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timothyafolami
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from langchain_openai import ChatOpenAI
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from langchain_core.prompts.prompt import PromptTemplate
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from langchain_core.output_parsers import StrOutputParser, JsonOutputParser
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import os
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import requests
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from loguru import logger
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from dotenv import load_dotenv
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load_dotenv()
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os.environ["OPENAI_API_KEY"] = os.getenv("OPENAI_API_KEY")
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PERPLEXITYAI_API_KEY = os.getenv('PERPLEXITY_AI_API')
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llm = ChatOpenAI(model="gpt-4o")
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def get_chat_completion(prompt, api_key=PERPLEXITYAI_API_KEY):
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url = "https://api.perplexity.ai/chat/completions"
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payload = {
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"model": "llama-3.1-sonar-small-128k-online",
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"messages": [
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{
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"role": "system",
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"content": "Be precise and concise."
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},
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{
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"role": "user",
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"content": prompt
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}
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],
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"temperature": 0.2,
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"top_p": 0.9,
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"return_citations": True,
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"search_domain_filter": ["perplexity.ai"],
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"return_images": False,
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"return_related_questions": False,
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"search_recency_filter": "month",
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"top_k": 0,
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"stream": False,
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"presence_penalty": 0,
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"frequency_penalty": 1
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}
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headers = {
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"Authorization": f"Bearer {api_key}",
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"Content-Type": "application/json"
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}
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response = requests.post(url, json=payload, headers=headers)
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# Check if the request was successful
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if response.status_code == 200:
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response_data = response.json()
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try:
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# Extract the message content
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message_content = response_data['choices'][0]['message']['content']
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return message_content
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except (KeyError, IndexError):
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return "Unexpected response format."
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else:
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return f"Request failed with status code: {response.status_code}"
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def influencer_data(search_result: str, prompt:str) -> dict:
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logger.info(f"Formatting Influencer Data")
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initiator_prompt = PromptTemplate(
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template="""<|begin_of_text|><|start_header_id|>system<|end_header_id|>
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You are a Influencer Data Extractor AI Agent tasked with extracting information from a search result\n
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Backstory: \n
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A business brand is looking for some influencer in a specific location or area. He used a tool called Perplexity AI to get this data. \n
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This is an amazing too, and yet it can struggle a bit (ai isn't perfect you know). \n
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The information returned by this ai can be incomplete, not properly structured and all. \n
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This brand needs youe help. \n
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This is how you will help the brand: \n
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1. You will be given two things, the ai search result and the prompt used to query the ai. \n
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2. Your major interest here is formatting and making the structure right. \n
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3. You will process the ai result, create a JSON structure of the key information needed by the user and add the corresponding values to it. \n
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4. The user prompt is your guide on of how the JSON should be formatted. \n
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5. If there's are missing data or something in the ai response, send it back as NA. \n
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6. At the end of your processing you want to return a structured response and also make sure it in the best order as expected by the user. \n
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Return a structured JSON or dictionary as output. \n
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Ensure that the data is properly arranged and in a good format. \n
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Please do this carefully and excellently.
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<|eot_id|><|start_header_id|>user<|end_header_id|>
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AI_SEARCH_RESULT: {search_result} \n
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PROMPT: {prompt}
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<|eot_id|><|start_header_id|>assistant<|end_header_id|>""",
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input_variables=["search_result", "prompt"],
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)
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initiator_router = initiator_prompt | llm | JsonOutputParser()
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output = initiator_router.invoke({"search_result":search_result, "prompt":prompt})
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return output
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def combined_influencer_data(prompt: str) -> dict:
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# Step 1: Get data using the get_chat_completion function
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logger.info("Using Perplexity Ai to get the influencer data")
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search_result = get_chat_completion(prompt)
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# Step 2: Process the search result using the influencer_data function
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logger.info("Formatting the data with OpenAI")
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formatted_data = influencer_data(search_result, prompt)
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# Step 3: Return the final output
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return formatted_data
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product_categories = [
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"Beauty & Skincare",
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"Fashion & Lifestyle",
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"Health & Fitness",
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"Travel & Adventure",
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"Food & Beverage",
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"Technology & Gadgets",
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"Gaming & Esports",
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"Parenting & Family",
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"Finance & Business",
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"Wellness & Mental Health",
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"Automotive & Motorsports",
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"Entertainment & Pop Culture",
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"Photography & Visual Arts",
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"Education & Learning",
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"Environmental & Sustainability"
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]
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def product_categorizer(product_lists: list, product_categories=product_categories) -> str:
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logger.info(f"Categorizing products")
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initiator_prompt = PromptTemplate(
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template="""<|begin_of_text|><|start_header_id|>system<|end_header_id|>
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You are a Brand AI Agent tasked with categorizing products into categories\n
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There are some categories given by the brand, these are the categories the products are expected to be categorized into.\n
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You will be given a list of products and asked to categorize them.\n
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You are meant to categorize all the given product into one category. \n
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You take the following steps:
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1. Looking into all the list of products. \n
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2. Understand where they belong to. \n
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3. Look into the kist of categories. \n
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4. Select the category that this products falls under. \n
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You should return the selected category as output. \n
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Please do this carefully and correctly.
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<|eot_id|><|start_header_id|>user<|end_header_id|>
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PRODUCT_LISTS: {product_lists} \n
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PRODUCT_CATEGORY: {product_categories}
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<|eot_id|><|start_header_id|>assistant<|end_header_id|>""",
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input_variables=["product_lists", "product_categories"],
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)
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initiator_router = initiator_prompt | llm | StrOutputParser()
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output = initiator_router.invoke({"product_lists":product_lists, "product_categories":product_categories})
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return output
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