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This commit is contained in:
Timothy Jaeryang Baek
2026-03-17 17:58:01 -05:00
parent fcf7208352
commit de3317e26b
220 changed files with 17200 additions and 22836 deletions
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349
backend/open_webui/retrieval/vector/dbs/s3vector.py
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@@ -28,18 +28,16 @@ class S3VectorClient(VectorDBBase):
# Simple validation - log warnings instead of raising exceptions
if not self.bucket_name:
log.warning("S3_VECTOR_BUCKET_NAME not set - S3Vector will not work")
log.warning('S3_VECTOR_BUCKET_NAME not set - S3Vector will not work')
if not self.region:
log.warning("S3_VECTOR_REGION not set - S3Vector will not work")
log.warning('S3_VECTOR_REGION not set - S3Vector will not work')
if self.bucket_name and self.region:
try:
self.client = boto3.client("s3vectors", region_name=self.region)
log.info(
f"S3Vector client initialized for bucket '{self.bucket_name}' in region '{self.region}'"
)
self.client = boto3.client('s3vectors', region_name=self.region)
log.info(f"S3Vector client initialized for bucket '{self.bucket_name}' in region '{self.region}'")
except Exception as e:
log.error(f"Failed to initialize S3Vector client: {e}")
log.error(f'Failed to initialize S3Vector client: {e}')
self.client = None
else:
self.client = None
@@ -48,8 +46,8 @@ class S3VectorClient(VectorDBBase):
self,
index_name: str,
dimension: int,
data_type: str = "float32",
distance_metric: str = "cosine",
data_type: str = 'float32',
distance_metric: str = 'cosine',
) -> None:
"""
Create a new index in the S3 vector bucket for the given collection if it does not exist.
@@ -66,21 +64,17 @@ class S3VectorClient(VectorDBBase):
dimension=dimension,
distanceMetric=distance_metric,
metadataConfiguration={
"nonFilterableMetadataKeys": [
"text",
'nonFilterableMetadataKeys': [
'text',
]
},
)
log.info(
f"Created S3 index: {index_name} (dim={dimension}, type={data_type}, metric={distance_metric})"
)
log.info(f'Created S3 index: {index_name} (dim={dimension}, type={data_type}, metric={distance_metric})')
except Exception as e:
log.error(f"Error creating S3 index '{index_name}': {e}")
raise
def _filter_metadata(
self, metadata: Dict[str, Any], item_id: str
) -> Dict[str, Any]:
def _filter_metadata(self, metadata: Dict[str, Any], item_id: str) -> Dict[str, Any]:
"""
Filter vector metadata keys to comply with S3 Vector API limit of 10 keys maximum.
"""
@@ -89,16 +83,16 @@ class S3VectorClient(VectorDBBase):
# Keep only the first 10 keys, prioritizing important ones based on actual Open WebUI metadata
important_keys = [
"text", # The actual document content
"file_id", # File ID
"source", # Document source file
"title", # Document title
"page", # Page number
"total_pages", # Total pages in document
"embedding_config", # Embedding configuration
"created_by", # User who created it
"name", # Document name
"hash", # Content hash
'text', # The actual document content
'file_id', # File ID
'source', # Document source file
'title', # Document title
'page', # Page number
'total_pages', # Total pages in document
'embedding_config', # Embedding configuration
'created_by', # User who created it
'name', # Document name
'hash', # Content hash
]
filtered_metadata = {}
@@ -117,9 +111,7 @@ class S3VectorClient(VectorDBBase):
if len(filtered_metadata) >= 10:
break
log.warning(
f"Metadata for key '{item_id}' had {len(metadata)} keys, limited to 10 keys"
)
log.warning(f"Metadata for key '{item_id}' had {len(metadata)} keys, limited to 10 keys")
return filtered_metadata
def has_collection(self, collection_name: str) -> bool:
@@ -128,9 +120,7 @@ class S3VectorClient(VectorDBBase):
This avoids pagination issues with list_indexes() and is significantly faster.
