Engineering Logs for Troubleshooting & Debugging
Problem – Empty Retrieval Results in a Development Sandbox Using ONNX Runtime for RAG In a sandbox environment the Retrieval‑Augmented Generation (RAG) pipeline is wired to an ONNX Runtime session that hosts a converted MiniLM‑v2 embedding model. When a user query is sent to the retriever the downstream FAISS (or similar) vector store returns an … Read more
Problem – RAG query decomposition fails during a rolling update During a rolling update of an MLflow‑served Retrieval‑Augmented Generation (RAG) model, inference requests start returning errors such as: Failed to decompose query: embedding dimension mismatch (expected 768, got 512) CacheKeyCollisionError: Duplicate cache entry for key ‘rag_decompose:customer_id=123’ detected during rolling update ModelVersionNotFoundError: Requested version ‘2’ for … Read more
ONNX Runtime RAG Reranker Timeout on Premises Server Problem Description The RAG (Retrieval‑Augmented Generation) pipeline uses an ONNX Runtime reranker model to score retrieved documents before generation. In a private data‑center deployment with limited CPU cores and memory, the reranker frequently exceeds the configured inference timeout during peak query load, returning errors such as: Error: … Read more
Problem Description – Retrieval Dominance After a Rolling Update A production Mistral AI Retrieval‑Augmented Generation (RAG) pipeline uses a hybrid search that combines: Vector‑based retrieval (weight = retrieval_weight) Transformer‑based reranking (weight = rerank_weight) During a rolling update that introduces a new reranker model version, operators observed: Hybrid search results suddenly favor raw retrieval scores. Relevance metrics dropped 30‑45 % (see … Read more
Problem Description – Retrieval Dominance in Qwen RAG API Multiple tenants of a cloud‑managed Qwen service reported that responses from the Retrieval‑Augmented Generation (RAG) endpoint were dominated by verbatim excerpts from the vector store. Typical symptoms included: Generated answers consisting of 70‑90% copied document passages. Redundant citation blocks appearing in every response. Truncated answers where … Read more
Problem – RAG Reranker Timeout During Inference Serving The Retrieval‑Augmented Generation (RAG) pipeline is deployed on a production inference service that uses ONNX Runtime for low‑latency model execution. Under moderate request load the reranker stage fails to finish within the allocated time budget, causing the entire request to time‑out. Typical symptoms observed in logs: Ort::Exception: … Read more
Problem Description During automated integration tests executed in a CI/CD pipeline, a Prometheus instance that is configured for Retrieval‑Augmented Generation (RAG) consistently returns empty result vectors. The same pipeline runs successfully on a developer’s workstation, but in the pipeline the query endpoint responds with messages such as: retrieval returned empty vector no data found for … Read more
Problem Description During a disaster‑recovery run, the RAG DocumentLoader used by a Hugging Face Transformers RagSequenceForGeneration pipeline crashes while ingesting documents from a cloud‑based backup. The restoration process aborts, leaving the knowledge base unavailable. Typical error output observed in the logs: Traceback (most recent call last): File “/opt/app/rag_loader.py”, line 112, in load_documents documents = loader.load() … Read more
Problem: RAG Embedding Dimension Mismatch after Model Update An event‑driven pipeline streams records into a PostgreSQL table that stores vector embeddings generated by a Retrieval‑Augmented Generation (RAG) model. After upgrading the model, inserts and similarity queries start failing with errors such as: ERROR: column “embedding” is of type vector(1536) but expression is of type vector(768) … Read more
Problem Description After a rolling update of the ONNX Runtime (ORT) library in a production Retrieval‑Augmented Generation (RAG) service, the retrieval component consistently returns empty result sets. The symptom manifests as: Search logs contain INFO: Retrieval returned 0 results – query embedding norm is 0.0, check model initialization. Embedding generation logs sometimes show WARN: Embedding … Read more