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@@ -1,47 +1,46 @@
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from elasticsearch import Elasticsearch, BadRequestError
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from typing import Optional
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import ssl
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-from elasticsearch.helpers import bulk, scan
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+from elasticsearch.helpers import bulk,scan
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from open_webui.retrieval.vector.main import VectorItem, SearchResult, GetResult
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from open_webui.config import (
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ELASTICSEARCH_URL,
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- ELASTICSEARCH_CA_CERTS,
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+ ELASTICSEARCH_CA_CERTS,
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ELASTICSEARCH_API_KEY,
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ELASTICSEARCH_USERNAME,
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- ELASTICSEARCH_PASSWORD,
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+ ELASTICSEARCH_PASSWORD,
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ELASTICSEARCH_CLOUD_ID,
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+ ELASTICSEARCH_INDEX_PREFIX,
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SSL_ASSERT_FINGERPRINT,
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+
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)
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+
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+
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class ElasticsearchClient:
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"""
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Important:
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- in order to reduce the number of indexes and since the embedding vector length is fixed, we avoid creating
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- an index for each file but store it as a text field, while seperating to different index
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+ in order to reduce the number of indexes and since the embedding vector length is fixed, we avoid creating
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+ an index for each file but store it as a text field, while seperating to different index
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baesd on the embedding length.
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"""
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-
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def __init__(self):
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- self.index_prefix = "open_webui_collections"
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+ self.index_prefix = ELASTICSEARCH_INDEX_PREFIX
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self.client = Elasticsearch(
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hosts=[ELASTICSEARCH_URL],
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ca_certs=ELASTICSEARCH_CA_CERTS,
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api_key=ELASTICSEARCH_API_KEY,
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cloud_id=ELASTICSEARCH_CLOUD_ID,
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- basic_auth=(
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- (ELASTICSEARCH_USERNAME, ELASTICSEARCH_PASSWORD)
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- if ELASTICSEARCH_USERNAME and ELASTICSEARCH_PASSWORD
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- else None
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- ),
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- ssl_assert_fingerprint=SSL_ASSERT_FINGERPRINT,
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+ basic_auth=(ELASTICSEARCH_USERNAME,ELASTICSEARCH_PASSWORD) if ELASTICSEARCH_USERNAME and ELASTICSEARCH_PASSWORD else None,
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+ ssl_assert_fingerprint=SSL_ASSERT_FINGERPRINT
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+
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)
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-
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- # Status: works
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- def _get_index_name(self, dimension: int) -> str:
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+ #Status: works
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+ def _get_index_name(self,dimension:int)->str:
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return f"{self.index_prefix}_d{str(dimension)}"
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-
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- # Status: works
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+
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+ #Status: works
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def _scan_result_to_get_result(self, result) -> GetResult:
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if not result:
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return None
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@@ -56,7 +55,7 @@ class ElasticsearchClient:
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return GetResult(ids=[ids], documents=[documents], metadatas=[metadatas])
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- # Status: works
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+ #Status: works
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def _result_to_get_result(self, result) -> GetResult:
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if not result["hits"]["hits"]:
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return None
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@@ -71,7 +70,7 @@ class ElasticsearchClient:
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return GetResult(ids=[ids], documents=[documents], metadatas=[metadatas])
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- # Status: works
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+ #Status: works
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def _result_to_search_result(self, result) -> SearchResult:
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ids = []
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distances = []
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@@ -85,16 +84,22 @@ class ElasticsearchClient:
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metadatas.append(hit["_source"].get("metadata"))
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return SearchResult(
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- ids=[ids],
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- distances=[distances],
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- documents=[documents],
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- metadatas=[metadatas],
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+ ids=[ids], distances=[distances], documents=[documents], metadatas=[metadatas]
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)
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-
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- # Status: works
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+ #Status: works
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def _create_index(self, dimension: int):
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body = {
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"mappings": {
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+ "dynamic_templates": [
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+ {
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+ "strings": {
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+ "match_mapping_type": "string",
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+ "mapping": {
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+ "type": "keyword"
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+ }
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+ }
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+ }
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+ ],
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"properties": {
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"collection": {"type": "keyword"},
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"id": {"type": "keyword"},
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@@ -110,51 +115,64 @@ class ElasticsearchClient:
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}
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}
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self.client.indices.create(index=self._get_index_name(dimension), body=body)
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-
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- # Status: works
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+ #Status: works
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def _create_batches(self, items: list[VectorItem], batch_size=100):
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for i in range(0, len(items), batch_size):
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- yield items[i : min(i + batch_size, len(items))]
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+ yield items[i : min(i + batch_size,len(items))]
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- # Status: works
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- def has_collection(self, collection_name) -> bool:
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+ #Status: works
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+ def has_collection(self,collection_name) -> bool:
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query_body = {"query": {"bool": {"filter": []}}}
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- query_body["query"]["bool"]["filter"].append(
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- {"term": {"collection": collection_name}}
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- )
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+ query_body["query"]["bool"]["filter"].append({"term": {"collection": collection_name}})
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try:
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- result = self.client.count(index=f"{self.index_prefix}*", body=query_body)
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-
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- return result.body["count"] > 0
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+ result = self.client.count(
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+ index=f"{self.index_prefix}*",
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+ body=query_body
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+ )
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+
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+ return result.body["count"]>0
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except Exception as e:
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return None
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+
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- # @TODO: Make this delete a collection and not an index
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- def delete_colleciton(self, collection_name: str):
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- # TODO: fix this to include the dimension or a * prefix
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- # delete_collection here means delete a bunch of documents for an index.
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- # We are simply adapting to the norms of the other DBs.
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- self.client.indices.delete(index=self._get_collection_name(collection_name))
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-
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- # Status: works
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+
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+ def delete_collection(self, collection_name: str):
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+ query = {
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+ "query": {
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+ "term": {"collection": collection_name}
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+ }
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+ }
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+ self.client.delete_by_query(index=f"{self.index_prefix}*", body=query)
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+ #Status: works
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def search(
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self, collection_name: str, vectors: list[list[float]], limit: int
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) -> Optional[SearchResult]:
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query = {
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"size": limit,
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- "_source": ["text", "metadata"],
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+ "_source": [
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+ "text",
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+ "metadata"
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+ ],
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"query": {
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"script_score": {
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"query": {
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- "bool": {"filter": [{"term": {"collection": collection_name}}]}
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+ "bool": {
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+ "filter": [
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+ {
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+ "term": {
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+ "collection": collection_name
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+ }
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+ }
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+ ]
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+ }
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},
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"script": {
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"source": "cosineSimilarity(params.vector, 'vector') + 1.0",
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"params": {
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"vector": vectors[0]
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- }, # Assuming single query vector
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+ }, # Assuming single query vector
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},
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}
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},
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@@ -165,8 +183,7 @@ class ElasticsearchClient:
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)
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return self._result_to_search_result(result)
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-
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- # Status: only tested halfwat
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+ #Status: only tested halfwat
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def query(
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self, collection_name: str, filter: dict, limit: Optional[int] = None
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) -> Optional[GetResult]:
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@@ -180,9 +197,7 @@ class ElasticsearchClient:
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for field, value in filter.items():
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query_body["query"]["bool"]["filter"].append({"term": {field: value}})
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- query_body["query"]["bool"]["filter"].append(
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- {"term": {"collection": collection_name}}
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- )
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+ query_body["query"]["bool"]["filter"].append({"term": {"collection": collection_name}})
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size = limit if limit else 10
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try:
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@@ -191,82 +206,109 @@ class ElasticsearchClient:
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body=query_body,
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size=size,
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)
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-
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+
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return self._result_to_get_result(result)
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except Exception as e:
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return None
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+ #Status: works
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+ def _has_index(self,dimension:int):
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+ return self.client.indices.exists(index=self._get_index_name(dimension=dimension))
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- # Status: works
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- def _has_index(self, dimension: int):
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- return self.client.indices.exists(
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- index=self._get_index_name(dimension=dimension)
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- )
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def get_or_create_index(self, dimension: int):
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if not self._has_index(dimension=dimension):
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self._create_index(dimension=dimension)
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-
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- # Status: works
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+ #Status: works
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def get(self, collection_name: str) -> Optional[GetResult]:
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# Get all the items in the collection.
