rag

PyTorch tensor shape mismatch in RAG context injection

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Problem – Tensor Shape Mismatch in RAG Context Injection When using a Retrieval‑Augmented Generation (RAG) pipeline inside a Docker‑based Jupyter sandbox, the model often drops or truncates tokens from the retrieved documents. The symptom manifests as missing context during generation and degraded answer quality. Typical log excerpt: context_input_ids.shape: torch.Size([3, 210]) context_attention_mask.shape: torch.Size([3, 128]) RuntimeError: Expected … Read more

RAG retrieval degradation after MLflow model validation

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Problem – Retrieval‑Augmented Generation (RAG) Hybrid Search Degradation During MLflow Validation During automated pre‑deployment validation runs orchestrated by an MLflow Project in the CI/CD pipeline, the hybrid retriever’s relevance metrics collapse: Recall@10 drops by 40‑50 % compared with manual runs. Logs contain errors such as ScoreNormalizationError: Sparse scores exceed dense scores range and warnings like HybridSearchWeightOverflow. … Read more

vLLM citation formatting errors after RAG prompt augmentation

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Problem Description – Inconsistent Citation Formatting in vLLM RAG Pipelines In a production Kubernetes deployment that serves high‑throughput GPU‑accelerated inference, the vLLM engine is used to generate responses augmented with retrieved documents (RAG). The downstream reference validator expects a strict JSON payload that contains a citations array, each citation delimited by the marker syntax defined … Read more

FAISS empty retrieval sets after tightening RAG metadata filters

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cable network

Problem – Empty Retrieval Sets After Tightening RAG Metadata Filters In a hybrid‑cloud RAG pipeline, the vector store is a FAISS index replicated from an on‑premises cluster to public‑cloud endpoints. After a recent rollout that added stricter boolean filters on the region, category, and source_id metadata fields, a growing number of queries returned zero results … Read more

RAG pipeline context injection failure after vector database query

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Problem: Intermittent RAG Context Injection Failure on Azure VM In a development sandbox running on an Azure Standard_DS3_v2 VM, a Retrieval‑Augmented Generation (RAG) pipeline executes the following steps: Load a FAISS index from /data/faiss_index. Query the index for the most relevant chunks. Inject the retrieved chunks into a PromptTemplate (LangChain or Haystack) and forward the … Read more

PostgreSQL vector search malformed JSON response after RAG pipeline update

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cable network

Problem Description After a routine update to the Retrieval‑Augmented Generation (RAG) pipeline, the citation extraction step began returning malformed JSON objects. Downstream the LLM serializer throws errors such as: JSON parsing error: Unexpected token ‘}’ at position 127 ERROR: invalid input syntax for type jsonb Observed symptoms in production logs: API responses missing source_id fields … Read more

Hugging Face RAG retrieval empty results on A100 GPU cluster

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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

ONNX Runtime RAG inference fails due to malformed prompt templates

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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

FAISS RAG pipeline chunk overlap configuration issues

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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

RAG retrieval empty results behind HAProxy in CI/CD pipeline

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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