCH_COLLECTION = f"{self.collection_prefix}_web_search"
self.HASH_BASED_COLLECTION = f"{self.collection_prefix}_hash_based"
self.shared_collections = [
self.MEMORY_COLLECTION,
self.KNOWLEDGE_COLLECTION,
self.FILE_COLLECTION,
self.WEB_SEARCH_COLLECTION,
self.HASH_BASED_COLLECTION,
]
def _get_collection_and_resource_id(self, collection_name: str) -> Tuple[str, str]:
"""
Maps the traditional collection name to multi-tenant collection and resource ID.
WARNING: This mapping relies on current Open WebUI naming conventions for
collection names. If Open WebUI changes how it generates collection names
(e.g., "user-memory-" prefix, "file-" prefix, web search patterns, or hash
formats), this mapping will break and route data to incorrect collections.
POTENTIALLY CAUSING HUGE DATA CORRUPTION, DATA CONSISTENCY ISSUES AND INCORRECT
DATA MAPPING INSIDE THE DATABASE.
"""
resource_id = collection_name
if collection_name.startswith("user-memory-"):
return self.MEMORY_COLLECTION, resource_id
elif collection_name.startswith("file-"):
return self.FILE_COLLECTION, resource_id
elif collection_name.startswith("web-search-"):
return self.WEB_SEARCH_COLLECTION, resource_id
elif len(collection_name) == 63 and all(
c in "0123456789abcdef" for c in collection_name
):
return self.HASH_BASED_COLLECTION, resource_id
else:
return self.KNOWLEDGE_COLLECTION, resource_id
def _create_shared_collection(self, mt_collection_name: str, dimension: int):
fields = [
FieldSchema(
name="id",
dtype=DataType.VARCHAR,
is_primary=True,
auto_id=False,
max_length=36,
),
FieldSchema(name="vector", dtype=DataType.FLOAT_VECTOR, dim=dimension),
FieldSchema(name="text", dtype=DataType.VARCHAR, max_length=65535),
FieldSchema(name="metadata", dtype=DataType.JSON),
FieldSchema(name=RESOURCE_ID_FIELD, dtype=DataType.VARCHAR, max_length=255),
]
schema = CollectionSchema(fields, "Shared collection for multi-tenancy")
collection = Collection(mt_collection_name, schema)
index_params = {
"metric_type": MILVUS_METRIC_TYPE,
"index_type": MILVUS_INDEX_TYPE,
"params": {},
}
if MILVUS_INDEX_TYPE == "HNSW":
index_params["params"] = {
"M": MILVUS_HNSW_M,
"efConstruction": MILVUS_HNSW_EFCONSTRUCTION,
}
elif MILVUS_INDEX_TYPE == "IVF_FLAT":
index_params["params"] = {"nlist": MILVUS_IVF_FLAT_NLIST}
collection.create_index("vector", index_params)
collection.create_index(RESOURCE_ID_FIELD)
log.info(f"Created shared collection: {mt_collection_name}")
return collection
def _ensure_collection(self, mt_collection_name: str, dimension: int):
if not utility.has_collection(mt_collection_name):
self._create_shared_collection(mt_collection_name, dimension)
def has_collection(self, collection_name: str) -> bool:
mt_collection, resource_id = self._get_collection_and_resource_id(
collection_name
)
if not utility.has_collection(mt_collection):
return False
collection = Collection(mt_collection)
collection.load()
res = collection.query(expr=f"{RESOURCE_ID_FIELD} == '{resource_id}'", limit=1)
return len(res) > 0
def upsert(self, collection_name: str, items: List[VectorItem]):
if not items:
return
mt_collection, resource_id = self._get_collection_and_resource_id(
collection_name
)
dimension = len(items[0]["vector"])
self._ensure_collection(mt_collection, dimension)
collection = Collection(mt_collection)
entities = [
{
"id": item["id"],
"vector": item["vector"],
"text": item["text"],
"metadata": item["metadata"],
RESOURCE_ID_FIELD: resource_id,
}
for item in items
]
collection.insert(entities)
def search(
self,
collection_name: str,
