README.md

metadata

title: LLMSearchEngine
emoji: 🏆
colorFrom: gray
colorTo: purple
sdk: docker
app_file: app.py
pinned: false

LLM Search Engine

This is a Flask-based web application that uses a large language model (LLM) to generate search engine-like results, styled to resemble Google’s classic search results page. Instead of querying an external search API, it prompts an LLM to create titles, snippets, and URLs for a given query, delivering a paginated, familiar interface.

Why We Built It

We created this app to explore how LLMs can mimic traditional search engines by generating results directly from their training data. It offers:

• A nostalgic, Google-like pagination design with clickable links.
• A proof-of-concept for LLM-driven search without real-time web access.
• A simple, self-contained alternative for queries within the model’s knowledge base.

Features

• Google-Styled Interface: Search bar, result list, and pagination styled with Google’s colors and layout.
• Generated Results: Titles, snippets, and URLs are fully produced by the LLM.
• Pagination: Displays 10 results per page, up to 30 total results across 3 pages.

Limitations

• Static Knowledge: Results are limited to the LLM’s training cutoff (e.g., pre-2025).
• Generated Content: URLs and snippets may not correspond to real web pages—use as a starting point.
• No Real-Time Data: Best for historical or established topics, not breaking news.

Using It on Hugging Face Spaces

Try the Demo

Deployed on Hugging Face Spaces, you can test it at https://codelion-llmsearchengine.hf.space:

1. Open the URL in your browser.
2. Type a query (e.g. "best Python libraries") in the search bar and press Enter or click "LLM Search".
3. Browse the paginated results, styled like Google, using "Previous" and "Next" links.

Using It as an API

API Endpoint

Your app doubles as an API when hosted on HF Spaces:

• URL: https://codelion-llmsearchengine.hf.space/
• Method: GET
• Parameters:
  • query: The search query (e.g., "best Python libraries").
  • page: Page number (1-3, defaults to 1).

Example Request

curl "https://codelion-llmsearchengine.hf.space/?query=best+Python+libraries&page=1"

Response

Returns raw HTML styled like a Google search results page.

---

Integration

You can fetch results programmatically and render or parse the HTML:

import requests
from urllib.parse import quote

query = "best Python libraries"
page = 1
url = f"https://codelion-llmsearchengine.hf.space/?query={quote(query)}&page={page}"
response = requests.get(url)
html_content = response.text  # Render or process as needed
print(html_content)

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How It Works

1. LLM Prompting

   • Queries trigger a prompt to the "gemini-2.0-flash-lite" model.
   • Generates 30 results in JSON format.

2. Rendering

   • Flask converts results into a Google-styled HTML page.
   • Includes a search bar, results, and pagination.

3. Deployment

   • Runs via Flask and Docker on HF Spaces.
   • Serves dynamic pages based on URL parameters.

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Setup Locally

Install dependencies

pip install -r requirements.txt

Set environment variables

export OPENAI_API_KEY="your-key"
export OPENAI_BASE_URL="your-url"

Run the app

python app.py

Visit http://localhost:5000.