LLocalSearch - Enhance Privacy with Local Large Language Models for Efficient Internet Search

Exploring LLocalSearch

What LLocalSearch Is

LLocalSearch is a fascinating tool designed to interact with local Large Language Models (LLMs), which are similar to well-known models like ChatGPT but on a smaller scale. This innovative project provides these models with access to various tools to enhance their capabilities. Among these tools is the ability to search the internet for real-time information in response to users' questions. The process is recursive, meaning the LLM can utilize these tools multiple times as it processes information from users and other sources.

Why Choose LLocalSearch?

Many people might wonder why they should use LLocalSearch instead of other popular choices. The key distinction lies in the approach to user interaction and privacy. LLocalSearch aims to present a more unbiased and transparent alternative. Unlike some big commercial names governed by business interests, LLocalSearch allows users to access information without the influence of advertisers or marketers. This makes it a suitable option for those who prefer less manipulation and more genuine information sourcing.

Unique Features

Privacy First: LLocalSearch operates entirely locally, eliminating the need for API keys. Thus, users' privacy is better protected.
Hardware Efficiency: The system is designed to run on lower-end hardware, demonstrated by a demo using a modestly priced 300€ GPU.
Transparent Processes: Users can view live logs and links within their answers, providing a clearer understanding of the agent's operations and the information's sources. This detail offers a solid base for further exploration.
Interactivity: The project supports follow-up questions, enhancing the conversational flow between users and the system.
User-Friendly Design: Mobile compatibility and adjustable display modes (dark and light) cater to users’ preferences.

Development Roadmap

Current Developments

LLama3 Support: Efforts are ongoing to ensure the project supports LLama3, dealing with issues around stop words that can cause misleading responses.
Interface Overhaul: The interface is being revamped for better functionality, inspired by the design philosophy of platforms like Obsidian.
Conversation History: Enhancements are planned to accommodate chat histories, requiring substantial internal adjustments to improve user interaction.

Future Plans

User Accounts: Preparing the system to manage private information safely, allowing users to upload personal documents and link to services like Google Drive.
Long-term Memory: There's an ambition to integrate persistent data storage, allowing the system to remember user preferences and other user-centric data over extended periods.

Installation Guide via Docker

Steps to Set Up

Clone the Repository: Start by cloning the LLocalSearch GitHub repository.
```
[email protected]:nilsherzig/LLocalSearch.git
cd LLocalSearch
```
Configure Environment: Users may need to create an .env file to customize settings, particularly if connecting to Ollama on different devices.
```
touch .env
code .env  # Open in VSCode
nvim .env  # Open in Neovim
```
Launch Containers: Use Docker Compose to start the containers.
```
docker-compose up -d
```

By exploring LLocalSearch, users can engage with locally powered language models to obtain and interact with information in a more private and efficient manner. This tool represents a step towards more user-focused technology, with continual improvements on the horizon.