autollama

autollama

<div align="center"> <img src="assets/autollama-logo.png" alt="AutoLlama Logo" width="200"/> <h1>The Context-Aware RAG Framework</h1> <h2><em>Your Documents Have Context. Now Your RAG Does Too.</em></h2> [](https://github.com/autollama/autollama/releases/latest) [](https://www.npmjs.com/package/autollama) [](https://docker.com) [](LICENSE) </div> **Context isn't a nice-to-have—it's the difference between information and understanding.** For too long, RAG has been about finding chunks, not understanding documents. AutoLlama changes that. Built on Anthropic's breakthrough contextual retrieval methodology, it's the first JavaScript-first RAG framework that actually comprehends your documents the way humans do. **It's like RAG finally learned how to read.** ## 📋 Table of Contents - [🚀 Quick Start (2 Minutes)](#-quick-start-2-minutes) - [🛠️ Installation Methods](#️-installation-methods) - [NPX Installation](#-npx-installation-early-access---v006) - [Docker Installation](#-docker-installation-zero-configuration) - [Native Installation](#-native-installation-maximum-control) - [⚙️ Configuration](#configuration) - [🏗️ Architecture](#architecture) - [📊 Experience AutoLlama](#experience-the-difference) - [🖼️ Screenshots](#screenshots---see-autollama-in-action) - [🔗 API Documentation](#api-documentation) - [🔧 Development](#development) - [🧪 The Science Behind Context](#the-science-behind-context) - [🚀 Deployment](#deployment) - [🚨 Troubleshooting](#troubleshooting) - [🆕 What's New in v3.0.3](#whats-new-in-v303) - [📞 Support](#support) ## Why Context Changes Everything **Traditional RAG**: "Here are 5 chunks that mention 'machine learning'" **AutoLlama**: "Here's how machine learning evolves through this research paper, building from foundational concepts in Chapter 2 to advanced applications in Chapter 7" See the difference? That's context at work. ## What Makes AutoLlama Revolutionary ### 🧠 **Anthropic's Contextual Retrieval** Stop getting random, disconnected chunks. AutoLlama implements Anthropic's breakthrough methodology that delivers **60% better accuracy** by understanding where each piece of information fits in the larger narrative. ### 🚀 **One-Command Setup** No more wrestling with complex configurations. Get started instantly: - **Global CLI**: `npm install -g autollama && autollama init` (2-minute setup) - **NPX**: `npx autollama init my-app` (instant, no installation) - **Docker**: `docker compose up -d` (60-second deploy) - **Native**: Clone and `npm run dev` (development mode) ### ⚡ **JavaScript-First Architecture** Built for developers who want power without pain. Full-stack JavaScript with multiple deployment modes: - **Local Development**: SQLite + embedded services - **Hybrid**: Mix of local and cloud services - **Production**: PostgreSQL + enterprise features ### 🔓 **Open Source Freedom** Your documents, your infrastructure, your control. No vendor lock-in, no usage limits, no monthly subscriptions. Just pure, customizable technology. ### 📊 **Real-Time Intelligence** Watch your documents transform from static files into living, searchable knowledge with real-time processing updates and interactive visualizations. ### 🔌 **Native OpenWebUI Integration** Chat with your documents instantly. Built-in RAG pipeline that OpenWebUI automatically discovers—no complex setup required. --- ## 🛠️ Installation Methods ## 🚀 Quick Start (2 Minutes) The fastest way to get AutoLlama running: ```bash # Install globally npm install -g autollama # Initialize new project with guided setup autollama init my-rag-project # Start your RAG framework autollama start ``` **That's it!** Open http://localhost:8080 and start uploading documents. ### Alternative Installation Methods ```bash # NPX (no global installation) npx autollama@latest init my-project # Docker (traditional method) git clone https://github.com/autollama/autollama.git cd autollama && docker compose up -d ``` --- ## 🛠️ CLI Commands AutoLlama includes a full-featured CLI for project management: ```bash # Project initialization autollama init [project-name] # Create new project with guided setup autollama doctor # Diagnose system and configuration issues # Service management autollama start # Start all services autollama stop # Stop services gracefully autollama status # Show service status and health # Docker shortcuts autollama docker:up # Start Docker containers autollama docker:down # Stop Docker containers ``` --- ## Prerequisites ### For NPM/CLI Installation - **Node.js**: 16+ (check with `node --version`) - **NPM**: 7+ (check with `npm --version`) - **Memory**: 4GB+ RAM recommended - **OpenAI API Key**: Get from https://platform.openai.com/api-keys ### For Docker Installation (Optional) - **Docker**: 20+ with Docker Compose - **Memory**: 8GB+ RAM recommended for full containerized setup ### Required Software (Docker Method Only) #### 1. Docker & Docker Compose Installation **For Ubuntu/Debian:** ```bash # Update package index sudo apt update # Install basic dependencies sudo apt install curl gnupg apt-transport-https ca-certificates lsb-release # Add Docker's official GPG key curl -fsSL https://download.docker.com/linux/debian/gpg | sudo gpg --dearmor -o /usr/share/keyrings/docker-archive-keyring.gpg # Add Docker repository echo "deb [arch=$(dpkg --print-architecture) signed-by=/usr/share/keyrings/docker-archive-keyring.gpg] https://download.docker.com/linux/debian $(lsb_release -cs) stable" | sudo tee /etc/apt/sources.list.d/docker.list > /dev/null # Update package index sudo apt update # Install Docker sudo apt install docker-ce docker-ce-cli containerd.io docker-compose-plugin # Add your user to docker group sudo usermod -aG docker $USER # Start and enable Docker sudo systemctl start docker sudo systemctl enable docker ``` **For CentOS/RHEL/Fedora:** ```bash # Install Docker sudo dnf install docker docker-compose # Start and enable Docker sudo systemctl start docker sudo systemctl enable docker # Add user to docker group sudo usermod -aG docker $USER ``` **For macOS:** Download Docker Desktop from https://www.docker.com/products/docker-desktop/ **After installation:** ```bash # Log out and back in, then verify installation docker --version docker compose version # Test Docker permissions docker ps ``` **Troubleshooting Docker Permissions:** If you encounter "permission denied" errors: ```bash # Refresh group membership without logging out newgrp docker # Or use sudo temporarily sudo docker compose up -d ``` #### 2. Tailscale Installation (Optional but Recommended) AutoLlama includes Tailscale integration for secure networking. Install Tailscale before running the containers: **Get your install script:** 1. Visit https://login.tailscale.com/admin/machines/new-linux 2. Copy your personalized install command 3. Run it (example format): ```bash curl -fsSL https://tailscale.com/install.sh | sh && sudo tailscale up --auth-key=tskey-auth-YOUR-KEY-HERE ``` **After Tailscale installation:** ```bash # Create required config directory mkdir -p ~/.config # Create tsauthkey file (required for Docker mount) touch ~/.config/tsauthkey chmod 600 ~/.config/tsauthkey # Verify Tailscale is running sudo tailscale status ``` ### Required Accounts & API Keys Before starting, obtain the following: - **OpenAI API key** from https://platform.openai.com/api-keys - **Qdrant Cloud account** from https://cloud.qdrant.io (or set up local Qdrant) - **PostgreSQL database** (can use cloud providers or local setup) ## Quick Start **Ready to experience contextual RAG? Choose your adventure:** ### 🦙 NPX Installation (Latest - v3.0.7) **New professional setup wizard experience:** ```bash # Create new project with guided setup npx autollama@latest init my-rag-app # Follow the 4-stage setup wizard, then: cd my-rag-app npx autollama start # 🌐 Your AutoLlama: http://localhost:8080 ``` **Features**: Interactive configuration, system validation, automated setup, state management for resume capability. **Perfect for**: Quick prototyping and testing the new setup experience. ### 🐳 Docker Installation (Zero-Configuration) **AutoLlama v3.0 features automatic database setup and migrations!** ```bash git clone https://github.com/autollama/autollama.git autollama cd autollama cp example.env .env # Add your OPENAI_API_KEY to .env docker compose up -d # That's it! No manual database fixes required! 🎉 ``` **Auto-Migration Features:** - ✅ **Automatic Schema Setup**: Creates all tables and indexes automatically - ✅ **PostgreSQL Wait Logic**: Waits for database readiness before starting - ✅ **Broken Installation Recovery**: Detects and fixes missing schema elements - ✅ **Extension Management**: Enables required PostgreSQL extensions - ✅ **Migration Tracking**: Prevents duplicate migrations with smart detection **Perfect for**: Production deployment, team development, enterprise use. ### 🔧 Native Installation (Maximum Control) **For developers who want full control:** ```bash git clone https://github.com/autollama/autollama.git autollama cd autollama npm install npm run setup npm run dev ``` **Perfect for**: Custom deployments, contributing to AutoLlama, advanced configurations. ## Configuration ### Environment Variables Key configuration options in `.env`: ```bash # AI Services