Top AI Repos — open-source AI, indexed and scored
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
A Python-based web-assisted large language model (LLM) search assistant using Llama.cpp
| Date | Stars |
|---|---|
| 2026-07-31 | 377 |
| 2026-08-01 | 377 |
| 2026-08-02 | 377 |
| 2026-08-06 | 377 |
Today
— stars today
This week
— stars this week
This month
— stars this month
Momentum
0.0
growth rate 0.00%/day
# Web-LLM Assistant ## Description Web-LLM Assistant is a simple web search assistant that leverages a large language model (LLM) running via either Llama.cpp or Ollama to provide informative and context-aware responses to user queries. This project combines the power of LLMs with real-time web searching capabilities, allowing it to access up-to-date information and synthesize comprehensive answers. Here is how it works in practice: You can ask the LLM a question, for example: "Is the boeing starliner still stuck on the international space station", then the LLM will decide on a search query and a time frame for which to get search results from, such as results from the last day or the last year, depending on the needs of your specific question. Then it will perform a web search, and collect the first 10 results and the information contained within them, it then will select 2 most relevant results and web scrape them to acquire the information contained within those results, after reviewing the information it will decide whether or not the information is sufficient to answer the your question. If it is then the LLM will answer the question, if it isn't then the LLM will perform a new search, likely rephrasing the search terms and/or time-frame, to find more appropriate and relevant information to use to answer your question, it can continue to do multiple searches refining the search terms or time-frame until it either has enough information to actually answer the User's question, or until it has done 5 separate searches, retrieving information from the the LLMs decided top 2 relevant results of each search, at which time if it hasn't been able to find the information needed to answer the User's question it will try it's best to provide whatever information it has acquired from the searches at that point to answer your question the best it can. Thus allowing you to ask it queries about recent events, or anything that may not actually be in it's training data. Which it can now, via this python program still determine the answer to your question, even if the answer is absent from the LLM's training data via web searching and retrieving information from those searches. ## Project Demonstration [](https://youtu.be/ZXbMCet5kjo "Web-LLM-Assistant Llama-cpp demonstration") Click the image above to watch a demonstration of the Web-LLM-Assistant Llama-cpp in action. ## Features - Local LLM usage via llama_cpp or ollama. - Web scraping of search results for full information for the LLM to utilise - Web searching using DuckDuckGo for privacy-focused searching for pages for scraping - Self-improving search mechanism that refines queries based on initial results - Rich console output with colorful and animated indicators for better user experience - Multi-attempt searching with intelligent evaluation of search results - Comprehensive answer synthesis using both LLM knowledge, web search results, and scraped information from the LLMs selected webpages ## Installation 1. Clone the repository: Clone the repository from GitHub using: git clone https://github.com/TheBlewish/Web-LLM-Assistant-Llamacpp-Ollama then navigate to the project directory. 3. Create a virtual environment (optional but recommended): python -m venv venv source venv/bin/activate # On Windows, use venv\Scripts\activate 4. Install the required dependencies: Install the required packages by running pip install -r requirements.txt 5. Install and setup either Ollama or Llama.cpp, then proceed to either the Ollama or Llama.cpp usage instructions below, depending on which you plan to use. ## Usage Using with Ollama: 1. Start the Ollama server: Run the command ollama serve to start the Ollama server. 2. Download your desired model using Ollama: Use the ollama pull command followed by the model name you wish to use, for example, ollama pull gemma2:9b-instruct-q5_K_M 3. Con
Excerpt of 6,868 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:bf6ca7c18533d7ca, llm:Repository description: "A Python-based web-assisted large language model (LLM) search assistant using Llama.cpp"
matched fp:bf6ca7c18533d7ca, llm:Repository description: "A Python-based web-assisted large language model (LLM) search assistant using Llama.cpp"
matched fp:bf6ca7c18533d7ca, llm:Repository description: "A Python-based web-assisted large language model (LLM) search assistant using Llama.cpp"