Perplexica/.github/copilot-instructions.md

GitHub Copilot Instructions for Perplexica

This file provides context and guidance for GitHub Copilot when working with the Perplexica codebase.

Project Overview

Perplexica is an open-source AI-powered search engine that uses advanced machine learning to provide intelligent search results. It combines web search capabilities with LLM-based processing to understand and answer user questions, similar to Perplexity AI but fully open source.

Key Components

• Frontend: Next.js application with React components (in /src/components and /src/app)
• Backend Logic: Node.js backend with API routes (in /src/app/api) and library code (in /src/lib)
• Search Engine: Uses SearXNG as a metadata search engine
• LLM Integration: Supports multiple models including OpenAI, Anthropic, Groq, Ollama (local models)
• Database: SQLite database managed with Drizzle ORM

Architecture

The system works through these main steps:

• User submits a query
• The system determines if web search is needed
• If needed, it searches the web using SearXNG
• Results are ranked using embedding-based similarity search
• LLMs are used to generate a comprehensive response with cited sources

Key Technologies

• Frontend: React, Next.js, Tailwind CSS
• Backend: Node.js
• Database: SQLite with Drizzle ORM
• AI/ML: LangChain for orchestration, various LLM providers
• Search: SearXNG integration
• Embedding Models: For re-ranking search results

Project Structure

• /src/app: Next.js app directory with page components and API routes
• /src/components: Reusable UI components
• /src/lib: Backend functionality
  • /lib/search: Search functionality and meta search agent
  • /lib/db: Database schema and operations
  • /lib/providers: LLM and embedding model integrations
  • /lib/prompts: Prompt templates for LLMs
  • /lib/chains: LangChain chains for various operations

Focus Modes

Perplexica supports multiple specialized search modes:

• All Mode: General web search
• Local Research Mode: Research and interact with local files with citations
• Chat Mode: Have a creative conversation
• Academic Search Mode: For academic research
• YouTube Search Mode: For video content
• Wolfram Alpha Search Mode: For calculations and data analysis
• Reddit Search Mode: For community discussions

Development Workflow

• Use npm run dev for local development
• Format code with npm run format:write before committing
• Database migrations: npm run db:push
• Build for production: npm run build
• Start production server: npm run start

Configuration

The application uses a config.toml file (created from sample.config.toml) for configuration, including:

• API keys for various LLM providers
• Database settings
• Search engine configuration
• Similarity measure settings

Common Tasks

When working on this codebase, you might need to:

• Add new API endpoints in /src/app/api
• Modify UI components in /src/components
• Extend search functionality in /src/lib/search
• Add new LLM providers in /src/lib/providers
• Update database schema in /src/lib/db/schema.ts
• Create new prompt templates in /src/lib/prompts
• Build new chains in /src/lib/chains

AI Behavior

• Avoid conciliatory language
• It is not necessary to apologize
• If you don't know the answer, ask for clarification
• Do not add additional packages or dependencies unless explicitly requested
• Only make changes to the code that are relevant to the task at hand