Spaces:
Sleeping
Sleeping
Commit History
-
feat: Wire LlamaIndex RAG into Simple Mode (Tiered Embedding) (#83)
Unverified signature* feat: wire LlamaIndex RAG service into embedding infrastructure This PR implements tiered embedding service selection per NEXT_TASK.md: ## Changes - Add EmbeddingServiceProtocol (embedding_protocol.py) for unified interface - Add async wrappers to LlamaIndexRAGService (add_evidence, search_similar, deduplicate) - Update service_loader.py with get_embedding_service() factory method - Update ResearchMemory to use service_loader instead of direct EmbeddingService - Update orchestrators to use EmbeddingServiceProtocol type hints ## Design Patterns Applied - Strategy Pattern: Tiered service selection (LlamaIndex or local) - Factory Method: get_embedding_service() creates appropriate service - Protocol Pattern: Structural typing for service interface - Dependency Injection: ResearchMemory accepts any protocol-compatible service ## Tiered Selection - Premium tier (OPENAI_API_KEY present): LlamaIndexRAGService with: - OpenAI embeddings (text-embedding-3-small) - Persistent ChromaDB s…7baf8ba
-
fix: add future annotations to workflow.py for runtime type compatibility
The string annotation "BaseCheckpointSaver[Any]" | None failed at runtime because Python can't use | with string literals. Adding `from __future__ import annotations` makes all annotations strings evaluated lazily, fixing the TypeError. Also removed now-unnecessary quotes per ruff UP037.586a3f1 -
feat(SPEC-08): Integrate shared memory layer + CodeRabbit fixes (#74)
Unverified signature## Summary - SPEC-08: Shared memory layer integration - CodeRabbit critical fix: async add_evidence() with proper deduplication - Security: Fixed CVEs in langgraph-checkpoint and urllib3 - CI: Hardened to fail on security vulnerabilities - Docstrings: 80%+ coverage All 178 tests pass. No known vulnerabilities.e0c585c -
feat: Enhance research workflow with embedding service integration
- Updated graph nodes to accept an optional EmbeddingService for improved evidence handling and deduplication. - Refactored search, judge, resolve, and synthesize nodes to utilize the embedding service for enhanced functionality. - Modified the research graph creation to bind the embedding service to worker nodes, ensuring seamless integration. - Added logging for better traceability during node execution. - Expanded unit and integration tests to cover new embedding service interactions and ensure robust functionality.f160233 -
feat: Integrate LangGraph for enhanced research orchestration
- Added LangGraph dependencies to pyproject.toml and uv.lock for improved state management and orchestration capabilities. - Implemented LangGraph-based orchestrator in src/orchestrators/langgraph_orchestrator.py, enabling structured research workflows. - Developed graph nodes and state management in src/agents/graph, including search, judge, and synthesis functionalities. - Updated app.py to support new "god" mode for advanced orchestration. - Enhanced documentation for active bugs and architectural gaps related to LangGraph integration. - Added unit and integration tests to ensure functionality and reliability of the new features.a820b5b