Splitter notebook: extend new strategy class (#242)

* Splitter notebook: extend new strategy class

fixes #240

* remove variable

Author: maxjakob

notebooks/document-chunking/with-langchain-splitters.ipynb

@@ -265,7 +265,6 @@
     "\n",
     "\n",
     "def parent_child_splitter(documents, chunk_size: int = 200):\n",
-    "\n",
     "    child_splitter = RecursiveCharacterTextSplitter(chunk_size=chunk_size)\n",
     "\n",
     "    docs = []\n",
@@ -550,22 +549,20 @@
    "source": [
     "from langchain_elasticsearch import (\n",
     "    ElasticsearchStore,\n",
-    "    ApproxRetrievalStrategy,\n",
+    "    DenseVectorStrategy,\n",
     ")\n",
     "from typing import List, Union\n",
     "from langchain_core.documents import Document\n",
     "\n",
     "\n",
-    "class CustomRetrievalStrategy(ApproxRetrievalStrategy):\n",
-    "\n",
-    "    def query(\n",
+    "class CustomRetrievalStrategy(DenseVectorStrategy):\n",
+    "    def es_query(\n",
     "        self,\n",
     "        query: Union[str, None],\n",
     "        filter: List[dict],\n",
     "        **kwargs,\n",
     "    ):\n",
-    "\n",
-    "        es_query = {\n",
+    "        return {\n",
     "            \"knn\": {\n",
     "                \"inner_hits\": {\"_source\": False, \"fields\": [\"passages.text\"]},\n",
     "                \"field\": \"passages.vector.predicted_value\",\n",
@@ -581,8 +578,6 @@
     "            }\n",
     "        }\n",
     "\n",
-    "        return es_query\n",
-    "\n",
     "\n",
     "vector_store = ElasticsearchStore(\n",
     "    index_name=INDEX_NAME,\n",