/RAG_Tutorial

Rag using different dataframework (mostly langchain)

Primary LanguageJupyter Notebook

Rag applications in different approaches

This repository contains the implementation of the RAG model in different approaches. The RAG model is a model that combines retrieval and generation in a single model. The model is composed of three components: retriever, reader, and generator. The retriever is responsible for retrieving relevant documents from a large corpus, the reader is responsible for reading the retrieved documents and extracting the relevant information, and the generator is responsible for generating the final answer. The RAG model is a powerful model that can be used in different applications such as question answering, text summarization, and text generation.

Approaches

  1. Basic rag application without any vector database
  2. Rag application with Haystack (RAG_in_Haystack.ipynb)
  3. Rag application with Huggingface + duck duck go search + Mistral-7b (local)/Gemini-2b(inference endpoint) (RAG_w_Huggingface.ipynb)
  4. Rag application with Huggingface + weaviate (vector database) + tavily search api