Building a Local RAG Pipeline with Python, Qdrant & OpenAI

1. 1Read a PDF file using Python
2. 2Split the text into manageable chunks
3. 3Embed the text using OpenAI's embedding models
4. 4Store the embedded vectors in a local Qdrant database
5. 5Configure the RAG pipeline to retrieve and generate text

Data scientists and AI engineers can benefit from this pipeline to improve their text generation models, while software engineers can appreciate the technical implementation details

💡 RAG pipelines can significantly enhance text generation capabilities by combining retrieval and generation models

🤖 Build a local RAG pipeline with Python, Qdrant & OpenAI to boost text generation #AI #NLP