mirror of
https://github.com/hwchase17/langchain
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Add Tecton example to the "Connecting to a Feature Store" example notebook (#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).
This commit is contained in:
brian-tecton-ai
GitHub
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{
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{
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"cell_type": "markdown",
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"cell_type": "markdown",
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"id": "ad0b5edf",
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"id": "ad0b5edf",
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"metadata": {},
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"metadata": {
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"tags": []
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},
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"source": [
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"source": [
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"## Feast\n",
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"## Feast\n",
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"\n",
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"\n",
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@ -211,6 +213,241 @@
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"metadata": {},
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"metadata": {},
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"outputs": [],
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"outputs": [],
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"source": []
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"source": []
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},
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{
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"cell_type": "markdown",
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"id": "c4049990-651d-44d3-82b1-0cd122da55c1",
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"metadata": {},
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"source": [
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"## Tecton\n",
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"\n",
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"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."
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]
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},
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{
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"cell_type": "markdown",
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"id": "7bb4dba1-0678-4ea4-be0a-d353c0b13fc2",
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"metadata": {
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"tags": []
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},
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"source": [
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"### Prerequisites\n",
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"\n",
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"* Tecton Deployment (sign up at [https://tecton.ai](https://tecton.ai))\n",
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"* `TECTON_API_KEY` environment variable set to a valid Service Account key"
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]
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},
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{
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"cell_type": "markdown",
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"id": "ac9eb618-8c52-4cd6-bb8e-9c99a150dfa6",
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"metadata": {
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"tags": []
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},
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"source": [
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"### Define and Load Features\n",
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"\n",
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"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",
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"\n",
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"```python\n",
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"user_transaction_metrics = FeatureService(\n",
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" name = \"user_transaction_metrics\",\n",
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" features = [user_transaction_counts]\n",
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")\n",
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"```\n",
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"\n",
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"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."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 60,
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"id": "32e9675d-a7e5-429f-906f-2260294d3e46",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"import tecton\n",
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"\n",
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"workspace = tecton.get_workspace(\"prod\")\n",
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"feature_service = workspace.get_feature_service(\"user_transaction_metrics\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "29b7550c-0eb4-4bd1-a501-1c63fb77aa56",
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"metadata": {},
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"source": [
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"### Prompts\n",
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"\n",
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"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",
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"\n",
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"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)."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 61,
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"id": "6fb77ea4-64c6-4e48-a783-bd1ece021b82",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"from langchain.prompts import PromptTemplate, StringPromptTemplate"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 77,
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"id": "02a98fbc-8135-4b11-bf60-85d28e426667",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"template = \"\"\"Given the vendor's up to date transaction stats, write them a note based on the following rules:\n",
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"\n",
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"1. If they had a transaction in the last day, write a short congratulations message on their recent sales\n",
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"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",
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"3. Always add a silly joke about chickens at the end\n",
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"\n",
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"Here are the vendor's stats:\n",
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"Number of Transactions Last Day: {transaction_count_1d}\n",
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"Number of Transactions Last 30 Days: {transaction_count_30d}\n",
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"\n",
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"Your response:\"\"\"\n",
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"prompt = PromptTemplate.from_template(template)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 78,
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"id": "a35cdfd5-6ccc-4394-acfe-60d53804be51",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"class TectonPromptTemplate(StringPromptTemplate):\n",
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" \n",
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" def format(self, **kwargs) -> str:\n",
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" user_id = kwargs.pop(\"user_id\")\n",
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" feature_vector = feature_service.get_online_features(join_keys={\"user_id\": user_id}).to_dict()\n",
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" kwargs[\"transaction_count_1d\"] = feature_vector[\"user_transaction_counts.transaction_count_1d_1d\"]\n",
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" kwargs[\"transaction_count_30d\"] = feature_vector[\"user_transaction_counts.transaction_count_30d_1d\"]\n",
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" return prompt.format(**kwargs)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 79,
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"id": "d5915df0-fb16-4770-8a82-22f885b74d1a",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"prompt_template = TectonPromptTemplate(input_variables=[\"user_id\"])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 80,
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"id": "a36abfc8-ea60-4ae0-a36d-d7b639c7307c",
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"metadata": {
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"tags": []
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Given the vendor's up to date transaction stats, write them a note based on the following rules:\n",
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"\n",
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"1. If they had a transaction in the last day, write a short congratulations message on their recent sales\n",
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"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",
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"3. Always add a silly joke about chickens at the end\n",
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"\n",
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"Here are the vendor's stats:\n",
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"Number of Transactions Last Day: 657\n",
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"Number of Transactions Last 30 Days: 20326\n",
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"\n",
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"Your response:\n"
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]
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}
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],
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"source": [
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"print(prompt_template.format(user_id=\"user_469998441571\"))"
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]
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},
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{
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"cell_type": "markdown",
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"id": "f8d4b905-1051-4303-9c33-8eddb65c1274",
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"metadata": {
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"tags": []
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},
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"source": [
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"### Use in a chain\n",
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"\n",
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"We can now use this in a chain, successfully creating a chain that achieves personalization backed by the Tecton Feature Platform"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 81,
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"id": "ffb60cd0-8e3c-4c9d-b639-43d766e12c4c",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"from langchain.chat_models import ChatOpenAI\n",
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"from langchain.chains import LLMChain"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 82,
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"id": "3918abc7-00b5-466f-bdfc-ab046cd282da",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"chain = LLMChain(llm=ChatOpenAI(), prompt=prompt_template)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 83,
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"id": "e7d91c4b-3e99-40cc-b3e9-a004c8c9193e",
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"metadata": {
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"tags": []
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"'Wow, congratulations on your recent sales! Your business is really soaring like a chicken on a hot air balloon! Keep up the great work!'"
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]
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},
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"execution_count": 83,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"chain.run(\"user_469998441571\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "f752b924-caf9-4f7a-b78b-cb8c8ada8c2e",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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}
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],
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],
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"metadata": {
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"metadata": {
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@ -229,7 +466,7 @@
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"name": "python",
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"name": "python",
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"nbconvert_exporter": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"pygments_lexer": "ipython3",
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"version": "3.9.1"
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"version": "3.9.13"
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}
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}
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},
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},
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"nbformat": 4,
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"nbformat": 4,
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