100% Local RAG with DeepSeek-R1, Ollama and LangChain - Build Document AI for Your Private Files

Introduction

This tutorial guides you through building a local document AI assistant using DeepSeek-R1, Ollama, and LangChain. You will learn to create a robust Retrieval-Augmented Generation (RAG) system that can analyze PDFs and other documents privately and freely. By following these steps, you will develop skills in document ingestion, contextual retrieval, and building a user-friendly interface.

Step 1: Understand RAG Architecture

• Familiarize yourself with the main components of the RAG architecture:
  • Chunking: Break down documents into manageable pieces for easier processing.
  • Contextual Retrieval: Use context to find relevant information from chunks.
  • Semantic Search: Implement search capabilities that understand the meaning of queries.
  • Local LLM Integration: Utilize the DeepSeek-R1 model for language processing.

Step 2: Set Up Your Development Environment

• Ensure you have Python installed on your machine.
• Install necessary libraries:

  pip install langchain ollama streamlit
  

• Clone the GitHub repository for reference:

  git clone https://github.com/curiousily/AI-Bootcamp
  

Step 3: Project Structure and Configuration

• Organize your project files:
  • Create folders for models, data, and configurations.
• Prepare a configuration file to define your settings for chunking and retrieval methods.

Step 4: Upload Files

• Implement a file upload feature:
  • Use Streamlit to create a simple file upload interface.
  • Ensure that users can upload various document types (e.g., PDFs, text files).

Step 5: File Ingestion Process

• Process uploaded files for retrieval:
  • Chunking: Divide documents into smaller segments.
  • Embeddings: Generate vector representations for each chunk.
  • Utilize BM25 for initial retrieval and reranking for improved accuracy:

    from langchain.embeddings import OpenAIEmbeddings
    from langchain.vectorstores import BM25
    

Step 6: Build the Chatbot Functionality

• Integrate Ollama and LangChain for chatbot features:
  • Stream conversations and maintain chat history.
  • Implement contextual responses based on user input.
• Example code for setting up the chatbot:

  import streamlit as st
  from ollama import Ollama
  
  chatbot = Ollama("deepseek-r1")
  user_input = st.text_input("Ask me anything:")
  response = chatbot.get_response(user_input)
  st.write(response)
  

Step 7: Create the User Interface with Streamlit

• Design a clean and interactive UI:
  • Display uploaded documents and chat history.
  • Use Streamlit components for buttons, text inputs, and conversation displays.

Step 8: Test Your RAG System

• Run the application locally and test its functionalities.
• Interact with the chatbot using sample queries related to the uploaded documents.
• Ensure that the AI provides accurate and contextually relevant responses.

Conclusion

In this tutorial, you built a local document AI assistant using DeepSeek-R1, Ollama, and LangChain. You learned about the architecture of RAG systems, how to set up your environment, and implement file ingestion and chatbot functionalities. As a next step, consider expanding the capabilities of your assistant by integrating more advanced features or different types of documents. Explore the resources provided for further learning and improvement.