We integrated our lives with advanced technologies like gpt, cloud technology but still we are facing problems with retrieving answers and data with the questions we have, especially when there are huge numbers of data documents. The college has a huge number of student data, it’s difficult for them to choose the best candidates for placements by checking the data manually and we have faced many problems in the Covid era to extract and retrieve the data from a huge number of multiple documents so there is need of model which solves the problem of retrieving answers and data from the large data documents. So here we propose the model which retrieves answers from the given data documents using HugginfFace LLM, LANGCHAIN and FAIRSEQ. LANGCHAIN is the framework for developing end-to-end applications for LLMs. FAIRSEQ is a deep learning model used for TEXT-TO-SPEECH Conversion. The FAIRSEQ model helps to train custom models for translation, summarization, language models, and other text generation tasks. This model can enhance semantic understanding. These models can capture complicated relationships and contextual cues, leading to more accurate and comprehensive answers where users can have easy handling of the model and provide comprehensive answers for given complex questions.