Types of RAG: Enhancing AI with Retrieval-Augmented Generation

Building smarter AI systems requires optimizing how they retrieve and generate information! That’s where Retrieval-Augmented Generation (RAG) plays a crucial role in enhancing AI’s accuracy and relevance.

Different types of RAG techniques improve how AI processes data:

🔹 Naïve RAG – A simple approach using basic vector search, ideal for static FAQ-based AI but sometimes leading to irrelevant results.
🔹 Agentic RAG (Router) – Intelligently picks the best data sources based on query type, ensuring more accurate retrieval.
🔹 Agentic RAG (Multi-Agent) – Uses multiple AI agents for specialized tasks like retrieval, ranking, and generation, boosting precision.
🔹 Multimodal RAG – Retrieves information from text, images, video, and audio, enabling AI to process richer contextual data.
🔹 Hybrid RAG – Merges keyword-based and vector search for balanced accuracy and contextual relevance.
🔹 Retrieve-and-Rank RAG – Applies deep learning models to refine search results, ensuring higher precision.
🔹 Graph RAG – Leverages knowledge graphs for structured retrieval, enhancing AI’s reasoning and understanding.

By leveraging the right RAG approach, AI can deliver more insightful and context-aware responses, unlocking new possibilities across industries.

You Should Know: Practical Implementation of RAG Techniques

1. Setting Up Naïve RAG with Python (FAISS + HuggingFace)

   from sentence_transformers import SentenceTransformer 
   import faiss 
   import numpy as np </li>
   </ol>
   
   <h1>Load pre-trained model</h1>
   
   model = SentenceTransformer('all-MiniLM-L6-v2')
   
   <h1>Sample documents</h1>
   
   documents = ["RAG improves AI responses.", "Vector search enables semantic retrieval."] 
   embeddings = model.encode(documents)
   
   <h1>Build FAISS index</h1>
   
   dimension = embeddings.shape[1] 
   index = faiss.IndexFlatL2(dimension) 
   index.add(embeddings)
   
   <h1>Query</h1>
   
   query = "How does RAG work?" 
   query_embedding = model.encode([query]) 
   k = 1 # Top-1 result 
   distances, indices = index.search(query_embedding, k) 
   print("Retrieved document:", documents[indices[0][0]]) 
   

   2. Agentic RAG with LangChain

   from langchain.agents import Tool, AgentExecutor 
   from langchain import OpenAI
   
   <h1>Define retrieval tool</h1>
   
   def retrieve_docs(query): 
   return "Relevant context based on query."
   
   tools = [Tool(name="Retriever", func=retrieve_docs, description="Fetches documents")] 
   agent = AgentExecutor.from_agent_and_tools(agent=OpenAI(temperature=0), tools=tools) 
   print(agent.run("Explain Agentic RAG.")) 
   

   3. Multimodal RAG with CLIP

   
   <h1>Install CLIP for image-text retrieval</h1>
   
   pip install git+https://github.com/openai/CLIP.git
   
   <h1>Encode images and text</h1>
   
   import clip 
   model, preprocess = clip.load("ViT-B/32") 
   image_features = model.encode_image(preprocess(image)) 
   text_features = model.encode_text(clip.tokenize(["RAG for images"])) 
   

   4. Hybrid RAG with Elasticsearch + BERT

   
   <h1>Elasticsearch hybrid search setup</h1>
   
   curl -X PUT "localhost:9200/rag_index" -H 'Content-Type: application/json' -d' 
   { 
   "mappings": { 
   "properties": { 
   "text": { "type": "text" }, 
   "vector": { "type": "dense_vector", "dims": 768 } 
   } 
   } 
   }' 
   

   5. Graph RAG with Neo4j

   [cypher]
   // Create a knowledge graph
   CREATE (rag:Concept {name: “Retrieval-Augmented Generation”})
   CREATE (ai:Concept {name: “Artificial Intelligence”})
   CREATE (rag)-[:RELATES_TO]->(ai)
   [/cypher]

   What Undercode Say

   RAG techniques bridge the gap between retrieval systems and generative AI, offering scalable solutions for dynamic knowledge integration. Key takeaways:
   – Naïve RAG is easy to implement but lacks sophistication.
   – Agentic RAG introduces dynamic routing for precision.
   – Multimodal RAG expands AI’s sensory capabilities.
   – Hybrid/Grap RAG combines structured and unstructured data for deeper insights.

   For Linux/Windows practitioners, integrating RAG with CLI tools enhances automation:

   
   <h1>Linux: Batch-process documents for embeddings</h1>
   
   find /data/docs -name "*.txt" | xargs -I {} python encode.py {}
   
   <h1>Windows PowerShell: Query Elasticsearch</h1>
   
   Invoke-RestMethod -Uri "http://localhost:9200/rag_index/_search" -Method Post -Body '{"query":{"match":{"text":"RAG"}}}' 
   

   Expected Output:

   A functional RAG pipeline delivering context-aware AI responses with optimized retrieval.

   URLs for Further Learning:

   • LangChain Documentation
   • FAISS GitHub
   • CLIP by OpenAI

   References:

   Reported By: Habib Shaikh – Hackers Feeds
   Extra Hub: Undercode MoN
   Basic Verification: Pass ✅

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