QueryPilot AI – Multi-User RAG Chatbot

@pkundu25

I am an experienced AI Developer. Your job caught my eye and looks to be quite interesting to me as I developed varieties of AI Agent and Agentic AI systems invloving RAG pipeline in recent past. I am well conversant with Generative AI and hands-on experience in developing AI applications using LangChain and LLMs. I am confident that I will be able to help you by developing multi-user RAG chatbot with React frontend and FastAPI backend. Similar work done in the past: - RAG based Semantic search engine - ChatPDF - AI Agent for Risk and Mitigation - AI Powered Copilot for Text2SQL Query - Document segmentation using Agentic AI Relevant Skills: - Python/FastAPI - Django/React.js - Agentic AI - LangGraph/n8n - GPT4o/Gemini/Llama3.2/Mistral - AWS/Thunder Compute - MySQL - TensorFlow - Google Colab Let's have a chat to understand the project objective and other relevant details. I assure you to deliver the best quality results and ensure the customer satisfaction. Looking forward to hearing from you soon. Thanks for the opportunity.

@srdank001

Hi there, I’m experienced in building multi-user AI applications like QueryPilot AI, a RAG chatbot with React frontend, FastAPI backend, LangChain orchestration, and ChromaDB vector search. Users can upload documents, ask questions, and receive context-aware responses. I can extend or customize this system, implement authentication, improve chat history, or deploy it for production. I follow clean, modular code practices and ensure scalable, maintainable architectures for GenAI applications. Best regards.

@NEHABHAT92

As a seasoned developer with 9+ years of experience, my proficiency in both Python and React.js makes me an ideal candidate for your QueryPilot AI project. Having worked on numerous web and mobile applications, I am familiar with the complexity and functionality required to deliver a production-level system such as this. Throughout my career, I have specialized in projects demanding innovation and performance, which perfectly aligns with QueryPilot AI's retrieval-augmented generation needs. My fluency with FastAPI and React frameworks ensures that I can efficiently develop the frontend/backend API layers as well as the user-friendly and responsive UI/UX you desire. I understand that the success of QueryPilot AI depends not only on its prototype but also on its potential for expansion and future improvements. With my comprehensive skill set covering various technologies like mobile app development (Android & iOS), PHP, Java, and more, I can guarantee a robust foundation for your project to grow. Together, let's turn your vision into reality. Let's get started!

@nikhilkutepatil1

Hi, I reviewed your QueryPilot AI project and it aligns well with my experience in React, FastAPI, LangChain, and scalable full-stack development. I can help improve and extend the RAG pipeline, optimize vector search and chat workflows, enhance the multi-user architecture, and prepare the application for production deployment. I also have experience with modular API design, authentication systems, cloud deployment, and AI-powered applications. My rate is $15/hour, and I’m available to start immediately for both feature development and long-term support.

@mussaibb7

I'm excited to take on the QueryPilot AI – Multi-User RAG Chatbot project. The key to success lies in retrieval accuracy, chunking, and grounding discipline, not just prompt wording. This requires a well-designed workflow, robust retrieval quality, and thoughtful memory/logging strategies. Having delivered a closely related Arabic Legal AI Retrieval System with fine-tuned LLaMA 3.1 and FAISS retrieval, I'm confident in tackling the delivery risks in this project. This experience has taught me the importance of modular orchestration, evaluation, and fallback behavior in LLM/RAG/AI agents. My execution plan involves delivering a modular workflow code, config or prompt structure, logging/fallback handling, and a README document. I'll work closely with you to clarify deployment targets, API boundaries, and whether this is a greenfield or existing service. Before we proceed, I'd like to clarify: Should I optimize first for reliability, latency, or ease of extension in the next phase?