"""
try:
self.client.get_index(
vectorBucketName=self.bucket_name, indexName=collection_name
)
self.client.get_index(vectorBucketName=self.bucket_name, indexName=collection_name)
return True
except Exception as e:
log.error(f"Error checking if index '{collection_name}' exists: {e}")
@@ -142,16 +132,12 @@ class S3VectorClient(VectorDBBase):
"""
if not self.has_collection(collection_name):
log.warning(
f"Collection '{collection_name}' does not exist, nothing to delete"
)
log.warning(f"Collection '{collection_name}' does not exist, nothing to delete")
return
try:
log.info(f"Deleting collection '{collection_name}'")
self.client.delete_index(
vectorBucketName=self.bucket_name, indexName=collection_name
)
self.client.delete_index(vectorBucketName=self.bucket_name, indexName=collection_name)
log.info(f"Successfully deleted collection '{collection_name}'")
except Exception as e:
log.error(f"Error deleting collection '{collection_name}': {e}")
@@ -162,10 +148,10 @@ class S3VectorClient(VectorDBBase):
Insert vector items into the S3 Vector index. Create index if it does not exist.
"""
if not items:
log.warning("No items to insert")
log.warning('No items to insert')
return
dimension = len(items[0]["vector"])
dimension = len(items[0]['vector'])
try:
if not self.has_collection(collection_name):
@@ -173,36 +159,36 @@ class S3VectorClient(VectorDBBase):
self._create_index(
index_name=collection_name,
dimension=dimension,
data_type="float32",
distance_metric="cosine",
data_type='float32',
distance_metric='cosine',
)
# Prepare vectors for insertion
vectors = []
for item in items:
# Ensure vector data is in the correct format for S3 Vector API
vector_data = item["vector"]
vector_data = item['vector']
if isinstance(vector_data, list):
# Convert list to float32 values as required by S3 Vector API
vector_data = [float(x) for x in vector_data]
# Prepare metadata, ensuring the text field is preserved
metadata = item.get("metadata", {}).copy()
metadata = item.get('metadata', {}).copy()
# Add the text field to metadata so it's available for retrieval
metadata["text"] = item["text"]
metadata['text'] = item['text']
# Convert metadata to string format for consistency
metadata = process_metadata(metadata)
# Filter metadata to comply with S3 Vector API limit of 10 keys
metadata = self._filter_metadata(metadata, item["id"])
metadata = self._filter_metadata(metadata, item['id'])
vectors.append(
{
"key": item["id"],
"data": {"float32": vector_data},
"metadata": metadata,
'key': item['id'],
'data': {'float32': vector_data},
'metadata': metadata,
}
)
@@ -215,15 +201,11 @@ class S3VectorClient(VectorDBBase):
indexName=collection_name,
vectors=batch,
)
log.info(
f"Inserted batch {i//batch_size + 1}: {len(batch)} vectors into index '{collection_name}'."
)
log.info(f"Inserted batch {i // batch_size + 1}: {len(batch)} vectors into index '{collection_name}'.")
log.info(
f"Completed insertion of {len(vectors)} vectors into index '{collection_name}'."
)
log.info(f"Completed insertion of {len(vectors)} vectors into index '{collection_name}'.")
except Exception as e:
log.error(f"Error inserting vectors: {e}")
log.error(f'Error inserting vectors: {e}')
raise
def upsert(self, collection_name: str, items: List[VectorItem]) -> None:
@@ -231,49 +213,47 @@ class S3VectorClient(VectorDBBase):
Insert or update vector items in the S3 Vector index. Create index if it does not exist.
"""
if not items:
log.warning("No items to upsert")
log.warning('No items to upsert')
return
dimension = len(items[0]["vector"])
log.info(f"Upsert dimension: {dimension}")
dimension = len(items[0]['vector'])
log.info(f'Upsert dimension: {dimension}')
try:
if not self.has_collection(collection_name):
log.info(
f"Index '{collection_name}' does not exist. Creating index for upsert."
)
log.info(f"Index '{collection_name}' does not exist. Creating index for upsert.")
