LLMOPS 02: Build Multi-Doc Chat with Advanced RAG Part-1| RAG in a Modular Manner (Logger, Config)

Описание: Welcome to the Complete LLMOPS Project Series!
In this End-to-End Advanced RAG Project, we’ll build a production-ready GenAI Document Chat System in step by step. From setup → RAG pipeline → FastAPI integration → testing → deployment on AWS ECS (Fargate) — everything is covered.

Series Syllabus

1️⃣ Setup & Installation – UV setup, project structure, requirements, Jupyter integration
2️⃣ RAG Implementation – Core concepts, logger, config, Pydantic models, multi-doc chat, advanced RAG (token counter, memory, evals, MMR)
3️⃣ FastAPI Integration – Overview, adding APIs, testing with Swagger UI
4️⃣ UI Integration – HTML & CSS basics, connect frontend with backend
5️⃣ Testing with Pytest – Unit & integration tests, fixtures, mocking, test cases for multi-doc chat
6️⃣ Deployment (AWS ECS Fargate) – CI/CD pipeline, GitHub Actions, workflow integration, cloud deployment

LangChain – RAG pipeline
DataStax AstraDB– Vector store
Python – Backend + AI logic
FastAPI – API layer
Pytest – Testing framework
AWS ECS (Fargate) – Cloud deployment
Streamlit/HTML/CSS – UI

Project Github: https://github.com/yashprogrammer/LLM...

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End-to-End-Langgraph-Project: https://github.com/sunnysavita10/doct...

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