RAG • Chapter 11

End-to-End Application Building

RAG engineering module on End-to-End Application Building.

6 note blocks4 exam topics

🎯 Exam Focus Areas

Evaluate chunking and embedding strategies.Understand Vector DB indexing architectures like HNSW.Analyze RAG prompts for injection vulnerabilities.Calculate and utilize RAGAS evaluation metrics.

Bringing it all together involves orchestrating the ingestion, retrieval, and generation pipelines into a unified, user-facing application.

Advanced System Mechanics

Modern RAG applications use orchestration frameworks like LangChain or LlamaIndex. A full-stack application typically features a React/Next.js frontend, a FastAPI backend, an OpenAI/Anthropic LLM, and a managed Vector DB like Pinecone. State management, conversation memory (chat history), and streaming responses via WebSockets or Server-Sent Events (SSE) are crucial for a smooth user experience.

1Understand the vector space implications of this concept.
2Identify potential hallucination risks.
3Optimize for low latency and high relevance.
4Ensure robust system prompts.

Implementation Blueprint

from fastapi import FastAPI
from langchain_openai import ChatOpenAI
from pydantic import BaseModel

app = FastAPI()
llm = ChatOpenAI()

class QueryRequest(BaseModel):
    question: str

@app.post("/chat")
def chat_endpoint(req: QueryRequest):
    # 1. Retrieve (mocked)
    context = "RAG combines retrieval and generation."
    
    # 2. Generate
    prompt = f"Context: {context}\nQuestion: {req.question}"
    response = llm.invoke(prompt)
    
    return {"answer": response.content}

📝 Quick Revision Points

1Review the differences between similarity metrics.
2Practice the LangChain/LlamaIndex code snippets.
3Understand the HyDE architecture deeply.
4Memorize the security guardrail implementations.

← PreviousSecurity, Privacy & Prompt Injection

Loading notes...

from fastapi import FastAPI from langchain_openai import ChatOpenAI from pydantic import BaseModel app = FastAPI() llm = ChatOpenAI() class QueryRequest(BaseModel): question: str @app.post("/chat") def chat_endpoint(req: QueryRequest): # 1. Retrieve (mocked) context = "RAG combines retrieval and generation." # 2. Generate prompt = f"Context: {context}\nQuestion: {req.question}" response = llm.invoke(prompt) return {"answer": response.content}