self._create_index(
index_name=collection_name,
dimension=dimension,
data_type="float32",
distance_metric="cosine",
data_type='float32',
distance_metric='cosine',
)
# Prepare vectors for upsert
vectors = []
for item in items:
# Ensure vector data is in the correct format for S3 Vector API
vector_data = item["vector"]
vector_data = item['vector']
if isinstance(vector_data, list):
# Convert list to float32 values as required by S3 Vector API
vector_data = [float(x) for x in vector_data]
# Prepare metadata, ensuring the text field is preserved
metadata = item.get("metadata", {}).copy()
metadata = item.get('metadata', {}).copy()
# Add the text field to metadata so it's available for retrieval
metadata["text"] = item["text"]
metadata['text'] = item['text']
# Convert metadata to string format for consistency
metadata = process_metadata(metadata)
# Filter metadata to comply with S3 Vector API limit of 10 keys
metadata = self._filter_metadata(metadata, item["id"])
metadata = self._filter_metadata(metadata, item['id'])
vectors.append(
{
"key": item["id"],
"data": {"float32": vector_data},
"metadata": metadata,
'key': item['id'],
'data': {'float32': vector_data},
'metadata': metadata,
}
)
@@ -283,12 +263,10 @@ class S3VectorClient(VectorDBBase):
batch = vectors[i : i + batch_size]
if i == 0: # Log sample info for first batch only
log.info(
f"Upserting batch 1: {len(batch)} vectors. First vector sample: key={batch[0]['key']}, data_type={type(batch[0]['data']['float32'])}, data_len={len(batch[0]['data']['float32'])}"
f'Upserting batch 1: {len(batch)} vectors. First vector sample: key={batch[0]["key"]}, data_type={type(batch[0]["data"]["float32"])}, data_len={len(batch[0]["data"]["float32"])}'
)
else:
log.info(
f"Upserting batch {i//batch_size + 1}: {len(batch)} vectors."
)
log.info(f'Upserting batch {i // batch_size + 1}: {len(batch)} vectors.')
self.client.put_vectors(
vectorBucketName=self.bucket_name,
@@ -296,11 +274,9 @@ class S3VectorClient(VectorDBBase):
vectors=batch,
)
log.info(
f"Completed upsert of {len(vectors)} vectors into index '{collection_name}'."
)
log.info(f"Completed upsert of {len(vectors)} vectors into index '{collection_name}'.")
except Exception as e:
log.error(f"Error upserting vectors: {e}")
log.error(f'Error upserting vectors: {e}')
raise
def search(
@@ -319,13 +295,11 @@ class S3VectorClient(VectorDBBase):
return None
if not vectors:
log.warning("No query vectors provided")
log.warning('No query vectors provided')
return None
try:
log.info(
f"Searching collection '{collection_name}' with {len(vectors)} query vectors, limit={limit}"
)
log.info(f"Searching collection '{collection_name}' with {len(vectors)} query vectors, limit={limit}")
# Initialize result lists
all_ids = []
@@ -335,10 +309,10 @@ class S3VectorClient(VectorDBBase):
# Process each query vector
for i, query_vector in enumerate(vectors):
log.debug(f"Processing query vector {i+1}/{len(vectors)}")
log.debug(f'Processing query vector {i + 1}/{len(vectors)}')
# Prepare the query vector in S3 Vector format
query_vector_dict = {"float32": [float(x) for x in query_vector]}
query_vector_dict = {'float32': [float(x) for x in query_vector]}
# Call S3 Vector query API
response = self.client.query_vectors(
@@ -356,24 +330,22 @@ class S3VectorClient(VectorDBBase):
query_metadatas = []
query_distances = []
result_vectors = response.get("vectors", [])
result_vectors = response.get('vectors', [])
for vector in result_vectors:
vector_id = vector.get("key")
vector_metadata = vector.get("metadata", {})
vector_distance = vector.get("distance", 0.0)
vector_id = vector.get('key')
vector_metadata = vector.get('metadata', {})
vector_distance = vector.get('distance', 0.0)
# Extract document text from metadata
document_text = ""
document_text = ''
if isinstance(vector_metadata, dict):
# Get the text field first (highest priority)
document_text = vector_metadata.get("text")
document_text = vector_metadata.get('text')
if not document_text:
# Fallback to other possible text fields
document_text = (
vector_metadata.get("content")
or vector_metadata.get("document")
or vector_id
vector_metadata.get('content') or vector_metadata.get('document') or vector_id
)
else:
document_text = vector_id
@@ -389,7 +361,7 @@ class S3VectorClient(VectorDBBase):
all_metadatas.append(query_metadatas)
all_distances.append(query_distances)
log.info(f"Search completed. Found results for {len(all_ids)} queries")
log.info(f'Search completed. Found results for {len(all_ids)} queries')
# Return SearchResult format
return SearchResult(
@@ -402,24 +374,20 @@ class S3VectorClient(VectorDBBase):
except Exception as e:
log.error(f"Error searching collection '{collection_name}': {str(e)}")
# Handle specific AWS exceptions
if hasattr(e, "response") and "Error" in e.response:
error_code = e.response["Error"]["Code"]
if error_code == "NotFoundException":
if hasattr(e, 'response') and 'Error' in e.response:
error_code = e.response['Error']['Code']
if error_code == 'NotFoundException':
log.warning(f"Collection '{collection_name}' not found")
return None
elif error_code == "ValidationException":
log.error(f"Invalid query vector dimensions or parameters")
elif error_code == 'ValidationException':
log.error(f'Invalid query vector dimensions or parameters')
return None
elif error_code == "AccessDeniedException":
log.error(
f"Access denied for collection '{collection_name}'. Check permissions."
