ti.sil/open-webui
1
0
Fork 0
mirror of https://github.com/open-webui/open-webui.git synced 2025-12-12 04:15:25 +00:00
Code Issues Projects Releases Packages Wiki Activity Actions 4

Merge pull request #15951 from 0xThresh/s3vector-support

Browse source 
feat: Add S3 Vector Buckets Support for Knowledge
This commit is contained in:
Tim Jaeryang Baek 2025-07-23 12:02:20 +04:00 committed by GitHub
commit bd18bf5c83
No known key found for this signature in database
GPG key ID: B5690EEEBB952194
5 changed files with 755 additions and 1 deletions
Show stats Download patch file Download diff file Expand all files Collapse all files

4
backend/open_webui/config.py
View file

@ -1957,6 +1957,10 @@ PINECONE_DIMENSION = int(os.getenv("PINECONE_DIMENSION", 1536)) # or 3072, 1024
PINECONE_METRIC = os.getenv("PINECONE_METRIC", "cosine")
PINECONE_CLOUD = os.getenv("PINECONE_CLOUD", "aws") # or "gcp" or "azure"
# S3 Vector
S3_VECTOR_BUCKET_NAME = os.environ.get("S3_VECTOR_BUCKET_NAME", None)
S3_VECTOR_REGION = os.environ.get("S3_VECTOR_REGION", None)
####################################
# Information Retrieval (RAG)
####################################

