- Implement parallel chunk processing with configurable concurrency
- Add direct NVIDIA API integration bypassing LangChain for better control
- Optimize for DGX Spark unified memory with batch processing
- Use concurrency of 4 for Ollama, 2 for other providers
- Add proper error handling and user stop capability
- Update NVIDIA model to Llama 3.3 Nemotron Super 49B v1.5
- Improve prompt engineering for triple extraction
- Update LangChain service to use Llama 3.3 Nemotron Super 49B v1.5
- Adjust temperature to 0.6 for better response quality
- Increase timeout to 120s for larger model
- Add top_p, frequency_penalty, and presence_penalty parameters
- Remove deprecated response_format configuration
- Update default NVIDIA model to Llama 3.3 Nemotron Super 49B v1.5
- Update model display name and description
- Replace deprecated 70B model with newer 49B Super model
- Replace button text with icons for compact display
- Add tooltips to Refresh, Disconnect, and Clear buttons
- Improve button spacing and alignment
- Import LogOut icon for disconnect action
- Switch traditional graph search to use LLM-enhanced endpoint
- Display LLM-generated answer prominently above triples
- Add llmAnswer state to store and display generated answers
- Update results section to show 'Supporting Triples' when answer exists
- Pass selected LLM model and provider to API
- Improve debug logging for query modes and results
- Integrate LLMSelectorCompact into RAG query component
- Make query mode cards more compact to accommodate LLM selector
- Update styling for better space utilization
- Add LLM selection section with descriptive label
- Create LLMSelectorCompact component for model selection
- Support Ollama and NVIDIA models
- Load available models from localStorage
- Persist selected model and dispatch selection events
- Compact design suitable for inline placement
- Update metrics endpoint to use getGraphDbService utility
- Support both ArangoDB and Neo4j database types
- Initialize graph database based on selected type
- Retrieve graph stats from the active database
- Add queryWithLLM method to BackendService
- Retrieves top K triples from graph and uses LLM to generate answers
- Supports configurable LLM model and provider selection
- Uses research-backed prompt structure for KG-enhanced RAG
- Includes fallback handling for LLM errors
- Create new /api/graph-query-llm endpoint for graph search + LLM generation
- Retrieves triples using graph search and generates answers using LLM
- Supports both traditional and vector-based graph search
- Makes traditional graph search comparable to RAG for benchmarking
