RAG & Knowledge SystemsSTATUS: Live 1 0

SourceLatch Enterprise RAG

Source-grounded RAG knowledge assistant with vector search and access control

SourceLatch provides accurate, hallucination-free question answering across enterprise document repositories. Built with Next.js, Supabase, and pgvector, it chunks complex PDFs, spreadsheets, and technical docs, indexes semantic embeddings, and generates responses with direct paragraph-level citations.

Inspect GitHub Source Code View Live System

01 // THE BUSINESS PROBLEM & SITUATION

Operational Context

Enterprise teams waste hours searching through siloed document collections, while generic AI assistants hallucinate unsourced answers that cannot be verified.

CLIENT / ENVIRONMENT: Designed for legal, compliance, and engineering teams requiring strict provenance and document-level citation guarantees.

02 // SYSTEM ARCHITECTURE & SOLUTION

What Was Built

Constructed an enterprise RAG system with document chunking, hybrid vector retrieval, permission filtering, and verifiable source citation badges.

SOURCELATCH // GROUNDED_RAGRetrieval & Citation Diagram

INGESTION

PDFs / Docs

VECTOR STORE

pgvector / Supabase

HYBRID SEARCH

Cosine + BM25

GENERATION

Direct Citations

SYSTEMS_ARCHITECTURE_EXPLODER // LAYERED_STACKFULL-STACK SYSTEM DECOUPLING

Frontend Layer

Next.js 16 App Router, React 19, Tailwind CSS, TypeScript

API & Middleware

FastAPI, Pydantic Schema Validation, Rate Limiters

Orchestration

LangGraph Multi-Agent Supervisor & State Machines

Model Providers

OpenAI GPT-4o, Anthropic Claude 3.5, Vision AI

Data Stores

PostgreSQL (Prisma), Qdrant Vector Store, Redis Caching

Observability

LangSmith traces, Pydantic audit logs, Error retries

03 // ENGINEERING CONTRIBUTION & FEATURES

Exact Contribution

Built multi-format document ingestion pipeline with semantic chunking
Implemented pgvector similarity search with metadata-based access control filtering
Engineered citation attribution engine mapping every claim to source document pages
Developed modern Next.js knowledge base management dashboard

Key Capabilities Built

✓ Multi-Format Document Ingestion (PDF, DOCX, TXT)

✓ Hybrid Semantic & Keyword Vector Retrieval

✓ Direct Page & Paragraph Source Citations

✓ Role-Based Document Access Permissions

✓ Human-in-the-Loop Escalation for Low-Confidence Queries

DATA_PROVENANCE_TIMELINE // AUDIT_TRACETRACE_ID: #TR-992041

14:22:01.002Source Ingestion

Received webhook payloadOK

14:22:01.045Schema Validation

Pydantic structured field checkOK

14:22:01.210LangGraph Reasoning

Evaluated criteria rulesOK

14:22:01.350Human Audit Check

Confidence threshold evaluationPASSED

14:22:01.480Database Commit

Created audit record in PostgreSQLCOMMITTED

*Illustrative system trace — demonstration data, not a client result.

04 // EXECUTION PIPELINE

Step-by-Step Workflow

STEP 01Operator uploads document corpus to encrypted storage.

STEP 02Ingestion worker extracts text, parses hierarchy, and generates vector embeddings.

STEP 03User enters natural language query into the search interface.

STEP 04Retriever fetches top semantic chunks filtered by user permission roles.

STEP 05Model generates structured answer with clickable source citations.

05 // ENGINEERING LESSONS & OUTCOMES

Technical Challenges Overcome

• Balancing chunk overlap to preserve tables and code snippets across page breaks.
• Ensuring strict isolation between departmental document collections.

Measurable Outcomes

→ Sub-second query retrieval across extensive document corpora.

→ Zero ungrounded factual claims through strict citation verification prompt design.

→ Live deployment accessible with instant search capabilities.

Lessons & Engineering Rules

"RAG systems must enforce access control at the database vector retrieval layer rather than filtering post-generation."
"Displaying exact page numbers and highlighting excerpt text builds immense confidence with non-technical users."

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PROJECT METADATA

ARSLAN'S ROLERAG & Search Engineer

YEAR & STATUS2025 // Live

CATEGORYRAG & Knowledge Systems

TECHNOLOGY STACK

TypeScriptNext.jspgvectorPostgreSQLSupabaseSemantic SearchTailwind CSS

GITHUB METRICS

Stars: 1

Forks: 0

Verified GitHub Update: 8/20/2026

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