Ollama: Provides a robust LLM server that runs locally on your machine.
  
  Langchain: A powerful library used to build the Retrieval-Augmented Generation (RAG) pipeline.
  
  Chroma: A vector storage solution that integrates well with Langchain.
  
  Streamlit: Creates a straightforward user interface for uploading PDFs and asking questions.

      Setting Up Ollama: Download and run the Ollama application suitable for your OS. Install the "Mistral-7B" LLM model within Ollama for its compact size and good performance.
      
      RAG Pipeline Construction: This core component handles ingesting and processing user queries.
      
      ingest(pdf_file_path):
      Loads the PDF using PyPDFLoader.
      
      Splits the document into manageable chunks with RecursiveCharacterTextSplitter.
      
      Uses Chroma for vector storage with FastEmbedEmbeddings for lightweight processing.
      
      Creates a retriever to find relevant passages based on user queries using vector similarity search.
      
      Builds a Langchain conversation chain to connect these functionalities.

      Takes the user's question and feeds it through the predefined chain, retrieving the answer from the LLM server.
      
      Drafting a Simple UI with Streamlit:
      
      The code utilizes Streamlit to create a user-friendly interface.
      
      Users can upload a PDF document.
      
      A chat window displays the conversation history.
      
      Users can type questions related to the uploaded document and receive answers generated by the LLM system.

      Save the code as app.py and execute streamlit run app.py to launch the application.