diff --git a/docs/extras/guides/expression_language/interface.ipynb b/docs/extras/guides/expression_language/interface.ipynb
index d263805bd9..98a1a38600 100644
--- a/docs/extras/guides/expression_language/interface.ipynb
+++ b/docs/extras/guides/expression_language/interface.ipynb
@@ -41,7 +41,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 1,
    "id": "466b65b3",
    "metadata": {},
    "outputs": [],
@@ -256,6 +256,131 @@
    "source": [
     "await chain.abatch([{\"topic\": \"bears\"}])"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0a1c409d",
+   "metadata": {},
+   "source": [
+    "## Parallelism\n",
+    "\n",
+    "Let's take a look at how LangChain Expression Language support parralel requests as much as possible. For example, when using a RunnableMapping (often written as a dictionary) it executes each element in parralel."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "e3014c7a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.schema.runnable import RunnableMap\n",
+    "chain1 = ChatPromptTemplate.from_template(\"tell me a joke about {topic}\") | model\n",
+    "chain2 = ChatPromptTemplate.from_template(\"write a short (2 line) poem about {topic}\") | model\n",
+    "combined = RunnableMap({\n",
+    "    \"joke\": chain1,\n",
+    "    \"poem\": chain2,\n",
+    "})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "08044c0a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 31.7 ms, sys: 8.59 ms, total: 40.3 ms\n",
+      "Wall time: 1.05 s\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "AIMessage(content=\"Why don't bears like fast food?\\n\\nBecause they can't catch it!\", additional_kwargs={}, example=False)"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "chain1.invoke({\"topic\": \"bears\"})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "22c56804",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 42.9 ms, sys: 10.2 ms, total: 53 ms\n",
+      "Wall time: 1.93 s\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "AIMessage(content=\"In forest's embrace, bears roam free,\\nSilent strength, nature's majesty.\", additional_kwargs={}, example=False)"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "chain2.invoke({\"topic\": \"bears\"})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "4fff4cbb",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 96.3 ms, sys: 20.4 ms, total: 117 ms\n",
+      "Wall time: 1.1 s\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "{'joke': AIMessage(content=\"Why don't bears wear socks?\\n\\nBecause they have bear feet!\", additional_kwargs={}, example=False),\n",
+       " 'poem': AIMessage(content=\"In forest's embrace,\\nMajestic bears leave their trace.\", additional_kwargs={}, example=False)}"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "combined.invoke({\"topic\": \"bears\"})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fab75d1d",
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
@@ -274,7 +399,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.10.1"
   }
  },
  "nbformat": 4,
diff --git a/docs/extras/use_cases/question_answering/how_to/multiple_retrieval.ipynb b/docs/extras/use_cases/question_answering/how_to/multiple_retrieval.ipynb
new file mode 100644
index 0000000000..96b6ef6986
--- /dev/null
+++ b/docs/extras/use_cases/question_answering/how_to/multiple_retrieval.ipynb
@@ -0,0 +1,166 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "66398b75",
+   "metadata": {},
+   "source": [
+    "# Multiple Retrieval Sources\n",
+    "\n",
+    "Often times you may want to do retrieval over multiple sources. These can be different vectorstores (where one contains information about topic X and the other contains info about topic Y). They could also be completely different databases altogether!\n",
+    "\n",
+    "A key part is is doing as much of the retrieval in parrelel as possible. This will keep the latency as low as possible. Luckily, [LangChain Expression Language](../../) supports parrellism out of the box.\n",
+    "\n",
+    "Let's take a look where we do retrieval over a SQL database and a vectorstore."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "1c5bab6a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.chat_models import ChatOpenAI"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "43a6210f",
+   "metadata": {},
+   "source": [
+    "## Set up SQL query"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "ab3bf8ba",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.utilities import SQLDatabase\n",
+    "from langchain.chains import create_sql_query_chain\n",
+    "\n",
+    "db = SQLDatabase.from_uri(\"sqlite:///../../../../../notebooks/Chinook.db\")\n",
+    "query_chain = create_sql_query_chain(ChatOpenAI(temperature=0), db)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a8585120",
+   "metadata": {},
+   "source": [
+    "## Set up vectorstore"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "b916b0b0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.indexes import VectorstoreIndexCreator\n",
+    "from langchain.schema.document import Document\n",
+    "index_creator = VectorstoreIndexCreator()\n",
+    "index = index_creator.from_documents([Document(page_content=\"Foo\")])\n",
+    "retriever = index.vectorstore.as_retriever()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a3b91816",
+   "metadata": {},
+   "source": [
+    "## Combine"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "4423211c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.prompts import ChatPromptTemplate\n",
+    "\n",
+    "system_message = \"\"\"Use the information from the below two sources to answer any questions.\n",
+    "\n",
+    "Source 1: a SQL database about employee data\n",
+    "<source1>\n",
+    "{source1}\n",
+    "</source1>\n",
+    "\n",
+    "Source 2: a text database of random information\n",
+    "<source2>\n",
+    "{source2}\n",
+    "</source2>\n",
+    "\"\"\"\n",
+    "\n",
+    "prompt = ChatPromptTemplate.from_messages([(\"system\", system_message), (\"human\", \"{question}\")])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "7ff87e0c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "full_chain = {\n",
+    "    \"source1\": {\"question\": lambda x: x[\"question\"]} | query_chain | db.run,\n",
+    "    \"source2\": (lambda x: x['question']) | retriever,\n",
+    "    \"question\": lambda x: x['question'],\n",
+    "} | prompt | ChatOpenAI()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "d6706410",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Number of requested results 4 is greater than number of elements in index 1, updating n_results = 1\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "content='There are 8 employees.' additional_kwargs={} example=False\n"
+     ]
+    }
+   ],
+   "source": [
+    "response = full_chain.invoke({\"question\":\"How many Employees are there\"})\n",
+    "print(response)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}