from opensearchpy import OpenSearch
from opensearchpy.helpers import bulk
from typing import Optional
from open_webui.retrieval.vector.main import (
VectorDBBase,
VectorItem,
SearchResult,
GetResult,
)
from open_webui.config import (
OPENSEARCH_URI,
OPENSEARCH_SSL,
OPENSEARCH_CERT_VERIFY,
OPENSEARCH_USERNAME,
OPENSEARCH_PASSWORD,
)
class OpenSearchClient(VectorDBBase):
def __init__(self):
self.index_prefix = "open_webui"
self.client = OpenSearch(
hosts=[OPENSEARCH_URI],
use_ssl=OPENSEARCH_SSL,
verify_certs=OPENSEARCH_CERT_VERIFY,
http_auth=(OPENSEARCH_USERNAME, OPENSEARCH_PASSWORD),
)
def _get_index_name(self, collection_name: str) -> str:
return f"{self.index_prefix}_{collection_name}"
def _result_to_get_result(self, result) -> GetResult:
if not result["hits"]["hits"]:
return None
ids = []
documents = []
metadatas = []
for hit in result["hits"]["hits"]:
ids.append(hit["_id"])
documents.append(hit["_source"].get("text"))
metadatas.append(hit["_source"].get("metadata"))
return GetResult(ids=[ids], documents=[documents], metadatas=[metadatas])
def _result_to_search_result(self, result) -> SearchResult:
if not result["hits"]["hits"]:
return None
ids = []
distances = []
documents = []
metadatas = []
for hit in result["hits"]["hits"]:
ids.append(hit["_id"])
distances.append(hit["_score"])
documents.append(hit["_source"].get("text"))
metadatas.append(hit["_source"].get("metadata"))
return SearchResult(
ids=[ids],
distances=[distances],
documents=[documents],
metadatas=[metadatas],
)
def _create_index(self, collection_name: str, dimension: int):
body = {
"settings": {"index": {"knn": True}},
"mappings": {
"properties": {
"id": {"type": "keyword"},
"vector": {
"type": "knn_vector",
"dimension": dimension, # Adjust based on your vector dimensions
"index": True,
"similarity": "faiss",
"method": {
"name": "hnsw",
"space_type": "innerproduct", # Use inner product to approximate cosine similarity
"engine": "faiss",
"parameters": {
"ef_construction": 128,
"m": 16,
},
},
},
"text": {"type": "text"},
"metadata": {"type": "object"},
}
},
}
self.client.indices.create(
index=self._get_index_name(collection_name), body=body
)
def _create_batches(self, items: list[VectorItem], batch_size=100):
for i in range(0, len(items), batch_size):
yield items[i : i + batch_size]
def has_collection(self, collection_name: str) -> bool:
# has_collection here means has index.
# We are simply adapting to the norms of the other DBs.
return self.client.indices.exists(index=self._get_index_name(collection_name))
def delete_collection(self, collection_name: str):
# delete_collection here means delete index.
# We are simply adapting to the norms of the other DBs.
self.client.indices.delete(index=self._get_index_name(collection_name))
def search(
self, collection_name: str, vectors: list[list[float | int]], limit: int
) -> Optional[SearchResult]:
try:
if not self.has_collection(collection_name):
return None
query = {
"size": limit,
"_source": ["text", "metadata"],
"query": {
"script_score": {
"query": {"match_all": {}},
"script": {
"source": "(cosineSimilarity(params.query_value, doc[params.field]) + 1.0) / 2.0",
"params": {
"field": "vector",
"query_value": vectors[0],
}, # Assuming single query vector
},
}
},
}
result = self.client.search(
index=self._get_index_name(collection_name), body=query
)
return self._result_to_search_result(result)
except Exception as e:
return None
def query(
self, collection_name: str, filter: dict, limit: Optional[int] = None
) -> Optional[GetResult]:
if not self.has_collection(collection_name):
return None
query_body = {
"query": {"bool": {"filter": []}},
"_source": ["text", "metadata"],
}
for field, value in filter.items():
query_body["query"]["bool"]["filter"].append(
{"match": {"metadata." + str(field): value}}
)
size = limit if limit else 10
try:
result = self.client.search(
index=self._get_index_name(collection_name),
body=query_body,
size=size,
)
return self._result_to_get_result(result)
except Exception as e:
return None
def _create_index_if_not_exists(self, collection_name: str, dimension: int):
if not self.has_collection(collection_name):
self._create_index(collection_name, dimension)
def get(self, collection_name: str) -> Optional[GetResult]:
query = {"query": {"match_all": {}}, "_source": ["text", "metadata"]}
result = self.client.search(
index=self._get_index_name(collection_name), body=query
)
return self._result_to_get_result(result)
def insert(self, collection_name: str, items: list[VectorItem]):
self._create_index_if_not_exists(
collection_name=collection_name, dimension=len(items[0]["vector"])
)
for batch in self._create_batches(items):
actions = [
{
"_op_type": "index",
"_index": self._get_index_name(collection_name),
"_id": item["id"],
"_source": {
"vector": item["vector"],
"text": item["text"],
"metadata": item["metadata"],
},
}
for item in batch
]
bulk(self.client, actions)
def upsert(self, collection_name: str, items: list[VectorItem]):
self._create_index_if_not_exists(
collection_name=collection_name, dimension=len(items[0]["vector"])
)
for batch in self._create_batches(items):
actions = [
{
"_op_type": "update",
"_index": self._get_index_name(collection_name),
"_id": item["id"],
"doc": {
"vector": item["vector"],
"text": item["text"],
"metadata": item["metadata"],
},
"doc_as_upsert": True,
}
for item in batch
]
bulk(self.client, actions)
def delete(
self,
collection_name: str,
ids: Optional[list[str]] = None,
filter: Optional[dict] = None,
):
if ids:
actions = [
{
"_op_type": "delete",
"_index": self._get_index_name(collection_name),
"_id": id,
}
for id in ids
]
bulk(self.client, actions)
elif filter:
query_body = {
"query": {"bool": {"filter": []}},
}
for field, value in filter.items():
query_body["query"]["bool"]["filter"].append(
{"match": {"metadata." + str(field): value}}
)
self.client.delete_by_query(
index=self._get_index_name(collection_name), body=query_body
)
def reset(self):
indices = self.client.indices.get(index=f"{self.index_prefix}_*")
for index in indices:
self.client.indices.delete(index=index)