Retrieval-Augmented Generation (RAG)

Overview

Retrieval-Augmented Generation (RAG) is an advanced approach that enhances large language models (LLMs) by retrieving relevant information from external knowledge sources before generating responses. This technique improves factual accuracy, reduces hallucinations, and enables dynamic knowledge updates without retraining the model.

Key Components

1. Retriever: Searches and fetches relevant documents from a knowledge base.
2. Embedder: Converts text into dense vector representations for efficient retrieval.
3. Vector Store: Stores and indexes embeddings for fast similarity search.
4. Generator (LLM): Generates responses based on retrieved documents.
5. Pipeline: Integrates all components to process user queries efficiently.

Implementation Steps

1. Environment Setup

pip install langchain transformers faiss-cpu sentence-transformers chromadb

2. Load and Preprocess Documents

from langchain.document_loaders import TextLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter

# Load documents
doc_loader = TextLoader("data/documents.txt")
documents = doc_loader.load()

# Split text into smaller chunks
text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=100)
document_chunks = text_splitter.split_documents(documents)

3. Create Embeddings and Store in Vector Database

from langchain.embeddings import HuggingFaceEmbeddings
from langchain.vectorstores import FAISS

# Initialize embedding model
embedding_model = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")

# Store embeddings in FAISS vector database
vector_store = FAISS.from_documents(document_chunks, embedding_model)

4. Build the RAG Pipeline

from langchain.chains import RetrievalQAWithSourcesChain
from langchain.llms import HuggingFacePipeline
from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer

# Load LLM
model_name = "distilgpt2"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Create pipeline for text generation
hf_pipeline = pipeline("text-generation", model=model, tokenizer=tokenizer)
llm = HuggingFacePipeline(pipeline=hf_pipeline, max_new_tokens=150)

# Initialize retrieval-based QA system
qa_chain = RetrievalQAWithSourcesChain.from_llm(llm=llm, retriever=vector_store.as_retriever())

5. Query the RAG System

query = "What is Retrieval-Augmented Generation?"
response = qa_chain({"question": query}, return_only_outputs=True)
print("Answer:", response['answer'])

Deployment Considerations

• Scalability: Use Weaviate, Pinecone, or ChromaDB for large-scale vector storage.
• Latency Optimization: Use optimized embedding models like BGE-M3 or FAISS-HNSW.
• Fine-Tuning: Adapt the LLM to domain-specific knowledge.
• API Integration: Deploy using FastAPI or Flask for production use.

Conclusion

Retrieval-Augmented Generation significantly improves LLM performance by incorporating external knowledge retrieval. Implementing RAG with LangChain and Hugging Face provides a powerful framework for knowledge-grounded AI applications.

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Contact Information

• Email: [email protected]
• WhatsApp: +8801834363533
• GitHub: Md-Emon-Hasan
• LinkedIn: Md Emon Hasan
• Facebook: Md Emon Hasan