Answer Engine Tutorial: The Open-Source Perplexity Search Replacement
TLDRDiscover an open-source alternative to Perplexity, the AI-powered answer engine. This innovative project, developed by Developer Digest, streamlines information retrieval by directly presenting answers and sources for user queries, akin to Google Search but with less hassle. The project, which leverages technologies like Verse, Gro, and Brave, is hosted on GitHub and has gained significant attention. By following a straightforward setup process, users can run the engine locally, potentially replacing their reliance on traditional search engines. The video also explores integrating the engine with local models using Ollama, offering a glimpse into the potential for fully decentralized information search.
Takeaways
- 🌟 The project discussed is an open-source alternative to the AI-powered answer engine, Perplexity.
- 🔍 Users can enter queries and receive direct answers with supporting sources, similar to Google Search but more streamlined.
- 📈 The project is gaining traction, with 300 stars on GitHub and the potential for more growth after the video tutorial.
- 🛠️ Developers Digest is the creator of the project, and they have open-sourced their JS LLM answer engine.
- 🖥️ The answer engine runs on Local Host 3000 and utilizes various technologies like Versel, Gro, Lang chain open AI embeddings, and Brave and Serper APIs.
- 💻 Installation instructions are provided, including cloning the repository and using VSS code for setup.
- 🔑 API keys from OpenAI, Grock, Brave, and Serper are required for the different functionalities of the answer engine.
- 🚀 The project can be run locally with a server in the background, offering a free alternative to Perplexity.
- 📦 An attempt was made to run the models completely locally using玉兰 (Olama), but it wasn't successful in the demonstration.
- 🎥 The video provides a step-by-step guide on setting up and running the answer engine, as well as troubleshooting tips.
Q & A
What is the main feature of the open-source project discussed in the video?
-The main feature is that it's an open-source version of an answer engine, similar to Perplexity, which uses artificial intelligence to directly answer user queries by compiling information into a page, instead of just providing a list of links.
How does the answer engine project differ from traditional search engines like Google?
-Unlike traditional search engines, the answer engine project assembles a direct answer to the query by pulling information from various sources and presenting it in a single, easy-to-understand format.
What technology is used for the inference process in this project?
-The project utilizes Versel and Gro for the inference process, which is noted for its speed. It also uses Mistal AI Labs Lang chain open AI embeddings and Brave and Serper API.
How can one install and run the answer engine project locally?
-To install and run it locally, one needs to clone the repository from GitHub, open it in VS Code, install the requirements using 'bun install', and then run the project using 'bun run dev'.
What are the API keys required for the project and where can they be obtained?
-The project requires API keys from OpenAI for embeddings, Grock for Mixol, Brave for search, and Serper API for search. These can be obtained by signing up for respective services and creating API keys.
What is the significance of the GitHub URL mentioned in the video?
-The GitHub URL is the repository where the open-source code for the answer engine project is hosted. It allows others to contribute to the project, download the code, report issues, and make suggestions.
How does the project handle video content within the search results?
-The project integrates video content by allowing users to click on any video thumbnail, which will then play the video in the same screen, providing a seamless experience.
What was the issue encountered when trying to run the models locally using玉兰(Olama)?
-The presenter suspected that there might be a compatibility issue with the Mistol model, as it didn't return any useful information when used in place of the Mixol model. This resulted in the answer engine not displaying any results on the interface.
What is the one requirement that still hits the web in the answer engine project?
-The requirement that still hits the web is the search functionality, as it performs a Google search to find some of the information needed to answer the queries.
What is the potential limitation of running the project with玉兰(Olama)?
-The potential limitation is that玉兰(Olama) may not be compatible with all the models used in the project, and it might not return useful information for the answer engine to process and display to the user.
What is the current status of the project in terms of community support?
-At the time of the video, the project was relatively new with only a few days since its creation and had received 300 stars on GitHub. The presenter hopes that the project will gain more momentum and support after the video.
Outlines
🌟 Introducing an Open-Source Answer Engine
The paragraph introduces an open-source version of Perplexity, an AI-powered answer engine that provides direct answers to queries instead of a list of search results. The speaker demonstrates how to use the engine by asking how to make ramen, showcasing the step-by-step instructions and sources provided. The project, developed by Devore Digest, is hosted on GitHub and has gained initial traction. The installation process is explained in detail, including setting up the environment in VS Code, cloning the repository, installing requirements with Bun, and configuring API keys for OpenAI, Grock, Brave, and Serper. The goal is to enable users to run a personal, local version of a search engine that leverages AI for direct answers.
🔧 Running the Models Locally with Olama
This paragraph discusses the process of running the answer engine's models locally using Olama. The speaker guides through downloading and installing Olama, downloading the mistol model for faster performance, and serving the model locally. The base URL in the project's configuration is replaced to point to the Olama server endpoint, and the API key is switched to use Olama. Despite facing some challenges in getting the local model to work, the speaker encourages viewers to experiment with the project and share their experiences. The video ends with a call to action for viewers to like, subscribe, and comment on their success with the project.
Mindmap
Keywords
💡Answer Engine
💡Open Source
💡Perplexity
💡Artificial Intelligence (AI)
💡Inference
💡GitHub
💡VS Code
💡API Key
💡Local Host
💡Olama
💡Mistol
Highlights
The project is an open-source version of Perplexity, an answer engine.
It uses artificial intelligence to directly answer queries.
Users can now utilize it as an alternative to Google Search.
The engine compiles a page with information, pictures, and videos related to the query.
Source information is provided for the content.
The project is hosted on Local Host 3000 and is called Answer Engine.
The engine uses Versel, Gro, and other open-source models for fast inference.
The GitHub repository for the project has been created and is gaining traction.
Instructions for installation and setup are provided in the tutorial.
API keys from OpenAI, Grock, Brave, and Serper are required for functionality.
The project can be run locally with a server in the background.
Models can be run completely locally with the use of玉兰 (Olama).
The base URL and API keys need to be adjusted for local model running.
OpenAI embeddings are the only paid third-party API used in the project.
The project requires internet access for Google search functionality.
Despite some issues, the project is highly regarded for its innovative approach.
The tutorial encourages users to experiment and provide feedback.
The video aims to showcase the benefits and practical use of the Answer Engine.