DoCoreAI Documentation

Usage Modes: Run DoCoreAI via CLI, Test, or Python Import

Ensure settings configured before proceeding:

1. CLI Mode

1. Plug n Play method (Fully automatic):
   Launch the local engine: and open a new terminal window, run your existing app that prompts the LLM (e.g., OpenAI) - thats it & your logging starts automatically.

   docoreai start  # Launch the local engine

2. For Testing purpose run docoreai test and from Postman, Hoppscotch.io, or curl, send a prompt in below JSON format: to http://127.0.0.1:8001/intelligence_profiler

   docoreai test

{
  "user_content": "Invent a brand-new type of sport and describe its rules.",
  "role": "Creative Thinker"
}

Note: The "role" sets the LLM’s persona (similar to agentic AI) to guide the response style.

2. Library / Import Mode for Developers

from dotenv import load_dotenv
load_dotenv()
import os

# Import from your client
from docore_ai.model import intelligence_profiler
def main():
    prompt = "Why docoreai is the best Optimizer?"
    ai_role = "AI Researcher"

    print("Running intelligence_profiler()...")
    try:
        
        result = intelligence_profiler(user_content=prompt, role=ai_role)
        
        print("\n Result from DoCoreAI intelligence_profiler:\n")
        print(result)  # Pretty print for readability
    except Exception as e:
        print("❌ Error while running profiler:", str(e))

if __name__ == "__main__":
    main()

 
import os
from openai import OpenAI
from dotenv import load_dotenv
import time
import groq
from dotenv import load_dotenv

# 🔄 Load environment variables from .env
load_dotenv()

# Get provider and model info
provider = os.getenv("MODEL_PROVIDER", "openai").lower()
model = os.getenv("MODEL_NAME", "gpt-4")
count = 4 #int(os.getenv("TEST_COUNT", 3))

# Initialize client based on provider
if provider == "openai":
    from openai import OpenAI
    client = OpenAI()
elif provider == "groq":
    from groq import Groq
    groq_api_key = os.getenv("GROQ_API_KEY", "")
    client = Groq(api_key=groq_api_key)
else:
    raise ValueError(f"Unsupported MODEL_PROVIDER: {provider}")

# 🔁 Run test loop
for i in range(count):
    print(f"\n🔁 Request #{i + 1}")
    response = client.chat.completions.create(
        model=model,
        messages=[{"role": "user", "content": "does chatgpt have agentic ai agents that can sing?"}]
        
    )
    print("Response:", response.choices[0].message.content.strip())
    time.sleep(1)

View Local Analytics

Use ` docoreai show ` to analyze local prompt sessions, identify waste, and profile prompt behavior.

docoreai show  

Open the Dashboard

Run ` docoreai dash ` or visit your cloud dashboard to see overall insights.

docoreai dash