)
elif error_code == 'AccessDeniedException':
log.error(f"Access denied for collection '{collection_name}'. Check permissions.")
return None
raise
def query(
self, collection_name: str, filter: Dict, limit: Optional[int] = None
) -> Optional[GetResult]:
def query(self, collection_name: str, filter: Dict, limit: Optional[int] = None) -> Optional[GetResult]:
"""
Query vectors from a collection using metadata filter.
"""
@@ -429,7 +397,7 @@ class S3VectorClient(VectorDBBase):
return GetResult(ids=[[]], documents=[[]], metadatas=[[]])
if not filter:
log.warning("No filter provided, returning all vectors")
log.warning('No filter provided, returning all vectors')
return self.get(collection_name)
try:
@@ -443,17 +411,13 @@ class S3VectorClient(VectorDBBase):
all_vectors_result = self.get(collection_name)
if not all_vectors_result or not all_vectors_result.ids:
log.warning("No vectors found in collection")
log.warning('No vectors found in collection')
return GetResult(ids=[[]], documents=[[]], metadatas=[[]])
# Extract the lists from the result
all_ids = all_vectors_result.ids[0] if all_vectors_result.ids else []
all_documents = (
all_vectors_result.documents[0] if all_vectors_result.documents else []
)
all_metadatas = (
all_vectors_result.metadatas[0] if all_vectors_result.metadatas else []
)
all_documents = all_vectors_result.documents[0] if all_vectors_result.documents else []
all_metadatas = all_vectors_result.metadatas[0] if all_vectors_result.metadatas else []
# Apply client-side filtering
filtered_ids = []
@@ -472,9 +436,7 @@ class S3VectorClient(VectorDBBase):
if limit and len(filtered_ids) >= limit:
break
log.info(
f"Filter applied: {len(filtered_ids)} vectors match out of {len(all_ids)} total"
)
log.info(f'Filter applied: {len(filtered_ids)} vectors match out of {len(all_ids)} total')
# Return GetResult format
if filtered_ids:
@@ -489,15 +451,13 @@ class S3VectorClient(VectorDBBase):
except Exception as e:
log.error(f"Error querying collection '{collection_name}': {str(e)}")
# Handle specific AWS exceptions
if hasattr(e, "response") and "Error" in e.response:
error_code = e.response["Error"]["Code"]
if error_code == "NotFoundException":
if hasattr(e, 'response') and 'Error' in e.response:
error_code = e.response['Error']['Code']
if error_code == 'NotFoundException':
log.warning(f"Collection '{collection_name}' not found")
return GetResult(ids=[[]], documents=[[]], metadatas=[[]])
elif error_code == "AccessDeniedException":
log.error(
f"Access denied for collection '{collection_name}'. Check permissions."
)
elif error_code == 'AccessDeniedException':
log.error(f"Access denied for collection '{collection_name}'. Check permissions.")
