Add example of running Q&A over structured data using the `Airbyte` loaders and `pandas` (#10069)

- Description: Added example of running Q&A over structured data using
the `Airbyte` loaders and `pandas`
  - Dependencies: any dependencies required for this change,
  - Tag maintainer: @hwchase17 
  - Twitter handle: @pelaseyed

docs/extras/integrations/toolkits/airbyte_structured_qa.ipynb

@@ -0,0 +1,118 @@
+{
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Airbyte Question Answering\n",
+    "This notebook shows how to do question answering over structured data, in this case using the `AirbyteStripeLoader`.\n",
+    "\n",
+    "Vectorstores often have a hard time answering questions that requires computing, grouping and filtering structured data so the high level idea is to use a `pandas` dataframe to help with these types of questions. "
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "1. Load data from Stripe using Airbyte. user the `record_handler` paramater to return a JSON from the data loader."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import pandas as pd\n",
+    "\n",
+    "from langchain.document_loaders.airbyte import AirbyteStripeLoader\n",
+    "from langchain.chat_models.openai import ChatOpenAI\n",
+    "from langchain.agents import AgentType, create_pandas_dataframe_agent\n",
+    "\n",
+    "stream_name = \"customers\"\n",
+    "config = {\n",
+    "    \"client_secret\": os.getenv(\"STRIPE_CLIENT_SECRET\"),\n",
+    "    \"account_id\": os.getenv(\"STRIPE_ACCOUNT_D\"),\n",
+    "    \"start_date\": \"2023-01-20T00:00:00Z\",\n",
+    "}\n",
+    "\n",
+    "def handle_record(record: dict, _id: str):\n",
+    "    return record.data\n",
+    "\n",
+    "loader = AirbyteStripeLoader(\n",
+    "    config=config,\n",
+    "    record_handler=handle_record,\n",
+    "    stream_name=stream_name,\n",
+    ")\n",
+    "data = loader.load()"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "2. Pass the data to `pandas` dataframe."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.DataFrame(data)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "3. Pass the dataframe `df` to the `create_pandas_dataframe_agent` and invoke\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "agent = create_pandas_dataframe_agent(\n",
+    "    ChatOpenAI(temperature=0, model=\"gpt-4\"),\n",
+    "    df,\n",
+    "    verbose=True,\n",
+    "    agent_type=AgentType.OPENAI_FUNCTIONS,\n",
+    ")"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "4. Run the agent"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "output = agent.run(\"How many rows are there?\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}