197 lines
6.3 KiB
Markdown
197 lines
6.3 KiB
Markdown
# OpenQuery
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**AI-powered search and answer system** that finds accurate, well-sourced answers to your questions by searching the web, extracting relevant content, and synthesizing intelligent responses.
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## ✨ Features
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- 🤖 **Smart Query Generation** - Automatically creates multiple diverse search queries from your question
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- ⚡ **Parallel Processing** - Fast concurrent searches, article fetching, and embedding generation
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- 🎯 **Semantic Search** - Uses vector embeddings to find the most relevant information
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- 📚 **Clean Article Extraction** - Intelligently extracts article content using SmartReader
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- 🔄 **Streaming Responses** - Watch the AI answer generate in real-time
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- ⚙️ **Fully Configurable** - Control queries, results, and context chunks
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- 🛡️ **Production Ready** - Built with rate limiting, retries, and error handling
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## 🚀 Quick Start
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### 1. Prerequisites
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- A **SearxNG** instance (Docker recommended):
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```bash
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docker run -d --name searxng -p 8002:8080 searxng/searxng:latest
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```
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- A **OpenRouter API key** from [openrouter.ai](https://openrouter.ai)
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### 2. Installation
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```bash
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# Clone and build
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git clone <your-repo-url>
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cd OpenQuery
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chmod +x install.sh
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./install.sh
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# Or build manually
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dotnet publish -c Release -r linux-x64 --self-contained true /p:PublishAot=true
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```
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### 3. Configuration
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```bash
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# Interactive setup
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openquery configure -i
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# Or set environment variables
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export OPENROUTER_API_KEY="sk-or-..."
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export SEARXNG_URL="http://localhost:8002" # default
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```
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### 4. Ask a Question
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```bash
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openquery "What is quantum entanglement and how does it work?"
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```
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That's it! The system will:
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1. Generate 3 search queries (configurable)
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2. Search the web via SearxNG
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3. Extract and chunk relevant articles
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4. Rank content by semantic relevance
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5. Stream a comprehensive answer with citations
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## 📖 Usage Examples
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```bash
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# Concise answer
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openquery -s "Who won the 2024 US presidential election?"
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# Detailed research
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openquery -l -q 5 -r 10 "Explain quantum computing and its applications"
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# See everything
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openquery -v "What are the health benefits of meditation?"
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# Customize
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openquery -c 5 -r 8 "Current state of SpaceX Starship development"
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```
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## 🔧 Options
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```
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-c, --chunks N Number of top context chunks (default: 3)
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-r, --results N Search results per query (default: 5)
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-q, --queries N Number of search queries to generate (default: 3)
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-s, --short Give a concise answer
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-l, --long Give a detailed answer
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-v, --verbose Show detailed progress
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```
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## 🌐 Supported Models
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OpenQuery works with any OpenRouter model. Popular choices:
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- `qwen/qwen3.5-flash-02-23` (default, fast & affordable)
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- `google/gemini-3-flash-preview`
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- `deepseek/deepseek-v3.2`
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- `moonshotai/kimi-k2.5`
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Configure your preferred model:
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```bash
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openquery configure --model "google/gemini-3-flash-preview"
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```
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## 📁 Project Structure
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```
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OpenQuery/
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├── README.md # This file
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├── docs/ # Detailed documentation
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│ ├── installation.md
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│ ├── configuration.md
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│ ├── usage.md
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│ ├── architecture.md
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│ ├── components/
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│ └── troubleshooting.md
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├── Program.cs # CLI entry point
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├── OpenQuery.cs # Main application logic
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├── Services/ # Business logic services
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├── Models/ # Data models
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├── Tools/ # Search orchestration
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└── ConfigManager.cs # Configuration management
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```
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## 🏗️ Architecture
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OpenQuery uses a multi-stage pipeline:
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```
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Query → Multiple Searches → Article Fetching → Embeddings → Ranking → AI Answer
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```
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1. **Query Expansion**: LLM generates diverse search queries
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2. **Parallel Search**: SearxNG executes all queries simultaneously
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3. **Content Extraction**: SmartReader pulls clean article text
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4. **Embedding Generation**: Vectorize query and chunks
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5. **Semantic Ranking**: Cosine similarity scoring
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6. **Answer Synthesis**: Final LLM response with sources
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## 🔍 How It Works
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1. **You ask a question** → OpenQuery generates 3 optimized search queries
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2. **Searches the web** → All queries run in parallel via SearxNG
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3. **Fetches articles** → Extracts clean content from top results
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4. **Splits into chunks** → ~500 character pieces for embedding
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5. **Ranks by relevance** → Semantic similarity to your question
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6. **Synthesizes answer** → LLM reviews top 3 chunks and responds with citations
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## 🐛 Troubleshooting
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**Common issues** and solutions:
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| Problem | Solution |
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|---------|----------|
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| "API Key is missing" | Run `openquery configure -i` or set `OPENROUTER_API_KEY` |
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| No search results | Check your SearxNG instance is running (`curl http://localhost:8002`) |
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| Slow performance | Reduce `--results` or `--queries` count |
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| Articles failing to fetch | Some sites block scrapers; try different queries |
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See [docs/troubleshooting.md](docs/troubleshooting.md) for detailed help.
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## 📚 Documentation
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- **[Installation Guide](docs/installation.md)** - Build and setup instructions
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- **[Configuration](docs/configuration.md)** - All config options and environment variables
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- **[Usage Guide](docs/usage.md)** - Complete CLI reference and examples
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- **[Architecture](docs/architecture.md)** - System design and patterns
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- **[Components](docs/components/)** - Deep dive into each module
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- **[Troubleshooting](docs/troubleshooting.md)** - Solve common problems
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- **[API Reference](docs/api-reference.md)** - Programmatic interfaces
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## 🤝 Contributing
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Contributions welcome! Please:
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1. Fork the repository
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2. Create a feature branch
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3. Make your changes
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4. Submit a pull request
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## 📄 License
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MIT License - see LICENSE file for details.
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## 🙏 Acknowledgments
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- [OpenRouter](https://openrouter.ai) - LLM API aggregation
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- [SearxNG](https://searx.space) - Privacy-respecting metasearch
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- [SmartReader](https://github.com/kfasten/SmartReader) - Article extraction
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- Built with [.NET](https://dotnet.microsoft.com)
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---
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**Need more details?** Check the comprehensive documentation in the [docs/](docs/) folder.
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