Unlocking Data With Generative Ai And Rag Pdf Jun 2026

from langchain.chat_models import ChatOpenAI llm = ChatOpenAI(temperature=0) relevant_chunks = compressed_retriever.get_relevant_documents( "What was the net profit in 2024?" ) response = llm.predict(prompt_template.format( chunks=relevant_chunks, query=user_query ))

solves this by acting as an open-book exam for the AI. Instead of relying solely on its internal training, the AI looks up relevant sections of your PDF before generating an answer. The Workflow unlocking data with generative ai and rag pdf

loader = PyPDFLoader("annual_report.pdf") docs = loader.load() from langchain

For a deeper dive, a PDF document on this topic might cover case studies, technical explanations, and future directions for generative AI and RAG in unlocking data potential. If you're looking for a specific document, you might search academic databases, tech company publications, or research institution resources. you might search academic databases