OPENAI_API_KEY=your_openai_api_key_here # Database Configuration DATABASE_URL=postgresql://user:password@host:5432/autollama QDRANT_URL=https://your-cluster.qdrant.io QDRANT_API_KEY=your_qdrant_api_key # Contextual Embeddings (Enhanced Processing) ENABLE_CONTEXTUAL_EMBEDDINGS=true CONTEXTUAL_EMBEDDING_MODEL=gpt-4o-mini # Service Configuration SERVICE_NAME=autollama DOMAIN=autollama.io ``` ### Web-Based Settings Configure advanced options through the settings interface: - **Connections Tab**: AI provider API keys and database connections - **Processing Tab**: Chunking parameters, AI models, and performance settings - **OpenWebUI Tab**: RAG pipeline configuration and API keys - **System Tab**: UI preferences, themes, and debug options ## Architecture ``` ┌─────────────────┐ ┌──────────────┐ ┌───────────────┐ │ Web Interface │ │ API Server │ │ Databases │ │ (React/Nginx) │◄──►│ (Node.js) │◄──►│ PostgreSQL + │ │ Port 8080 │ │ Port 3001 │ │ Qdrant Vector │ └─────────────────┘ └──────────────┘ └───────────────┘ │ │ │ │ ┌────────▼────────┐ │ │ │ Processing │ │ │ │ Pipeline │ │ │ │ │ │ └──────────────┤ • Content Fetch │───────────┘ │ • AI Analysis │ │ • Chunking │ │ • Embedding │ │ • Storage │ └─────────────────┘ ``` ### Core Services - **Frontend**: React application with Tailwind CSS - **API Server**: Node.js/Express with comprehensive middleware - **Database**: PostgreSQL for structured metadata storage - **Vector Database**: Qdrant for semantic search and embeddings - **BM25 Service**: Fast lexical search for hybrid retrieval - **Background Queue**: Async processing with progress tracking ## Experience the Difference ### Transform Any Document Into Intelligent Knowledge **Web Interface** (Recommended for first-time users): 1. Visit http://localhost:8080 and drop in a PDF or paste a URL 2. Watch the real-time Flow View as AutoLlama processes your document 3. See contextual analysis, sentiment mapping, and entity extraction in action 4. Search your content with natural language and get meaningful, connected results **API Integration** (For developers): ```bash # Process any URL with contextual awareness curl -X POST http://localhost:8080/api/process-url-stream \ -H "Content-Type: application/json" \ -d '{"url":"https://example.com/research-paper.pdf"}' -N # Upload and intelligently process files curl -X POST http://localhost:8080/api/process-file-stream \ -F "[email protected]" -N ``` ### Search That Actually Understands **Contextual Search:** ```bash curl "http://localhost:8080/api/search?q=how+does+the+methodology+evolve+throughout+the+paper" ``` *Returns: Connected insights showing how research methods build and evolve across chapters* **Hybrid Intelligence (Vector + BM25):** ```bash curl "http://localhost:8080/api/search/grouped?q=machine+learning+performance+metrics" ``` *Returns: Grouped results showing performance discussions in context of experimental design* ### Chat With Your Documents **OpenWebUI Integration** - Because your documents deserve a conversation: 1. **One-Click Setup**: AutoLlama's pipeline auto-discovers in OpenWebUI 2. **Intelligent Responses**: Ask "What are the key findings?" and get document-aware answers 3. **Contextual Memory**: Follow-up questions understand document structure and narrative flow ```bash # Your OpenWebUI configuration: Pipeline URL: http://autollama-on-hstgr:3001/api/openwebui API Key: 0p3n-w3bu! ``` **Try asking**: "How do the conclusions in Chapter 5 relate to the hypotheses presented in Chapter 1?" ## Screenshots - See AutoLlama in Action Real screenshots from the AutoLlama platform—experience the interface that makes contextual RAG effortless. ### 📊 Document Processing Overview  Complete document processing analytics including AI analysis results (content type, technical level, sentiment), content statistics (character counts, processing time, storage size), and comprehensive topic/entity extraction with clickable tags. ### 📊 Main Dashboard  Comprehensive document management with interactive processed documents, real-time processing queue, and intuitive search interface. ### 📋 Document Overview  Dedicated document overview showing processing statistics, chunk completion status, and progress tracking with total chunks, completed items, and processing status at a glance. ### 🧠 Document Analysis Dashboard  Advanced document intelligence with topic distribution analysis, processing quality metrics including contextual enhancement percentages, entity recognition scores, and sentiment analysis results. ### 🔬 Chunk Inspector - Embedding Analysis  Deep dive into individual chunk embeddings with detailed vector analysis, embedding