llm-answer-engine

Perplexity Inspired Answer Engine

open-sourceagent-frameworks
5.0k
Stars
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Releases (6m)

Star Growth

4.9k5.0k5.1kMar 27Apr 1

Overview

A sophisticated answer engine inspired by Perplexity that combines multiple AI services to deliver comprehensive search results. Built with Next.js and the Vercel AI SDK, it integrates Groq's processing power, Mistral AI's Mixtral model, Langchain.JS for text operations, Brave Search for privacy-focused web results, Serper API, and OpenAI. The engine returns not just text answers, but also relevant sources, images, videos, and intelligent follow-up questions based on user queries. With over 5,000 GitHub stars and comprehensive YouTube tutorials, it serves as an excellent foundation for developers building natural language processing and search applications. The tool emphasizes privacy through Brave Search integration while maintaining the sophisticated multi-modal response capabilities that make modern AI search engines so effective. Its modular architecture allows developers to understand and customize how different AI services work together to create intelligent, contextual responses.

Deep Analysis

Key Differentiator

vs Perplexity / SearchGPT: open-source Perplexity clone built on Next.js + Vercel AI SDK — combines Brave/Serper search, Groq/OpenAI generation, and function calling (Maps, Shopping, Stocks) in a deployable package

Capabilities

  • Perplexity-style answer engine with streaming responses
  • Multi-source retrieval (web, images, videos) with citations
  • Follow-up question generation for deeper exploration
  • Function calling for Maps, Shopping, Stocks, Spotify (beta)
  • Semantic caching for faster repeated queries
  • Rate limiting via Upstash Redis
  • RAG with configurable search providers

🔗 Integrations

Groq (Mixtral-8x7b)OpenAIOllamaBrave SearchSerper APIVercel AI SDKLangChain.jsUpstash RedisCheerio

Best For

  • Building custom Perplexity-style AI search experiences
  • Learning RAG + streaming + search integration patterns
  • Prototyping answer engines with multi-source retrieval

Not Ideal For

  • Production search engines requiring high reliability
  • Simple chatbot applications without search
  • Teams wanting a hosted solution without infrastructure management

Languages

TypeScript (Next.js)

Deployment

Vercel (one-click)Dockerlocal (npm/bun)

Known Limitations

  • Ollama follow-up questions not yet supported
  • Document upload/RAG incomplete
  • Settings not configurable from UI
  • Requires multiple API keys for full functionality

Pros

  • + Comprehensive multi-modal results including sources, answers, images, videos, and follow-up questions in a single query response
  • + Privacy-focused architecture using Brave Search for web results while maintaining advanced AI capabilities
  • + Strong developer support with extensive YouTube tutorials and active community (5,000+ GitHub stars)

Cons

  • - Complex setup requiring multiple API keys and service configurations (Groq, Mistral, OpenAI, Serper, Brave Search)
  • - Potentially high operational costs due to multiple paid AI and search services
  • - Heavy dependency stack that may require ongoing maintenance as services update their APIs

Use Cases

  • Building AI-powered research platforms that need comprehensive, multi-format answers with source attribution
  • Creating privacy-focused search applications for educational or enterprise environments
  • Developing prototypes for next-generation search engines with conversational AI capabilities

Getting Started

1. Clone the repository and install dependencies with npm install 2. Configure API keys for Groq, Mistral AI, OpenAI, Brave Search, and Serper in environment variables 3. Run the development server with npm run dev and test queries through the web interface

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