TLD.lat

Real-Time Web Intelligence Layer for Generative AI


What is TLD.lat?

TLD.lat is a lightweight real-time web scanning layer that enhances GPT and HuggingFace transformer-based chatbots with near real-time event awareness.

It fetches live web data, summarizes it using pretrained models, and injects contextual intelligence into chatbot prompts.

How It Works

Python Implementation


"""
TLD.lat Real-Time AI Web Scanner Layer
Adds near real-time event awareness to GPT / HuggingFace chatbots
"""

import requests
from transformers import pipeline
from datetime import datetime

# ---------------------------
# CONFIG
# ---------------------------

SEARCH_API = "https://api.duckduckgo.com/"
USER_AGENT = {"User-Agent": "TLD-Lat-Scanner/1.0"}

# Choose summarization model
summarizer = pipeline(
    "summarization",
    model="facebook/bart-large-cnn"
)

# ---------------------------
# WEB SEARCH FUNCTION
# ---------------------------

def search_web(query):
    params = {
        "q": query,
        "format": "json"
    }
    response = requests.get(SEARCH_API, params=params, headers=USER_AGENT)
    data = response.json()

    results = []
    if "RelatedTopics" in data:
        for item in data["RelatedTopics"][:5]:
            if "Text" in item:
                results.append(item["Text"])

    return results


# ---------------------------
# SUMMARIZE LIVE DATA
# ---------------------------

def summarize_results(results):
    combined = " ".join(results)[:3000]

    if not combined:
        return "No recent information found."

    summary = summarizer(
        combined,
        max_length=180,
        min_length=60,
        do_sample=False
    )

    return summary[0]["summary_text"]


# ---------------------------
# AUGMENT GPT PROMPT
# ---------------------------

def augment_prompt(user_query):
    print("Fetching live data...")
    live_results = search_web(user_query)

    print("Summarizing...")
    live_summary = summarize_results(live_results)

    augmented_prompt = f"""
You are a real-time AI assistant.

Current date: {datetime.utcnow().strftime('%Y-%m-%d %H:%M UTC')}

Live Web Context:
{live_summary}

User Question:
{user_query}

Answer using the live context above when relevant.
"""

    return augmented_prompt


# ---------------------------
# EXAMPLE GPT CALL
# ---------------------------

def send_to_gpt(augmented_prompt, openai_api_key):
    import openai

    openai.api_key = openai_api_key

    response = openai.ChatCompletion.create(
        model="gpt-4",
        messages=[
            {"role": "system", "content": "You are a helpful AI."},
            {"role": "user", "content": augmented_prompt}
        ]
    )

    return response["choices"][0]["message"]["content"]


# ---------------------------
# MAIN EXECUTION
# ---------------------------

if __name__ == "__main__":
    query = input("Ask something about recent events: ")

    prompt = augment_prompt(query)

    print("\n--- Augmented Prompt ---\n")
    print(prompt)

    # To enable GPT response:
    # api_key = "YOUR_OPENAI_KEY"
    # answer = send_to_gpt(prompt, api_key)
    # print("\n--- GPT Response ---\n")
    # print(answer)

Use Cases