Pass config specs through ensemble retriever (#15917)

<!-- Thank you for contributing to LangChain!

Please title your PR "<package>: <description>", where <package> is
whichever of langchain, community, core, experimental, etc. is being
modified.

Replace this entire comment with:
  - **Description:** a description of the change, 
  - **Issue:** the issue # it fixes if applicable,
  - **Dependencies:** any dependencies required for this change,
- **Twitter handle:** we announce bigger features on Twitter. If your PR
gets announced, and you'd like a mention, we'll gladly shout you out!

Please make sure your PR is passing linting and testing before
submitting. Run `make format`, `make lint` and `make test` from the root
of the package you've modified to check this locally.

See contribution guidelines for more information on how to write/run
tests, lint, etc: https://python.langchain.com/docs/contributing/

If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on
network access,
2. an example notebook showing its use. It lives in
`docs/docs/integrations` directory.

If no one reviews your PR within a few days, please @-mention one of
@baskaryan, @eyurtsev, @hwchase17.
 -->

---------

Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>

docs/docs/modules/data_connection/retrievers/ensemble.ipynb

@@ -24,7 +24,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -35,22 +35,31 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
-    "doc_list = [\n",
+    "doc_list_1 = [\n",
     "    \"I like apples\",\n",
     "    \"I like oranges\",\n",
     "    \"Apples and oranges are fruits\",\n",
     "]\n",
     "\n",
     "# initialize the bm25 retriever and faiss retriever\n",
-    "bm25_retriever = BM25Retriever.from_texts(doc_list)\n",
+    "bm25_retriever = BM25Retriever.from_texts(\n",
+    "    doc_list_1, metadatas=[{\"source\": 1}] * len(doc_list_1)\n",
+    ")\n",
     "bm25_retriever.k = 2\n",
     "\n",
+    "doc_list_2 = [\n",
+    "    \"You like apples\",\n",
+    "    \"You like oranges\",\n",
+    "]\n",
+    "\n",
     "embedding = OpenAIEmbeddings()\n",
-    "faiss_vectorstore = FAISS.from_texts(doc_list, embedding)\n",
+    "faiss_vectorstore = FAISS.from_texts(\n",
+    "    doc_list_2, embedding, metadatas=[{\"source\": 2}] * len(doc_list_2)\n",
+    ")\n",
     "faiss_retriever = faiss_vectorstore.as_retriever(search_kwargs={\"k\": 2})\n",
     "\n",
     "# initialize the ensemble retriever\n",
@@ -61,26 +70,92 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[Document(page_content='I like apples'),\n",
-       " Document(page_content='Apples and oranges are fruits')]"
+       "[Document(page_content='You like apples', metadata={'source': 2}),\n",
+       " Document(page_content='I like apples', metadata={'source': 1}),\n",
+       " Document(page_content='You like oranges', metadata={'source': 2}),\n",
+       " Document(page_content='Apples and oranges are fruits', metadata={'source': 1})]"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "docs = ensemble_retriever.get_relevant_documents(\"apples\")\n",
+    "docs = ensemble_retriever.invoke(\"apples\")\n",
     "docs"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Runtime Configuration\n",
+    "\n",
+    "We can also configure the retrievers at runtime. In order to do this, we need to mark the fields as configurable"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_core.runnables import ConfigurableField"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "faiss_retriever = faiss_vectorstore.as_retriever(\n",
+    "    search_kwargs={\"k\": 2}\n",
+    ").configurable_fields(\n",
+    "    search_kwargs=ConfigurableField(\n",
+    "        id=\"search_kwargs_faiss\",\n",
+    "        name=\"Search Kwargs\",\n",
+    "        description=\"The search kwargs to use\",\n",
+    "    )\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ensemble_retriever = EnsembleRetriever(\n",
+    "    retrievers=[bm25_retriever, faiss_retriever], weights=[0.5, 0.5]\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "config = {\"configurable\": {\"search_kwargs_faiss\": {\"k\": 1}}}\n",
+    "docs = ensemble_retriever.invoke(\"apples\", config=config)\n",
+    "docs"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Notice that this only returns one source from the FAISS retriever, because we pass in the relevant configuration at run time"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,