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query = {
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- "query": {"bool": {"filter": [{"term": {"collection": collection_name}}]}},
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- "_source": ["text", "metadata"],
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- }
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+ "query": {
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+ "bool": {
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+ "filter": [
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+ {
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+ "term": {
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+ "collection": collection_name
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+ }
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+ }
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+ ]
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+ }
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+ }, "_source": ["text", "metadata"]}
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results = list(scan(self.client, index=f"{self.index_prefix}*", query=query))
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-
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+
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return self._scan_result_to_get_result(results)
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- # Status: works
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+ #Status: works
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def insert(self, collection_name: str, items: list[VectorItem]):
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if not self._has_index(dimension=len(items[0]["vector"])):
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self._create_index(dimension=len(items[0]["vector"]))
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+
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for batch in self._create_batches(items):
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actions = [
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- {
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- "_index": self._get_index_name(dimension=len(items[0]["vector"])),
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- "_id": item["id"],
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- "_source": {
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- "collection": collection_name,
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- "vector": item["vector"],
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- "text": item["text"],
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- "metadata": item["metadata"],
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- },
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- }
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+ {
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+ "_index":self._get_index_name(dimension=len(items[0]["vector"])),
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+ "_id": item["id"],
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+ "_source": {
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+ "collection": collection_name,
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+ "vector": item["vector"],
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+ "text": item["text"],
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+ "metadata": item["metadata"],
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+ },
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+ }
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for item in batch
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]
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- bulk(self.client, actions)
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+ bulk(self.client,actions)
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- # Status: should work
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+ # Upsert documents using the update API with doc_as_upsert=True.
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def upsert(self, collection_name: str, items: list[VectorItem]):
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if not self._has_index(dimension=len(items[0]["vector"])):
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- self._create_index(collection_name, dimension=len(items[0]["vector"]))
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-
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+ self._create_index(dimension=len(items[0]["vector"]))
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for batch in self._create_batches(items):
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actions = [
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{
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- "_index": self._get_index_name(dimension=len(items[0]["vector"])),
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+ "_op_type": "update",
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+ "_index": self._get_index_name(dimension=len(item["vector"])),
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"_id": item["id"],
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- "_source": {
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+ "doc": {
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+ "collection": collection_name,
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"vector": item["vector"],
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"text": item["text"],
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"metadata": item["metadata"],
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},
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+ "doc_as_upsert": True,
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}
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for item in batch
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]
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- self.client.bulk(actions)
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-
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- # TODO: This currently deletes by * which is not always supported in ElasticSearch.
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- # Need to read a bit before changing. Also, need to delete from a specific collection
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- def delete(self, collection_name: str, ids: list[str]):
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- # Assuming ID is unique across collections and indexes
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- actions = [
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- {"delete": {"_index": f"{self.index_prefix}*", "_id": id}} for id in ids
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- ]
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- self.client.bulk(body=actions)
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+ bulk(self.client,actions)
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+
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+
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+ # Delete specific documents from a collection by filtering on both collection and document IDs.
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+ def delete(
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+ self,
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+ collection_name: str,
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+ ids: Optional[list[str]] = None,
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+ filter: Optional[dict] = None,
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+ ):
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+
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+ query = {
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+ "query": {
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+ "bool": {
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+ "filter": [
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+ {"term": {"collection": collection_name}}
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+ ]
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+ }
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+ }
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+ }
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+ #logic based on chromaDB
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+ if ids:
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+ query["query"]["bool"]["filter"].append({"terms": {"_id": ids}})
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+ elif filter:
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+ for field, value in filter.items():
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+ query["query"]["bool"]["filter"].append({"term": {f"metadata.{field}": value}})
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+
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+
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+ self.client.delete_by_query(index=f"{self.index_prefix}*", body=query)
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def reset(self):
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indices = self.client.indices.get(index=f"{self.index_prefix}*")
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