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Building a Powerful Local AI Web Search Assistant with Llama 3.2:

Building a Powerful Local AI Web Search Assistant with Llama 3.2:

Combine Large Language Models with Real-Time Web Search for Up-to-Date Answers and Context-Aware Insights

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Introduction

Large Language Models (LLMs) have revolutionized how we interact with technology. From summarizing vast amounts of text to generating creative content, their capabilities are impressive. But what happens when we need answers to questions about current events or information outside their training data? This is where the Web-LLM Assistant steps in. By combining LLM power with real-time web search, this project elevates the assistant’s utility to unprecedented levels.

In this blog, I’ll explore how the Web-LLM Assistant works, its standout features, and how you can set it up using Llama 3.2 — a robust, versatile LLM.

What Is the Web-LLM Assistant?

The Web-LLM Assistant is an intelligent system that integrates the computational capabilities of a large language model with web-search functionality. This hybrid approach enables the assistant to provide accurate, up-to-date responses, even for topics not covered in its training data. It achieves this by leveraging web scraping and self-refining search strategies.

For example, if you ask, “Is the Boeing Starliner still stuck on the International Space Station?”, the assistant will:

  1. Interpret your query.
  2. Perform a web search using privacy-focused DuckDuckGo.
  3. Scrape and analyze the most relevant results.
  4. Combine the LLM’s pre-trained knowledge with the retrieved information to generate a detailed answer.

Key Features of the Web-LLM Assistant

1. Local LLM Integration

The assistant supports local execution of models through Llama.cpp or Ollama, ensuring data privacy and reducing reliance on external APIs.

It uses DuckDuckGo for searching the web, prioritizing user privacy. This allows it to fetch up-to-date information on recent events or obscure topics.

3. Web Scraping

By scraping the most relevant results, the assistant retrieves rich contextual data, enabling more comprehensive answers.

If the initial search doesn’t yield sufficient information, the assistant refines its query terms and time frames, conducting up to five search iterations for optimal results.

5. Enhanced User Experience

With colorful, animated console outputs, interacting with the assistant is engaging and intuitive.

6. Flexible Model Configuration

The assistant supports diverse instruct models, letting users experiment with various configurations to suit their needs and hardware.

Why Use Llama 3.2?

Llama 3.2 stands out as a high-performing instruct model. It’s designed for conversational tasks, making it an ideal choice for the Web-LLM Assistant. Its smaller memory footprint ensures compatibility with local machines, even those with limited resources.

Hands-On Tutorial: Setting Up Web-LLM Assistant with Llama 3.2

This step-by-step guide will help you set up the Web-LLM Assistant using Llama 3.2 and explore its powerful features. You’ll also learn how it handles real-time search queries, synthesizing comprehensive responses.

Step 1: Clone the Repository and Install Dependencies

First, clone the Web-LLM Assistant repository and install its dependencies:

git clone https://github.com/TheBlewish/Web-LLM-Assistant-Llamacpp-Ollama
cd Web-LLM-Assistant-Llamacpp-Ollama
pip install -r requirements.txt

Step 2: Set Up Ollama and Download Required Models

  1. Install Ollama Follow the Ollama installation guide to set up the Ollama server on your local machine.
  2. Download Llama 3.2 and Nomic Embedding Models Start the Ollama server:
ollama serve

Pull the required models:

ollama pull llama3.2
ollama pull nomic-embed-text

Step 3: Update the Configuration

Modify the llm_config.py file to use the Llama 3.2 model with Ollama. Look for the section:

## LLM setting for Ollama
LLM_CONFIG_OLLAMA = {
    "model_name": "ollama model name",  # Change this to "llama3.2"
    ...
}

Update "ollama model name" to "llama3.2".

Step 4: Run the Web-LLM Assistant

Launch the assistant by running the script:

python3 WEB-LLM.py

Step 5: Interact with the Assistant

You can now start interacting with the Web-LLM Assistant. To ask a web-based query, prepend your message with a forward slash (/).

For example:

/Md Monsur Ali LLM Medium blogger

How the Assistant Works

  • The assistant performs a web search based on your query.
  • It fetches the top 10 results, selects the most relevant ones, and scrapes their content.
  • Finally, it synthesizes an answer using both the web search data and Llama 3.2’s pre-trained knowledge.

