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https://github.com/ItzCrazyKns/Perplexica.git
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95 lines
3.5 KiB
Markdown
95 lines
3.5 KiB
Markdown
# GitHub Copilot Instructions for Perplexica
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This file provides context and guidance for GitHub Copilot when working with the Perplexica codebase.
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## Project Overview
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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.
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## Key Components
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- **Frontend**: Next.js application with React components (in `/src/components` and `/src/app`)
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- **Backend Logic**: Node.js backend with API routes (in `/src/app/api`) and library code (in `/src/lib`)
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- **Search Engine**: Uses SearXNG as a metadata search engine
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- **LLM Integration**: Supports multiple models including OpenAI, Anthropic, Groq, Ollama (local models)
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- **Database**: SQLite database managed with Drizzle ORM
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## Architecture
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The system works through these main steps:
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- User submits a query
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- The system determines if web search is needed
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- If needed, it searches the web using SearXNG
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- Results are ranked using embedding-based similarity search
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- LLMs are used to generate a comprehensive response with cited sources
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## Key Technologies
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- **Frontend**: React, Next.js, Tailwind CSS
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- **Backend**: Node.js
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- **Database**: SQLite with Drizzle ORM
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- **AI/ML**: LangChain for orchestration, various LLM providers
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- **Search**: SearXNG integration
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- **Embedding Models**: For re-ranking search results
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## Project Structure
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- `/src/app`: Next.js app directory with page components and API routes
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- `/src/components`: Reusable UI components
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- `/src/lib`: Backend functionality
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- `/lib/search`: Search functionality and meta search agent
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- `/lib/db`: Database schema and operations
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- `/lib/providers`: LLM and embedding model integrations
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- `/lib/prompts`: Prompt templates for LLMs
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- `/lib/chains`: LangChain chains for various operations
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## Focus Modes
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Perplexica supports multiple specialized search modes:
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- All Mode: General web search
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- Local Research Mode: Research and interact with local files with citations
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- Chat Mode: Have a creative conversation
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- Academic Search Mode: For academic research
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- YouTube Search Mode: For video content
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- Wolfram Alpha Search Mode: For calculations and data analysis
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- Reddit Search Mode: For community discussions
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## Development Workflow
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- Use `npm run dev` for local development
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- Format code with `npm run format:write` before committing
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- Database migrations: `npm run db:push`
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- Build for production: `npm run build`
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- Start production server: `npm run start`
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## Configuration
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The application uses a `config.toml` file (created from `sample.config.toml`) for configuration, including:
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- API keys for various LLM providers
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- Database settings
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- Search engine configuration
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- Similarity measure settings
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## Common Tasks
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When working on this codebase, you might need to:
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- Add new API endpoints in `/src/app/api`
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- Modify UI components in `/src/components`
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- Extend search functionality in `/src/lib/search`
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- Add new LLM providers in `/src/lib/providers`
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- Update database schema in `/src/lib/db/schema.ts`
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- Create new prompt templates in `/src/lib/prompts`
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- Build new chains in `/src/lib/chains`
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## AI Behavior
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- Avoid conciliatory language
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- It is not necessary to apologize
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- If you don't know the answer, ask for clarification
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- Do not add additional packages or dependencies unless explicitly requested
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- Only make changes to the code that are relevant to the task at hand
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