# Day 5 of learning AI Engineering: built a small RAG app over a PDF
📰 Dev.to AI
Learn to build a RAG app that answers questions from a PDF using LangChain, OpenAI embeddings, and Qdrant
Action Steps
- Build a RAG pipeline using LangChain and OpenAI embeddings to generate answers from a PDF
- Run Qdrant in Docker to store and query vector embeddings
- Configure the RAG app to accept user questions and return answers with page numbers
- Test the app with a sample PDF and evaluate its performance
- Apply this pipeline to other data sources, such as web pages or documents, to build similar AI-powered applications
Who Needs to Know This
AI engineers and developers can benefit from this tutorial to build RAG-powered applications, while data scientists can explore new ways to interact with PDF data
Key Insight
💡 The same RAG pipeline can be used with different data sources to build various AI-powered applications
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Build a RAG app that answers questions from a PDF using LangChain, OpenAI embeddings, and Qdrant #AI #RAG #LLM
DeepCamp AI