745
backend/open_webui/retrieval/vector/dbs/s3vector.py Normal file
View file

@ -0,0 +1,745 @@
from open_webui.retrieval.vector.main import (
VectorDBBase,
VectorItem,
GetResult,
SearchResult,
)
from open_webui.config import S3_VECTOR_BUCKET_NAME, S3_VECTOR_REGION
from open_webui.env import SRC_LOG_LEVELS
from typing import List, Optional, Dict, Any, Union
import logging
import boto3
log = logging.getLogger(__name__)
log.setLevel(SRC_LOG_LEVELS["RAG"])
class S3VectorClient(VectorDBBase):
"""
AWS S3 Vector integration for Open WebUI Knowledge.
"""
def __init__(self):
self.bucket_name = S3_VECTOR_BUCKET_NAME
self.region = S3_VECTOR_REGION
# 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")
if not self.region:
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}'"
)
except Exception as e:
log.error(f"Failed to initialize S3Vector client: {e}")
self.client = None
else:
self.client = None
def _create_index(
self,
index_name: str,
dimension: int,
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.
"""
if self.has_collection(index_name):
log.debug(f"Index '{index_name}' already exists, skipping creation")
return
try:
self.client.create_index(
vectorBucketName=self.bucket_name,
indexName=index_name,
dataType=data_type,
dimension=dimension,
distanceMetric=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]:
"""
Filter vector metadata keys to comply with S3 Vector API limit of 10 keys maximum.
"""
if not isinstance(metadata, dict) or len(metadata) <= 10:
return metadata
# 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
]
filtered_metadata = {}
# First, add important keys if they exist
for key in important_keys:
if key in metadata:
filtered_metadata[key] = metadata[key]
if len(filtered_metadata) >= 10:
break
# If we still have room, add other keys
if len(filtered_metadata) < 10:
for key, value in metadata.items():
if key not in filtered_metadata:
filtered_metadata[key] = value
if len(filtered_metadata) >= 10:
break
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:
"""
Check if a vector index (collection) exists in the S3 vector bucket.
"""
try:
response = self.client.list_indexes(vectorBucketName=self.bucket_name)
indexes = response.get("indexes", [])
return any(idx.get("indexName") == collection_name for idx in indexes)
except Exception as e:
log.error(f"Error listing indexes: {e}")
return False
def delete_collection(self, collection_name: str) -> None:
"""
Delete an entire S3 Vector index/collection.
"""
if not self.has_collection(collection_name):
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
)
log.info(f"Successfully deleted collection '{collection_name}'")
except Exception as e:
log.error(f"Error deleting collection '{collection_name}': {e}")
raise
def insert(self, collection_name: str, items: List[VectorItem]) -> None:
"""
Insert vector items into the S3 Vector index. Create index if it does not exist.
"""
if not items:
log.warning("No items to insert")
return
dimension = len(items[0]["vector"])
try:
if not self.has_collection(collection_name):
log.info(f"Index '{collection_name}' does not exist. Creating index.")
self._create_index(
index_name=collection_name,
dimension=dimension,
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"]
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()
# Add the text field to metadata so it's available for retrieval
metadata["text"] = item["text"]
# Filter metadata to comply with S3 Vector API limit of 10 keys
metadata = self._filter_metadata(metadata, item["id"])
vectors.append(
{
"key": item["id"],
"data": {"float32": vector_data},
"metadata": metadata,
}
)
# Insert vectors
self.client.put_vectors(
vectorBucketName=self.bucket_name,
indexName=collection_name,
vectors=vectors,
)
log.info(f"Inserted {len(vectors)} vectors into index '{collection_name}'.")
except Exception as e:
log.error(f"Error inserting vectors: {e}")
raise
def upsert(self, collection_name: str, items: List[VectorItem]) -> None:
"""
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")
return
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."
)
self._create_index(
index_name=collection_name,
dimension=dimension,
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"]
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()
# Add the text field to metadata so it's available for retrieval
metadata["text"] = item["text"]
# Filter metadata to comply with S3 Vector API limit of 10 keys
metadata = self._filter_metadata(metadata, item["id"])
vectors.append(
{
"key": item["id"],
"data": {"float32": vector_data},
"metadata": metadata,
}
)
# Upsert vectors (using put_vectors for upsert semantics)
log.info(
f"Upserting {len(vectors)} vectors. First vector sample: key={vectors[0]['key']}, data_type={type(vectors[0]['data']['float32'])}, data_len={len(vectors[0]['data']['float32'])}"
)
self.client.put_vectors(
vectorBucketName=self.bucket_name,
indexName=collection_name,
vectors=vectors,
)
log.info(f"Upserted {len(vectors)} vectors into index '{collection_name}'.")
except Exception as e:
log.error(f"Error upserting vectors: {e}")
raise
def search(
self, collection_name: str, vectors: List[List[Union[float, int]]], limit: int
) -> Optional[SearchResult]:
"""
Search for similar vectors in a collection using multiple query vectors.
"""
if not self.has_collection(collection_name):
log.warning(f"Collection '{collection_name}' does not exist")
return None
if not vectors:
log.warning("No query vectors provided")
return None
try:
log.info(
f"Searching collection '{collection_name}' with {len(vectors)} query vectors, limit={limit}"
)
# Initialize result lists
all_ids = []
all_documents = []
all_metadatas = []
all_distances = []
# Process each query vector
for i, query_vector in enumerate(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]}
# Call S3 Vector query API
response = self.client.query_vectors(
vectorBucketName=self.bucket_name,
indexName=collection_name,
topK=limit,
queryVector=query_vector_dict,
returnMetadata=True,
returnDistance=True,
)
# Process results for this query
query_ids = []
query_documents = []
query_metadatas = []
query_distances = []
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)
# Extract document text from metadata
document_text = ""
if isinstance(vector_metadata, dict):
# Get the text field first (highest priority)
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
)
else:
document_text = vector_id
query_ids.append(vector_id)
query_documents.append(document_text)
query_metadatas.append(vector_metadata)
query_distances.append(vector_distance)
# Add this query's results to the overall results
all_ids.append(query_ids)
all_documents.append(query_documents)
all_metadatas.append(query_metadatas)
all_distances.append(query_distances)
log.info(f"Search completed. Found results for {len(all_ids)} queries")
# Return SearchResult format
return SearchResult(
ids=all_ids if all_ids else None,
documents=all_documents if all_documents else None,
metadatas=all_metadatas if all_metadatas else None,