@mubashir021

I am excited about the opportunity to develop the QueryPilot AI Multi-User RAG Chatbot. With a solid background in Python, React, and FastAPI, I can create a seamless multi-user document Q&A system that leverages the latest technologies to provide context-aware responses. My approach will involve a thorough understanding of your requirements, followed by an agile development process that includes frequent communication and iterations to ensure we meet your expectations. I will utilize AI tools to enhance productivity and streamline tasks such as code generation and testing, while maintaining a high standard of quality. The proposed architecture will ensure efficient document handling and retrieval, facilitating an engaging chat interface for users. I am committed to delivering a production-ready solution within the stipulated timeframe while allowing for any necessary revisions. Let's collaborate to bring your vision to life and create an intelligent assistant that transforms how users interact with their documents.

@arshkumar213

I can implement and extend your QueryPilot AI (React + FastAPI + LangChain + ChromaDB). I’ve built similar RAG systems with multi-user chat, document ingestion, vector search, and LLM pipelines. I can add auth, streaming responses, better chat history, and production-ready Docker deployment. Clean modular architecture, scalable for enterprise use.

@vinayakg6

Hi, Your QueryPilot AI project strongly matches my experience with AI-powered MERN and RAG-based applications. I’ve already built AI tools involving document processing, chat systems, intelligent responses, and modern React interfaces, so I understand both the frontend UX and backend AI pipeline required for this platform. I can help improve and extend: ✔ Multi-user document Q&A workflows ✔ FastAPI + LangChain integration ✔ ChromaDB/vector search pipelines ✔ React chat UI and document management ✔ Authentication, session handling & streaming responses ✔ Scalable and production-ready architecture I also have experience building AI-based developer tools and offline AI assistants, which helps me understand real-world GenAI workflows beyond basic chatbot implementations. What I like about your project is that it’s already structured professionally and has strong future scalability for enterprise AI assistants and knowledge systems. I focus on: • Clean modular code • Fast communication • Scalable backend architecture • Smooth UI/UX • Reliable deployment support I’d be excited to work on this project and can start immediately.

@menttallist

Hello, I will build your QueryPilot AI as a fully production-ready multi-user RAG system using FastAPI + React. Backend (FastAPI): Modular architecture (authentication, chat system, document upload & processing) PDF/DOCX parsing → text chunking → embeddings generation Vector database (ChromaDB or FAISS depending on scale) Isolated chat sessions for each user (multi-user support) AI Layer: RAG pipeline (retrieval + context injection + LLM response generation) Streaming responses for real-time chat experience Frontend (React): Clean chat UI with document upload functionality Multi-user session support with persistent chat history Deployment: Dockerized setup Ready for VPS or AWS deployment I have already built similar FastAPI + React systems involving document Q&A, chatbot workflows, and LLM/API integrations.

@hosnearay

Hello, I’m inactive to Freelancer.com and currently building my profile, but I’m highly motivated to prove myself by taking on challenging projects like this one. The technologies you’ve outlined—Python, FastAPI, React, LangChain, RAG pipelines, and vector databases—are exactly the areas I’m eager to work in. I may not have reviews yet, but I bring enthusiasm, adaptability, and a commitment to learning quickly and delivering results. Here’s what I can promise: Dedication: I’ll put in the extra effort to understand your requirements and implement them carefully. Clean, functional code: My focus is always on execution—making sure the solution works exactly as expected. Transparency: I’ll keep communication clear and consistent, so you always know where the project stands. Growth mindset: Since I’m new learned here, I’m determined to go above and beyond to earn your trust and build my reputation. If you’re open to giving a motivated newcomer the chance, I’ll bring energy, persistence, and a fresh perspective to your project. I’m excited about the opportunity to collaborate and help bring this RAG chatbot into production. Best regards, Mubtasim J

@aaronrobs

⭐Perfection is what we Guarantee⭐ With extensive experience building multi-user Retrieval-Augmented Generation chatbots, we excel in delivering robust solutions like QueryPilot AI. Our expertise in React and FastAPI ensures seamless integration of document upload, vector search, and LLM response generation. Core Deliverables ➡️ ➡️ Multi-user chat workflows ➡️ Document upload/manage/delete features ➡️ Semantic vector search and RAG pipeline ➡️ Clean, responsive React frontend ➡️ Scalable FastAPI backend Our Approach ➡️ ➡️ Understand full project scope through discussion ➡️ Develop modular, production-ready APIs ➡️ Implement efficient vector search and retrieval ➡️ Prioritize UX with intuitive UI design ➡️ Ensure easy extensibility and future-proofing I’d love to hear more to create a detailed timeline and deliver a high-quality product aligned with your goals. Kind regards, Happy Screen Solutions Aaron Roberts

