RAG + Face Detection Offline: Smart Search, Identity Intelligence & Domain-Specific RAG — A Practical MVP

How an offline Retrieval-Augmented Generation (RAG) platform with a Document Hygiene engine and face-detection pipeline enables fast, private, domain-specific intelligence across law enforcement, housing societies, manufacturing and finance.

We built an offline RAG platform that combines:

• A Document Hygiene engine (sentence transformation + vector ingestion)
• Local LLM-driven smart search → Neo4j Cypher
• Vector search (ChromaDB) for semantic retrieval
• Face-detection + vector matching pipeline
  This stack powers natural-language smart search, identity resolution, and relationship discovery — all running on-premises for privacy and performance.

Problem Statement

Organizations need fast, accurate search across heterogeneous data (records, documents, images) while maintaining privacy and control. Cloud APIs can be costly, raise compliance issues, and require network connectivity. Teams need:

• Natural-language querying of structured and unstructured data
• Fast face-matching and identity lookups (offline)
• Relationship mapping across people, places and events
• A reusable platform that adapts to different domains

Our Solution (High-level)

We developed an offline RAG platform built around a Document Hygiene Engine and multi-modal retrieval:

• Document Hygiene: cleans, segments, transforms sentences for optimal semantic vectors
• Vector DB (ChromaDB): stores embeddings for semantic retrieval
• Graph DB (Neo4j): stores structured records, relationships, and master hierarchies
• Local LLM (Mistral/Llama family): converts NL queries to Cypher (with validator/sanitizer)
• Face pipeline: local face encoder → ChromaDB similarity lookup → ranked matches
• Frontend: React UI with Smart Search, Manage Records, Face Detection and card-based results

Architecture (summary)

• Frontend: React (Vite) + Tailwind — SmartSearchTab, ManageRecordsTab, FaceDetectionTab
• API: FastAPI (Uvicorn) — routers for smart_search, records, face_detection, images
• Retrieval: ChromaDB for embeddings, local encoder for vectors
• Graph: Neo4j for Person nodes, master nodes (Country/State/District/City), and relationships
• Orchestration: Local LLM prompts (with schema included), sanitizers, fallbacks and cleanup hooks

Domain Use Cases

1. Manufacturing (Quality, Maintenance, Compliance)

• Use case examples:
  • Find maintenance procedures for machines with similar failure patterns
  • Search QA reports for recurring defect descriptions
  • Trace supplier documentation and contract clauses related to a part
• Why it fits:
  • Documents (specs, tickets, logs) are transformed into high-quality vectors
  • Graph DB models relationships between parts, machines, vendors
  • RAG answers contextual queries like “Which suppliers’ parts caused repeated bearing failures?”

2. Housing Societies (Security, Resident Management)

• Use case examples:
  • Identify visitors/unknown persons via face matching against resident DB
  • Search resident records by partial address, vehicle number, or complaint text
  • Quickly list residents with specific access privileges or past incidents
• Why it fits:
  • Face-detection pipeline handles photo-based lookups offline
  • Master data (city/district/police_station) ensures consistent filtering
  • Smart search handles casual user queries (“find person living in Dehradun society A”)

3. Financial Services & Fraud Detection

• Use case examples:
  • “Show accounts with sudden large deposits followed by quick withdrawals”
  • Natural-language compliance queries across transaction logs and policy docs
  • Relationship traversal to find intermediaries or shell accounts
• Why it fits:
  • Graph DB is ideal for transactional linkage and chain analysis
  • RAG with hygiene tooling ingests policy/regulatory docs for combined reasoning

4. Additional applicable verticals

• Healthcare (EHR search, research paper retrieval)
• Legal (contract clause search, precedent retrieval)
• Public Sector & Emergency Response (citizen records, incident triage)

Technical & Operational Considerations

• Offline-first: all components can run on-premises in air-gapped environments
• Resource guidance: 32+ GB RAM recommended for comfortable performance with 7B LLMs; GPU recommended for embedding and LLM inference
• Security: role-based access, audit logs, data lineage for compliance
• Extensibility: add domain modules (entity extractors, phrase→field mapping) and custom embedding models

Business Benefits

• Privacy & compliance by design (no external API calls)
• Faster discovery and decision making (natural-language queries)
• Reusable platform economics — one hygiene engine + vector DB supports many domains
• Demonstrable ROI in time saved, faster investigations, reduced manual processing