Answer Engine Tutorial: The Open-Source Perplexity Search Replacement

Innovative Open-Source Answer Engine

In this section, the speaker introduces an open-source answer engine project that functions similarly to Google Search but provides direct answers without the need to click through multiple links.

Introduction to the Project

• The project is an open-source version of perplexity, serving as an answer engine powered by artificial intelligence. It aims to provide direct answers to user queries without extensive link navigation.

• Users can input questions into the system, and it utilizes AI to generate a page with step-by-step answers along with relevant sources for the information provided.

Running the Open-Source Model Locally

• The demonstration showcases running the open-source answer engine locally on Local Host 3000, offering a seamless experience similar to commercial search engines.

• The project leverages various technologies such as Versel, Grok for inference, Mistal AI Labs Lang chain, OpenAI embeddings, Brave, and Serper API for efficient functioning.

Installation Process and API Key Setup

This section details the installation process of the open-source answer engine project and setting up essential API keys for its functionality.

Installation Steps

• To install the project, users need to clone the repository from GitHub using Git commands in VS Code terminal.

• After cloning, users should navigate into the project directory and install necessary requirements using npm or Bun package manager.

Setting Up API Keys

• Users are guided through obtaining and configuring essential API keys including OpenAI key for embeddings, Grok key for inference, Brave key for search functionality, and Serper API key.

Running Models Locally

This segment focuses on running models locally by downloading Olama and specific models like Mistol while adjusting base URLs and integrating necessary API keys.

Local Model Execution

• The speaker demonstrates downloading Olama and specific models like Mistol locally for efficient model execution on personal machines.

• By replacing base URLs with Olama served endpoints and integrating appropriate API keys like Olama's key for third-party APIs such as OpenAI embeddings ensures smooth local model operation.

New Section

In this section, the speaker discusses the potential for local processing in a project and the necessity of web-based searches for certain functionalities.

Local Processing vs. Web-Based Searches

• The project aims to run locally for speed but may transition to local embeddings with sufficient momentum, retaining web-based search as the only online component.

• After successful downloading, ensuring proper serving is essential, involving replacing base URLs and API keys.

• Saving configurations like using "olama" as an API key and restarting servers are crucial steps in setting up the system.

New Section

This part delves into troubleshooting efforts related to setting up the base URL and API key configurations.

Troubleshooting Base URL and API Key

• The speaker encounters issues with configuring the base URL (Local Host:11434/slv1) and API key (olama), seeking assistance or identifying potential errors.

• Adjustments made to host settings (000000 AMA serve) due to recent issues with "olama," highlighting the need for specific configurations.

New Section

Here, adjustments are made in code-wide searches for model references to ensure smooth operation of the system.

Code-Wide Search Optimization

• Conducting code-wide searches for model references reveals discrepancies requiring corrections like replacing "mixol" with "mistol" for improved functionality.