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import os |
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import dspy |
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from dspy.predict.react import Tool |
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from tavily import TavilyClient |
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lm = dspy.LM('huggingface/meta-llama/Llama-3.2-1B') |
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dspy.configure(lm=lm) |
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search_client = TavilyClient(api_key=os.environ["T_TOKEN"]) |
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INST="""Recommend banking financial product based on verbatim""" |
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def web_search(query: str) -> list[str]: |
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"""Run a web search and return the personal banking product from the top 5 search results""" |
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response = search_client.search(query) |
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return [r["content"] for r in response["results"]] |
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agent = dspy.ReAct("verbatim -> product", tools=[Tool(web_search)]) |
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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." |
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def rival_product(customer:str): |
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prediction = agent(verbatim=f"Which banking product best serve this customer needs, pain points: {customer}") |
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return prediction.product |
