ai_insight.py

from llama_index.core import SummaryIndex
from llama_index.readers.mongodb import SimpleMongoReader
from llama_index.llms.openai import OpenAI
from datetime import datetime
import os
from dotenv import load_dotenv
import asyncio
# from llama_index.vector_stores import FaissVectorStore
load_dotenv()

llm = OpenAI(model="GPT-4o-Mini", api_key=os.getenv("OPENAI_API_KEY"), max_output_tokens=100)  # Use environment variable for API key
mongo_uri = os.getenv("MONGO_URI")
db_name = "telegram_bot_db"
collection_name = "token_contracts_analytics_data"

host = mongo_uri
port = 27017

# Specify the required fields using dot notation
field_names = ["all_token_data","num_times_all_mentioned","last_mention_date"]
reader = SimpleMongoReader(host, port)

documents = reader.load_data(
    db_name, collection_name, field_names
)


async def ai_insight():
   
    try:
         
        prompt = f"""Today's date is {datetime.now().strftime('%d/%m/%Y')}.\n
            You are a crypto advisor and professional researcher responsible for gathering information for daily reports.
            Identify unusual token patterns, price and volume trends. Provide actionable insights in Markdown format.

            Format:
            Example: "Hi! I've noticed an unusual spike in mentions of token XYZ, which is associated with a 20% increase in volume over the last 24 hours. This token may be of interest to you!"
            Example: "Token ABC is trending upward in mentions and liquidity. Based on historical patterns, similar tokens have seen a 15%-30% increase in the last 48 hours."
            (Include relevant links like x, telegram and origin)
            Write differently every time.
            use data like mention time and number and token analytics needed all data
            Write in Markdown format, within 500 characters.
            """
       
        # vector_store = FaissVectorStore.from_documents(documents)
        # index = SummaryIndex.from_vector_store(vector_store)   
        index = SummaryIndex.from_documents(documents)
        query_engine = index.as_query_engine(llm=llm, streaming=True, similarity_top_k=5)  # Pass the LLM to the query engine

        print("starting query...",query_engine)
        start_time = datetime.now()
        
        response = query_engine.query(prompt)  # Ensure this is awaited

        # Measure response time
        end_time = datetime.now()
        print(f"Query response received in {end_time - start_time} seconds.")
        # print(f"Querying for-----------------------: {query_text}")
        print(f"Query response received.-----------:{response}")
        return response
       
    except Exception as e:
        print(f"An error occurred: {e}")
        return "An error occurred while processing your request."

# Usage example
# asyncio.run(chat_bot("What is the highest token price change in the last 24 hours?"))
asyncio.run(ai_insight())