Engineering Logs for Troubleshooting & Debugging
Problem Description – Empty Retrieval Results on A100/H100 GPU Cluster When executing a Retrieval‑Augmented Generation (RAG) pipeline on a multi‑node GPU cluster (A100/H100), the RAGRetriever consistently returns an empty document list despite: Valid natural‑language queries. A fully populated FAISS index built from the knowledge base. Successful generation of query embeddings on the GPU. Typical log … Read more
Problem – RAG inference fails because the prompt template is malformed In a distributed micro‑services deployment the Retrieval‑Augmented Generation (RAG) service invokes an ONNX Runtime sidecar via gRPC. The sidecar builds a prompt by substituting variables (e.g., {{question}}, {{context}}) into a JSON‑encoded template before feeding the concatenated string to a transformer model. Symptoms observed in … Read more
Problem – Retrieval Degradation from Improper Chunk Overlap In a local development sandbox a Retrieval‑Augmented Generation (RAG) pipeline builds a FAISS index from a corpus of text documents. The chunk_size and overlap parameters used during document splitting are mis‑configured, causing two distinct symptoms: Excessive overlap (e.g., overlap = 0.9 * chunk_size) produces many near‑duplicate vectors. … Read more
Problem – RAG retrieval returns empty results behind HAProxy in CI/CD pipelines During automated end‑to‑end tests a Retrieval‑Augmented Generation (RAG) service intermittently returns no documents. The symptom is observed only when the request traverses an HAProxy instance that load‑balances traffic across several identical backend replicas. Typical error messages: HAProxy log entry: <…> 0/0/0/30/30 504 0 … Read more
Problem Description During a rollout of an A/B test that adds dynamic metadata fields (e.g., promotion_code, confidence_score, user_attributes), the Gemini RAG pipeline starts rejecting a large fraction of candidate documents. The symptoms observed in production logs are: Retrieval failures with HTTP 400 responses. Log entries such as: 2026-07-08T12:34:56.789Z ERROR gemini.retrieval.filter Metadata schema validation failed: missing required … Read more
Problem During a canary rollout of a Retrieval‑Augmented Generation (RAG) pipeline on Google Cloud Compute Engine, the DocumentLoader component crashes intermittently while ingesting documents. The failure manifests as partial ingestion, missing vectors in the downstream vector store, and occasional container termination. Typical symptoms observed in the canary instance logs: 2024-07-08T12:34:56.123Z stdout FileLockError: could not acquire … Read more
Problem: Inconsistent RAG Citation Formatting in Real‑Time Streaming Queries When using the Qwen model in streaming mode for Retrieval‑Augmented Generation (RAG), engineers have observed that generated citations deviate from the required [1], [2] syntax. Typical symptoms include: Missing opening bracket: 1] or source-??] Extra closing bracket: [1]] Incorrect or duplicated source identifiers: [source‑], [source‑1][source‑1] Intermittent … Read more
Problem: Inconsistent RAG Answer Extraction Between Blue and Green Deployments In a production RAG pipeline accelerated with NVIDIA TensorRT, the same user query yields different answer strings when routed to the blue deployment versus the green deployment. The discrepancy appears intermittently during traffic shifts and can cause downstream ranking failures, SLA breaches, and user‑visible errors. … Read more
Problem Description When using Meta LLaMA for Retrieval‑Augmented Generation (RAG) on a workstation with 16 GB RAM and an NVIDIA RTX 3060, the DocumentLoader crashes as soon as it encounters a text file larger than roughly 10 GB. The failure manifests as a series of memory‑related exceptions and, eventually, a hard termination of the Python process. Typical error output: … Read more
Problem Description During a canary rollout, a subset of production traffic was routed to a staging Pinecone index that contains only 5 k vectors. The Retrieval‑Augmented Generation (RAG) pipeline applies a metadata filter to restrict results to the appropriate document_type and language. Engineers observed: Frequent hallucinated answers from the LLM because the retrieved context was empty … Read more