March 2025: Implemented end-to-end distributed tracing for the RAG pipeline to enhance observability and debugging. Integrated Google Cloud Trace with OpenTelemetry, enabling backend trace export and enabling trace-based performance insights. Added documentation and screenshots to demonstrate tracing functionality and flows. This work improves diagnosability, reduces MTTR for RAG-related issues, and establishes the foundation for ongoing observability-driven optimizations.

December 2024 delivered a major upgrade to the embedding model used in RAG examples across multiple backends (Elasticsearch, Postgres-pgvector, Qdrant, and Weaviate). The update included the chatbot and document embedding scripts and a Weaviate notebook, resulting in higher retrieval accuracy and lower latency for RAG workflows. The change is captured in a single, auditable commit: 17f93c994631fb240255864697238872a2830b15 with the message “Update out-dated embedding model for the RAG examples (#1539). No major bugs were reported this month. Overall impact: improved search relevance for end users, easier future upgrades of embedding models, and demonstrated cross-backend consistency in the embedding pipeline. Technologies demonstrated: embeddings, RAG, Elasticsearch, Postgres-pgvector, Qdrant, Weaviate, Python scripts, notebooks.