return GetResult(ids=[[]], documents=[[]], metadatas=[[]])
raise
@@ -524,47 +484,43 @@ class S3VectorClient(VectorDBBase):
while True:
# Prepare request parameters
request_params = {
"vectorBucketName": self.bucket_name,
"indexName": collection_name,
"returnData": False, # Don't include vector data (not needed for get)
"returnMetadata": True, # Include metadata
"maxResults": 500, # Use reasonable page size
'vectorBucketName': self.bucket_name,
'indexName': collection_name,
'returnData': False, # Don't include vector data (not needed for get)
'returnMetadata': True, # Include metadata
'maxResults': 500, # Use reasonable page size
}
if next_token:
request_params["nextToken"] = next_token
request_params['nextToken'] = next_token
# Call S3 Vector API
response = self.client.list_vectors(**request_params)
# Process vectors in this page
vectors = response.get("vectors", [])
vectors = response.get('vectors', [])
for vector in vectors:
vector_id = vector.get("key")
vector_data = vector.get("data", {})
vector_metadata = vector.get("metadata", {})
vector_id = vector.get('key')
vector_data = vector.get('data', {})
vector_metadata = vector.get('metadata', {})
# Extract the actual vector array
vector_array = vector_data.get("float32", [])
vector_array = vector_data.get('float32', [])
# For documents, we try to extract text from metadata or use the vector ID
document_text = ""
document_text = ''
if isinstance(vector_metadata, dict):
# Get the text field first (highest priority)
document_text = vector_metadata.get("text")
document_text = vector_metadata.get('text')
if not document_text:
# Fallback to other possible text fields
document_text = (
vector_metadata.get("content")
or vector_metadata.get("document")
or vector_id
vector_metadata.get('content') or vector_metadata.get('document') or vector_id
)
# Log the actual content for debugging
log.debug(
f"Document text preview (first 200 chars): {str(document_text)[:200]}"
)
log.debug(f'Document text preview (first 200 chars): {str(document_text)[:200]}')
else:
document_text = vector_id
@@ -573,37 +529,29 @@ class S3VectorClient(VectorDBBase):
all_metadatas.append(vector_metadata)
# Check if there are more pages
next_token = response.get("nextToken")
next_token = response.get('nextToken')
if not next_token:
break
log.info(
f"Retrieved {len(all_ids)} vectors from collection '{collection_name}'"
)
log.info(f"Retrieved {len(all_ids)} vectors from collection '{collection_name}'")
# Return in GetResult format
# The Open WebUI GetResult expects lists of lists, so we wrap each list
if all_ids:
return GetResult(
ids=[all_ids], documents=[all_documents], metadatas=[all_metadatas]
)
return GetResult(ids=[all_ids], documents=[all_documents], metadatas=[all_metadatas])
else:
return GetResult(ids=[[]], documents=[[]], metadatas=[[]])
except Exception as e:
log.error(
f"Error retrieving vectors from collection '{collection_name}': {str(e)}"
)
log.error(f"Error retrieving vectors from collection '{collection_name}': {str(e)}")
# Handle specific AWS exceptions
if hasattr(e, "response") and "Error" in e.response:
error_code = e.response["Error"]["Code"]
if error_code == "NotFoundException":
if hasattr(e, 'response') and 'Error' in e.response:
error_code = e.response['Error']['Code']
if error_code == 'NotFoundException':
log.warning(f"Collection '{collection_name}' not found")
return GetResult(ids=[[]], documents=[[]], metadatas=[[]])
elif error_code == "AccessDeniedException":
log.error(
f"Access denied for collection '{collection_name}'. Check permissions."
)
elif error_code == 'AccessDeniedException':
log.error(f"Access denied for collection '{collection_name}'. Check permissions.")
return GetResult(ids=[[]], documents=[[]], metadatas=[[]])
raise
@@ -618,20 +566,16 @@ class S3VectorClient(VectorDBBase):
"""
if not self.has_collection(collection_name):
log.warning(
f"Collection '{collection_name}' does not exist, nothing to delete"
)
log.warning(f"Collection '{collection_name}' does not exist, nothing to delete")
return
# Check if this is a knowledge collection (not file-specific)
is_knowledge_collection = not collection_name.startswith("file-")
is_knowledge_collection = not collection_name.startswith('file-')
try:
if ids:
# Delete by specific vector IDs/keys
log.info(
f"Deleting {len(ids)} vectors by IDs from collection '{collection_name}'"
)
log.info(f"Deleting {len(ids)} vectors by IDs from collection '{collection_name}'")
self.client.delete_vectors(
vectorBucketName=self.bucket_name,
indexName=collection_name,
@@ -641,15 +585,13 @@ class S3VectorClient(VectorDBBase):
