Lean RAG: AI-powered Q&A Assistant for Private Docs

·Reading time: 4 min
AILean RAG
Lean RAG Overview
Loading...

Do you have private documents and need quick answers to specific questions? Or are you looking for a way to extract insights from a collection of files in an organized manner?

Then Lean RAG might be exactly what you need.

Lean RAG is designed to serve as a Q&A assistant for private documents. By leveraging advanced AI technologies, the application processes uploaded documents, creates a searchable vector database, and allows users to interact with their data through a chat interface. It ensures fast, precise, and structured responses to user queries, offering an innovative way to engage with your content.

Links to the app:

Core Features

  1. Document Processing: Lean RAG accepts text (TXT) and PDF files, extracts content, and standardizes formatting for seamless analysis.
  2. Custom Vector Database: Uploaded files are processed to generate a unique vector database, enabling efficient and accurate information retrieval.
  3. Interactive Q&A Interface: Users can ask questions directly about their documents, and the system retrieves and displays the most relevant information.
  4. Configurable Parameters: Options to adjust token size, overlap, and the number of chunks retrieved during searches provide flexibility to meet various use cases.
  5. Session-Based Privacy: Data is stored in-memory during the session and deleted upon closing the application, ensuring user confidentiality.
Lean RAG Document Query Interface
Loading...
Lean RAG Document Query Interface

Under the Hood - Implementation Details

1. Ingestion Service

  • Approach:
    • Accepts PDF and TXT files.
    • Extracts and standardizes text content.
    • Chunks text into segments (configurable size: 512-2048 tokens).
    • Adds overlap between chunks for better context (configurable: 64-256 tokens).
  • Tech Stack:
    • PyPDF2 for PDF reading.
    • LangChain for text chunking.

2. Indexing Service

  • Approach:
    • Generates embeddings for each text chunk.
    • Stores vectors and original text in an in-memory FAISS index.
  • Tech Stack:
    • OpenAIEmbeddings for vector creation.
    • FAISS for efficient vector storage.

3. Query Processing

  • Approach:
    • Converts user questions into vector embeddings.
    • Searches for similarity matches in the vector database.
    • Retrieves the most relevant chunks (configurable: top 3-10).
  • Tech Stack:
    • OpenAIEmbeddings for query vectorization.
    • FAISS for similarity search.

4. Prompt Augmentation

  • Approach:
    • Combines retrieved context chunks.
    • Constructs a structured prompt with context and user queries.
    • Adds instructions for formatting the LLM’s response.
  • Tech Stack:
    • Custom prompt template.
    • Logic for context assembly.

5. Retrieval-Augmented Generation

  • Approach:
    • Sends the augmented prompt to the LLM.
    • Receives and formats the response.
    • Displays results in the Streamlit chat interface.
  • Tech Stack:
    • OpenAI API (GPT-4o or GPT-4o-mini).
    • Configurable temperature settings (0.0-1.0).

Conclusion

Lean RAG exemplifies the potential of integrating document processing with AI-powered Q&A systems. It transforms static files into dynamic, searchable resources, making information retrieval more accessible and efficient. Whether for personal use or business applications, Lean RAG is a powerful tool to unlock the value of your documents with the power of AI.