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Conversational Document Assistant using RAG

Retrieval Augmented Generation with Huggingface model Zephyr-7b and ChromaDB

Steps:

  • Load Model and Tokenizer: Load a quantized conversational model and initialize its tokenizer.
  • Mount Google Drive: Mount Google Drive to access documents.
  • Process Text from PDFs: Extract text from PDF documents, remove links, references, stopwords, and blank lines, then save the processed text as text files.
  • Generate Embeddings and Vectorization: Use Sentence Transformers for text embeddings, then vectorize using Chroma.
  • Build Chatbot Pipeline: Construct a conversational retrieval chain with memory (RAG) using LangChain, integrating the model, retriever, and memory.
  • Create UI with Gradio: Develop a user interface using Gradio for users to interact with the chatbot.