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Is your feature request related to a problem? Please describe. Most large language models can only provide information based on the corpus of data that they’ve been trained on. These models might hallucinate if they don't have the required data or context. This is where RAG or Retrieval-Augmented-Generation helps.
By incorporating a retriever, RAG pulls relevant information from external knowledge sources, such as databases or documents, to enrich the generated output with up-to-date, contextually accurate information. This approach helps to mitigate the limitations of static model training data, enabling real-time responses that adapt to the specific needs of each query.
Describe the solution you'd like Create a RAG based chatbot using the following technologies:
Describe alternatives you've considered An inefficient alternate might be to manually search all the way through the text and find the answers or pass that content to a LLM and then query it.
Approach to be followed (optional) Make a RAG model which will firstly retrieve the most relevant content from the external document. This will be done using similarity search. Next, this relevant content will be passed along with the user query to an LLM to frame the entire output.
Additional context Such RAG systems are highly beneficial for research purposes, educational institutions and courses, etc.
@Neilblaze @SaiNivedh26 will you please assign me for this issue?