Upgrade Your AI Using Web Search - The Ollama Course

Summary

In this video, Matt Williams, a founding member of Ollama, explains how to integrate web search into AI models using tools like Ollama and SEARXNG, a privacy-focused meta search engine. He demonstrates how search queries are processed, cleaned, and fed to models like Llama for real-time responses. Matt emphasizes the importance of privacy and customization, discusses the power of Retrieval-Augmented Generation (RAG) for querying vector databases, and walks through the code implementation. He concludes by sharing his excitement for future AI tools and encouraging viewers to explore integrating web search into their AI projects.

Takeaways

• 🔍 Web search integration is essential for AI models to access the latest information, as models can't search the web independently.
• 🖥️ Web search capabilities are added by the software wrapping the model, not the model itself.
• 🌐 Matt Williams, former Ollama team member, now creates content about AI tools, with a focus on Ollama, which he endorses for its simplicity and privacy benefits.
• 📚 Retrieval-Augmented Generation (RAG) enables AI models to retrieve relevant content from a vector store to answer queries based on stored knowledge.
• 🔒 Privacy concerns arise when using mainstream search engines like Google or Bing due to their data handling practices.
• 🔎 SEARXNG is a meta-search engine recommended for enhanced privacy, allowing users to query multiple search engines with data obfuscation.
• 🚀 SEARXNG can be run locally using Docker or hosted on external servers, which provides additional privacy and customization.
• 📜 Williams outlines a step-by-step process of using SEARXNG with Ollama to retrieve and clean search results, integrating them with the AI model for informed responses.
• 🧹 Text cleanup involves removing irrelevant HTML elements using libraries like Cheerio, focusing on extracting only the useful content.
• 💻 The demo project is coded in TypeScript and uses tools like Deno, emphasizing the ease of integrating search functionalities in AI-driven applications.

Q & A

What is websearch and how does it enhance AI models?

Websearch allows AI models to access the latest information from the web, providing real-time data and updates that the model didn't learn during its training. This capability helps users receive current and relevant responses, but it requires additional software to integrate with search engines.

Can AI models search the web on their own?

No, AI models cannot search the web by themselves. Any model that appears to search the web has software around it to provide that functionality, integrating it with search engines.

What is RAG (Retrieval Augmented Generation) and how does it work?

RAG is a method where the model takes a user query, embeds it, and compares it to a vector store containing relevant information. The model then uses both the query and the retrieved data to generate a more accurate and specific response.

What is the key difference between RAG and websearch?

RAG relies on pre-existing information stored in a vector database, while websearch requires an API to access real-time data from search engines, making it more current but also more dependent on external resources.

What privacy concerns are associated with using public search engines for websearch?

Using public search engines like Google or Microsoft Bing raises privacy concerns because these platforms often collect user data. To mitigate this, tools like SearxNG can be used to anonymize search requests.

What is SearxNG and why is it useful for websearch?

SearxNG is a meta-search engine that allows users to search multiple engines at once while protecting their privacy by removing personal information from requests before they reach the search engine.

How can SearxNG be set up for personal use?

SearxNG can be hosted on your own machine using a Docker image or on an external host. There are several options for running it, including using a Docker Compose file for more advanced setups.

What are the steps for integrating websearch with AI models like Ollama?

First, the user provides a search query, which is sent to SearxNG. URLs related to the query are fetched, cleaned up to extract relevant text, and then fed to the AI model along with the original query to generate a response.

What library is used for cleaning up HTML in the provided example, and how does it work?

The example uses Cheerio, a Node.js library similar to jQuery, to clean up HTML by removing unnecessary elements like scripts, images, and styles, leaving only the relevant text for the model to process.

What technology stack is used in the example code for the websearch integration?

The example is written in Typescript and uses the Llama 3.2 model with only 1 billion parameters. It also utilizes the SearxNG API, Cheerio for HTML processing, and Deno as the runtime environment.