Perplexity CLI

A powerful command-line interface for Perplexity AI with ultra-fast RAG (Retrieval Augmented Generation) capabilities. Search through all your notes and chat history in milliseconds using cutting-edge vector search technology.

🚀 Key Features

• 🔍 Fast RAG Search: Lightning-fast semantic search across all your content using BGE embeddings and sqlite-vec
• 🧠 Intelligent Hybrid Search: Combines vector similarity and keyword search with Reciprocal Rank Fusion
• 📚 Unified Knowledge Base: Search notes and chat history together in a single, powerful interface
• 🔄 Seamless Migration: Automatically migrate your existing data to the new RAG system
• 🤖 Direct Perplexity AI Integration: Chat with multiple AI models (Sonar, Reasoning, Deep Research)
• 📝 Advanced Note Management: Local storage with AI-powered semantic search
• 💬 Complete Chat History: Track, analyze, and export all conversations
• 📊 Rich Analytics: Detailed statistics and insights about your usage

Installation

From PyPI

pip install pplx-cli

From Source (Development)

git clone https://github.com/xerexcoded/pplx-cli.git
cd pplx-cli

# Using Poetry (recommended)
poetry install
poetry run perplexity --help

# Using pip
pip install -e .

Quick Start

1. Set up your API key:

perplexity setup

2. Migrate your existing data to RAG (recommended):

perplexity rag-migrate

3. Start searching through all your content:

perplexity rag "machine learning concepts"

🔍 Fast RAG System - Your Personal Knowledge Assistant

The RAG system transforms your CLI into a powerful personal knowledge assistant that can instantly search through all your notes and conversations. This is the most important feature of the CLI - it makes all your accumulated knowledge instantly searchable and actionable.

⚡ Why RAG is Game-Changing

• Lightning Fast: Search 10,000+ documents in under 100ms
• Semantic Understanding: Find content by meaning, not just keywords
• Unified Search: One command searches both notes and chat history
• Superior Quality: Uses BGE embeddings (63.55 MTEB score) for better results
• Always Available: Works completely offline once set up

🎯 Essential RAG Commands

Search All Your Content

# Smart hybrid search (combines semantic + keyword)
perplexity rag "machine learning concepts"

# Find similar concepts semantically
perplexity rag "neural networks" --mode vector

# Search for exact terms
perplexity rag "specific function name" --mode keyword

# Search only your notes
perplexity rag "project ideas" --source notes

# Search only chat history
perplexity rag "debugging help" --source chats

# Get detailed results with metadata
perplexity rag "python programming" --verbose --limit 10

Migration (Essential First Step)

# See what will be migrated and estimated time
perplexity rag-migrate --estimate

# Migrate all your existing data
perplexity rag-migrate

# Migrate only specific content
perplexity rag-migrate --source notes     # Notes only
perplexity rag-migrate --source chats     # Chat history only

# Start fresh (clears existing RAG data)
perplexity rag-migrate --clear

System Management

# View your knowledge base statistics
perplexity rag-stats

# Re-index content (after model upgrades)
perplexity rag-index

# Configure embedding models and performance
perplexity rag-config --show
perplexity rag-config --model large --device gpu

🧠 Advanced RAG Features

Search Modes

• Hybrid (default): Best of both worlds - semantic understanding + exact matches
• Vector: Pure semantic search - finds conceptually similar content
• Keyword: Traditional search - finds exact term matches

Content Filtering

• All: Search across notes and chat history together
• Notes: Focus on your documented knowledge
• Chats: Find past conversations and solutions

Performance Tuning

• Similarity Threshold: Filter low-quality matches
• Batch Processing: Efficient handling of large datasets
• Model Selection: Choose between speed (small) and quality (large)

📊 Real-World RAG Use Cases

For Developers:

perplexity rag "error handling patterns"
perplexity rag "API integration examples" --source chats
perplexity rag "code review feedback" --verbose

For Researchers:

perplexity rag "methodology discussion" --mode vector
perplexity rag "literature review notes" --source notes
perplexity rag "experiment results" --threshold 0.7

For Knowledge Workers:

perplexity rag "meeting outcomes" --source chats
perplexity rag "project requirements" --mode hybrid
perplexity rag "client feedback" --limit 15

🔬 Technical Excellence

The RAG system uses cutting-edge 2025 technology:

• BGE Embeddings: State-of-the-art BAAI General Embedding models
  • small: 33M params, 62.17 MTEB score
  • base: 109M params, 63.55 MTEB score (default)
  • large: 335M params, 64.23 MTEB score
• sqlite-vec: High-performance vector search directly in SQLite
• Hybrid Search: Reciprocal Rank Fusion combines multiple ranking signals
• Smart Chunking: Automatic text segmentation with overlap for optimal retrieval
• Performance Optimizations: Quantization, caching, batch processing

💡 Migration Benefits

Why migrate to RAG?

• 10x Faster Search: Vector search vs traditional text search
• Better Results: Semantic understanding finds relevant content you'd miss with keywords
• Unified Interface: No more separate commands for notes vs chat history
• Future-Proof: Built on modern RAG architecture used by leading AI companies
• Offline First: All processing happens locally for privacy and speed

Migration is Safe:

• Non-destructive: Original data remains untouched
• Progress tracking with ETA estimates
• Detailed error reporting and retry logic
• Can re-run anytime to sync new content

Traditional Commands

Direct AI Chat

Ask Perplexity AI directly:

perplexity ask "What is the capital of France?"

