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import re
import uuid
from typing import Any, Dict, List, Optional, Union
from open_webui.retrieval.vector.main import (
VectorDBBase,
VectorItem,
SearchResult,
GetResult,
)
from open_webui.retrieval.vector.utils import process_metadata
from open_webui.config import (
WEAVIATE_HTTP_HOST,
WEAVIATE_GRPC_HOST,
WEAVIATE_HTTP_PORT,
WEAVIATE_GRPC_PORT,
WEAVIATE_API_KEY,
WEAVIATE_HTTP_SECURE,
WEAVIATE_GRPC_SECURE,
WEAVIATE_SKIP_INIT_CHECKS,
)
def _convert_uuids_to_strings(obj: Any) -> Any:
"""
Recursively convert UUID objects to strings in nested data structures.
This function handles:
- UUID objects -> string
- Dictionaries with UUID values
- Lists/Tuples with UUID values
- Nested combinations of the above
Args:
obj: Any object that might contain UUIDs
Returns:
The same object structure with UUIDs converted to strings
"""
if isinstance(obj, uuid.UUID):
return str(obj)
elif isinstance(obj, dict):
return {key: _convert_uuids_to_strings(value) for key, value in obj.items()}
elif isinstance(obj, (list, tuple)):
return type(obj)(_convert_uuids_to_strings(item) for item in obj)
elif isinstance(obj, (str, int, float, bool, type(None))):
return obj
else:
return obj
class WeaviateClient(VectorDBBase):
def __init__(self):
self.url = WEAVIATE_HTTP_HOST
try:
# Build connection parameters
connection_params = {
"http_host": WEAVIATE_HTTP_HOST,
"http_port": WEAVIATE_HTTP_PORT,
"http_secure": WEAVIATE_HTTP_SECURE,
"grpc_host": WEAVIATE_GRPC_HOST,
"grpc_port": WEAVIATE_GRPC_PORT,
"grpc_secure": WEAVIATE_GRPC_SECURE,
"skip_init_checks": WEAVIATE_SKIP_INIT_CHECKS,
}
# Only add auth_credentials if WEAVIATE_API_KEY exists and is not empty
if WEAVIATE_API_KEY:
connection_params["auth_credentials"] = (
weaviate.classes.init.Auth.api_key(WEAVIATE_API_KEY)
)
self.client = weaviate.connect_to_custom(**connection_params)
self.client.connect()
except Exception as e:
raise ConnectionError(f"Failed to connect to Weaviate: {e}") from e
def _sanitize_collection_name(self, collection_name: str) -> str:
"""Sanitize collection name to be a valid Weaviate class name."""
if not isinstance(collection_name, str) or not collection_name.strip():
raise ValueError("Collection name must be a non-empty string")
# Requirements for a valid Weaviate class name:
# The collection name must begin with a capital letter.
# The name can only contain letters, numbers, and the underscore (_) character. Spaces are not allowed.
# Replace hyphens with underscores and keep only alphanumeric characters
name = re.sub(r"[^a-zA-Z0-9_]", "", collection_name.replace("-", "_"))
name = name.strip("_")
if not name:
raise ValueError(
"Could not sanitize collection name to be a valid Weaviate class name"
)
# Ensure it starts with a letter and is capitalized
if not name[0].isalpha():
name = "C" + name
return name[0].upper() + name[1:]
def has_collection(self, collection_name: str) -> bool:
sane_collection_name = self._sanitize_collection_name(collection_name)
return self.client.collections.exists(sane_collection_name)
def delete_collection(self, collection_name: str) -> None:
sane_collection_name = self._sanitize_collection_name(collection_name)
if self.client.collections.exists(sane_collection_name):
self.client.collections.delete(sane_collection_name)
def _create_collection(self, collection_name: str) -> None:
self.client.collections.create(
name=collection_name,
vector_config=weaviate.classes.config.Configure.Vectors.self_provided(),
properties=[
weaviate.classes.config.Property(
name="text", data_type=weaviate.classes.config.DataType.TEXT
),
],
)
def insert(self, collection_name: str, items: List[VectorItem]) -> None:
sane_collection_name = self._sanitize_collection_name(collection_name)
if not self.client.collections.exists(sane_collection_name):
self._create_collection(sane_collection_name)
collection = self.client.collections.get(sane_collection_name)
with collection.batch.fixed_size(batch_size=100) as batch:
for item in items:
item_uuid = str(uuid.uuid4()) if not item["id"] else str(item["id"])
properties = {"text": item["text"]}
if item["metadata"]:
clean_metadata = _convert_uuids_to_strings(
process_metadata(item["metadata"])
)
clean_metadata.pop("text", None)
properties.update(clean_metadata)
batch.add_object(
properties=properties, uuid=item_uuid, vector=item["vector"]
)
def upsert(self, collection_name: str, items: List[VectorItem]) -> None:
sane_collection_name = self._sanitize_collection_name(collection_name)
if not self.client.collections.exists(sane_collection_name):
self._create_collection(sane_collection_name)
collection = self.client.collections.get(sane_collection_name)
with collection.batch.fixed_size(batch_size=100) as batch:
