deep-tutor

create a fully function AI dsa tutor with the following functionalities Your project **“AI-Powered DSA Learning & Document Analysis Platform”** combines two major systems: * **DSA Tutor (Intelligent Learning Assistant)** * **Project SISO (RAG-based Document Intelligence System)** Together they form a **multi-capability AI platform** for learning, coding practice, and document analysis. Below is a **clear, structured list of the combined functionalities**. --- # Combined Functionalities of AI Tutor + Project SiSo ## 1. Intelligent DSA Learning System Provides **concept explanations for Data Structures and Algorithms**. **Capabilities** * Step-by-step explanations * Text-based diagrams * Concept breakdown from basic → advanced * Context-aware follow-up questions **Example** User asks: *“Explain Dynamic Programming”* System provides: * Definition * Intuition * Example problems * Diagram explanation * Complexity discussion --- # 2. Coding Practice & Evaluation Engine Users can **solve coding problems and receive automated evaluation**. ### Features * Multi-language coding support * Python * Java * C++ * JavaScript * Code analysis includes: * Correctness validation * Time complexity analysis * Space complexity analysis * Optimal vs brute-force detection * Improvement suggestions **Example** User submits solution for **Two Sum** System evaluates: ``` Correctness: Passed Time Complexity: O(n²) Optimal Solution: O(n) using HashMap ``` --- # 3. MCQ Test Generation & Knowledge Assessment System can generate **adaptive quizzes for concept reinforcement.** ### Capabilities * Dynamic MCQ generation * Difficulty levels (Easy / Medium / Hard) * Instant evaluation * Explanation for correct answer * Performance feedback --- # 4. Conversational AI Tutor Acts as a **general AI assistant for learning discussions.** ### Capabilities * Free chat mode * Doubt solving * Interview preparation * Concept comparisons Example queries: * “Difference between BFS and DFS” * “Explain heap vs priority queue” --- # 5. Intelligent Intent Detection The system automatically **detects what the user wants**. ### Possible intents | User Input | System Mode | | ---------------------- | ------------- | | Explain Graphs | Learning Mode | | Solve LeetCode problem | Coding Mode | | Give MCQs on Trees | Test Mode | | General question | Chat Mode | This routing ensures **correct handler execution**. --- # 6. Multi-Document Question Answering (RAG) Project SiSo allows users to **ask questions across multiple documents.** ### Supported documents * DOCX * Study material * Notes * Resume * Job descriptions ### Process ``` Document Upload → Parsing + OCR → Chunking → Embedding generation → Weaviate indexing → Retrieval → LLM answer generation ``` --- # 7. Citation-Based Answer Generation All answers from documents include **precise citations.** Format: ``` [doc_id:page_number:chunk_index] ``` Example: ``` The transformer architecture relies on self-attention [doc1:3:2] ``` This ensures **traceability and reliability of information**. --- # 8. Resume vs Job Description Analysis The system performs **career gap analysis**. ### Features * Resume parsing * JD parsing * Skill gap identification * Improvement suggestions Example output: ``` Missing Skills: - Kubernetes - Docker - GraphQL Recommendation: Learn container orchestration and API frameworks. ``` --- # 9. Study Material Question Answering Students can upload **lecture notes or textbooks**. Capabilities: * Ask conceptual questions * Extract summaries * Generate explanations * Retrieve exact references Example: ``` Q: Explain Gradient Descent ``` Answer retrieved from uploaded lecture notes. --- # 10. High-Availability AI Architecture System ensures **reliable LLM responses**. ### Model pipeline Primary model ``` Google Gemini ``` Fallback model ``` Ollama (Local Llama 3) ``` Mechanism: ``` Gemini request → if failure → automatic fallback to Ollama ``` --- # 11. Asynchronous Processing Pipeline Heavy tasks run **as background jobs**. Architecture: ``` User Request ↓ FastAPI API ↓ Redis Queue ↓ Worker ↓ Processing ↓ Result Retrieval ``` Benefits: * Scalable * Non-blocking API * Handles large documents --- # 12. Hybrid Search Retrieval System Uses **two retrieval methods together**. | Method | Purpose | | ------------- | ---------------- | | Vector Search | semantic meaning | | BM25 | keyword matching | This hybrid search improves **retrieval accuracy**. --- # 13. Multi-Document Knowledge Indexing The system supports **grouped document search**. Metadata stored in vector database: ``` doc_id doc_group_id source_file page_number chunk_index ``` This allows: * Cross-document answers * grouped search results --- # 14. Job Status Tracking System Users can track processing progress. Endpoints: ``` POST /submit_job GET /job_status/{job_id} GET /get_result/{doc_id} ``` Example status: ``` Processing: Chunking documents Processing: Generating embeddings Completed ``` --- # 15. Interactive Frontend Workflow The UI supports **three stages**: ### 1️⃣ Input Stage * Upload documents * Enter questions ### 2️⃣ Processing Stage * Progress polling * Loading indicators ### 3️⃣ Result Stage * AI generated answers * Citation references * Analysis reports --- # Final Combined Platform Vision The system ultimately acts as a **unified AI knowledge platform** capable of: | Domain | Capability | | ------------ | --------------------- | | Learning | DSA tutor | | Coding | Practice + evaluation | | Testing | MCQ quizzes | | Conversation | AI discussion | | Documents | RAG Q&A | | Career | Resume analysis | --- ✅ **In one sentence (interview ready):** > The platform integrates an AI-driven DSA tutoring system with a Retrieval-Augmented Generation (RAG) document intelligence engine, enabling interactive algorithm learning, automated code evaluation, adaptive testing, multi-document question answering with citations, and AI-based resume-job description gap analysis within a unified scalable architecture.