Engineering Logs for Troubleshooting & Debugging
Problem: “StorageClass not found” when deploying a Hugging Face Transformers model During the CI/CD pipeline a Deployment that mounts a PersistentVolumeClaim for the model artifacts fails with the following error: error: failed to create pod sandbox: rpc error: code = Unknown desc = failed to provision volume with StorageClass “fast-ssd”: storageclass.storage.k8s.io “fast-ssd” not found The … Read more
Problem In a production Retrieval‑Augmented Generation (RAG) pipeline that streams responses from Anthropic Claude, the generated citations no longer match the required JSON schema. Symptoms include: Missing required fields such as source_id or relevance_score. Incorrect delimiters (e.g., using ; instead of the mandated ,). Citation blocks appearing outside the citations array or after the final … Read more
Problem When ChromaDB is deployed with multi‑region replication, Retrieval‑Augmented Generation (RAG) prompts occasionally render with the placeholder {{retrieved_text}} left untouched. The generated output contains the literal placeholder instead of the expected document snippets, causing downstream language model calls to fail or produce nonsensical answers. Typical symptom in the application logs: 2024-10-08 14:22:31,842 ERROR rag_engine – … Read more
Problem After a recent configuration change on an edge‑computing node running vLLM, the Retrieval‑Augmented Generation (RAG) hybrid search started to favor one modality almost exclusively. In practice: Keyword‑based retrieval (bm25) returns no results even when the query contains exact terms. Dense vector retrieval dominates the relevance score, causing unrelated passages to be selected. Latency spikes … Read more
Problem: Claude Returns Empty or Irrelevant Answers Despite Relevant Vector Retrieval In a development sandbox we integrate Anthropic Claude via its API with a local FAISS vector store. The retrieval step consistently returns passages that contain the answer, but the final response from Claude is either an empty string or a generic answer that does … Read more
Problem: Gemini RAG pipeline cannot connect to the vector store after autoscaling The Gemini Retrieval‑Augmented Generation (RAG) pipeline is deployed on a managed cloud service that scales compute instances automatically. After a scale‑out event, the pipeline consistently fails to retrieve documents from the configured managed vector store. The failure manifests as connection timeouts and, intermittently, … Read more
Problem The Retrieval‑Augmented Generation (RAG) pipeline uses RabbitMQ to stream document metadata to a GPU‑accelerated inference service. After a recent change to the metadata filter, the pipeline began discarding documents that should have been included in the context set. The LLM responses became fragmented, missing critical facts, and the generation_quality_score metric dropped from 0.87 to … Read more
Problem During peak traffic periods the API gateway that fronts a Haystack‑based search service starts returning 504 Gateway Timeout, 502 Bad Gateway and occasional 429 Too Many Requests errors. The downstream Haystack document store logs show repeated failures when trying to fetch source documents from the cloud storage provider (e.g., AWS S3, GCP Cloud Storage, … Read more
Problem: Malformed JSON‑LD Citations in RAG Responses after Model Version Upgrade The AI service running on Google Compute Engine (GCE) VMs generates Retrieval‑Augmented Generation (RAG) answers that include citations. After upgrading the underlying LLM from v1.2.0 to v1.3.0, the citation strings no longer conform to the required JSON‑LD schema. Missing fields (@type, url), stray delimiters, … Read more
Problem During Retrieval‑Augmented Generation (RAG) inference, the Weaviate NearVector similarity query returns an empty result set even though the target documents are present in the vector store. The downstream LLM receives no context, leading to hallucinations or generic answers. Root Cause The empty results stem from one or more of the following intertwined issues: Vector … Read more