Opensearch test and documentation (#11981)

llama-index-integrations/vector_stores/llama-index-vector-stores-opensearch/llama_index/vector_stores/opensearch/base.py

@@ -358,12 +358,12 @@ class OpensearchVectorClient:
             is_aoss=self.is_aoss,
         )
 
-    async def delete_doc_id(self, doc_id: str) -> None:
+    async def delete_by_doc_id(self, doc_id: str) -> None:
         """
-        Delete a document.
+        Deletes all OpenSearch documents corresponding to the given LlamaIndex `Document` ID.
 
         Args:
-            doc_id (str): document id
+            doc_id (str): a LlamaIndex `Document` id
         """
         search_query = {
             "query": {"term": {"metadata.doc_id.keyword": {"value": doc_id}}}
@@ -494,10 +494,10 @@ class OpensearchVectorStore(BasePydanticVectorStore):
 
     def delete(self, ref_doc_id: str, **delete_kwargs: Any) -> None:
         """
-        Delete nodes using with ref_doc_id.
+        Delete nodes using a ref_doc_id.
 
         Args:
-            ref_doc_id (str): The doc_id of the document to delete.
+            ref_doc_id (str): The doc_id of the document whose nodes should be deleted.
 
         """
         asyncio.get_event_loop().run_until_complete(
@@ -506,13 +506,13 @@ class OpensearchVectorStore(BasePydanticVectorStore):
 
     async def adelete(self, ref_doc_id: str, **delete_kwargs: Any) -> None:
         """
-        Async delete nodes using with ref_doc_id.
+        Async delete nodes using a ref_doc_id.
 
         Args:
-            ref_doc_id (str): The doc_id of the document to delete.
+            ref_doc_id (str): The doc_id of the document whose nodes should be deleted.
 
         """
-        await self._client.delete_doc_id(ref_doc_id)
+        await self._client.delete_by_doc_id(ref_doc_id)
 
     def query(self, query: VectorStoreQuery, **kwargs: Any) -> VectorStoreQueryResult:
         """

llama-index-integrations/vector_stores/llama-index-vector-stores-opensearch/tests/test_opensearch_client.py

@@ -4,7 +4,7 @@ import pytest
 import uuid
 from typing import List, Generator
 
-from llama_index.core.schema import TextNode
+from llama_index.core.schema import NodeRelationship, RelatedNodeInfo, TextNode
 from llama_index.vector_stores.opensearch import (
     OpensearchVectorClient,
     OpensearchVectorStore,
@@ -68,7 +68,7 @@ def node_embeddings() -> List[TextNode]:
         TextNode(
             text="lorem ipsum",
             id_="c330d77f-90bd-4c51-9ed2-57d8d693b3b0",
-            # relationships={NodeRelationship.SOURCE: RelatedNodeInfo(node_id="test-0")},
+            relationships={NodeRelationship.SOURCE: RelatedNodeInfo(node_id="test-0")},
             metadata={
                 "author": "Stephen King",
                 "theme": "Friendship",
@@ -78,7 +78,7 @@ def node_embeddings() -> List[TextNode]:
         TextNode(
             text="lorem ipsum",
             id_="c3d1e1dd-8fb4-4b8f-b7ea-7fa96038d39d",
-            # relationships={NodeRelationship.SOURCE: RelatedNodeInfo(node_id="test-1")},
+            relationships={NodeRelationship.SOURCE: RelatedNodeInfo(node_id="test-1")},
             metadata={
                 "director": "Francis Ford Coppola",
                 "theme": "Mafia",
@@ -88,7 +88,7 @@ def node_embeddings() -> List[TextNode]:
         TextNode(
             text="lorem ipsum",
             id_="c3ew11cd-8fb4-4b8f-b7ea-7fa96038d39d",
-            # relationships={NodeRelationship.SOURCE: RelatedNodeInfo(node_id="test-2")},
+            relationships={NodeRelationship.SOURCE: RelatedNodeInfo(node_id="test-2")},
             metadata={
                 "director": "Christopher Nolan",
             },
@@ -97,8 +97,8 @@ def node_embeddings() -> List[TextNode]:
         TextNode(
             text="I was taught that the way of progress was neither swift nor easy.",
             id_="0b31ae71-b797-4e88-8495-031371a7752e",
-            # relationships={NodeRelationship.SOURCE: RelatedNodeInfo(node_id="text-3")},
-            metadate={
+            relationships={NodeRelationship.SOURCE: RelatedNodeInfo(node_id="test-3")},
+            metadata={
                 "author": "Marie Curie",
             },
             embedding=[0.0, 0.0, 0.9],
@@ -109,8 +109,8 @@ def node_embeddings() -> List[TextNode]:
                 + " Curiosity has its own reason for existing."
             ),
             id_="bd2e080b-159a-4030-acc3-d98afd2ba49b",
-            # relationships={NodeRelationship.SOURCE: RelatedNodeInfo(node_id="text-4")},
-            metadate={
+            relationships={NodeRelationship.SOURCE: RelatedNodeInfo(node_id="test-4")},
+            metadata={
                 "author": "Albert Einstein",
             },
             embedding=[0.0, 0.0, 0.5],
@@ -121,8 +121,8 @@ def node_embeddings() -> List[TextNode]:
                 + " I am a free human being with an independent will."
             ),
             id_="f658de3b-8cef-4d1c-8bed-9a263c907251",
-            # relationships={NodeRelationship.SOURCE: RelatedNodeInfo(node_id="text-5")},
-            metadate={
+            relationships={NodeRelationship.SOURCE: RelatedNodeInfo(node_id="test-5")},
+            metadata={
                 "author": "Charlotte Bronte",
             },
             embedding=[0.0, 0.0, 0.3],
@@ -153,6 +153,6 @@ def test_functionality(
     query_result = os_store.query(query)
     assert query_result.nodes
     assert query_result.nodes[0].get_content() == exp_node.text
-    # delete
-    os_store.delete(exp_node.id_)
+    # delete one node using its associated doc_id
+    os_store.delete("test-1")
     assert count_docs_in_index(os_store) == len(node_embeddings) - 1