Enterprise Operations
Enterprise Knowledge Retrieval & RAG Pipeline
Private Hybrid Vector Retrieval with Deterministic Citations
Executive Summary
A private Retrieval-Augmented Generation (RAG) system with hybrid dense-sparse vector search, enabling staff to query thousands of technical documents with cited sources.
The Architectural Challenge
Internal support and operations staff spent hours hunting through fragmented documentation across PDFs, Notion, and databases, causing prolonged ticket response times.
Engineering Approach & Systems Design
Architected a private hybrid vector indexing pipeline using Qdrant and LangChain, complete with strict tenant isolation, PII guardrails, and cited source document links.
Architecture Implementation Details
- Qdrant hybrid dense-sparse vector cluster with tenant metadata filters
- OpenAI Text-Embedding-3-Large with automated chunk deduplication
- Python FastAPI microservices with strict PII sanitization middleware
- Next.js 15 chat workspace with clickable PDF source citations
Technology Stack
LangChainQdrantOpenAI EmbeddingsNext.js 15Python FastAPIPostgreSQL
Qualitative Outcome
Significantly streamlined internal knowledge discovery, reduced repetitive tier-1 support queries, and ensured 100% data confidentiality.
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