AI RAG Engine (LLM)

Large Language Models (LLMs) like GPT-4 are powerful but frozen in time and lack knowledge of your private data. Retrieval-Augmented Generation (RAG) bridges this gap by injecting relevant context from your own documents into the LLM’s prompt window.

This blueprint establishes a production-grade RAG pipeline capable of digesting PDFs, databases, and internal wikis to answer user queries accurately without hallucinations.

Architecture

The workflow consists of two main pipelines: Ingestion and Inference.

1. Ingestion:

   • Load: Extract text from varied sources (S3, SQL, Notion).
   • Split: Chunk text into manageable pieces (e.g., 500 tokens).
   • Embed: Convert chunks into vector embeddings (numbers representing meaning).
   • Store: Save vectors in a specialized Vector Database.

2. Inference (Query):

   • User Question: “What is our refund policy?”
   • Search: Convert question to vector -> Find nearest neighbors in DB.
   • Augment: Append found text chunks to a system prompt.
   • Generate: LLM provides the final answer based only on the provided context.

Use Cases

• Internal Knowledge Base: Chatbot that answers HR questions based on Confluence pages.
• Legal Contract Analysis: “Summarize the indemnification clause in these 50 PDFs.”
• Customer Support: Auto-draft responses based on past resolved tickets.
• Code Assistant: Query your own codebase for implementation details.

Implementation Guide

We will build a Python-based RAG engine using LangChain, Qdrant (Vector DB), and OpenAI.

Prerequisites

• Python 3.10+
• Docker (for Qdrant)
• OpenAI API Key

Step 1: Setup Vector Database

We utilize Qdrant for its speed and ease of use.

# docker-compose.yml
version: '3'
services:
  qdrant:
    image: qdrant/qdrant
    ports:
      - "6333:6333"
    volumes:
      - ./qdrant_storage:/qdrant/storage

Run it:

docker-compose up -d

Step 2: Ingestion Pipeline

Create ingest.py to load data and populate the Vector DB.

"""
ingest.py - Loads data into Qdrant
Dependencies: pip install langchain qdrant-client openai tiktoken langchain-community
"""
import os
from langchain_community.document_loaders import TextLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_community.vectorstores import Qdrant
from langchain_openai import OpenAIEmbeddings

# configuration
os.environ["OPENAI_API_KEY"] = "sk-..." 
COLLECTION_NAME = "my_knowledge_base"

def ingest():
    # 1. Load Data
    loader = TextLoader("./company_policy.txt")
    documents = loader.load()

    # 2. Split Text
    text_splitter = RecursiveCharacterTextSplitter(
        chunk_size=1000,
        chunk_overlap=200
    )
    chunks = text_splitter.split_documents(documents)
    
    print(f"Split into {len(chunks)} chunks")

    # 3. Embed & Store
    embeddings = OpenAIEmbeddings()
    
    Qdrant.from_documents(
        chunks,
        embeddings,
        url="http://localhost:6333",
        collection_name=COLLECTION_NAME
    )
    print("Ingestion Complete!")

if __name__ == "__main__":
    ingest()

Step 3: Query Engine

Create query.py to ask questions.

"""
query.py - Chat with your data
"""
import os
from langchain.chains import RetrievalQA
from langchain_openai import ChatOpenAI
from langchain_community.vectorstores import Qdrant
from langchain_openai import OpenAIEmbeddings
import qdrant_client

os.environ["OPENAI_API_KEY"] = "sk-..."
COLLECTION_NAME = "my_knowledge_base"

def ask(question):
    # Connect to DB
    client = qdrant_client.QdrantClient(url="http://localhost:6333")
    embeddings = OpenAIEmbeddings()
    
    vector_store = Qdrant(
        client=client, 
        collection_name=COLLECTION_NAME, 
        embeddings=embeddings
    )

    # Setup LLM & Chain
    llm = ChatOpenAI(model_name="gpt-4", temperature=0)
    qa_chain = RetrievalQA.from_chain_type(
        llm=llm,
        chain_type="stuff",
        retriever=vector_store.as_retriever(search_kwargs={"k": 3}),
        return_source_documents=True
    )

    # Execute
    print(f"User: {question}")
    result = qa_chain.invoke({"query": question})
    
    print(f"AI: {result['result']}")
    print("\nSources:")
    for doc in result['source_documents']:
        print(f"- {doc.page_content[:50]}...")

if __name__ == "__main__":
    ask("What is the policy on remote work?")

Production Readiness Checklist

Deploying LLMs requires strict guardrails.

[ ] Chunking Strategy: Verified that chunk_size (e.g., 500 vs 2000) matches the complexity of the documents. [ ] Embedding Model: Selected the right model (e.g., text-embedding-3-small vs Ada-002) for cost/performance. [ ] Vector Persistence: Qdrant volume is mounted to persistent disk (EBS/PVC). [ ] Rate Limiting: Implemented exponential backoff for OpenAI API calls (429 Too Many Requests). [ ] Hallucination Check: Added a system prompt instruction: “If you don’t know the answer based on the context, say ‘I don’t know’.” [ ] Cost Estimation: Calculated daily token usage. (Avg query = 500 input + 200 output tokens). [ ] Privacy: Ensured PII is masked before sending text to external Embedding APIs.