Feb 15
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Building a RAG system is powerful. But production systems require: API endpoints Containerization Deployment flexibility Scalability Let’s turn your RAG project into a production-ready AI microservice. ✅ Step 1 — Install Required Tools pip install fastapi uvicorn langchain openai faiss-cpu You’ll also need Docker installed. ✅ Step 2 — Create the RAG System (Simplified Setup) Assume you already built a vector store: from langchain.embeddings.openai import OpenAIEmbeddings from langchain.vectorstores import FAISS from langchain.chat_models import ChatOpenAI from langchain.chains import RetrievalQA embeddings = OpenAIEmbeddings() db = FAISS.load_local("faiss_index", embeddings) llm = ChatOpenAI(model="gpt-4o") rag_chain = RetrievalQA.from_chain_type( llm=llm, retriever=db.as_retriever() ) This is your AI engine. ✅ Step 3 — Wrap It With FastAPI Create a file called app.py: from fastapi import FastAPI from pydantic import BaseModel app = FastAPI() class Query(BaseModel): question: str @app.post("/ask") def ask_question(query: Query): response = rag_chain.run(query.question) return {"answer": response} Now your AI is accessible via HTTP. ✅ Step 4 — Run the API Locally uvicorn app:app --reload Open: http://127.0.0.1:8000/docs You now have an interactive Swagger UI. ✅ Step 5 — Dockerize It Create a Dockerfile: FROM python:3.10 WORKDIR /app COPY . . RUN pip install -r requirements.txt CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "8000"] Build and run: docker build -t rag-api . docker run -p 8000:8000 rag-api Now it runs anywhere: Cloud Server Kubernetes VM 🔁 Why This Automation Matters Without automation: AI stays in notebooks No team access No scaling No integration With automation: Any frontend can call your AI Slack bots can connect Websites can integrate Apps can consume responses You just turned RAG into a real backend product. 🧠 Interview Questions & Answers ❓ Why deploy ML/AI models behind APIs? ✅ APIs allow applications, services, and users to interact with AI models in a scalable and structured way. ❓ Why use FastAPI for ML deployment? ✅ It is lightweight, fast, async-ready, and ideal for production AI services. ❓ Why containerize AI applications? ✅ Containers ensure consistent environments across development and production systems. ❓ What is the benefit of Docker in ML workflows? ✅ It eliminates dependency conflicts and simplifies deployment. ❓ How does this fit into MLOps? ✅ It automates model serving, making deployment repeatable and scalable. #AI #Automation #RAG #MLOps #CodeVisium