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Add Tecton example to the "Connecting to a Feature Store" example not…
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…ebook (langchain-ai#3626)

This PR adds a similar example to the Feast example, using the [Tecton
Feature Platform](https://www.tecton.ai/) and features from the [Tecton
Fundamentals
Tutorial](https://docs.tecton.ai/docs/tutorials/tecton-fundamentals).
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brian-tecton-ai authored Apr 27, 2023
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241 changes: 239 additions & 2 deletions docs/modules/prompts/prompt_templates/examples/connecting_to_a_feature_store.ipynb
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{
"cell_type": "markdown",
"id": "ad0b5edf",
"metadata": {},
"metadata": {
"tags": []
},
"source": [
"## Feast\n",
"\n",
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"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"id": "c4049990-651d-44d3-82b1-0cd122da55c1",
"metadata": {},
"source": [
"## Tecton\n",
"\n",
"Above, we showed how you could use Feast, a popular open source and self-managed feature store, with LangChain. Our examples below will show a similar integration using Tecton. Tecton is a fully managed feature platform built to orchestrate the complete ML feature lifecycle, from transformation to online serving, with enterprise-grade SLAs."
]
},
{
"cell_type": "markdown",
"id": "7bb4dba1-0678-4ea4-be0a-d353c0b13fc2",
"metadata": {
"tags": []
},
"source": [
"### Prerequisites\n",
"\n",
"* Tecton Deployment (sign up at [https://tecton.ai](https://tecton.ai))\n",
"* `TECTON_API_KEY` environment variable set to a valid Service Account key"
]
},
{
"cell_type": "markdown",
"id": "ac9eb618-8c52-4cd6-bb8e-9c99a150dfa6",
"metadata": {
"tags": []
},
"source": [
"### Define and Load Features\n",
"\n",
"We will use the user_transaction_counts Feature View from the [Tecton tutorial](https://docs.tecton.ai/docs/tutorials/tecton-fundamentals) as part of a Feature Service. For simplicity, we are only using a single Feature View; however, more sophisticated applications may require more feature views to retrieve the features needed for its prompt.\n",
"\n",
"```python\n",
"user_transaction_metrics = FeatureService(\n",
" name = \"user_transaction_metrics\",\n",
" features = [user_transaction_counts]\n",
")\n",
"```\n",
"\n",
"The above Feature Service is expected to be [applied to a live workspace](https://docs.tecton.ai/docs/applying-feature-repository-changes-to-a-workspace). For this example, we will be using the \"prod\" workspace."
]
},
{
"cell_type": "code",
"execution_count": 60,
"id": "32e9675d-a7e5-429f-906f-2260294d3e46",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"import tecton\n",
"\n",
"workspace = tecton.get_workspace(\"prod\")\n",
"feature_service = workspace.get_feature_service(\"user_transaction_metrics\")"
]
},
{
"cell_type": "markdown",
"id": "29b7550c-0eb4-4bd1-a501-1c63fb77aa56",
"metadata": {},
"source": [
"### Prompts\n",
"\n",
"Here we will set up a custom TectonPromptTemplate. This prompt template will take in a user_id , look up their stats, and format those stats into a prompt.\n",
"\n",
"Note that the input to this prompt template is just `user_id`, since that is the only user defined piece (all other variables are looked up inside the prompt template)."
]
},
{
"cell_type": "code",
"execution_count": 61,
"id": "6fb77ea4-64c6-4e48-a783-bd1ece021b82",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"from langchain.prompts import PromptTemplate, StringPromptTemplate"
]
},
{
"cell_type": "code",
"execution_count": 77,
"id": "02a98fbc-8135-4b11-bf60-85d28e426667",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"template = \"\"\"Given the vendor's up to date transaction stats, write them a note based on the following rules:\n",
"\n",
"1. If they had a transaction in the last day, write a short congratulations message on their recent sales\n",
"2. If no transaction in the last day, but they had a transaction in the last 30 days, playfully encourage them to sell more.\n",
"3. Always add a silly joke about chickens at the end\n",
"\n",
"Here are the vendor's stats:\n",
"Number of Transactions Last Day: {transaction_count_1d}\n",
"Number of Transactions Last 30 Days: {transaction_count_30d}\n",
"\n",
"Your response:\"\"\"\n",
"prompt = PromptTemplate.from_template(template)"
]
},
{
"cell_type": "code",
"execution_count": 78,
"id": "a35cdfd5-6ccc-4394-acfe-60d53804be51",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"class TectonPromptTemplate(StringPromptTemplate):\n",
" \n",
" def format(self, **kwargs) -> str:\n",
" user_id = kwargs.pop(\"user_id\")\n",
" feature_vector = feature_service.get_online_features(join_keys={\"user_id\": user_id}).to_dict()\n",
" kwargs[\"transaction_count_1d\"] = feature_vector[\"user_transaction_counts.transaction_count_1d_1d\"]\n",
" kwargs[\"transaction_count_30d\"] = feature_vector[\"user_transaction_counts.transaction_count_30d_1d\"]\n",
" return prompt.format(**kwargs)"
]
},
{
"cell_type": "code",
"execution_count": 79,
"id": "d5915df0-fb16-4770-8a82-22f885b74d1a",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"prompt_template = TectonPromptTemplate(input_variables=[\"user_id\"])"
]
},
{
"cell_type": "code",
"execution_count": 80,
"id": "a36abfc8-ea60-4ae0-a36d-d7b639c7307c",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Given the vendor's up to date transaction stats, write them a note based on the following rules:\n",
"\n",
"1. If they had a transaction in the last day, write a short congratulations message on their recent sales\n",
"2. If no transaction in the last day, but they had a transaction in the last 30 days, playfully encourage them to sell more.\n",
"3. Always add a silly joke about chickens at the end\n",
"\n",
"Here are the vendor's stats:\n",
"Number of Transactions Last Day: 657\n",
"Number of Transactions Last 30 Days: 20326\n",
"\n",
"Your response:\n"
]
}
],
"source": [
"print(prompt_template.format(user_id=\"user_469998441571\"))"
]
},
{
"cell_type": "markdown",
"id": "f8d4b905-1051-4303-9c33-8eddb65c1274",
"metadata": {
"tags": []
},
"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 Tecton Feature Platform"
]
},
{
"cell_type": "code",
"execution_count": 81,
"id": "ffb60cd0-8e3c-4c9d-b639-43d766e12c4c",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"from langchain.chat_models import ChatOpenAI\n",
"from langchain.chains import LLMChain"
]
},
{
"cell_type": "code",
"execution_count": 82,
"id": "3918abc7-00b5-466f-bdfc-ab046cd282da",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"chain = LLMChain(llm=ChatOpenAI(), prompt=prompt_template)"
]
},
{
"cell_type": "code",
"execution_count": 83,
"id": "e7d91c4b-3e99-40cc-b3e9-a004c8c9193e",
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"'Wow, congratulations on your recent sales! Your business is really soaring like a chicken on a hot air balloon! Keep up the great work!'"
]
},
"execution_count": 83,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"chain.run(\"user_469998441571\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f752b924-caf9-4f7a-b78b-cb8c8ada8c2e",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
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"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.1"
"version": "3.9.13"
}
},
"nbformat": 4,
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