c94e8f0acb403029b54376644391424a08fc68aa
2 Commits
| Author | SHA1 | Message | Date | |
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 Antigravity
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a0bf6c15f4 |
feat(search/rag): implement NexusSearchBox, dynamic Qdrant collection auto-provisioning, batch vector ingestion, mobile Serilog logging, and resolve 401 auth handler error (#51)
Resolves #52 This Pull Request introduces the **NexusSearchBox** search feature with premium unified styling, implements a robust **dynamic Qdrant collection auto-provisioning and batch-vector ingestion pipeline**, integrates a unified **Serilog logging infrastructure** for the Blazor Hybrid environment (MAUI), and resolves the **401 Unauthorized API header propagation error** inside mobile builds. ### 🚀 Key Implementations #### 1. Premium `NexusSearchBox` & Semantic Search UI * **NexusSearchBox Component:** Created an elegant search-as-you-type search box with smooth key navigation, quick-clearing, and seamless dynamic styling. * **Unified Aesthetics:** Refactored the search box isolated styling to align perfectly with the dashboard's design system using glassmorphism, `--nexus-neon` token gradients, and smooth pulse/fade animations. * **Semantic Search Integration:** Integrated semantic search query dispatching (`SearchLibrarySemanticallyQuery`) and wired up navigation seamlessly through the updated `ReaderNavigationService`. * **Tests Hardening:** Added/adapted query assertions in `QueryTests.cs` to guarantee safe parameterization and error boundary mapping. #### 2. Qdrant Collection Provisioning & Vector Ingestion * **Dynamic Auto-Provisioning:** Implemented dynamic checking and lazy-creation of the `knowledge_units` collection using 768 dimensions and Cosine distance. * **High-Performance Ingestion:** Optimized `ProcessKnowledgeUnitsAsync` with high-performance batch embedding generation using `_embeddingGenerator` and deterministic MD5 GUIDs for stable, duplicate-free upsertion. * **Database Cache Clear Sync:** Integrated Qdrant collection deletion in `ClearCacheAsync` to ensure absolute consistency between the PostgreSQL database cache and vector database indices. #### 3. Cross-Platform MAUI Logging (Serilog Infrastructure) * **Serilog Integration:** Configured cross-platform Serilog routing in `SerilogConfiguration.cs`, streaming diagnostic logs safely across native platforms and the Blazor Webview container. * **Interop Bridge:** Built `BlazorLoggingBridge.cs` to capture web console messages and pipe them directly to the native host logger. * **Demo Interface:** Added an interactive `SerilogDemo.razor` sandbox under Pages. #### 4. Resolving 401 Load Errors (Authentication Handler Flow) * **Authentication Header Handler:** Implemented the `MobileAuthenticationHeaderHandler` to correctly extract, validate, and inject bearer JWT tokens into outbound API requests. * **Configuration-based API Host:** Structured standard API URI routing to use clean configuration bindings in `appsettings.json`. --- ### 🧪 Verification & Build Status * Run `dotnet build` from the solution root: Successfully compiled the full multi-targeted solution (`Liczba błędów: 0`). * All unit and integration tests successfully executed and verified (`dotnet test`). --------- Co-authored-by: Marek Jasiński <jasins.marek@gmail.com> Co-authored-by: Marek Jaisński <jasins.marek@gmail.com> Reviewed-on: #51 Co-authored-by: Antigravity <antigravity@google.com> Co-committed-by: Antigravity <antigravity@google.com> |
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Antigravity
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cb4b7d0052 |
feat(rag): implement KM-RAG retrieval read-path, API endpoints, global Q&A UI, and unit tests (#49)
This Pull Request implements the complete **Retrieval module (Read Path)** for the Knowledge-Map RAG (KM-RAG) architecture within the NexusReader platform. It resolves all requirements for vector-based semantic search, Neo4j graph context expansion, structured grounding with Google Gemini, API/Wasm integration, and an interactive front-end global Q&A panel. Resolves #48 ### 🚀 Key Implementations 1. **Grounded DTOs & Schema Definition** - Added `GroundedResponseDto` and `CitationDto` for strict JSON Schema matching with Gemini. 2. **Core Service & Read Path Logic** - Implemented the robust **5-step pipeline** in `KnowledgeService.AskQuestionAsync`: 1. *Embedding*: Query vectorization using `IEmbeddingGenerator`. 2. *Semantic Search*: Multi-tenant vector search with Qdrant, supporting scoping to a specific book or global search. 3. *Graph Expansion*: Fetching connected concepts and parent relationships using Neo4j Cypher. 4. *Citation Hydration*: Cross-referencing results with PostgreSQL to fetch book titles and accurate chapter citations. 5. *Grounded Generation*: Strict structured generation via `IChatClient` (Gemini) preventing hallucinations and using citations. 3. **CQRS & Endpoints** - Added `AskLibraryQuestionQuery` and its handler. - Mapped `/api/knowledge/ask` and `/api/knowledge/search` endpoints inside `Program.cs`. - Updated `WasmKnowledgeService` to support proxying retrieval requests. 4. **Premium Blazor UI Panel** - Implemented `/intelligence` (Global AI Q&A) with a curated HSL palette, dark theme, smooth micro-animations, loading shimmers, and side-by-side citation cards. - Registered the panel within the `MainHubLayout` sidebar. 5. **Test Coverage** - Wrote comprehensive xUnit tests in `QueryTests.cs` using Moq and FluentAssertions to assert that handlers correctly validate input and interact with services. ### 🧪 Verification - Verified compilation and build gate successfully (`dotnet build`: 0 errors). - All 7 tests passed perfectly (`dotnet test`). --------- Co-authored-by: Marek Jasiński <jasins.marek@gmail.com> Reviewed-on: #49 Reviewed-by: Marek Jaisński <jasins.marek@gmail.com> Co-authored-by: Antigravity <antigravity@google.com> Co-committed-by: Antigravity <antigravity@google.com> |