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import os
import dspy
from dspy.predict.react import Tool
from tavily import TavilyClient
#lm = dspy.LM('ollama_chat/deepseek-r1', api_base='http://localhost:11434', api_key='')
#lm = dspy.LM('huggingface/Qwen/Qwen2.5-Coder-32B-Instruct')
#lm = dspy.LM('huggingface/meta-llama/Llama-3.2-1B')
lm = dspy.LM('groq/qwen-qwq-32b')
dspy.configure(lm=lm)
search_client = TavilyClient(api_key=os.environ["T_TOKEN"])
INST="""Recommend banking financial product based on verbatim"""
def web_search(query: str) -> list[str]:
"""Run a web search to return the top 3 personal banking product that satisfy the query"""
response = search_client.search(query)
return [r["content"] for r in response["results"]]
agent = dspy.ReAct("verbatim -> product", tools=[Tool(web_search)])
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."
def rival_product(customer:str):
prediction = agent(verbatim=f"Which banking product name best serve this customer needs: {customer}")
return prediction.product |