model information, vector statistics, and similarity analysis tools for finding related chunks. ### 🔍 Vector Analysis - Heatmap Visualization  Interactive heatmap visualization of embedding vectors showing positive/negative value distribution across the 1536-dimensional space with color-coded intensity mapping. ### 📊 Vector Analysis - Statistical Breakdown  Comprehensive statistical analysis of embedding vectors including dimensions, magnitude, value distribution (positive/negative/zero counts), range analysis, and sparsity metrics. ### 🔢 Vector Analysis - Raw Data Access  Direct access to raw vector values with copy-to-clipboard functionality, allowing developers to inspect and analyze the actual numerical embeddings for debugging and research. ### 🧩 Chunks Grid View  Visual representation of processed chunks with completion status and easy navigation to individual chunk details. ### ⚡ Real-Time Processing  Processing queue showing Llama actively processing items with real-time status updates. ### 🔍 AI Analysis Panel  Comprehensive AI-powered document analysis showing processing progress, content type detection, sentiment analysis, technical level assessment, and detailed topic/entity extraction with contextual embeddings support. ### 🚀 Ultra-Fast Contextual Search  Lightning-fast BM25 search across contextually enhanced content, tags, entities, sentiment, and AI-generated metadata - find exactly what you need instantly. ### 🔗 OpenWebUI Integration #### One-Click Setup  Configure your pipeline with just a URL and API key. AutoLlama handles the rest. #### Native Integration  AutoLlama appears as 'autollama-rag' in your model dropdown. Switch seamlessly between AI models. #### Intelligent Responses  Get precise answers from your documents with contextual understanding and follow-up suggestions. ### 🎯 What You're Seeing - **✓ Real-Time Processing**: Watch documents transform into intelligent chunks - **✓ Visual Analytics**: Comprehensive insights and quality metrics - **✓ Contextual Intelligence**: Every chunk remembers its place in the story - **✓ Enterprise Ready**: Production interface built for scale ## API Documentation ### Key Endpoints - `GET /api/health` - System health check - `POST /api/process-url-stream` - Process URL with real-time updates - `POST /api/process-file-stream` - Process file upload with progress - `GET /api/search` - Semantic search across processed content - `GET /api/documents` - List processed documents - `GET /api/settings` - Retrieve system configuration - `POST /api/settings` - Update system settings ### Interactive Documentation Visit `/docs` for complete API documentation with interactive examples. ## Development ### Local Development ```bash # Frontend development (hot reload) cd config/react-frontend npm run dev # API development (with nodemon) cd api npm run dev # Build and restart specific services docker compose build autollama-api --no-cache docker compose up -d autollama-api ``` ### Testing ```bash # Run API tests cd api npm test # Run specific test suites npm run test:unit npm run test:integration npm run test:performance ``` ### Performance Monitoring **Health Endpoints:** - `/health` - Basic service health - `/api/health/comprehensive` - Detailed system status - `/api/knowledge-base/stats` - Database statistics ## The Science Behind Context **This isn't just another RAG implementation.** AutoLlama is built on cutting-edge research from Anthropic that solves the fundamental problem plaguing traditional RAG systems. ### The Problem with Traditional RAG Most RAG systems treat documents like a bag of disconnected chunks. They find text that matches your query but lose the narrative thread that makes information meaningful. ### AutoLlama's Contextual Breakthrough - **Document-Aware Processing**: Every chunk knows its place in the larger story - **Semantic Boundary Detection**: Respects natural content flow instead of arbitrary splits - **Hierarchical Understanding**: Maintains document structure and relationships - **60% Accuracy Improvement**: Proven performance gains over traditional chunking ### Real-World Impact Instead of getting fragments like "machine learning accuracy improved," you get the full picture: "The research team's machine learning model achieved 94% accuracy in Chapter 4's clinical trials, building on the 87% baseline established in Chapter 2's preliminary testing." **That's the power of context.