vectors: List[List[float]],
filter: Optional[Dict] = None,
limit: int = 10,
) -> Optional[SearchResult]:
if not vectors:
return None
mt_collection, resource_id = self._get_collection_and_resource_id(
collection_name
)
if not utility.has_collection(mt_collection):
return None
collection = Collection(mt_collection)
collection.load()
search_params = {"metric_type": MILVUS_METRIC_TYPE, "params": {}}
results = collection.search(
data=vectors,
anns_field="vector",
param=search_params,
limit=limit,
expr=f"{RESOURCE_ID_FIELD} == '{resource_id}'",
output_fields=["id", "text", "metadata"],
)
ids, documents, metadatas, distances = [], [], [], []
for hits in results:
batch_ids, batch_docs, batch_metadatas, batch_dists = [], [], [], []
for hit in hits:
batch_ids.append(hit.entity.get("id"))
batch_docs.append(hit.entity.get("text"))
batch_metadatas.append(hit.entity.get("metadata"))
batch_dists.append(hit.distance)
ids.append(batch_ids)
documents.append(batch_docs)
metadatas.append(batch_metadatas)
distances.append(batch_dists)
return SearchResult(
ids=ids, documents=documents, metadatas=metadatas, distances=distances
)
def delete(
self,
collection_name: str,
ids: Optional[List[str]] = None,
filter: Optional[Dict[str, Any]] = None,
):
mt_collection, resource_id = self._get_collection_and_resource_id(
collection_name
)
if not utility.has_collection(mt_collection):
return
collection = Collection(mt_collection)
# Build expression
expr = [f"{RESOURCE_ID_FIELD} == '{resource_id}'"]
if ids:
# Milvus expects a string list for 'in' operator
id_list_str = ", ".join([f"'{id_val}'" for id_val in ids])
expr.append(f"id in [{id_list_str}]")
if filter:
for key, value in filter.items():
expr.append(f"metadata['{key}'] == '{value}'")
collection.delete(" and ".join(expr))
def reset(self):
for collection_name in self.shared_collections:
if utility.has_collection(collection_name):
utility.drop_collection(collection_name)
def delete_collection(self, collection_name: str):
mt_collection, resource_id = self._get_collection_and_resource_id(
collection_name
)
if not utility.has_collection(mt_collection):
return
collection = Collection(mt_collection)
collection.delete(f"{RESOURCE_ID_FIELD} == '{resource_id}'")
def query(
self, collection_name: str, filter: Dict[str, Any], limit: Optional[int] = None
) -> Optional[GetResult]:
mt_collection, resource_id = self._get_collection_and_resource_id(
collection_name
)
if not utility.has_collection(mt_collection):
return None
collection = Collection(mt_collection)
collection.load()
expr = [f"{RESOURCE_ID_FIELD} == '{resource_id}'"]
if filter:
for key, value in filter.items():
if isinstance(value, str):
expr.append(f"metadata['{key}'] == '{value}'")
else:
expr.append(f"metadata['{key}'] == {value}")
iterator = collection.query_iterator(
expr=" and ".join(expr),
output_fields=["id", "text", "metadata"],
limit=limit if limit else -1,
)
all_results = []
while True:
batch = iterator.next()
if not batch:
iterator.close()
break
all_results.extend(batch)
ids = [res["id"] for res in all_results]
documents = [res["text"] for res in all_results]
metadatas = [res["metadata"] for res in all_results]
return GetResult(ids=[ids], documents=[documents], metadatas=[metadatas])
def get(self, collection_name: str) -> Optional[GetResult]:
return self.query(collection_name, filter={}, limit=None)
def insert(self, collection_name: str, items: List[VectorItem]):
return self.upsert(collection_name, items)
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