qa_on_private_documents_rag

• Developed an advanced question-answering system utilising the OpenAI, Pinecone, and LangChain (OPL) stack, enabling dynamic information retrieval from nonpublic or recent documents not covered in the model's training data.
• Implemented a retrieval-augmented generation approach using embeddings to efficiently process and query large text corpora, integrating technologies like OpenAI's text embedding ada 002 and the Pinecone vector database to manage and search document chunks.
• Built a complete application capable of real-time querying on custom data, demonstrating the ability to generate accurate answers from documents published beyond the model's training scope, exemplifying cutting-edge NLP application in information retrieval.