RAG Developers’ Stack: Building Advanced AI Applications

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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 ✅

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