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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
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feat(phase7): implement Hypothesis Agent for mechanistic reasoning
Add HypothesisAgent that generates Drug → Target → Pathway → Effect hypotheses based on collected evidence. Key features: Models: - MechanismHypothesis: structured hypothesis with confidence scoring - HypothesisAssessment: container for hypotheses with knowledge gaps Utilities: - MMR (Maximal Marginal Relevance) for diverse evidence selection - Sentence-aware truncation for clean text processing Agent: - HypothesisAgent generates 2-4 testable hypotheses per query - Integrated with EmbeddingService for semantic evidence selection Orchestrator: - Refactored MagenticOrchestrator into smaller methods - Added "hypothesizing" event type for UI feedback - Workflow: Search → Hypothesize → Targeted Search → Judge Tests: 53/53 passing, mypy clean, ruff cleanc690006