# How to use website search functionality with my LLM **URL:** \ **Category:** Beginners\ **Created:** [January 11, 2025, 11:13am UTC](https://discuss.huggingface.co/t/how-to-use-website-search-functionality-with-my-llm/135257 "2025-01-11T11:13:25Z")\ **Posts on this page:** 3\ **Page:** 1
**Author:** ![nyri](https://sea2.discourse-cdn.com/hellohellohello/user_avatar/discuss.huggingface.co/nyri/32/38988_2.png) [@nyri](https://discuss.huggingface.co/u/nyri)\ **Post date:** [January 11, 2025, 11:13am UTC](https://discuss.huggingface.co/t/how-to-use-website-search-functionality-with-my-llm/135257/1 "2025-01-11T11:13:26Z")
Hi everyone, I’m a beginner who discovered Hugging Face a few days ago and I’m really impressed by what we can do here. I was wondering if it’s possible to replicate the “domain search” feature (like in HuggingChat) for my own custom chatbots, essentially using it as a RAG approach. Is there a straightforward way to crawl or connect data from a website URL for that purpose? If so, could you please explain how or point me to any relevant tools or examples? Thanks a lot ! nyri ![Capture d’écran 2025-01-11 à 12.03.05](https://us1.discourse-cdn.com/hellohellohello/original/3X/7/b/7b0bf8138d3eba801d7aa95a69c272adfdc73461.png) ---
**Author:** ![John6666](https://sea2.discourse-cdn.com/hellohellohello/user_avatar/discuss.huggingface.co/john6666/32/27664_2.png) [@John6666](https://discuss.huggingface.co/u/John6666)\ **Post date:** [January 11, 2025, 11:51am UTC](https://discuss.huggingface.co/t/how-to-use-website-search-functionality-with-my-llm/135257/2 "2025-01-11T11:51:01Z")
Hello. There are two broad methods. One is to process the results of a normal web search using a programming language such as Python and pass the results to LLM yourself. The other is a method called **Function Calling** , in which you instruct LLM to execute a search tool and return the results. (There are various names for this method.) In the case of the former, there are various useful libraries, so you should try searching for them. If you can use the latter, it is usually built into the LLM execution environment, so it is often found somewhere in the documentation. ### Pass the results of a normal web search to LLM > **[duckduckgo-search](https://pypi.org/project/duckduckgo-search/)** > > Search for words, documents, images, news, maps and text translation using the DuckDuckGo.com search engine. [https://github.com/alby13/ollama-internet-search-tool](https://github.com/alby13/ollama-internet-search-tool) ### Function Calling > **[Tool support · Ollama Blog](https://ollama.com/blog/tool-support)** > > Ollama now supports tool calling with popular models such as Llama 3.1. This enables a model to answer a given prompt using tool(s) it knows about, making it possible for models to perform more complex tasks or interact with the outside world. ---
**Author:** ![olivicarter](https://avatars.discourse-cdn.com/v4/letter/o/ea666f/32.png) [@olivicarter](https://discuss.huggingface.co/u/olivicarter)\ **Post date:** [July 24, 2026, 11:42am UTC](https://discuss.huggingface.co/t/how-to-use-website-search-functionality-with-my-llm/135257/3 "2026-07-24T11:42:01Z")
Hi! One approach that works well is to build a structured knowledge base from your website instead of relying only on raw page crawling. If your site already has FAQs, service pages, pricing information, and educational content, you can chunk that content, generate embeddings, and store it in a vector database for RAG. For local business websites, users often ask the same question in different ways, such as: - How much does junk removal cost? - Can I recycle old electronics? - Where should I dispose of a mattress? - What items are accepted for pickup? A combination of semantic search and keyword search usually returns more reliable results than using only one approach, especially for conversational queries. I’m also interested to know what tools others are using to crawl websites and keep the knowledge base updated automatically.