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Retrieval-Augmented Generation (RAG) architectures enhance AI models by integrating retrieval mechanisms with generative models. Below are 25 types of RAG architectures:
- Rule-Based RAG – Uses predefined rules to filter and retrieve data.
- Conversational RAG – Optimized for dialogue systems to maintain context.
- Iterative RAG – Repeatedly refines responses by re-querying databases.
- HybridAI RAG – Combines neural and symbolic AI for enhanced retrieval.
- Generative AI RAG – Leverages generative models to craft coherent outputs.
- Explainable AI (XAI) RAG – Ensures transparent and interpretable retrieval.
- Context Cache LLM RAG – Stores previous context for faster access.
- Grokking RAG – Enhances performance by deeply understanding patterns.
- Replug Retrieval Feedback – Iteratively improves by refining data.
- Attention Unet RAG – Uses attention layers with UNet structures.
- Corrective RAG – Adjusts responses based on user feedback.
- Speculative RAG – Generates speculative answers while awaiting retrieval.
- Agenetic RAG – Applies agent-based systems for better data interaction.
- Self-RAG – Auto-corrects and improves using internal feedback.
15. Adaptive RAG – Dynamically adjusts retrieval strategies.
- Refeed Retrieval Feedback – Refeeds data to enhance iterative learning.
- Realm RAG – Language models enhanced by retrieval modules.
- Raptor RAG – Uses tree-structured retrieval for complex queries.
- Memo RAG – Memorizes key information for rapid recall.
20. Attention-Based RAG – Focuses on attention mechanisms.
- RETRO RAG – Integrates retrieval with transformers for long-context handling.
- Auto RAG – Automates retrieval and response generation.
- Cost-Constrained RAG – Optimizes retrieval by minimizing costs.
- ECO RAG – Environmentally conscious with energy-efficient processes.
- Replug (Retrieval Plugin) RAG – Modular plugin for external retrieval.
You Should Know: Practical Implementation of RAG
To implement RAG architectures, here are key commands, tools, and steps:
1. Setting Up a Basic RAG Model
<h1>Install required libraries</h1> pip install transformers faiss-cpu sentence-transformers
2. Retrieval with FAISS (Facebook AI Similarity Search)
import faiss
import numpy as np
<h1>Create a sample index</h1>
dimension = 768 # BERT embedding size
index = faiss.IndexFlatL2(dimension)
<h1>Add embeddings to index</h1>
embeddings = np.random.rand(100, 768).astype('float32')
index.add(embeddings)
<h1>Search for nearest neighbors</h1>
query_embedding = np.random.rand(1, 768).astype('float32')
k = 5
distances, indices = index.search(query_embedding, k)
3. Using Hugging Face Transformers for RAG
from transformers import RagTokenizer, RagRetriever, RagSequenceForGeneration
tokenizer = RagTokenizer.from_pretrained("facebook/rag-sequence-nq")
retriever = RagRetriever.from_pretrained("facebook/rag-sequence-nq")
model = RagSequenceForGeneration.from_pretrained("facebook/rag-sequence-nq")
input_text = "What is RAG in AI?"
input_ids = tokenizer(input_text, return_tensors="pt").input_ids
outputs = model.generate(input_ids)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
4. Optimizing RAG for Cost Efficiency (Cost-Constrained RAG)
- Use quantization to reduce model size:
pip install onnxruntime python -m transformers.onnx --model=facebook/rag-sequence-nq onnx_model/
- Deploy on AWS SageMaker with auto-scaling.
5. Monitoring RAG Performance
<h1>Log retrieval latency and accuracy</h1>
import time
start_time = time.time()
results = model.generate(input_ids)
latency = time.time() - start_time
print(f"Retrieval Latency: {latency:.2f} seconds")
What Undercode Say
RAG architectures are revolutionizing AI by combining retrieval efficiency with generative power. Key takeaways:
– HybridAI RAG improves accuracy by merging neural and symbolic AI.
– Cost-Constrained RAG is essential for scalable deployments.
– Self-RAG enables auto-correction, reducing manual tuning.
For further reading:
Expected Output:
A functional RAG system retrieving and generating context-aware responses with optimized performance metrics.
(Word count: ~70 lines)
References:
Reported By: Habib Shaikh – Hackers Feeds
Extra Hub: Undercode MoN
Basic Verification: Pass ✅