** ## Why Developers Choose AutoLlama ### vs. LangChain/LlamaIndex **Them**: Complex Python ecosystems with endless configuration **AutoLlama**: JavaScript-first simplicity with enterprise power ### vs. Pinecone/Weaviate **Them**: Vendor lock-in with usage-based pricing **AutoLlama**: Your infrastructure, your control, zero ongoing costs ### vs. Traditional RAG **Them**: "Here are chunks that match your keywords" **AutoLlama**: "Here's how these concepts connect across your entire document" ### vs. Anthropic Claude (API only) **Them**: Pay per request with limited document context **AutoLlama**: Process unlimited documents with full contextual understanding **The result?** Developers ship faster, costs stay predictable, and results actually make sense. ## Deployment ### Production Deployment 1. **Environment Setup** ```bash cp docker-compose.yaml docker-compose.prod.yaml # Edit production configuration ``` 2. **SSL Configuration** ```bash # Configure nginx for HTTPS # Update domain settings in .env ``` 3. **Database Setup** ```bash # Run migrations docker exec autollama-api npm run migrate ``` 4. **Monitoring** ```bash # Check service health curl http://your-domain/health curl http://your-domain/api/health/comprehensive ``` ### Scaling Considerations - **Database**: PostgreSQL read replicas for scaling search queries - **Vector Storage**: Qdrant horizontal scaling for large document collections - **Processing**: Increase batch sizes and concurrent processing limits - **Caching**: Redis integration for frequently accessed embeddings ## Troubleshooting ### Docker Auto-Migration (v3.0+) **Check migration status:** ```bash # View migration logs docker compose logs autollama-api | grep "Migration" # Verify database schema docker exec autollama-postgres psql -U autollama -d autollama -c "\dt" # Check critical v2.3 columns docker exec autollama-postgres psql -U autollama -d autollama -c "SELECT column_name FROM information_schema.columns WHERE table_name = 'processed_content' AND column_name IN ('record_type', 'upload_source', 'updated_at');" ``` **Test API after migration:** ```bash curl http://localhost:8080/api/health # Should return "healthy" curl http://localhost:8080/api/documents # Should return JSON response ``` **Manual migration (emergency only):** ```bash # If auto-migration fails completely docker exec autollama-api node migrate-docker.js --auto --docker docker compose restart autollama-api ``` ### Installation Issues **Docker not found:** ```bash # Verify Docker installation which docker docker --version # If not installed, follow installation steps above ``` **Docker permission denied:** ```bash # Refresh group membership newgrp docker # Or add user to docker group and re-login sudo usermod -aG docker $USER # Log out and back in ``` **Missing tsauthkey file:** ```bash # Create required file mkdir -p ~/.config touch ~/.config/tsauthkey chmod 600 ~/.config/tsauthkey # Restart containers docker compose down && docker compose up -d ``` **Package installation errors (Debian/Ubuntu):** ```bash # Install missing dependencies sudo apt install curl gnupg apt-transport-https ca-certificates # Update package lists sudo apt update ``` ### Runtime Issues **Services not starting:** ```bash # Check logs docker compose logs -f autollama-api docker compose logs -f autollama # Verify configuration docker compose config ``` **Processing failures:** ```bash # Check API connectivity curl http://localhost:8080/api/health # Monitor processing logs docker compose logs -f autollama-api | grep "processing" ``` **Database connectivity:** ```bash # Test database connection docker exec autollama-api node -e " const { Pool } = require('pg'); const pool = new Pool({ connectionString: process.env.DATABASE_URL }); pool.query('SELECT NOW()').then(r => console.log('✓ Database connected:', r.rows[0])); " ``` ### Performance Issues - **Slow processing**: Adjust `CONTEXT_GENERATION_BATCH_SIZE` in settings - **High memory usage**: See Memory Optimization section below - **API timeouts**: Increase timeout values in nginx configuration ### Memory Optimization **Monitor memory usage**: ```bash # Check current memory status ./scripts/memory-monitor.sh # Container memory breakdown docker stats --no-stream ``` **Regular maintenance** (run weekly): ```bash # Clean Docker build cache and unused images docker system prune -f # For severe memory pressure: docker system prune -a -f ``` **Memory limits configured**: - PostgreSQL: 256MB (optimized for small datasets) - API Service: 1GB (Node.js with heap optimization) - Qdrant: 512MB (vector database with compression) - BM25: 256MB (Python text indexing) - Frontend: 128MB (nginx static files) **Total container limits**: 2.2GB maximum vs unlimited before ### Common Error Messages **Documents not appearing after processing**: Missing database column - see Database Schema Issues below **"HTTP 502: Bad Gateway" on file upload**: Nginx proxy misconfiguration - see Docker Networking Issues below **"bind source path does not exist"**: Missing tsauthkey file - create it as shown above **"unable to get image"**: Docker permission issue - run `newgrp docker` or use sudo **"Package not found"**: Update package lists with `sudo apt update` ### Docker Networking Issues **Problem**: File uploads fail with "HTTP 502: Bad Gateway" after container restart **Cause**: Nginx configuration using `localhost` instead of Docker service names **Solution**: 1. **Update nginx proxy configuration** in `config/react-frontend/nginx.conf`: ```bash # Change all instances of: proxy_pass http://localhost:3001/... # To: proxy_pass http://autollama-api:3001/... # Change BM25 service: proxy_pass http://localhost:3002/... # To: proxy_pass http://autollama-bm25:3002/... ``` 2. **Rebuild frontend container**: ```bash docker compose build autollama-frontend --no-cache docker compose up -d autollama-frontend ``` 3. **Verify fix**: ```bash curl http://localhost:8080/api/health # Should return: healthy ``` **Why this happens**: Docker containers communicate via service names (`autollama-api`) within the Docker network, not `localhost`. The nginx container can't reach the API container using `localhost:3001`. ### Database Schema Issues **Problem**: Processing completes successfully but documents don't appear on homepage **Cause**: Missing `upload_source` column in `processed_content` table **Solution**: ```bash # Add missing column to database docker exec autollama-api node -e " const { Pool } = require('pg'); const pool = new Pool({ connectionString: process.env.DATABASE_URL }); pool.query('ALTER TABLE processed_content ADD COLUMN IF NOT EXISTS upload_source VARCHAR(50) DEFAULT \\'user\\'') .then(r => { console.log('✅ Added upload_source column'); pool.end(); })" ``` **Verify fix**: ```bash # Check documents API returns data curl http://localhost:8080/api/documents # Should return document list instead of empty array ``` **Why this happens**: The `createDocumentRecord` function expects an `upload_source` column that may be missing from older database schemas. This causes silent failures where processing succeeds but documents aren't stored. ## What's New in v3.0.3 ### 🔍 **Search & AI Chat RAG Restored** ✅ **Fixed**: Search functionality completely restored - no more 0 results ✅ **Fixed**: AI Chat now shows `🔍 RAG ACTIVE` with document sources ✅ **Fixed**: BM25 indexing now automatic during document processing ✅ **Fixed**: Startup verification rebuilds search index if empty ### 📄 **Enhanced PDF Processing** ✅ **Fixed**: PDF uploads no longer fail with "Cannot find module pdf.js" error ✅ **Fixed**: Runtime PDF symlink verification with automatic recovery ✅ **Fixed**: Robust Dockerfile fixes for all environments ✅ **Fixed**: Special characters in filenames now handled properly ### 🛡️ **Regression Prevention** - **Multi-Layer Protection**: Dockerfile + runtime checks prevent PDF failures - **Auto-Recovery**: BM25 index rebuilds automatically if missing - **Health Monitoring**: Startup verification ensures core functionality - **Future-Proof**: Cannot regress to v3.0.2 search/PDF issues ### 🚀 **Immediate Benefits** - Documents upload and process reliably across all environments - Search returns relevant results immediately after processing - AI Chat provides contextual responses from your documents - Zero manual fixes required for new installations --- ## Contributing We welcome contributions! Please see our [contributing guidelines](.github/CONTRIBUTING.md) for details. ### Development Setup 1. Fork the repository 2. Create a feature branch 3. Make your changes with tests 4. Submit a pull request ### Code Standards - Follow existing code style and conventions - Add tests for new functionality - Update documentation for API changes - Use conventional commits for clear history ## License This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details. ## Support - **Documentation**: Visit `/docs` for comprehensive API documentation - **Issues**: Report bugs and request features via GitHub Issues - **Discussions**: Join the community discussion for questions and support --- <div align="center"> **🦙 AutoLlama v3.0.3** *Your Documents Have Context. Now Your RAG Does Too.* **[Live Demo](https://autollama.io)** • **[Documentation](/docs)** • **[GitHub Issues](https://github.com/autollama/autollama/issues)** • **[Releases](https://github.com/snedea/autollama/releases)** *Built by developers, for developers who believe context changes everything.* </div>