@gavenl

Hi, I can build and polish QueryPilot as a production-style multi-user RAG app using FastAPI, React/Vite, LangChain and ChromaDB. I can implement the document upload/list/delete flow, session-based chat history, robust retrieval pipeline, clean API schemas, error handling, and a usable React interface. For authentication I would default to JWT/OAuth2-compatible FastAPI auth unless you prefer another method. I can also add Docker/Linux run instructions and keep the code modular so streaming responses or general chat mode can be added cleanly later.

@shivsaxena2878

Hi, I checked out your QueryPilot AI project and honestly, it’s a really interesting build. I like the way you’ve structured the RAG workflow with FastAPI, React, LangChain, and ChromaDB — it already feels close to a production-style GenAI application. I’ve been working with FastAPI, React, LangChain, vector databases, and AI-based applications, and I’d love to help improve and extend this project further. The multi-user document chat flow, semantic retrieval, and modular backend architecture are exactly the kind of systems I enjoy working on. I can help with things like: * improving the RAG pipeline and retrieval quality * chat history/session handling * authentication and user management * streaming responses * frontend improvements and better UX * deployment and production setup * optimizing vector search and document processing I also appreciate that the project is designed with scalability in mind instead of being just a demo chatbot. That makes a huge difference for long-term usability. I’m comfortable with FastAPI backend development, React/Vite frontend work, LangChain integrations, and building clean API-driven systems. I can adapt to your existing codebase and continue development in a structured way. Would love to discuss the current status of the project and what features or improvements you want to prioritize next. Looking forward to hearing from you. Shiv Saxena

@sniegul

**Proposal for QueryPilot AI – Multi-User RAG Chatbot** I understand you’re building a production-grade RAG chatbot that serves multiple users simultaneously—likely with document ingestion, real-time retrieval from a vector store, and LLM-powered answers. This goes beyond a simple PoC; it requires robust user management, context handling, low-latency inference, and reliable deployment. I have delivered similar systems for enterprise clients, including a multi-tenant document Q&A platform that handled 500+ concurrent users with sub-2-second response times. My approach would be: 1. **Data pipeline** – Build an ingestion service that chunks and embeds documents (PDFs, markdown, etc.) into a vector database (e.g., Qdrant or Pinecone) with metadata for user/tenant isolation. 2. **RAG orchestration** – Use LangChain or a custom chain to combine retrieval with an LLM (open-source or API-based) while handling conversation history and context pruning. 3. **Multi-user architecture** – Implement session-based authentication (JWT/OAuth) and rate limiting, plus a message queue for asynchronous processing if needed. 4. **Deployment & monitoring** – Containerize with Docker, deploy on your infrastructure, and add logging/observability (e.g., Grafana, PgBouncer for PostgreSQL). I can start immediately and provide a working MVP within two weeks, followed by iterative improvements. Let’s discuss your specific retrieval sources and scale requirements to tailor the solution precisely.

@MariaaBano

I can help turn QueryPilot AI from a strong GenAI demo into a cleaner, more production-ready multi-user RAG application. Your stack is practical: React/Vite frontend, FastAPI backend, LangChain, ChromaDB, configurable embeddings/LLMs, and a modular API structure. I can work directly with this architecture and improve the parts that matter most for reliability, UX, and scalability. My focus would be: 1. Clean up FastAPI routes and backend structure 2. Improve document upload, indexing, deletion, and vector cleanup 3. Strengthen RAG retrieval quality with chunking, metadata, and filtering 4. Improve chat sessions, history handling, and multi-user separation 5. Add streaming responses if needed 6. Prepare Docker deployment and README/setup documentation 7. Fix edge cases around failed uploads, empty retrieval, duplicate files, and model errors I have experience with RAG systems, FastAPI, React, LangChain, vector databases, API design, and AI chatbot workflows. The key is making sure each user only sees their own documents, retrieval stays accurate, and the app remains easy to extend. Quick questions: Do you want authentication added now? Should this stay document-only, or include general chat mode too? Are you planning local models or API-based LLMs? Send me the GitHub repo and I can quickly review the current structure and suggest the best next improvements.

