- 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
- Add optional Pinecone and sentence-transformers services for vector search
- Configure NVIDIA GPU support with proper environment variables
- Add new environment variables for embeddings and Pinecone
- Add docker compose profiles to optionally enable vector-search
- Improve CUDA configuration for Ollama service
- Add pinecone-net network for service communication
