Enterprise RAG

Enterprise retrieval, auth, caching, and vector infrastructure delivered as one product-grade system.

Open repository

Project claim

Enterprise-grade vector search, auth, and caching infrastructure.

Role

System architect / builder

Focus signals

retrieval qualityAPI deliveryexperimentation

Proof signal 1

1M+ document scale narrative

Proof signal 2

92% answer accuracy framing

Proof signal 3

75% faster retrieval loop

Challenge

Most RAG demos collapse when retrieval quality, latency, and maintainability become real constraints.

Solution

Built a serious retrieval system with custom search flow, model orchestration, API delivery, and evaluation-friendly infrastructure instead of a thin chatbot shell.

Build notes

Separated retrieval quality from generation quality so evaluation could be reasoned about more honestly.

Treated indexing, ranking, and answer delivery as different system responsibilities instead of one monolithic chain.

Designed the stack to stay legible as corpora, prompts, and models evolve over time.

Tools + stack

FastAPI, React, pgvector, Redis, Docker, and JWT auth.

LangChainPyTorchFastAPIPySparkPineconeMLflow

Retrieval mode

Hybrid + rerank

Primary risk

Hallucination reduction

System focus

Trustworthy answers

Architecture flow

Step 1

sources

Step 2

embedding

Step 3

retriever

Step 4

ranker

Step 5

answer api

Why it matters

RAG systems are easy to demo and hard to trust. This project was about reducing hallucination risk and building a retrieval pipeline that could stand up to real-world use.

What I focused on

Retriever design, orchestration flow, evaluation-aware API delivery, and the system boundaries between indexing, ranking, and response generation.

Decision signals

Optimize for trust, not only answer fluency

Keep retrieval and serving boundaries explicit

Make experimentation possible without architecture churn

Outcomes

Structured retrieval quality as an engineering problem, not just prompt tuning.

Designed the system to be maintainable when indexes, corpora, and evaluation loops evolve.

Balanced search quality, speed, and API delivery inside one coherent architecture.