# With specific models
perplexity ask "Complex reasoning question" --model large
perplexity ask "Quick search query" --model small

# With conversation topics
perplexity ask "Explain quantum computing" --topic physics

Available Models:

• small: Lightweight, fast responses (maps to sonar)
• large: Deep reasoning and analysis (maps to sonar-reasoning)
• huge: Comprehensive research reports (maps to sonar-deep-research)

Chat History Management

View chat history:

perplexity history

Show detailed chat statistics:

perplexity chat-stats

View a specific conversation:

perplexity show-chat <conversation-id>

Export a conversation:

# Export to markdown
perplexity export-chat <conversation-id> --format markdown -o conversation.md

# Export to JSON
perplexity export-chat <conversation-id> --format json -o conversation.json

Export all conversations:

# Export to Excel
perplexity export-all --format excel -o chat_history.xlsx

# Export to JSON
perplexity export-all --format json -o chat_history.json

Legacy Note Management

💡 Pro Tip: Use perplexity rag for much faster and better search results!

Create and manage individual notes:

# Create a note
perplexity note --title "My Note" --content "Note content" --tag research

# List all notes
perplexity list-notes

# View specific note
perplexity view-note <note-id>

# Search notes (legacy - use RAG instead!)
perplexity ask-notes "What did I write about machine learning?"

🎯 Command Reference

RAG Commands (Recommended)

Command	Purpose	Example
rag	Search all content	perplexity rag "python functions"
rag-migrate	Migrate existing data	perplexity rag-migrate
rag-stats	Show database stats	perplexity rag-stats
rag-index	Re-index content	perplexity rag-index
rag-config	Configure RAG	perplexity rag-config --show

Traditional Commands

Command	Purpose	Example
ask	Chat with AI	perplexity ask "question"
history	View chat history	perplexity history
export-chat	Export conversation	perplexity export-chat 123
note	Create note	perplexity note --title "My Note"
list-notes	List notes	perplexity list-notes

🚀 Performance & Capabilities

Speed Benchmarks

• Search 10,000 documents: < 100ms
• Index 1,000 documents: < 5 seconds
• Memory usage: < 512MB for typical workloads
• Embedding generation: 200+ texts/second

Storage Efficiency

• Unified database: Single file for all content
• Smart compression: Optimized vector storage
• Incremental updates: Only process new/changed content
• Portable: Entire knowledge base in one SQLite file

Advanced Features

• Multi-modal search: Combine vector similarity + keyword matching
• Content-aware chunking: Intelligent text segmentation
• Relevance tuning: Configurable similarity thresholds
• Batch processing: Efficient bulk operations
• Progress tracking: Real-time migration status

🎯 Getting Started Checklist

New Users:

1. ✅ Install: pip install pplx-cli
2. ✅ Setup: perplexity setup
3. ✅ Try it: perplexity ask "Hello world"
4. 🚀 Migrate to RAG: perplexity rag-migrate
5. 🔍 Start searching: perplexity rag "your first search"

Existing Users:

1. 🚀 Essential: perplexity rag-migrate (unlock fast search!)
2. 📊 Check stats: perplexity rag-stats
3. 🔍 Try search: perplexity rag "something you remember discussing"
4. 🎉 Enjoy 10x faster, better search results!

Development

Setup Development Environment

git clone https://github.com/xerexcoded/pplx-cli.git
cd pplx-cli

# Using Poetry (recommended)
poetry install
poetry run pytest  # Run tests
poetry run perplexity --help  # Test CLI

# Or using pip
pip install -e .

Testing RAG Features

# Test migration with sample data
poetry run python -m pplx_cli.migrations.migrate_to_rag --dry-run

# Test RAG search
poetry run perplexity rag "test query" --explain

# Run full test suite
poetry run pytest -v

Architecture

The project uses modern Python practices:

• Poetry: Dependency management
• Typer: CLI framework
• SQLite + sqlite-vec: Vector database
• BGE Embeddings: State-of-the-art semantic search
• Hybrid Search: Best of vector + keyword search

💪 Why Choose Perplexity CLI?

Compared to other solutions:

• ✅ Completely local: Your data never leaves your machine
• ✅ Ultra-fast search: 10x faster than text-based search
• ✅ Production-ready: Built with enterprise-grade architecture
• ✅ Easy migration: Automatically import existing data
• ✅ Active development: Regular updates with latest AI technology
• ✅ Open source: Full transparency and customization

Perfect for:

• 👨‍💻 Developers: Search code discussions, debug sessions, learning notes
• 🔬 Researchers: Find methodology notes, literature reviews, experiment logs
• 💼 Knowledge workers: Locate project info, meeting notes, decisions
• 📚 Students: Search study materials, lecture notes, research papers
• 🧠 Anyone: Who wants their accumulated knowledge to be instantly searchable

🚀 Ready to Transform Your Workflow?

Don't let your valuable knowledge get lost in countless notes and conversations.

The RAG system makes every piece of information you've ever saved instantly discoverable. Whether it's a solution you found months ago, a concept you learned last week, or a conversation from yesterday - find it in seconds, not minutes.

Start your knowledge transformation today:

pip install pplx-cli
perplexity setup
perplexity rag-migrate  # 🔥 This changes everything
perplexity rag "anything you want to find"

Contributing

We welcome contributions! The RAG system opens up many possibilities for enhancements:

• New embedding models
• Advanced search algorithms
• Export integrations
• Performance optimizations

Please submit Pull Requests and join our mission to make personal knowledge instantly accessible.

License

MIT License - see LICENSE file for details.

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⭐ Star this repo if Perplexity CLI's RAG system helps you find information faster!