Building a Private AI: A Comprehensive Look at a Retrieval-Augmented Generation (RAG) Streamlit Project

Posted on by Naresh Matta

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Our Retrieval-Augmented Generation (RAG) system is powered by a local language model that incorporates internal chain-of-thought reasoning. This means that, internally, the model works through intermediate steps and logical reasoning to generate a robust, context-aware response. However, to ensure clarity and simplicity for end users, our system is engineered to only display the final, concise answer.

Key Points:

  • Internal Reasoning: The model internally processes the context and question through multiple reasoning steps. This helps in formulating a detailed and accurate response.
  • Clean Output: Through our advanced prompt engineering and post-processing (using regex to remove any <think>…</think> markers), the system strips out the internal chain-of-thought details, showing only the final answer.
  • Benefits:

This approach allows our RAG system to function as a reasoning model—leveraging sophisticated internal processing while keeping the user experience straightforward and focused solely on the answer.

Table of Contents

  1. Introduction
  2. Understanding Retrieval-Augmented Generation (RAG)
  3. Project Goals and High-Level Architecture
  4. Key Components and Libraries
  5. Document Ingestion and Text Extraction
  6. Chunking and Embedding
  7. FAISS for Similarity Search
  8. Language Model and Post-Processing
  9. Streamlit UI and Workflow
  10. Detailed Walkthrough of the Code Project Structure Text Cleaning Helpers Post-Processing of Model Output The DocumentRAG Class Streamlit UI Logic
  11. Deployment Considerations
  12. Challenges and Future Enhancements
  13. Conclusion
  14. Sample LinkedIn Post

1. Introduction

In the rapidly evolving landscape of artificial intelligence (AI), Retrieval-Augmented Generation (RAG) has emerged as a powerful technique that combines document retrieval with language model generation to produce more accurate, context-aware answers. This article provides a deep dive into a Private AI RAG Streamlit project, explaining how each piece fits together and how you can build or extend such a system for your own needs.

We’ll explore document ingestion, embedding with SentenceTransformers, similarity search with FAISS, language model inference (on GPU if available), and the Streamlit user interface. Additionally, we’ll detail the post-processing steps that ensure the final AI response is free of chain-of-thought markers like <think>…</think>—making the system more user-friendly and production-ready.

This article is meant for engineers, data scientists, and AI enthusiasts looking to understand how to build a RAG system that respects user privacy and can run on local or on-premise hardware.

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2. Understanding Retrieval-Augmented Generation (RAG)

Retrieval-Augmented Generation is a technique that addresses one of the key limitations of large language models: context windows. Traditional LLMs rely on the text input you provide (the “prompt”) and their internal parameters, but they can’t directly “see” or “search” external documents. This often leads to hallucinations or incomplete answers, especially when the model’s training data is outdated or limited.

RAG mitigates this by introducing a retrieval step:

  1. Index relevant documents (PDFs, DOCX, PPTX, or others) using embeddings.
  2. Search for relevant chunks using a query.
  3. Feed the retrieved chunks into the language model as context.
  4. Generate a final answer that is informed by the specific content found in your local corpus.

By doing so, RAG ensures the AI system can provide up-to-date, context-specific responses without relying solely on the LLM’s parametric memory. This approach is especially beneficial for private or proprietary documents, as it keeps everything on your local machine or private server.

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3. Project Goals and High-Level Architecture

3.1 Goals

  1. Local AI: All processing remains on your own machine or server, protecting sensitive data.
  2. Multiple Document Types: Ingest and index PDFs, DOCX, and PPTX files.
  3. GPU Support: If available, leverage CUDA for faster inference.
  4. Concise, Polished Answers: Post-process the model’s output to remove chain-of-thought and ensure a final, user-friendly response.
  5. Streamlit UI: Provide an intuitive web-based chat interface.

3.2 High-Level Architecture

  1. Document Ingestion:
  2. Embedding and Indexing:
  3. Query:
  4. Generation:
  5. Streamlit:

4. Key Components and Libraries

4.1 Streamlit

  • A Python library for creating interactive web applications.
  • In this project, Streamlit powers the chat interface, the sidebar for uploading documents, and the real-time display of model outputs.

4.2 pdfplumber, python-docx, python-pptx

  • pdfplumber: More advanced than PyPDF2, often better at extracting text from multi-column or complex PDFs.
  • python-docx: Allows reading .docx files to extract paragraph text.
  • python-pptx: Allows reading .pptx slides and shapes to extract textual content.

4.3 SentenceTransformers

  • Provides pre-trained embedding models (like all-mpnet-base-v2).
  • We encode each text chunk into a dense vector for similarity search.

4.4 FAISS

  • A library developed by Facebook AI Research for fast similarity search.
  • Stores embeddings in an index (here, IndexFlatL2), allowing quick retrieval of top-k similar chunks.

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