{ "product_vision": "Multi-agent LLM financial trading framework that mirrors real-world trading firm dynamics for research-based market analysis and trading decisions", "existing_features": [ "news_domain_95_complete", "google_news_client", "article_scraper_client", "news_repository_with_embeddings", "postgresql_timescaledb_stack", "agent_toolkit_rag_integration", "openrouter_llm_provider" ], "architecture": { "layer_pattern": "Router → Service → Repository → Entity → Database", "database": "PostgreSQL + TimescaleDB + pgvectorscale", "llm_provider": "OpenRouter unified interface", "agent_orchestration": "LangGraph workflows", "data_pipeline": "Dagster (planned, not implemented)", "domain_structure": "news (95% complete), marketdata (planned), socialmedia (planned)", "testing_strategy": "Domain-specific: mocks for services, real DB for repositories, pytest-vcr for HTTP" }, "news_implementation_status": { "core_components": { "NewsService": "Business logic with company/global news context", "NewsRepository": "Async PostgreSQL with batch upsert, vector embeddings", "GoogleNewsClient": "RSS feed client for live data", "ArticleScraperClient": "newspaper4k with paywall detection" }, "data_models": { "NewsArticle": "Domain dataclass with validation", "NewsArticleEntity": "SQLAlchemy model with 1536-dim vector embeddings" }, "key_features": [ "URL-based deduplication", "Vector embeddings for similarity", "Paywall detection and fallback", "Comprehensive test coverage with pytest-vcr" ] }, "dagster_status": "Planned but not implemented - documentation references exist but no pipeline code", "technical_patterns": { "async_operations": "All repository methods async with session management", "batch_operations": "upsert_batch for performance", "error_handling": "Graceful degradation with logging", "vector_search": "Semantic similarity for RAG", "connection_management": "DatabaseManager with asyncpg and pooling" } }