提示模板连接到特征存储

from feast import FeatureStore
 
# You may need to update the path depending on where you stored it
feast_repo_path = "../../../../../my_feature_repo/feature_repo/"
store = FeatureStore(repo_path=feast_repo_path)
 

from langchain.prompts import PromptTemplate, StringPromptTemplate
 

template = """Given the driver's up to date stats, write them note relaying those stats to them.
If they have a conversation rate above .5, give them a compliment. Otherwise, make a silly joke about chickens at the end to make them feel better
 
Here are the drivers stats:
Conversation rate: {conv_rate}
Acceptance rate: {acc_rate}
Average Daily Trips: {avg_daily_trips}
 
Your response:"""
prompt = PromptTemplate.from_template(template)
 

class FeastPromptTemplate(StringPromptTemplate):
 
    def format(self, **kwargs) -> str:
        driver_id = kwargs.pop("driver_id")
        feature_vector = store.get_online_features(
            features=[
                'driver_hourly_stats:conv_rate',
                'driver_hourly_stats:acc_rate',
                'driver_hourly_stats:avg_daily_trips'
            ],
            entity_rows=[{"driver_id": driver_id}]
        ).to_dict()
        kwargs["conv_rate"] = feature_vector["conv_rate"][0]
        kwargs["acc_rate"] = feature_vector["acc_rate"][0]
        kwargs["avg_daily_trips"] = feature_vector["avg_daily_trips"][0]
        return prompt.format(**kwargs)
 

prompt_template = FeastPromptTemplate(input_variables=["driver_id"])
 

print(prompt_template.format(driver_id=1001))
 

Given the driver's up to date stats, write them note relaying those stats to them.
If they have a conversation rate above .5, give them a compliment. Otherwise, make a silly joke about chickens at the end to make them feel better
 
Here are the drivers stats:
Conversation rate: 0.4745151400566101
Acceptance rate: 0.055561766028404236
Average Daily Trips: 936
 
Your response:
 

from langchain.chat_models import ChatOpenAI
from langchain.chains import LLMChain
 

chain = LLMChain(llm=ChatOpenAI(), prompt=prompt_template)
 

chain.run(1001)
 

"Hi there! I wanted to update you on your current stats. Your acceptance rate is 0.055561766028404236 and your average daily trips are 936. While your conversation rate is currently 0.4745151400566101, I have no doubt that with a little extra effort, you'll be able to exceed that .5 mark! Keep up the great work! And remember, even chickens can't always cross the road, but they still give it their best shot."
 

user_transaction_metrics = FeatureService(
    name = "user_transaction_metrics",
    features = [user_transaction_counts]
)
 

import tecton
 
workspace = tecton.get_workspace("prod")
feature_service = workspace.get_feature_service("user_transaction_metrics")
 

from langchain.prompts import PromptTemplate, StringPromptTemplate
 

template = """Given the vendor's up to date transaction stats, write them a note based on the following rules:
 
1. If they had a transaction in the last day, write a short congratulations message on their recent sales
2. If no transaction in the last day, but they had a transaction in the last 30 days, playfully encourage them to sell more.
3. Always add a silly joke about chickens at the end
 
Here are the vendor's stats:
Number of Transactions Last Day: {transaction_count_1d}
Number of Transactions Last 30 Days: {transaction_count_30d}
 
Your response:"""
prompt = PromptTemplate.from_template(template)
 

class TectonPromptTemplate(StringPromptTemplate):
 
    def format(self, **kwargs) -> str:
        user_id = kwargs.pop("user_id")
        feature_vector = feature_service.get_online_features(join_keys={"user_id": user_id}).to_dict()
        kwargs["transaction_count_1d"] = feature_vector["user_transaction_counts.transaction_count_1d_1d"]
        kwargs["transaction_count_30d"] = feature_vector["user_transaction_counts.transaction_count_30d_1d"]
        return prompt.format(**kwargs)
 

prompt_template = TectonPromptTemplate(input_variables=["user_id"])
 

print(prompt_template.format(user_id="user_469998441571"))
 

Given the vendor's up to date transaction stats, write them a note based on the following rules:
 
1. If they had a transaction in the last day, write a short congratulations message on their recent sales
2. If no transaction in the last day, but they had a transaction in the last 30 days, playfully encourage them to sell more.
3. Always add a silly joke about chickens at the end
 
Here are the vendor's stats:
Number of Transactions Last Day: 657
Number of Transactions Last 30 Days: 20326
 
Your response:
 

from langchain.chat_models import ChatOpenAI
from langchain.chains import LLMChain
 

chain = LLMChain(llm=ChatOpenAI(), prompt=prompt_template)
 

chain.run("user_469998441571")
 

'Wow, congratulations on your recent sales! Your business is really soaring like a chicken on a hot air balloon! Keep up the great work!'