- Add queryMode field to QueryLogSummary interface
- Update getQueryLogs to group by both query AND queryMode
- Use composite key (query|||queryMode) for proper separation
- Enables separate tracking of Pure RAG vs Graph Search queries
Previously, queries with the same text but different modes were
merged together, causing metrics to only show one aggregate value.
Now each mode's performance is tracked independently.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
- Add triple structure logging in API route for debugging
- Update graph-db-service imports for multi-hop fields
- Improve embeddings generator UI responsiveness
- Enable data pipeline verification for depth/pathLength fields
These changes help diagnose issues with multi-hop data flow
and ensure proper propagation of metadata through the stack.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
- Extract ALL edges from graph traversal paths, not just endpoints
- Add depth field (edge position in path: 0, 1, 2...)
- Add pathLength field (total edges in path)
- Use numeric index iteration for AQL compatibility
- Apply depth penalty to edge scoring (earlier edges weighted higher)
- Enable visualization of knowledge chains in graph queries
- Increase topK default to 40 for richer multi-hop context
This allows Traditional Graph to show how information is connected
across multiple hops in the knowledge graph, similar to GraphRAG.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
- Integrate NVIDIA API as alternative to Ollama for graph queries
- Implement thinking tokens API with /think system message
- Add min_thinking_tokens (1024) and max_thinking_tokens (2048)
- Format reasoning_content with <think> tags for UI parsing
- Support dynamic model/provider selection per query
- Maintain Ollama fallback for backward compatibility
This enables Traditional Graph to use NVIDIA's reasoning models
(e.g., nvidia-nemotron-nano-9b-v2) with visible chain-of-thought.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
- Add query mode badge to answer section showing Pure RAG/Traditional Graph/GraphRAG
- Add collapsible reasoning section for <think> tags in answers
- Add markdown rendering support (bold/italic) in answers
- Fix Pure RAG to properly display answers using llmAnswer state
- Hide empty results message for Pure RAG mode
- Update metrics sidebar to show query times by mode instead of overall average
- Add queryTimesByMode field to metrics API and frontend interfaces
- Disable GraphRAG button with "COMING SOON" badge (requires GNN model)
- Fix Qdrant vector store document mapping with contentPayloadKey
- Update console logs to reflect Qdrant instead of Pinecone
- Add @qdrant/js-client-rest dependency to package.json
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
- Migrate from Pinecone to Qdrant vector database for native ARM64 support
- Add Qdrant service with automatic collection initialization in docker-compose
- Implement QdrantService with UUID-based point IDs to meet Qdrant requirements
- Update all API routes and frontend components to use Qdrant
- Enhance Storage Connections UI with detailed stats (vectors, status, dimensions)
- Add icons and tooltips to Vector DB section matching Graph DB UX
- 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
- 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
