OpenSearch top k parameter fix (#5216)

For most queries it's the `size` parameter that determines final number
of documents to return. Since our abstractions refer to this as `k`, set
this to be `k` everywhere instead of expecting a separate param. Would
be great to have someone more familiar with OpenSearch validate that
this is reasonable (e.g. that having `size` and what OpenSearch calls
`k` be the same won't lead to any strange behavior). cc @naveentatikonda

Closes #5212

docs/modules/indexes/vectorstores/examples/opensearch.ipynb

@@ -12,7 +12,8 @@
     "\n",
     "This notebook shows how to use functionality related to the `OpenSearch` database.\n",
     "\n",
-    "To run, you should have the opensearch instance up and running: [here](https://opensearch.org/docs/latest/install-and-configure/install-opensearch/index/)\n",
+    "To run, you should have an OpenSearch instance up and running: [see here for an easy Docker installation](https://hub.docker.com/r/opensearchproject/opensearch).\n",
+    "\n",
     "`similarity_search` by default performs the Approximate k-NN Search which uses one of the several algorithms like lucene, nmslib, faiss recommended for\n",
     "large datasets. To perform brute force search we have other search methods known as Script Scoring and Painless Scripting.\n",
     "Check [this](https://opensearch.org/docs/latest/search-plugins/knn/index/) for more details."
@@ -23,7 +24,8 @@
    "id": "94963977-9dfc-48b7-872a-53f2947f46c6",
    "metadata": {},
    "source": [
-    "## Installation"
+    "## Installation\n",
+    "Install the Python client."
    ]
   },
   {
@@ -61,7 +63,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "id": "aac9563e",
    "metadata": {},
    "outputs": [],
@@ -74,7 +76,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "id": "a3c3999a",
    "metadata": {},
    "outputs": [],
@@ -98,6 +100,32 @@
     "`similarity_search` using `Approximate k-NN` Search with Custom Parameters"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "803fe12b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "docsearch = OpenSearchVectorSearch.from_documents(\n",
+    "    docs, \n",
+    "    embeddings, \n",
+    "    opensearch_url=\"http://localhost:9200\"\n",
+    ")\n",
+    "\n",
+    "# If using the default Docker installation, use this instantiation instead:\n",
+    "# docsearch = OpenSearchVectorSearch.from_documents(\n",
+    "#     docs, \n",
+    "#     embeddings, \n",
+    "#     opensearch_url=\"https://localhost:9200\", \n",
+    "#     http_auth=(\"admin\", \"admin\"),     \n",
+    "#     use_ssl = False,\n",
+    "#     verify_certs = False,\n",
+    "#     ssl_assert_hostname = False,\n",
+    "#     ssl_show_warn = False,\n",
+    "# )"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -109,10 +137,8 @@
    },
    "outputs": [],
    "source": [
-    "docsearch = OpenSearchVectorSearch.from_documents(docs, embeddings, opensearch_url=\"http://localhost:9200\")\n",
-    "\n",
     "query = \"What did the president say about Ketanji Brown Jackson\"\n",
-    "docs = docsearch.similarity_search(query)"
+    "docs = docsearch.similarity_search(query, k=10)"
    ]
   },
   {
@@ -283,7 +309,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.6"
+   "version": "3.11.3"
   }
  },
  "nbformat": 4,

langchain/vectorstores/opensearch_vector_search.py

@@ -153,13 +153,12 @@ def _default_text_mapping(
 
 def _default_approximate_search_query(
     query_vector: List[float],
-    size: int = 4,
     k: int = 4,
     vector_field: str = "vector_field",
 ) -> Dict:
     """For Approximate k-NN Search, this is the default query."""
     return {
-        "size": size,
+        "size": k,
         "query": {"knn": {vector_field: {"vector": query_vector, "k": k}}},
     }
 
@@ -167,14 +166,13 @@ def _default_approximate_search_query(
 def _approximate_search_query_with_boolean_filter(
     query_vector: List[float],
     boolean_filter: Dict,
-    size: int = 4,
     k: int = 4,
     vector_field: str = "vector_field",
     subquery_clause: str = "must",
 ) -> Dict:
     """For Approximate k-NN Search, with Boolean Filter."""
     return {
-        "size": size,
+        "size": k,
         "query": {
             "bool": {
                 "filter": boolean_filter,
@@ -189,13 +187,12 @@ def _approximate_search_query_with_boolean_filter(
 def _approximate_search_query_with_lucene_filter(
     query_vector: List[float],
     lucene_filter: Dict,
-    size: int = 4,
     k: int = 4,
     vector_field: str = "vector_field",
 ) -> Dict:
     """For Approximate k-NN Search, with Lucene Filter."""
     search_query = _default_approximate_search_query(
-        query_vector, size, k, vector_field
+        query_vector, k=k, vector_field=vector_field
     )
     search_query["query"]["knn"][vector_field]["filter"] = lucene_filter
     return search_query
@@ -382,8 +379,6 @@ class OpenSearchVectorSearch(VectorStore):
         Optional Args for Approximate Search:
             search_type: "approximate_search"; default: "approximate_search"
 
-            size: number of results the query actually returns; default: 4
-
             boolean_filter: A Boolean filter consists of a Boolean query that
             contains a k-NN query and a filter.
 
@@ -438,7 +433,6 @@ class OpenSearchVectorSearch(VectorStore):
         vector_field = _get_kwargs_value(kwargs, "vector_field", "vector_field")
 
         if search_type == "approximate_search":
-            size = _get_kwargs_value(kwargs, "size", 4)
             boolean_filter = _get_kwargs_value(kwargs, "boolean_filter", {})
             subquery_clause = _get_kwargs_value(kwargs, "subquery_clause", "must")
             lucene_filter = _get_kwargs_value(kwargs, "lucene_filter", {})
@@ -449,15 +443,19 @@ class OpenSearchVectorSearch(VectorStore):
                 )
             if boolean_filter != {}:
                 search_query = _approximate_search_query_with_boolean_filter(
-                    embedding, boolean_filter, size, k, vector_field, subquery_clause
+                    embedding,
+                    boolean_filter,
+                    k=k,
+                    vector_field=vector_field,
+                    subquery_clause=subquery_clause,
                 )
             elif lucene_filter != {}:
                 search_query = _approximate_search_query_with_lucene_filter(
-                    embedding, lucene_filter, size, k, vector_field
+                    embedding, lucene_filter, k=k, vector_field=vector_field
                 )
             else:
                 search_query = _default_approximate_search_query(
-                    embedding, size, k, vector_field
+                    embedding, k=k, vector_field=vector_field
                 )
         elif search_type == SCRIPT_SCORING_SEARCH:
             space_type = _get_kwargs_value(kwargs, "space_type", "l2")