WebRAG – Scalable RAG Engine

The Problem

Building production-ready RAG systems that can handle high concurrency while maintaining low latency is challenging. Most implementations fail to scale beyond prototype stage.

The goal: Engineer a scalable RAG engine that handles concurrent requests with consistent low-latency retrieval.

Technical Implementation

Architecture Decisions

Built a high-concurrency RAG system with three core components:

1. Embedding Pipeline: Document ingestion → Gemini-embedding-001 → Qdrant vector storage
2. Async Processing: FastAPI + Celery for non-blocking document processing
3. Metadata Persistence: PostgreSQL for document metadata and tracking

Key Technical Implementations

Component	Implementation	Purpose
Embeddings	Gemini-embedding-001	High-quality semantic representations
Vector DB	Qdrant	Low-latency similarity search
Text Chunking	RecursiveCharacterTextSplitter	Optimized chunk sizing for embedding storage
Task Queue	Celery + Redis	Async document processing pipeline
Deployment	Docker Compose	Reproducible, scalable deployment

Tech Stack Rationale

Why Qdrant over alternatives?

• Native support for payload filtering
• Excellent performance at scale
• Simple deployment with Docker

Why Celery?

• Reliable async task execution
• Redis as broker for fast message passing
• Easy horizontal scaling for document processing

What I Learned

Things That Worked

1. Gemini-embedding-001 quality: Consistently high-quality embeddings improved retrieval accuracy significantly.

2. Async-first architecture: FastAPI + Celery combination handled concurrent loads efficiently.

3. RecursiveCharacterTextSplitter: LangChain’s intelligent chunking preserved semantic context better than naive splitting.

Things I’d Improve

1. Add hybrid search: Combine vector search with BM25 for better keyword matching.

2. Implement caching layer: Cache frequent queries to reduce embedding API calls.

3. Add evaluation pipeline: Systematic evaluation of retrieval quality.

Links

• GitHub Repository