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The RAG (Retrieval-Augmented Generation) stack is revolutionizing AI development by combining powerful language models with efficient data retrieval systems. Here’s a breakdown of the key components and how they work together:
🌟 LLMs (Large Language Models)
Advanced transformer-based models like GPT-4, LLaMA, and Mistral form the core of RAG applications. Open-source models (e.g., LLaMA-3, Falcon) and proprietary ones (e.g., OpenAI’s GPT) provide flexibility.
You Should Know:
- Run LLaMA locally:
ollama pull llama3 ollama run llama3
- Use OpenAI’s API:
import openai response = openai.ChatCompletion.create(model="gpt-4", messages=[{"role": "user", "content": "Explain RAG."}])
🌟 Frameworks (LangChain, Llama Index)
These frameworks simplify RAG development by providing pre-built modules for retrieval, generation, and evaluation.
You Should Know:
- Install LangChain:
pip install langchain
- Basic RAG pipeline:
from langchain.document_loaders import WebBaseLoader from langchain.embeddings import OpenAIEmbeddings from langchain.vectorstores import FAISS </li> </ul> loader = WebBaseLoader("https://example.com") docs = loader.load() db = FAISS.from_documents(docs, OpenAIEmbeddings()) retriever = db.as_retriever()🌟 Vector Databases (Milvus, Qdrant, Pinecone)
Store and retrieve embeddings efficiently for semantic search.
You Should Know:
- Run Qdrant locally:
docker pull qdrant/qdrant docker run -p 6333:6333 qdrant/qdrant
- Query embeddings in Python:
from qdrant_client import QdrantClient client = QdrantClient("localhost", port=6333)
🌟 Data Extraction (PDF, Web Scraping)
Extract structured data from unstructured sources.
You Should Know:
- Extract text from PDFs:
pip install pdfplumber
import pdfplumber with pdfplumber.open("doc.pdf") as pdf: text = "\n".join([page.extract_text() for page in pdf.pages])
🌟 Open LLMs Access (Ollama, Hugging Face, Groq)
Run LLMs locally or via APIs.
You Should Know:
- Use Hugging Face models:
from transformers import pipeline generator = pipeline("text-generation", model="gpt2") print(generator("RAG is"))
🌟 Text Embeddings (OpenAI, Sentence Transformers)
Convert text into numerical vectors for retrieval.
You Should Know:
- Generate embeddings:
from sentence_transformers import SentenceTransformer model = SentenceTransformer('all-MiniLM-L6-v2') embeddings = model.encode("RAG applications")
🌟 Evaluation (Ragas, Giskard)
Ensure RAG systems perform accurately.
You Should Know:
- Install Ragas:
pip install ragas
- Evaluate retrieval:
from ragas import evaluate results = evaluate(dataset, metrics=[answer_relevancy, faithfulness])
What Undercode Say
RAG is a game-changer in AI, blending retrieval efficiency with generative power. Mastering these tools—LLMs, vector databases, and evaluation frameworks—will elevate your AI projects.
Expected Output:
A fully functional RAG pipeline integrating LangChain, Qdrant, and OpenAI for seamless AI-driven applications.
Relevant URLs:
References:
Reported By: Habib Shaikh – Hackers Feeds
Extra Hub: Undercode MoN
Basic Verification: Pass ✅Join Our Cyber World:
- Run Qdrant locally:
