Architecture Reference Blueprint
Enterprise RAG Pipeline: Hybrid Search & Re-ranking
A production architecture for enterprise document Q&A that eliminates hallucinations, retrieves verified citations, and respects document access permissions.
System Constraints
Non-Negotiable Architecture Constraints
Component Topology
System Components & Technologies
Modular subsystems designed with decoupled responsibilities, clear contracts, and scalable storage layers.
Enterprise RAG Pipeline: Hybrid Search & Re-ranking Stack Topology
Document Ingestion Engine
Embedding & Vector Store
Sparse Keyword Engine
Cross-Encoder Re-ranker
Document Ingestion Engine
OCR, layout-aware PDF parsing, and semantic chunking
LlamaIndex + Unstructured.io
Embedding & Vector Store
Dense vector indexing with metadata permission filtering
Qdrant / PostgreSQL pgvector
Sparse Keyword Engine
Lexical BM25 search for exact acronym and code retrieval
Elasticsearch / OpenSearch
Cross-Encoder Re-ranker
Contextual re-ranking of top 50 retrieved candidates down to top 5
Cohere Rerank / ColBERT
LLM Generation & Guardrail
Context grounding, citation synthesis, and PII redaction
vLLM / Anthropic Claude 3.5 Sonnet
Data Lifecycle
End-to-End Data Flow Sequence
User submits a query along with their enterprise identity and group permissions token.
Query is converted into dense vector embedding and sparse BM25 keyword tokens in parallel.
Hybrid search queries vector store and lexical engine, filtering candidates strictly by user's group permissions.
Reciprocal Rank Fusion (RRF) merges results, passing top 40 candidate chunks to the ColBERT cross-encoder.
Re-ranked top 5 chunks are injected into the system prompt with strict citation instructions; LLM streams answer.
Reliability & Resilience
Failure modes & automated mitigations
Context Window Saturation with Irrelevant Noise
Apply semantic chunking and strict score cutoff thresholds on the cross-encoder re-ranker.
Hallucination of Unsupported Statistics
Implement an automated validator (DeepEval) that cross-checks all generated numbers against retrieved chunks.
High Embedding API Latency
Cache embedding vectors for frequent queries in Redis and run local embedding models (BGE-Large) on dedicated inference GPUs.
Architecture FAQs
Frequently asked blueprint questions
Dense embeddings excel at conceptual meaning but frequently fail on exact part numbers, acronyms, and names. Combining dense vectors with BM25 keyword search achieves 95%+ retrieval recall.
Document permission ACLs are stored as metadata attributes on each vector chunk and evaluated dynamically at query time against the user's active session roles.
Senior engineering teams that build for long-term production health
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