groq_helper.py

import os
from groq import Groq

client = Groq(
    api_key=os.getenv('GROQ_API_KEY'),
)

def generate_chat_completion(USER_INPUT, related_vectors):

    SYSTEM_PROMPT = f'''
    You are a system that converts natural language queries into a structured filter schema.
    The filter schema consists of a list of conditions, each represented as:
    {{
        "attribute": "<attribute_name>",
        "op": "<operator>",
        "value": "<value>"
    }}
    There can be any number of conditions. You have to list them all.

    Supported attributes and their operators are:
    {related_vectors}

    Example:
    Input: "Show campaigns where spend is greater than 11"
    Output: [{{"attribute": "spend", "op": ">", "value": 11}}]

    Input: "Find ads with clicks less than 100 and impressions greater than 500"
    Output: [
        {{"attribute": "clicks", "op": "<", "value": 100}},
        {{"attribute": "impressions", "op": ">", "value": 500}}
    ]

    STRICLY PROVIDE IN THE ABOVE JSON FORMAT WITHOUT ANY METADATA

    '''
    
    chat_completion = client.chat.completions.create(
        messages=[
            {
                "role": "system",
                "content": SYSTEM_PROMPT,
            },
            {
                "role": "user",
                "content": USER_INPUT,
            },
        ],
        model="llama3-8b-8192",
    )
    return chat_completion.choices[0].message.content

#Test input
# USER_INPUT = "Show campaigns where spend is greater than 11 and labels include holiday and with impressions less than 500"
# print(generate_chat_completion(SYSTEM_PROMPT, USER_INPUT))