Example Interaction

Query:

/Md Monsur Ali LLM Medium blogger

Search Attempt Output:

-=- Search attempt: 1 -=-

@ Searching...
Original query: Md Monsur Ali LLM Medium blogger
Formulated query: Md Monsur Ali LLM Medium Blogger
Time range: none
Search query sent to DuckDuckGo: Md Monsur Ali LLM Medium Blogger
Time range sent to DuckDuckGo: none
Number of results: 10

Search Results:
Result 1:
Title: About Me — Md Monsur Ali
URL: https://medium.com/about-me-stories/about-me-md-monsur-ali-6606e94f4695

Result 2:
Title: The Journey of a Data Scientist: My Story and Beyond
URL: https://medium.com/@monsuralirana/the-journey-of-a-data-scientist-my-story-and-beyond-faa7b693f36d
...

Scraping Content from:
URL: https://medium.com/about-me-stories/about-me-md-monsur-ali-6606e94f4695
Content:
Hi, I’m Md Monsur Ali, and I’m excited to welcome you into my world of curiosity, exploration, and creativity!

I’m a Data Scientist currently living in Germany, though my journey began in Bangladesh. After relocating to Germany to pursue my master’s degree, I successfully completed my studies and began my professional career here, eventually making Germany my permanent home. My work focuses on AI, data science, and technology, and over the years, I’ve had the privilege of tackling complex challenges that keep me fascinated with the potential of innovative technologies. Writing about my experiences has become a passion of mine, as it allows me to share knowledge and insights while connecting with others who share the same curiosity.

More Details:

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Practical Applications

  1. Research Assistant Quickly retrieve and synthesize information on any topic, including recent developments.
  2. Personalized Learning Use it to explore complex subjects with interactive, real-time assistance.
  3. Customer Support Integrate it into customer support workflows for real-time, informed responses.

Summary

The Web-LLM Assistant seamlessly combines the power of large language models (LLMs) like Llama 3.2 with real-time web search and scraping capabilities. This innovative tool addresses a key limitation of LLMs — their inability to access recent or niche information — by retrieving and analyzing up-to-date web content. Using privacy-focused DuckDuckGo, it refines its search process iteratively, scrapes relevant pages, and synthesizes insightful answers.

Key steps to get started include cloning the repository, setting up Ollama, pulling the necessary models (Llama 3.2 and nomic-embed-text), and configuring the llm_config.py file. Once set up, users can ask queries with ease, and the assistant will intelligently combine LLM knowledge with live web data to provide accurate responses.

Conclusion

The Web-LLM Assistant represents a significant advancement in AI-assisted research and interaction. By marrying LLM capabilities with real-time data retrieval, it overcomes the static nature of pre-trained models, making it highly versatile for applications like research, education, and real-time Q&A systems.

Whether you’re a developer exploring the boundaries of AI, a researcher looking for precise and up-to-date answers, or simply an enthusiast, this tool offers an exceptional platform for innovation. Its flexibility to operate locally with Llama.cpp or Ollama ensures data privacy while delivering robust performance.

References

[1] TheBlewish, “Web-LLM-Assistant-Llamacpp-Ollama: GitHub Repository,” 2024. Available: https://github.com/TheBlewish/Web-LLM-Assistant-Llamacpp-Ollama

[2] Ollama, “Ollama: Local LLM Inference Platform,” 2024. Available: https://ollama.com/

[3] Ollama, “Llama 3.2 Model Overview: Library,” 2024. Available: https://ollama.com/library/llama3.2

Happy coding! 🎉

👨🏾‍💻 GitHub ⭐️ | 👔 LinkedIn | 📝 Medium | ☕️ Ko-fi

Thank you for your time in reading this post!

Make sure to leave your feedback and comments. 👏 Clap for the story and follow for stories. See you in the next blog; stay tuned 📢

Enjoyed this article? Check out more of my work:

  • Building a Custom Documents Agent with Elasticsearch, Ollama, LLaMA 3.1, and LangChain: Explore how to set up a personalized document retrieval agent using LLaMA 3.1 and Ollama for seamless information retrieval. Read the full tutorial here.
  • Building Your Personal AI Assistant with Memory Using Ollama’s LLaMA3.1, LLaMA3.2 Models, Streamlit UI, and Locally: Discover how to develop an AI assistant that remembers past interactions using the latest LLaMA models and a user-friendly Streamlit interface. Read the full tutorial here.
  • OpenAI Swarm: A Lightweight Framework for Multi-Agent Orchestration: Dive into a new framework designed for managing multiple AI agents efficiently, enhancing your AI project management. Read the full tutorial here.
  • How to Use Molmo-7B for Multimodal AI: Extract Text and Images with an Open-Source Vision-Language Model: Learn how to harness the power of the Molmo-7B model for extracting both text and images, revolutionizing your approach to multimodal AI. Read the full tutorial here.

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