for item in items:
item_uuid = str(item["id"]) if item["id"] else None
properties = {"text": item["text"]}
if item["metadata"]:
clean_metadata = _convert_uuids_to_strings(
process_metadata(item["metadata"])
)
clean_metadata.pop("text", None)
properties.update(clean_metadata)
batch.add_object(
properties=properties, uuid=item_uuid, vector=item["vector"]
)
def search(
self,
collection_name: str,
vectors: List[List[Union[float, int]]],
filter: Optional[dict] = None,
limit: int = 10,
) -> Optional[SearchResult]:
sane_collection_name = self._sanitize_collection_name(collection_name)
if not self.client.collections.exists(sane_collection_name):
return None
collection = self.client.collections.get(sane_collection_name)
result_ids, result_documents, result_metadatas, result_distances = (
[],
[],
[],
[],
)
for vector_embedding in vectors:
try:
response = collection.query.near_vector(
near_vector=vector_embedding,
limit=limit,
return_metadata=weaviate.classes.query.MetadataQuery(distance=True),
)
ids = [str(obj.uuid) for obj in response.objects]
documents = []
metadatas = []
distances = []
for obj in response.objects:
properties = dict(obj.properties) if obj.properties else {}
documents.append(properties.pop("text", ""))
metadatas.append(_convert_uuids_to_strings(properties))
# Weaviate has cosine distance, 2 (worst) -> 0 (best). Re-ordering to 0 -> 1
raw_distances = [
(
obj.metadata.distance
if obj.metadata and obj.metadata.distance
else 2.0
)
for obj in response.objects
]
distances = [(2 - dist) / 2 for dist in raw_distances]
result_ids.append(ids)
result_documents.append(documents)
result_metadatas.append(metadatas)
result_distances.append(distances)
except Exception:
result_ids.append([])
result_documents.append([])
result_metadatas.append([])
result_distances.append([])
return SearchResult(
**{
"ids": result_ids,
"documents": result_documents,
"metadatas": result_metadatas,
"distances": result_distances,
}
)
def query(
self, collection_name: str, filter: Dict, limit: Optional[int] = None
) -> Optional[GetResult]:
sane_collection_name = self._sanitize_collection_name(collection_name)
if not self.client.collections.exists(sane_collection_name):
return None
collection = self.client.collections.get(sane_collection_name)
weaviate_filter = None
if filter:
for key, value in filter.items():
prop_filter = weaviate.classes.query.Filter.by_property(name=key).equal(
value
)
weaviate_filter = (
prop_filter
if weaviate_filter is None
else weaviate.classes.query.Filter.all_of(
[weaviate_filter, prop_filter]
)
)
try:
response = collection.query.fetch_objects(
filters=weaviate_filter, limit=limit
)
ids = [str(obj.uuid) for obj in response.objects]
documents = []
metadatas = []
for obj in response.objects:
properties = dict(obj.properties) if obj.properties else {}
documents.append(properties.pop("text", ""))
metadatas.append(_convert_uuids_to_strings(properties))
return GetResult(
**{
"ids": [ids],
"documents": [documents],
"metadatas": [metadatas],
}
)
except Exception:
return None
def get(self, collection_name: str) -> Optional[GetResult]:
sane_collection_name = self._sanitize_collection_name(collection_name)
if not self.client.collections.exists(sane_collection_name):
return None
collection = self.client.collections.get(sane_collection_name)
ids, documents, metadatas = [], [], []
try:
for item in collection.iterator():
ids.append(str(item.uuid))
properties = dict(item.properties) if item.properties else {}
documents.append(properties.pop("text", ""))
metadatas.append(_convert_uuids_to_strings(properties))
if not ids:
return None
return GetResult(
**{
"ids": [ids],
"documents": [documents],
"metadatas": [metadatas],
}
)
except Exception:
return None
def delete(
self,
collection_name: str,
ids: Optional[List[str]] = None,
filter: Optional[Dict] = None,
) -> None:
sane_collection_name = self._sanitize_collection_name(collection_name)
if not self.client.collections.exists(sane_collection_name):
return
collection = self.client.collections.get(sane_collection_name)
try:
if ids:
for item_id in ids:
collection.data.delete_by_id(uuid=item_id)
elif filter:
weaviate_filter = None
for key, value in filter.items():
prop_filter = weaviate.classes.query.Filter.by_property(
name=key
).equal(value)
weaviate_filter = (
prop_filter
if weaviate_filter is None
else weaviate.classes.query.Filter.all_of(
[weaviate_filter, prop_filter]
)
)
if weaviate_filter:
collection.data.delete_many(where=weaviate_filter)
except Exception:
pass
def reset(self) -> None:
try:
for collection_name in self.client.collections.list_all().keys():
self.client.collections.delete(collection_name)
except Exception:
pass
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