distances=all_distances if all_distances else None,
)
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":
log.warning(f"Collection '{collection_name}' not found")
return None
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."
)
return None
raise
def query(
self, collection_name: str, filter: Dict, limit: Optional[int] = None
) -> Optional[GetResult]:
"""
Query vectors from a collection using metadata filter.
"""
if not self.has_collection(collection_name):
log.warning(f"Collection '{collection_name}' does not exist")
return GetResult(ids=[[]], documents=[[]], metadatas=[[]])
if not filter:
log.warning("No filter provided, returning all vectors")
return self.get(collection_name)
try:
log.info(f"Querying collection '{collection_name}' with filter: {filter}")
# For S3 Vector, we need to use list_vectors and then filter results
# Since S3 Vector may not support complex server-side filtering,
# we'll retrieve all vectors and filter client-side
# Get all vectors first
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")
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 []
)
# Apply client-side filtering
filtered_ids = []
filtered_documents = []
filtered_metadatas = []
for i, metadata in enumerate(all_metadatas):
if self._matches_filter(metadata, filter):
if i < len(all_ids):
filtered_ids.append(all_ids[i])
if i < len(all_documents):
filtered_documents.append(all_documents[i])
filtered_metadatas.append(metadata)
# Apply limit if specified
if limit and len(filtered_ids) >= limit:
break
log.info(
f"Filter applied: {len(filtered_ids)} vectors match out of {len(all_ids)} total"
)
# Return GetResult format
if filtered_ids:
return GetResult(
ids=[filtered_ids],
documents=[filtered_documents],
metadatas=[filtered_metadatas],
)
else:
return GetResult(ids=[[]], documents=[[]], metadatas=[[]])
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":
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."
)
return GetResult(ids=[[]], documents=[[]], metadatas=[[]])
raise
def get(self, collection_name: str) -> Optional[GetResult]:
"""
Retrieve all vectors from a collection.
"""
if not self.has_collection(collection_name):
log.warning(f"Collection '{collection_name}' does not exist")
return GetResult(ids=[[]], documents=[[]], metadatas=[[]])
try:
log.info(f"Retrieving all vectors from collection '{collection_name}'")
# Initialize result lists
all_ids = []
all_documents = []
all_metadatas = []
# Handle pagination
next_token = None
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
}
if 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", [])
for vector in vectors:
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", [])
# For documents, we try to extract text from metadata or use the vector ID
document_text = ""
if isinstance(vector_metadata, dict):
# Get the text field first (highest priority)
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
)
# Log the actual content for debugging
log.debug(
f"Document text preview (first 200 chars): {str(document_text)[:200]}"
)
else:
document_text = vector_id
all_ids.append(vector_id)
all_documents.append(document_text)
all_metadatas.append(vector_metadata)
# Check if there are more pages
next_token = response.get("nextToken")
if not next_token:
break
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]
)
else:
return GetResult(ids=[[]], documents=[[]], metadatas=[[]])
except Exception as 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":
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."
)
return GetResult(ids=[[]], documents=[[]], metadatas=[[]])
raise
def delete(
self,
collection_name: str,
ids: Optional[List[str]] = None,
filter: Optional[Dict] = None,
) -> None:
"""
Delete vectors by ID or filter from a collection.
"""
if not self.has_collection(collection_name):
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-")
try:
if ids:
# Delete by specific vector IDs/keys
log.info(
f"Deleting {len(ids)} vectors by IDs from collection '{collection_name}'"
)
self.client.delete_vectors(
vectorBucketName=self.bucket_name,
indexName=collection_name,
keys=ids,
)
log.info(f"Deleted {len(ids)} vectors from index '{collection_name}'")
elif filter:
# Handle filter-based deletion
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 self.has_collection(file_collection_name):
log.info(
f"Found related file-specific collection '{file_collection_name}', deleting it to prevent duplicates"
)
self.delete_collection(file_collection_name)
# For the main collection, implement query-then-delete
# First, query to get IDs matching the filter
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"
)
# Delete the matching vectors by ID
self.client.delete_vectors(
vectorBucketName=self.bucket_name,
indexName=collection_name,
keys=matching_ids,
)
log.info(
f"Deleted {len(matching_ids)} vectors from index '{collection_name}' using filter"
)
else:
log.warning("No vectors found matching the filter criteria")
else:
log.warning("No IDs or filter provided for deletion")
except Exception as e:
log.error(
f"Error deleting vectors from collection '{collection_name}': {e}"
)
raise
def reset(self) -> None:
"""
Reset/clear all vector data. For S3 Vector, this deletes all indexes.
"""
try:
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", [])
if not indexes:
log.warning("No indexes found to delete")
return
# Delete all indexes
deleted_count = 0
for index in indexes:
index_name = index.get("indexName")
if index_name:
try:
self.client.delete_index(
vectorBucketName=self.bucket_name, indexName=index_name
)
deleted_count += 1
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")
except Exception as e:
log.error(f"Error during reset: {e}")
raise
def _matches_filter(self, metadata: Dict[str, Any], filter: Dict[str, Any]) -> bool:
"""
Check if metadata matches the given filter conditions.
"""
if not isinstance(metadata, dict) or not isinstance(filter, dict):
return False
# Check each filter condition
for key, expected_value in filter.items():
# Handle special operators
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 == "$or":
# At least one condition must match
if not isinstance(expected_value, list):
continue
any_match = False
for condition in expected_value:
if self._matches_filter(metadata, condition):
any_match = True
break
if not any_match:
return False
continue
# Get the actual value from metadata
actual_value = metadata.get(key)
# Handle different types of expected values
if isinstance(expected_value, dict):
# Handle comparison operators
for op, op_value in expected_value.items():
if op == "$eq":
if actual_value != op_value:
return False
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
):
return False
elif op == "$nin":
if isinstance(op_value, list) and actual_value in op_value:
return False
elif op == "$exists":
if bool(op_value) != (key in metadata):
return False
# Add more operators as needed
else:
# Simple equality check
if actual_value != expected_value:
return False
return True

