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51 lines
1.9 KiB
Python
51 lines
1.9 KiB
Python
from __future__ import annotations
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import typing as t
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from langchain.chains import LLMChain
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from langchain.llms import OpenLLM
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from langchain.prompts import PromptTemplate
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from pydantic import BaseModel
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import bentoml
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from bentoml.io import JSON, Text
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class Query(BaseModel):
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industry: str
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product_name: str
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keywords: t.List[str]
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llm_config: t.Dict[str, t.Any]
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def gen_llm(model_name: str, model_id: str | None = None, **attrs: t.Any) -> OpenLLM:
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lc_llm = OpenLLM(model_name=model_name, model_id=model_id, embedded=False, **attrs)
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lc_llm.runner.download_model()
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return lc_llm
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llm = gen_llm("llama", model_id="TheBloke/Llama-2-13B-chat-GPTQ", quantize="gptq")
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prompt = PromptTemplate(input_variables=["industry", "product_name", "keywords"],
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template="""
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You are a Facebook Ads Copywriter with a strong background in persuasive
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writing and marketing. You craft compelling copy that appeals to the target
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audience's emotions and needs, peruading them to take action or make a
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purchase. You are given the following context to create a facebook ad copy.
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It should provide an attention-grabbing headline optimizied for capivating
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leads and perusaive calls to action.
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Industry: {industry}
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Product: {product_name}
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Keywords: {keywords}
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Facebook Ads copy:
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""")
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chain = LLMChain(llm=llm, prompt=prompt)
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svc = bentoml.Service("fb-ads-copy", runners=[llm.runner])
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SAMPLE_INPUT = Query(industry="SAAS",
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product_name="BentoML",
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keywords=["open source", "developer tool", "AI application platform", "serverless", "cost-efficient"],
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llm_config=llm.runner.config.model_dump())
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@svc.api(input=JSON.from_sample(sample=SAMPLE_INPUT), output=Text())
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def generate(query: Query):
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return chain.run({"industry": query.industry, "product_name": query.product_name, "keywords": ", ".join(query.keywords)})
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