AI-Assisted SOP Search System (RAG)

Status

🚧 In Progress — actively building toward a production-ready, compliance-aware RAG system for regulated SOP search.

Overview

This project implements an AI-assisted SOP search system using a Retrieval-Augmented Generation (RAG) architecture. The goal is to enable semantic search and grounded question answering across SOP documentation while supporting regulated environment needs (GxP), including traceability, explainability, and validation readiness.

Planned Architecture

1. Ingestion: parse documents, extract metadata, chunk text
2. Embedding & Indexing: generate embeddings and store in a vector database
3. Retrieval: top-k similarity search + metadata filtering
4. Generation: grounded responses constrained to retrieved context
5. Deployment: containerized service with audit-friendly logging

Tech Stack (Target)

• Python
• Docker
• GCP Cloud Run (deployment target)
• Vector DB (Milvus or equivalent)
• Transformer embeddings + RAG pipeline

Milestones

• Define document schema + metadata strategy
• Build ingestion + chunking pipeline
• Implement embedding + vector indexing
• Add retrieval evaluation (precision@k / recall@k)
• Add grounded generation with citations to source chunks
• Containerize and deploy to Cloud Run
• Add monitoring hooks + audit logging

Validation & Governance (Design Goals)

• Traceability from answer → retrieved chunks → source document
• Explainability through surfaced retrieval results
• Controlled model/version changes aligned with change management
• Audit-friendly logs and reproducible retrieval behavior

Next Steps

Initial scaffolding and architecture documentation are underway. Code and deployment artifacts will be added as core milestones are completed.