4
backend/open_webui/retrieval/vector/factory.py
View file

@ -30,6 +30,10 @@ class Vector:
from open_webui.retrieval.vector.dbs.pinecone import PineconeClient
return PineconeClient()
case VectorType.S3VECTOR:
from open_webui.retrieval.vector.dbs.s3vector import S3VectorClient
return S3VectorClient()
case VectorType.OPENSEARCH:
from open_webui.retrieval.vector.dbs.opensearch import OpenSearchClient

1
backend/open_webui/retrieval/vector/type.py
View file

@ -9,3 +9,4 @@ class VectorType(StrEnum):
ELASTICSEARCH = "elasticsearch"
OPENSEARCH = "opensearch"
PGVECTOR = "pgvector"
S3VECTOR = "s3vector"

2
uv.lock
View file

@ -5245,4 +5245,4 @@ wheels = [
{ url = "https://files.pythonhosted.org/packages/f2/61/ac78a1263bc83a5cf29e7458b77a568eda5a8f81980691bbc6eb6a0d45cc/zstandard-0.23.0-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:a8fffdbd9d1408006baaf02f1068d7dd1f016c6bcb7538682622c556e7b68e35", size = 5191313, upload-time = "2024-07-15T00:16:09.758Z" },
{ url = "https://files.pythonhosted.org/packages/e7/54/967c478314e16af5baf849b6ee9d6ea724ae5b100eb506011f045d3d4e16/zstandard-0.23.0-cp312-cp312-win32.whl", hash = "sha256:dc1d33abb8a0d754ea4763bad944fd965d3d95b5baef6b121c0c9013eaf1907d", size = 430877, upload-time = "2024-07-15T00:16:11.758Z" },
{ url = "https://files.pythonhosted.org/packages/75/37/872d74bd7739639c4553bf94c84af7d54d8211b626b352bc57f0fd8d1e3f/zstandard-0.23.0-cp312-cp312-win_amd64.whl", hash = "sha256:64585e1dba664dc67c7cdabd56c1e5685233fbb1fc1966cfba2a340ec0dfff7b", size = 495595, upload-time = "2024-07-15T00:16:13.731Z" },
]
]