S84_ANU_NextGenLingo NextGenLingo NextGenLingo is an advanced, next-generation conversational AI agent designed to revolutionize information access, automation, and personalized learning. By integrating state-of-the-art language models with document retrieval, function calling, and structured output, NextGenLingo delivers correct, efficient, and scalable solutions for knowledge querying and workflow automation.
🌟 Project Overview NextGenLingo enables users to interact naturally using chat, upload documents, request tasks (like scheduling or emailing), and receive personalized language guidance. It acts as an intelligent bridge between human requests and digital actions or data—grounding every response in reliable sources and high technical rigor.
🚀 Key Features Document Question Answering (with Citations): Ask questions on uploaded files (PDFs, docs, images). The agent retrieves, analyzes, and answers—referencing specific source passages.
Personalized Language Tutoring: Interactive quizzes, grammar correction, and tailored lessons leveraging user mistakes and learning history.
Workflow Automation (Function Calling): Trigger external tools, schedule events, or fetch real-time info (e.g., calendar, CRM, weather) securely within the chat.
Multi-turn Contextual Memory: Remembers previous conversations, user preferences, and document history for deeply personalized, coherent, multi-step dialogs.
Structured Output: Returns answers in user-specified formats (JSON, Markdown, tables) for seamless reporting, analytics, or system integration.
Multi-source Retrieval Augmented Generation (RAG): Combines company docs, web resources, and databases for comprehensive, well-grounded answers.
✨ Example Use Cases Legal Compliance: Upload a contract, ask "What are all the deadlines?", and get a cited, structured table.
Language Learning: "Correct my email draft" or "Quiz me on past-tense verbs." Receive instant, personalized feedback.
Business Automation: "Schedule demo with client and share product deck"—the agent books meetings & sends files.
Custom Data Extraction: "List all expenses by category from this report in JSON"—handy for finance or analytics.
Research & Discovery: "Get latest AI news and summarize key trends in a Markdown table."
🏗️ High-Level Technical Architecture text +----------------------+ | User / UI | +----------------------+ │ ▼ (chat, files/requests) +----------------------------+ | Backend Orchestrator | (Python/Node.js) +----------------------------+ │ │ │ ▼ ▼ ▼ [System/User Prompts] [Conversation Memory] │ │ ▼ │ +-----------------------------------------+ | Intent/Task Detection | +-----------------------------------------+ │ │ │ ▼ ▼ ▼ [RAG Engine] [Function Calling] [Output Formatter] │ │ │ ▼ ▼ ▼ [Docs/DB/Web] [APIs/Tools] [JSON/Markdown/Table] │ │ ▼ ▼ Result Assembled & Returned to UI 🧑💻 Detailed Project Workflow User Input: User sends a question, uploads a file, or requests an action via the chat interface.
Prompt & Context Assembly: Backend builds the context: incorporates system prompt (AI's role/goals), user prompt, & historical conversation for context continuity.
Intent & Task Recognition:
Information Retrieval Needed: LLM uses RAG to query documents or knowledge bases, extracts supporting snippets.
Action/Function Required: LLM triggers an external API or function (e.g., calendar, database).
Structured Output Desired: LLM formats the response per user/system requirements (JSON, table, Markdown).
Response Generation & Delivery:
AI generates response using retrieved data or action results.
Results are grounded, well-referenced, and formatted.
UI presents answer, showing sources and structured data if requested.
Contextual Memory Update: Session data, preferences, and past exchanges are logged for future interactions, improving personalization and conversational flow.
🏆 Evaluation Criteria Alignment Correctness
Answers and actions always validated against source data/documents.
Function calls adhere to secure, well-defined API schemas.
Structured outputs checked for format integrity.
Efficiency
Real-time retrieval from fast vector databases.
Lightweight backend orchestration minimizes latency.
Asynchronous operations enable snappy responses even under load.
Scalability
Modular, stateless APIs support high concurrent usage.
Cloud-native design (e.g., containerization, horizontal scaling).
Optimized data pipelines efficiently handle large files and batch requests.
📈 Implementation Details Backend: Python/Node.js serving REST APIs and orchestrating LLMs, RAG, functions.
LLM & RAG: OpenAI GPT / open-source models; vector DBs (Pinecone/FAISS/LlamaIndex).
Front-End: Modern chat UI (React/Streamlit).
Function Calling: Standardized API schemas (JSON), plugins for real-world services (calendar, email, etc.).
Structured Output: Output validation/parsing modules; customizable formats.
Deployment: Containerized for easy cloud hosting and scaling.
📝 Getting Started Clone the repository: git clone https://github.com/kalviumcommunity/S84_ANU_NextGenLingo.git
Install dependencies: Use pip, npm, or relevant manager for backend/frontend.
Configure environment: Set API keys (LLM, DBs), document storage, and endpoint URLs in .env.
Start backend & vector database: Follow docs to initialize RAG services or knowledge sources.
Launch front-end UI: Start the chat interface, interact with your AI assistant!
🙌 Contributions Your ideas are welcome! Add integrations, new features, or polish the UI. Please open Pull Requests or Issues.