This roadmap outlines our vision and planned development trajectory for the RagE system, our Pathway-powered RAG (Retrieval Augmented Generation) platform.
- Core RAG functionality using Pathway's vector processing pipeline
- Streamlit UI as primary interface for document interaction
- Multi-model support (OpenAI, Gemini, Hugging Face)
- User authentication and document isolation
- Legacy Flask UI for API compatibility
- Advanced hybrid search (dense + sparse vectors)
- Fine-tuned reranking for domain-specific relevance
- Streaming response capabilities from Pathway to Streamlit
- Improved context handling for longer documents
- Optimized embedding generation for large document sets
- Advanced document visualization tools
- Interactive query builder
- User preference management
- Document relationship visualization
- Result explanation and evidence highlighting
- Enhanced document processing pipeline
- Table extraction and structured data handling
- Image content extraction via multimodal models
- Metadata enrichment and filtering
- Advanced caching strategies
- Improved error handling and recovery
- Telemetry for system monitoring
- Multi-hop reasoning
- Answer synthesis from multiple document sources
- Fact-checking and validation mechanisms
- Dynamic context window optimization
- Query-specific retrieval strategy selection
- RBAC (Role-Based Access Control)
- Audit logging and compliance features
- Data retention policies and enforcement
- Integration with enterprise identity providers
- On-premises deployment configurations
- Real-time document indexing with change detection
- Streaming embeddings for continuous updates
- Custom Pathway nodes for specialized document handling
- Distributed processing for very large document collections
- Advanced query understanding with Pathway transformers
- Video content analysis and retrieval
- Audio transcription and semantic search
- Image understanding and visual question answering
- Complex document layout understanding
- Cross-modal reasoning capabilities
- Self-improving retrieval based on user feedback
- Automatic knowledge base construction
- Customized model fine-tuning based on corpus
- Automated document summarization and knowledge extraction
- Context-aware query planning
- Public API for third-party applications
- Developer SDK for custom extensions
- Plugin architecture for specialized processors
- Integration with popular knowledge management systems
- Advanced visualization tools and dashboards
- Novel retrieval algorithms optimized for Pathway
- Document chunking optimization research
- Embedding efficiency studies
- Multi-vector representations per document
- Evaluation frameworks for RAG quality