Conceptual Python-based outline to illustrate the key steps ~RAG system
Ashish Mohan
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Ashish Mohan
AI/ML Expert | Digital Transformation & Generative AI Specialist | Responsible AI & Ethics Advocate | Corporate Trainer & Fintech Educator | Empowering the Future of Tech in 2025(MS Computer Science)PHD Scholar
import langchain
# Library for RAG operations # Load a pre-trained LLM and a vector database
llm = langchain.load_llm("gpt4")
# Example using a hypothetical LLM vector_db = langchain.load_vector_db("path/to/vector_database")
# Retrieve relevant documents based on user query
query = "What are the best practices for building RAG LLM applications?" retrieved_docs = vector_db.query(query, k=5)
# Retrieve top 5 relevant documents
# Combine retrieved documents with query and generate response
response = llm(query, retrieved_docs)
print(response)