@adarshr180

Hi, I’m a full-stack developer who has worked with React, FastAPI, and AI-based projects. I’ve also explored RAG and LLM integrations, so I’m familiar with how document-based AI chat systems work. I can help with both frontend and backend development and I genuinely enjoy working on modern AI applications like this. I focus on writing clean code, understanding the project properly, and communicating clearly during development. Your project matches the kind of work I’m interested in, and I’d love to contribute and help improve it. Looking forward to hearing from you.

@ammarkhan0151

Hello, QueryPilot AI is a strong production-style RAG project, and your architecture using React, FastAPI, LangChain, and ChromaDB is well aligned with modern GenAI system design. I’d be happy to help enhance, optimize, and extend the platform. I have experience with: • FastAPI backend development • React/Vite frontend workflows • LangChain orchestration • Vector databases including ChromaDB • RAG pipelines & semantic search • AI integrations and API systems • Linux-based development environments I can assist with: • Improving retrieval accuracy and chunking strategy • Multi-user session handling • Authentication integration • Streaming responses • Dockerized deployment • API optimization and modular backend improvements • Chat history and memory workflows • UI/UX enhancements for the React frontend • Production-ready deployment architecture My cybersecurity and backend engineering background also helps ensure secure API handling, document isolation, and scalable system design for future enterprise use cases. I’m comfortable working with GitHub workflows, Linux servers, VSCode, and collaborative development environments. I can quickly understand your existing structure and continue development cleanly without disrupting the current pipeline. I’d be happy to discuss the current development stage and the exact improvements or features you’d like implemented next. Best regards, Ammar

@Ayush0701

Hi — Read the brief. You have v1 working — you need the Future Improvements shipped: auth, general chat, streaming, history, Docker. 7-day fixed ₹7,000 plan: D1 — JWT auth + per-user ChromaDB isolation (re: Mubashir's Q — JWT > OAuth2 unless you need SSO) D2 — General chat toggle (same LLM, bypass retriever) D3 — SSE streaming endpoint + React EventSource client D4 — Persistent chat history (SQLite), sidebar reload D5 — docker-compose: backend + frontend + ChromaDB, one-cmd D6-7 — testing, polish, handoff docs Stack fit: Python/FastAPI/LangChain/React all in my daily use. NLP/Agentic AI is my day-job at PharmaACE. Proof: Intelligent CV — live on Play Store, built solo (Flutter + Firebase + AI). freelancer.in/u/Ayush0701 Building reviews on Freelancer right now — happy to deliver this at the tight rate for a fair 5★. 2 milestones suggested: ₹3k after D3 demo, ₹4k on delivery. Quick Qs: 1. LLM provider — OpenAI, Anthropic, or Ollama? 2. Deploy target — AWS, Render, or your VPS? Can start today. GitHub access on hire. — Ayush

@priyanshsingh11

Hello, I’m Priyansh Singh, an AI/ML Engineer and Full-Stack Developer with hands-on experience building RAG-based applications using FastAPI, React, LangChain, FAISS/Vector DBs, and LLM integrations. Your QueryPilot AI project strongly matches the kind of systems I already work on, including document-based chat applications, semantic search pipelines, multi-user workflows, and production-style AI architectures. I can help you build and improve the complete system including document upload pipelines, vector search, chat session management, modular FastAPI APIs, React/Vite frontend integration, and optimized RAG workflows. I also have experience with deployment, Docker-ready structures, authentication systems, streaming responses, and scalable backend design. I can deliver a clean, well-documented, production-style implementation with full source code, setup instructions, and deployment guidance within the required timeline.