Skip to content

README.md

Latest commit

 

History

History
58 lines (42 loc) · 3.42 KB

README.md

File metadata and controls

58 lines (42 loc) · 3.42 KB

Scalable LLMOps Pipeline

License: MIT Python LangChain RAG

Production-grade blueprint for building, deploying, and scaling reliable LLM-powered applications

This repository contains a comprehensive guide and reference architecture for LLMOps — the specialized extension of MLOps tailored to large language models (LLMs). It covers the full lifecycle: prompt engineering, RAG pipelines, agent orchestration, observability, evaluation, guardrails, cost optimization, and continuous iteration.

Whether you're building customer support chatbots, internal knowledge assistants, agentic financial tools, or voice-enabled agents, this pipeline helps you move from prototype to production with reliability, auditability, and cost control.

Why LLMOps Matters

LLMs bring unique production challenges:

  • Non-deterministic outputs & hallucinations
  • Prompt brittleness & high inference costs
  • Rapid model/provider changes & data freshness issues
  • Ethical risks (bias, toxicity, jailbreaks)

Without structured LLMOps, most prototypes fail at scale due to drift, runaway costs, poor reliability, and compliance gaps.

This guide bridges traditional MLOps → modern LLMOps with practical patterns used in enterprise settings (e.g., RAG chatbots at banks, agentic flows like BlackRock's Aladdin Copilot).

Key Features & Coverage

  • 📊 Detailed comparison: LLMOps vs. Traditional MLOps (table included)
  • ⚙️ End-to-end pipeline: Foundations → Build → Deploy → Observe → Scale
  • 🏗️ Layered architecture: User → Orchestration → Retrieval → Inference → Observability
  • 🔧 Core components table: Prompt Registry, Vector Stores, Orchestration Engines, Inference Gateways, Guardrails, etc. (with tools & engineering notes)
  • 🔄 Data flows: Typical production RAG + agent processing (with guardrails)
  • ⚖️ Stateless vs. Stateful apps: Trade-offs, patterns, and when to add memory
  • 🛡️ Real-world focus: Latency targets (<2-3s), cost drivers, feedback loops, A/B/canary rollouts, enterprise compliance

Core Tech Stack (Reference)

  • Orchestration: LangChain / LangGraph / LlamaIndex
  • Retrieval: Pinecone / Weaviate / Chroma / PGVector
  • Embeddings: OpenAI / Sentence Transformers / Cohere
  • Inference: OpenAI / Anthropic / Groq / vLLM / Ray Serve / TGI
  • Tracing & Observability: LangSmith / Helicone / Phoenix / OpenLLMetry
  • Evaluation: DeepEval / RAGAS / LLM-as-Judge / LangSmith Datasets
  • Guardrails: NeMo Guardrails / Llama Guard / Patronus
  • Serving & Scaling: FastAPI / Kubernetes / Redis caching / LiteLLM / Portkey
  • Other: GitOps / CI/CD (GitHub Actions), Prometheus alerts

Real-world example stack (enterprise RAG chatbot):
LangGraph → Pinecone → OpenAI → Helicone tracing → LangSmith eval → Ray Serve / Kubernetes → Prometheus

Getting Started

This repo is primarily a reference guide & architecture blueprint (based on a detailed 9-page document). To use it:

  1. Clone the repo: 
    git clone https://github.com/YOUR_USERNAME/scalable-llmops-pipeline.git
cd scalable-llmops-pipeline