Askzono

A local, AI-powered chat application that allows users to engage with their documents through natural language, providing relevant insights from PDFs and markdown files using efficient retrieval and query-answering systems.

GitHub

Local AI-Powered Document Chat Application

AskZono is local, AI-powered chat application designed to help users interact with their documents directly through natural language. Using retrieval-augmented generation (RAG) techniques, AskZono extracts relevant information from PDF and markdown files and delivers precise, context-aware answers, making document navigation easier and more efficient.

The Motivation Behind AskZono

Finding the right information can be time-consuming. With AskZono, users can simply ask questions and receive tailored answers, all within a secure, local environment.

By leveraging LangChain and local models like Ollama, the system maintains privacy while delivering fast and accurate responses.

Core Features of AskZono

  • Document Upload and Parsing: Users can upload PDFs and markdown files to AskZono, which automatically parses the content and stores it in an embeddings-based vector database.

  • AI-Powered Retrieval: The application uses a retrieval-augmented generation (RAG) system to pull relevant sections of documents based on user queries. It retrieves multiple documents using optimized search algorithms to ensure precise results.

  • Local: Powered by local models, AskZono doesn’t rely on cloud-based services, ensuring full data privacy and control over information.

Under the Hood: Technical Overview

AskZono relies on the following components to power its intelligent document chat experience:

  1. LangChain: For document retrieval and question-answering, the LangChain framework connects various modules to manage document embeddings, vector databases, and chain logic for interacting with documents.

  2. Embedding Models: It uses the OllamaEmbeddings to create high-quality, dense representations of document content, allowing for efficient vector-based searches within the uploaded text.

  3. Retrieval-Augmented Generation (RAG): The RAG Chain combines a query generation system with a vector-based retriever to pull context from documents, which is then used to formulate accurate, context-based responses to user queries.

  4. Streamlit Interface: The application is built with Streamlit, offering an interactive, user-friendly interface that enables real-time document interaction and feedback.

How It Works: Step-by-Step

  1. Upload Documents: Users upload their files (PDF or markdown), which are parsed and split into smaller text chunks. These chunks are embedded into a vector database for easy retrieval.

  2. Ask Questions: The user inputs a question through the chat interface.

  3. Query and Retrieve: AskZono generates a search query based on the conversation history, retrieves relevant document sections, and presents them in context.

  4. AI-Driven Answer Generation: Using the context provided by the retrieved documents, AskZono generates a coherent answer to the user’s question.

  5. Response Delivery: The answer is streamed back to the user, and relevant documents are highlighted for easy reference.

By combining vector search with LLM-powered answer generation, AskZono ensures that users can interact with their documents conversationally, all in a local, privacy-first environment.

Why AskZono Matters

The project emphasizes data privacy and local AI, ensuring that users maintain full control over their information while benefiting from powerful AI capabilities.