elif filter:
# Handle filter-based deletion
log.info(
f"Deleting vectors by filter from collection '{collection_name}': {filter}"
)
log.info(f"Deleting vectors by filter from collection '{collection_name}': {filter}")
# If this is a knowledge collection and we have a file_id filter,
# also clean up the corresponding file-specific collection
if is_knowledge_collection and "file_id" in filter:
file_id = filter["file_id"]
file_collection_name = f"file-{file_id}"
if is_knowledge_collection and 'file_id' in filter:
file_id = filter['file_id']
file_collection_name = f'file-{file_id}'
if self.has_collection(file_collection_name):
log.info(
f"Found related file-specific collection '{file_collection_name}', deleting it to prevent duplicates"
@@ -661,9 +603,7 @@ class S3VectorClient(VectorDBBase):
query_result = self.query(collection_name, filter)
if query_result and query_result.ids and query_result.ids[0]:
matching_ids = query_result.ids[0]
log.info(
f"Found {len(matching_ids)} vectors matching filter, deleting them"
)
log.info(f'Found {len(matching_ids)} vectors matching filter, deleting them')
# Delete the matching vectors by ID
self.client.delete_vectors(
@@ -671,17 +611,13 @@ class S3VectorClient(VectorDBBase):
indexName=collection_name,
keys=matching_ids,
)
log.info(
f"Deleted {len(matching_ids)} vectors from index '{collection_name}' using filter"
)
log.info(f"Deleted {len(matching_ids)} vectors from index '{collection_name}' using filter")
else:
log.warning("No vectors found matching the filter criteria")
log.warning('No vectors found matching the filter criteria')
else:
log.warning("No IDs or filter provided for deletion")
log.warning('No IDs or filter provided for deletion')
except Exception as e:
log.error(
f"Error deleting vectors from collection '{collection_name}': {e}"
)
log.error(f"Error deleting vectors from collection '{collection_name}': {e}")
raise
def reset(self) -> None:
@@ -690,36 +626,32 @@ class S3VectorClient(VectorDBBase):
"""
try:
log.warning(
"Reset called - this will delete all vector indexes in the S3 bucket"
)
log.warning('Reset called - this will delete all vector indexes in the S3 bucket')
# List all indexes
response = self.client.list_indexes(vectorBucketName=self.bucket_name)
indexes = response.get("indexes", [])
indexes = response.get('indexes', [])
if not indexes:
log.warning("No indexes found to delete")
log.warning('No indexes found to delete')
return
# Delete all indexes
deleted_count = 0
for index in indexes:
index_name = index.get("indexName")
index_name = index.get('indexName')
if index_name:
try:
self.client.delete_index(
vectorBucketName=self.bucket_name, indexName=index_name
)
self.client.delete_index(vectorBucketName=self.bucket_name, indexName=index_name)
deleted_count += 1
log.info(f"Deleted index: {index_name}")
log.info(f'Deleted index: {index_name}')
except Exception as e:
log.error(f"Error deleting index '{index_name}': {e}")
log.info(f"Reset completed: deleted {deleted_count} indexes")
log.info(f'Reset completed: deleted {deleted_count} indexes')
except Exception as e:
log.error(f"Error during reset: {e}")
log.error(f'Error during reset: {e}')
raise
def _matches_filter(self, metadata: Dict[str, Any], filter: Dict[str, Any]) -> bool:
@@ -732,15 +664,15 @@ class S3VectorClient(VectorDBBase):
# Check each filter condition
for key, expected_value in filter.items():
# Handle special operators
if key.startswith("$"):
if key == "$and":
if key.startswith('$'):
if key == '$and':
# All conditions must match
if not isinstance(expected_value, list):
continue
for condition in expected_value:
if not self._matches_filter(metadata, condition):
return False
elif key == "$or":
elif key == '$or':
# At least one condition must match
if not isinstance(expected_value, list):
continue
@@ -760,22 +692,19 @@ class S3VectorClient(VectorDBBase):
if isinstance(expected_value, dict):
# Handle comparison operators
for op, op_value in expected_value.items():
if op == "$eq":
if op == '$eq':
if actual_value != op_value:
return False
elif op == "$ne":
elif op == '$ne':
if actual_value == op_value:
return False
elif op == "$in":
if (
not isinstance(op_value, list)
or actual_value not in op_value
):
elif op == '$in':
if not isinstance(op_value, list) or actual_value not in op_value:
return False
elif op == "$nin":
elif op == '$nin':
if isinstance(op_value, list) and actual_value in op_value:
return False
elif op == "$exists":
elif op == '$exists':
if bool(op_value) != (key in metadata):
return False
# Add more operators as needed