IntelliDocs AI is a robust, submission-ready internship project designed to extract insights from PDF documents using Retrieval-Augmented Generation (RAG). This solution allows users to interact with complex documents, transforming static text into an accessible, data-driven conversational experience. 💡
Professionals and students often face the challenge of scanning through lengthy, dense documentation to find specific information. Manually searching for details is time-consuming, prone to error, and inefficient, especially when dealing with massive datasets or technical reports. 📉
This project leverages advanced Large Language Models (LLMs) and Vector Databases to provide an intelligent document-querying interface. Key highlights include:
- 🔹 Intelligent Retrieval: Uses semantic search to find precise document sections. 🎯
- 🔹 Persistent Storage: Utilizes ChromaDB for efficient, on-disk document indexing. 💾
- 🔹 Scalability: Handles large PDF files without memory bottlenecks. ⚖️
- 🔹 High-Performance Architecture: Streamlined pipeline for fast, accurate response generation. ⚡
- 🐍 Python: Core development language.
- 🦜 LangChain: Framework for building the RAG pipeline.
- 🧠 Google Gemini: Advanced LLM for natural language processing.
- 📊 ChromaDB: Persistent vector database for document storage.
- 🌐 Streamlit: Framework for the responsive web interface.
This system supports any standard PDF file. It breaks down documents into contextually relevant "chunks" using RecursiveCharacterTextSplitter, ensuring that the AI has the most accurate information to generate answers based on:
- 📄 Document content extraction
- 🔍 Context-aware semantic searching
- 💡 Precise answer generation using Gemini's reasoning
- 🏆 Build a production-ready RAG application for document analysis.
- 📌 Minimize "hallucinations" by grounding model responses in provided document context.
- 🚀 Optimize retrieval latency for a better user experience.
- Prerequisites: Python 3.11+
- Setup:
git clone https://github.com/AaineeSinha/INTELLIDOCS
cd INTELLIDOCS
python -m venv venv
.\venv\Scripts\activate
pip install -r requirements.txt
- Execution:
streamlit run app.py
- 🚀 Support for multi-format document uploads (Docx, Txt, Images).
- 📊 Integration of persistent user chat history using a database like PostgreSQL.
- 🧠 Implement agentic workflows for summarizing complex multi-document sets.
Happy querying! 📄✨ Enjoy exploring your documents with data-driven precision. 🥂