RAG Architecture Blueprint for Confidential Corporate Knowledge Base
Technical architecture diagram, vector database selection matrix, chunking strategies, and security protocols.
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Table of Contents & Section Overview
16 Pages TotalSystem Architecture & Ingestion Pipeline
Data flow from document repos to vector index.
Chunking & Embedding Optimization
Recursive character vs semantic chunking algorithms.
Hybrid Search & Reciprocal Rank Fusion
Combining sparse BM25 with dense vector embeddings.
ACL Security Trimming at Retrieval Layer
Enforcing document-level user permissions in real time.
Appendix: Benchmark Test Suite
Evaluation metrics for precision and recall tracking.
Problem Statement
"Deploying RAG for internal corporate documents requires solving permission trimming, OCR table extraction, and hallucination control simultaneously while keeping latency under 1.5 seconds."
Section 3 Excerpt: Hybrid Search & Reciprocal Rank Fusion
Dense embeddings capture broad semantic meaning but frequently miss exact alphanumerics such as policy reference numbers or code identifiers. Implementing sparse BM25 search alongside vector search is mandatory for enterprise accuracy.
Combine search results using Reciprocal Rank Fusion (RRF) with k=60 to recalculate rank scores across sparse and dense candidate sets prior to feeding context to the LLM.
Vector DB Performance Comparison
| Database | Index Type | Filtered Search Support | Self-Hosted Ease |
|---|---|---|---|
| pgvector (Postgres) | HNSW / IVFFlat | Native SQL Pre-Filtering | Very High |
| Qdrant | HNSW | Single-Stage Filtered Graph | High |
| Pinecone | Proprietary | Post & Pre-Filtering | Managed Cloud Only |
Related Engineering Resources & Articles
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