|
import dspy |
|
|
|
import pandas as pd |
|
|
|
|
|
lm = dspy.LM('huggingface/Qwen/Qwen2.5-Coder-32B-Instruct') |
|
dspy.configure(lm=lm) |
|
|
|
df = pd.read_csv("product2.csv") |
|
df['content']=df['product']+"; "+df['purpose']+"; "+df['benefit']+"; "+df['fee'] |
|
|
|
corpus = [row['content'] for i,row in df.iterrows()] |
|
|
|
embedder = dspy.Embedder('openai/text-embedding-3-small', dimensions=512) |
|
class RecommendProduct(dspy.Signature): |
|
""" |
|
Recommend RBC financial product based on verbatim |
|
""" |
|
context = dspy.InputField(desc="may contain relevant product information") |
|
verbatim = dspy.InputField() |
|
product = dspy.OutputField(desc="product name with benefit") |
|
|
|
class RAG(dspy.Module): |
|
def __init__(self, num_passages=3): |
|
super().__init__() |
|
self.retrieve = dspy.retrievers.Embeddings(embedder=embedder, corpus=corpus, k=num_passages) |
|
self.recommender = dspy.ChainOfThought(RecommendProduct) |
|
|
|
def forward(self, verbatim): |
|
context = self.retrieve(verbatim).passages |
|
prediction = self.recommender(context=context, verbatim=verbatim) |
|
return dspy.Prediction(context=context, product=prediction.product) |
|
|
|
|
|
customer="Low APR and great customer service. I would highly recommend if you’re looking for a great credit card company and looking to rebuild your credit. I have had my credit limit increased annually and the annual fee is very low." |
|
|
|
|
|
qa = RAG(num_passages=2) |
|
|
|
def rbc_product(customer:str): |
|
response = qa(verbatim=f"Which RBC personal banking product best serve the follow customer needs, pain points: {customer}") |
|
return response.product |
