Add example on how to use Featureform with langchain (#4337)

Added an example on how to use Featureform to
connecting_to_a_feature_store.ipynb .

docs/modules/prompts/prompt_templates/examples/connecting_to_a_feature_store.ipynb

@@ -448,6 +448,152 @@
    "metadata": {},
    "outputs": [],
    "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a0691cd9",
+   "metadata": {},
+   "source": [
+    "## Featureform\n",
+    "\n",
+    "Finally, we will use [Featureform](https://github.com/featureform/featureform) an open-source and enterprise-grade feature store to run the same example. Featureform allows you to work with your infrastructure like Spark or locally to define your feature transformations."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "44320d68",
+   "metadata": {},
+   "source": [
+    "### Initialize Featureform\n",
+    "\n",
+    "You can follow in the instructions in the README to initialize your transformations and features in Featureform."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e64ada9d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import featureform as ff\n",
+    "\n",
+    "client = ff.Client(host=\"demo.featureform.com\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b28914a2",
+   "metadata": {},
+   "source": [
+    "### Prompts\n",
+    "\n",
+    "Here we will set up a custom FeatureformPromptTemplate. This prompt template will take in the average amount a user pays per transactions.\n",
+    "\n",
+    "Note that the input to this prompt template is just avg_transaction, since that is the only user defined piece (all other variables are looked up inside the prompt template)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "75d4a34a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.prompts import PromptTemplate, StringPromptTemplate"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "88253bcb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "template = \"\"\"Given the amount a user spends on average per transaction, let them know if they are a high roller. Otherwise, make a silly joke about chickens at the end to make them feel better\n",
+    "\n",
+    "Here are the user's stats:\n",
+    "Average Amount per Transaction: ${avg_transcation}\n",
+    "\n",
+    "Your response:\"\"\"\n",
+    "prompt = PromptTemplate.from_template(template)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "61f72476",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class FeatureformPromptTemplate(StringPromptTemplate):\n",
+    "    \n",
+    "    def format(self, **kwargs) -> str:\n",
+    "        user_id = kwargs.pop(\"user_id\")\n",
+    "        fpf = client.features([(\"avg_transactions\", \"quickstart\")], {\"user\": user_id})\n",
+    "        return prompt.format(**kwargs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "994a644c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "prompt_template = FeatureformPrompTemplate(input_variables=[\"user_id\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "79b2b0cb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(prompt_template.format(user_id=\"C1410926\"))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f09ddfdd",
+   "metadata": {},
+   "source": [
+    "### Use in a chain\n",
+    "\n",
+    "We can now use this in a chain, successfully creating a chain that achieves personalization backed by the Featureform Feature Platform"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5e89216f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.chat_models import ChatOpenAI\n",
+    "from langchain.chains import LLMChain"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9d3d558c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "chain = LLMChain(llm=ChatOpenAI(), prompt=prompt_template)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b5412626",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "chain.run(\"C1410926\")"
+   ]
   }
  ],
  "metadata": {