IMPROVEMENT Increase flexibility of ElasticVectorSearch (#6863)

Hey @rlancemartin, @eyurtsev ,

I did some minimal changes to the `ElasticVectorSearch` client so that
it plays better with existing ES indices.

Main changes are as follows:

1. You can pass the dense vector field name into `_default_script_query`
2. You can pass a custom script query implementation and the respective
parameters to `similarity_search_with_score`
3. You can pass functions for building page content and metadata for the
resulting `Document`

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docs/docs/integrations/vectorstores/elasticsearch.ipynb

@@ -776,6 +776,40 @@
     "print(results[0])"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Customize the Document Builder\n",
+    "\n",
+    "With ```doc_builder``` parameter at search, you are able to adjust how a Document is being built using data retrieved from Elasticsearch. This is especially useful if you have indices which were not created using Langchain."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from typing import Dict\n",
+    "from langchain.docstore.document import Document\n",
+    "\n",
+    "def custom_document_builder(hit: Dict) -> Document:\n",
+    "    src = hit.get(\"_source\", {})\n",
+    "    return Document(\n",
+    "        page_content=src.get(\"content\", \"Missing content!\"),\n",
+    "        metadata={\"page_number\": src.get(\"page_number\", -1), \"original_filename\": src.get(\"original_filename\", \"Missing filename!\")},\n",
+    "    )\n",
+    "\n",
+    "results = db.similarity_search(\n",
+    "    \"What did the president say about Ketanji Brown Jackson\",\n",
+    "    k=4,\n",
+    "    doc_builder=custom_document_builder,\n",
+    ")\n",
+    "print(\"Results:\")\n",
+    "print(results[0])"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "3242fd42",
@@ -929,7 +963,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.3"
+   "version": "3.9.7"
   }
  },
  "nbformat": 4,

libs/langchain/langchain/vectorstores/elasticsearch.py

@@ -727,6 +727,8 @@ class ElasticsearchStore(VectorStore):
         fields: Optional[List[str]] = None,
         filter: Optional[List[dict]] = None,
         custom_query: Optional[Callable[[Dict, Union[str, None]], Dict]] = None,
+        doc_builder: Optional[Callable[[Dict], Document]] = None,
+        **kwargs: Any,
     ) -> List[Tuple[Document, float]]:
         """Return Elasticsearch documents most similar to query, along with scores.
 
@@ -781,6 +783,14 @@ class ElasticsearchStore(VectorStore):
             source=fields,
         )
 
+        def default_doc_builder(hit: Dict) -> Document:
+            return Document(
+                page_content=hit["_source"].get(self.query_field, ""),
+                metadata=hit["_source"]["metadata"],
+            )
+
+        doc_builder = doc_builder or default_doc_builder
+
         docs_and_scores = []
         for hit in response["hits"]["hits"]:
             for field in fields:
@@ -792,10 +802,7 @@ class ElasticsearchStore(VectorStore):
 
             docs_and_scores.append(
                 (
-                    Document(
-                        page_content=hit["_source"].get(self.query_field, ""),
-                        metadata=hit["_source"]["metadata"],
-                    ),
+                    doc_builder(hit),
                     hit["_score"],
                 )
             )

libs/langchain/tests/integration_tests/vectorstores/test_elasticsearch.py

@@ -254,6 +254,35 @@ class TestElasticsearch:
         )
         assert output == [Document(page_content="foo", metadata={"page": 1})]
 
+    def test_similarity_search_with_doc_builder(
+        self, elasticsearch_connection: dict, index_name: str
+    ) -> None:
+        texts = ["foo", "foo", "foo"]
+        metadatas = [{"page": i} for i in range(len(texts))]
+        docsearch = ElasticsearchStore.from_texts(
+            texts,
+            FakeEmbeddings(),
+            metadatas=metadatas,
+            **elasticsearch_connection,
+            index_name=index_name,
+        )
+
+        def custom_document_builder(_: Dict) -> Document:
+            return Document(
+                page_content="Mock content!",
+                metadata={
+                    "page_number": -1,
+                    "original_filename": "Mock filename!",
+                },
+            )
+
+        output = docsearch.similarity_search(
+            query="foo", k=1, doc_builder=custom_document_builder
+        )
+        assert output[0].page_content == "Mock content!"
+        assert output[0].metadata["page_number"] == -1
+        assert output[0].metadata["original_filename"] == "Mock filename!"
+
     def test_similarity_search_exact_search(
         self, elasticsearch_connection: dict, index_name: str
     ) -> None: