AI-Driven RAG Chatbot & Test Automation

@mayankinnovative

Hello I can develop your AI driven RAG chatbot with document ingestion vector search contextual responses conversation management API integration and automated testing for reliable production performance With 20 years of software development experience I can work with Python FastAPI OpenAI APIs RAG pipelines embeddings vector databases PostgreSQL REST APIs Docker CI CD and automated testing frameworks A few quick questions Which document formats and data sources need to be included in the RAG pipeline Which LLM and vector database are you currently using or planning to use Do you need automated unit integration API and end to end tests as part of the delivery I’ll focus on building an accurate and maintainable RAG architecture with secure document processing relevant context retrieval reliable responses and comprehensive automated testing for the chatbot APIs and core workflows I’m available to start immediately Thanks

@michaelbukachi

AI-QE-RAG Hi, I specialize in building production RAG pipelines and autonomous agentic workflows in Python, with hands-on experience in Playwright test generation and LLM evaluation. Here is the proposed architecture for your two-module MVP: 1. RAG Chatbot Engine: - Document Processing: Semantic chunking for PDF, DOCX, and text with metadata tagging (page, section, timestamp). - Retrieval & Guardrails: PostgreSQL with pgvector (or Qdrant) paired with strict prompt grounding to eliminate hallucination, returning direct citations. - Evaluation & Cost Tracking: Integrated DeepEval/Ragas metrics (faithfulness, answer relevance) and per-request token/cost logging. 2. AI Test Case & Playwright Automation Pipeline: - Ingestion to Test Cases: Parser reading PRDs/BDD specs, generating structured test cases with requirement traceability, exportable to Excel via openpyxl. - Playwright Generation: Templated Page Object Model (POM) Python Playwright scripts with strict linting, retry logic, and headless test runners in Docker. - Results Dashboard: Execution reports capturing pass/fail/skipped stats, failure screenshots, and execution traces. 3. Architecture & Milestones: - Built in Python + FastAPI backend, PostgreSQL/pgvector, and Docker containerization. - Milestone 1: Knowledge-base ingestion & citation-backed RAG chatbot. - Milestone 2: Spec-to-Playwright generation, test execution runner, and reporting dashboard. Ready to demonstrate past RAG & Playwright agent architectures upon contact.

@AZTURK1

AI-QE-RAG You need an extensible MVP combining a grounded RAG chatbot with an automated requirement-to-Playwright test generation and execution pipeline. I can architect and deploy this using FastAPI, PostgreSQL with pgvector, and containerized worker agents. ✅ Deploy FastAPI core with PostgreSQL/pgvector handling document ingestion (PDF, DOCX), semantic chunking, citation retrieval, guardrails, and Ragas/DeepEval evaluation hooks. ✅ Build the multi-agent test engine converting PRDs into structured test cases, Excel exports, and executable Playwright scripts with historical run reporting. ✅ Full Docker Compose orchestration with isolated worker environments, infinite-loop timeouts, token cost tracking, and automated database snapshot routines. ✅ 10+ years production experience | 400+ completed projects | 320+ five-star reviews ✅ Microsoft® Certified: MCSA | MCSE | MCT & cPanel Certified (CWSA-2) For the Playwright runner, will executions run headlessly inside isolated Docker worker containers on the host, or do you prefer dispatching to an external grid? I can deliver this in 3 days for 32000 INR and can jump on it right now.

@techmadigital

Hi, AI-QE-RAG. I build full-stack AI products end to end, and this two-module MVP fits my verified work in RAG, agentic workflows, dashboards, and Playwright automation. I would build the document ingestion, chunking, embeddings, vector retrieval, and citation-grounded answers, plus the PRD to test case to Playwright pipeline with review, Excel export, execution results, and historical dashboards. I would add authentication, guardrails, evaluations, model abstraction, cost tracking, logging, and Docker deployment. I favor a simple, extensible architecture over unnecessary complexity, using off-the-shelf components where practical. I can share architecture and milestone details next. Thanks, Nikhil

@FreeLancers360

Hello, I just finished reading your brief for "AI-Driven RAG Chatbot & Test Automation", and I'm genuinely excited about the chance to work on it. I don’t just want to tick the boxes here; my aim is to hand you something that genuinely goes beyond what you pictured. From your brief I can see this involves ai, llm, chatbot — all areas we handle in-house. We specialise in Python, Software Architecture, PostgreSQL, Docker, which lines up directly with what you need. How we'd approach it: - Clarify the use-case, inputs and the exact output you expect - Build and integrate the model / automation pipeline - Evaluate accuracy and tune against real examples - Deploy with monitoring and a clear handover From kickoff to final handover, I’ll keep things transparent, on schedule, and squarely focused on your goals. I can start right away and keep you updated at every step — let’s make this a great one. Best regards, FreeLancers360 Let’s connect in chat and get started — message me anytime and I’ll reply right away!

@TechizeBuilder

Dear Client, I read "AI-Driven RAG Chatbot & Test Automation" carefully and understand you need a WhatsApp chatbot that automates customer interactions and streamlines your business processes. My hands-on experience with Python, PostgreSQL, Chatbot aligns directly with what you need. I've built WhatsApp chatbot solutions with automated conversations, product catalogs, order placement, lead generation, appointment booking, payment integration, AI-powered responses, CRM/API integrations, multilingual support, notifications, and admin dashboards — all designed to improve customer engagement and reduce manual effort. A few quick questions to get us started: 1. Will the chatbot use the WhatsApp Business Cloud API or another WhatsApp API provider? 2. What are the primary use cases (sales, customer support, bookings, order tracking, etc.)? 3. Do you need integrations with your existing CRM, ERP, website, or payment gateway? Thanks & Regards, Deepak

@bojans9

AI-QE-RAG Hi, Bojan here, from Serbia. Your AI platform—RAG chatbot plus test automation—requires careful foundation work: document handling, vector search, RAG evaluation, and multi-agent orchestration to control costs and avoid hallucinations. I'm a senior Python developer with 8+ years' experience in FastAPI, Docker, PostgreSQL, and full-stack development. The real challenge: RAG systems hallucinate if evaluation and guardrails aren't baked in from the start. Common mistake: building the chatbot first without RAG evaluation, leading to unreliable answers. My plan: first, set up PostgreSQL + pgvector and FastAPI backend with LangChain for core RAG. Then, build the chatbot UI with memory and guardrails. Finally, add test case generation with Playwright output and execution dashboard. I've built backend systems in FastAPI and PostgreSQL, so I focus on data integrity and avoiding AI pitfalls: hallucinations, cost spirals, and infinite loops. A few questions to better understand the scope: 1. For RAG evaluation, do you need automated thresholds, or manual review? 2. Should Phase 1 focus on the chatbot alone? I'd recommend yes—it's the foundation. 3. How will you manage LLM API costs—should the system track spend per project? I can start right away. Happy to discuss architecture and phasing here in chat. Looking forward to hearing from you, Bojan

@GobindSawarn

AI-QE-RAG Hello! I hope this message finds you well. My name is Govind, and I’m a seasoned AI developer with a strong focus on Python and deep knowledge in Docker and PostgreSQL, all of which are invaluable assets for your AI-driven RAG Chatbot and Test Automation project. Over the past 6 years, many clients have entrusted me to deliver polished, efficient and timely work - all of which would be put to maximum use by delivering an excellent product for your organisation. Before diving in - I think your preference for simple, practical but extensible architecture is spot-on, and aligns perfectly with my way of thinking. In terms of my experience with the technologies you've mentioned: I’ve worked heavily with Playwright, OpenAI/Anthropic/Gemini amongst others. I can link projects where similar technologies were employed. Understanding the scale of your needs, I believe my deep understanding of multi-agent orchestration along with agent/tool communication can be highly advantageous as well as my knack for observability in logging and error/timeout handling. Lastly, my experience in managing tokens and costs alongside providing historical dashboards would suit the budgeting requirements of your project.

@hemchand4315

Hello, I'm Hemchand Saini, a highly experienced AI developer with a strong Python background. While WordPress and PHP development are where I began my career over a decade ago, my passion for AI led me to specialize in AI, Automation, and most importantly for your project - Retrieval-Augmented Generation (RAG). Throughout my career, I've successfully integrated RAG technology into various platforms and worked heavily with the likes of OpenAI/Gemini, DeepEval/RAGAS which are quite relevant to your project. In terms of timeline and cost estimation, I believe in providing realistic yet ambitious milestones while making utmost efforts to stick to them. My quote reflects fair payment structures that are important to both parties. Finally, I want to emphasize on the value of long-term support & maintenance which means that even after completion of the project, you can rely on me for any future requirements or updates related to your AI platform. With my experience-driven expertise and robust problem-solving skills , I assure you that selecting me would be the next best step towards achieving an **AI-driven RAG Chatbot & Test Automation system** that not only caters to your immediate needs but also provides a solid foundation for future enhancements and scalability. I’m enthusiastic about the possibility of working on this project with you, so let's get in touch to discuss the details further.

@MuhammadAkif8

AI-QE-RAG! The priority is building the MVP around one shared RAG and AI platform, so the chatbot and test automation pipeline can reuse document processing, retrieval, model abstraction, evaluations, memory, guardrails and cost tracking instead of becoming two disconnected systems. Which sources should be supported first for the MVP Jira/BDD/PRD plus uploaded files and do you already have a preferred LLM provider? Also, should Playwright execution run in isolated Docker workers for controlled and parallel test runs? Workflow: Requirements & Architecture → Document Ingestion → Chunking & Embeddings → PostgreSQL/pgvector RAG → Citation & Retrieval Layer → LLM Abstraction → Guardrails & Prompt-Injection Protection → RAG Evaluation → Test Case Generation → Requirement Traceability → Review & Excel Export → Playwright Code Generation → Isolated Test Execution → Results & Historical Dashboards → Multi-Agent Orchestration → MCP Integration → Token/Cost Tracking → Logging & Observability → Retry/Timeout/Loop Protection → Authentication & Secure Storage → Docker Deployment Lets have a quick chat to review the MVP priorities and architecture so we can define the milestones and get the first working version moving. Warm regards, Akif S

@B1zt

AI-QE-RAG Hi, Both modules depend on the same foundation: a document store that answers with real sources. If that is solid, the test generator mostly reuses it. So I would build the chatbot core first and keep it simple. Milestone 1, which this price covers: - Upload PDF, DOCX and text files, split them up and store them in PostgreSQL with pgvector - Answers that cite the exact source passages, with conversation history - Logins, with each user seeing only their own documents - One switch between OpenAI, Anthropic or Gemini, with token and cost tracking per question - A simple React screen for uploading and chatting, all running in Docker Build vs off-the-shelf: FastAPI, pgvector, the providers' own SDKs and RAGAS for evaluation. No heavy agent framework until it earns its place. Milestone 2 adds evaluation and guardrails. Milestone 3 is test case generation, Excel export, Playwright runs and dashboards. I would quote each one once the first is working. Running costs: a small server is around INR 2,000 a month, and LLM usage at MVP volume is usually a few thousand INR more. Milestone 1: INR 37,500, delivered in 14 days. One question: roughly how many documents and users should the first version handle? Happy to walk you through the architecture on a call before you decide. Thanks, Bhagya

@designerbros

AI-QE-RAG At Designer Bros, we're known for our transformative approach to the digital world; a commitment to turning visions into reality that aligns perfectly with your AI-driven RAG Chatbot & Test Automation project. Our talents in Python and Software Architecture make us an ideal fit for developing both your RAG Chatbot and AI Test Generation module. We've collaborated on projects similar to yours in the past, successfully building secure chatbots for multiple industries. Our expertise extends beyond programming languages alone – we bring deep knowledge in LLM evaluation, Agentic AI, and Playwright, crucial skills for the successful implementation of your project. Beyond these assignments, we understand the significance of offering practical & extensible architecture; this includes our ability to generate structured manual test cases with requirement traceability and play-writing targeted automation. Regarding the proposed timeline and cost, we ensure every project follows an organized protocol, with each milestone achievable within agreed timelines. Understanding the importance of cost efficiency for businesses, we assure you of our best efforts in optimizing not only the initial costs but also the hosting/LLM running costs.

@alfatrade2

Groundedness scoring and hallucination detection alone usually take longer to tune than the retrieval pipeline itself, so cramming both modules plus multi-LLM support and cost tracking into one MVP is worth splitting into phases rather than building all at once. I've built AI agents on Claude and OpenAI APIs for classification and generation pipelines, which covers the chunking-to-citation flow you're describing. Which document types are the priority for the first working version - PDF only, or DOCX too?

@ravikumarsingh1

I’m AI Automation & DevOps Engineer with experience in RAG knowledge bases, LLM integrations, Python automation, and Docker-based deployments. I can build a practical MVP combining a source-grounded RAG chatbot with AI-powered test generation and Playwright execution. Proposed stack: React + Vite, Python + FastAPI, PostgreSQL + pgvector, OpenAI/Gemini APIs, Playwright, and Docker. Implementation plan: RAG chatbot: Document ingestion, chunking, embeddings, vector search, citations, conversation history, guardrails, and evaluation. AI test generation: Convert PRDs, BDD, and documents into structured test cases with requirement traceability. Test automation: Review/edit test cases, Excel export, Playwright script generation, controlled execution, and result tracking. Dashboard: Historical trends, test coverage, token usage, and cost tracking. Deployment: Authentication, logging, retries, execution limits, and Docker deployment. I recommend building the core workflow with proven libraries and adding multi-agent orchestration and MCP only where needed. Estimated timeline: 6–8 weeks. Budget: To be finalized after scope and milestones are agreed. Hosting and LLM usage are separate. I have worked on RAG knowledge-base and AI automation projects. You can check my profile for relevant experience.

@ronakkataria1

AI-QE-RAG One step in your flow will quietly fake the coverage number: PRD → RAG → test cases. Retrieval hands the generator the top few chunks. A requirement that's never retrieved never gets a test, and the dashboard can't show a gap it never saw. So I'd parse the PRD, BDD or Jira export into requirements with IDs and walk every one. RAG only adds context, like a glossary or linked spec. Coverage is then tested requirements over all, and a test citing a made-up ID is rejected. Second catch: a PRD says "user logs in", not which button. Playwright written from documents guesses selectors, so the first run goes red for reasons that aren't bugs. The generator reads the live page's roles and labels first. Generated code runs in a throwaway container with no secrets, since an uploaded doc can steer what the model writes. Built: both flows, review screen, Excel export, dashboards. Off the shelf: pgvector, RAGAS, LiteLLM for switching models, Langfuse for tokens and cost. Every agent gets step and cost caps, which is your loop protection. Milestone 1, two weeks: chatbot with citations, memory and an eval report. Milestone 2, two weeks: test generation, review, Playwright runs, dashboards. Half the bid each. Background: enterprise RAG on GPT-4, LLaMA, Qwen and Claude, LangGraph agents across development, testing and validation, and LLMOps with evals and guardrails. Is there a staging copy of the app, with test logins, for the generated tests to run against? Ronak

@romelc58

Hello Hope you are doing well "Build Secure RAG Chatbot And AI Testing" - I can develop an extensible platform using Python, FastAPI, PostgreSQL, LangChain, and Playwright. I can implement document ingestion, vector retrieval, cited answers, guardrails, evaluations, test-case generation, Excel export, Playwright automation, dashboards, and secure AI workflows. I will integrate multi-agent orchestration, model abstraction, cost tracking, observability, authentication, Docker deployment, retries, loop protection, and historical testing metrics with practical architecture. Would you like the MVP architecture optimized initially for OpenAI, Anthropic, or Gemini models? Looking forward to working with you.

@Ayesham836

AI-QE-RAG I can build the Python/FastAPI MVP around the core RAG workflow, with document ingestion, chunking, embeddings, vector retrieval, source-grounded responses, conversation context, and a clean API architecture. I have built an AI PDF Q&A application using Python, Streamlit, PyPDFLoader, HuggingFace embeddings and FAISS, and I have also worked on a Reality Checker system involving RAG, vector storage, semantic caching and research workflows. For this MVP, I would keep the architecture practical and extensible rather than over-engineering it: Python/FastAPI for the backend, a suitable vector store, structured document processing, logging, and clear separation between retrieval, generation and evaluation components. The test-automation pipeline can then be developed as a separate milestone around the same foundation. I can provide the implementation in milestones covering: 1. RAG/document pipeline and chatbot 2. Evaluation, guardrails and usage/cost tracking 3. AI test-case generation and Excel workflow 4. Playwright execution and dashboard integration 5. Docker/deployment and documentation I would first review the PRD/BDD/Jira input format and confirm the preferred LLM/vector database before finalising the architecture. Best regards

@Wonitaf

Hi there, AI-QE-RAG! I’ve tackled AI-driven projects before, especially with chatbots and test automation. I understand the intricacies of building secure systems that process various document types and manage conversation context effectively. I noticed you want a clean, professional RAG chatbot that can provide source-grounded answers and handle multiple LLMs. It’s crucial to ensure relevance and control hallucinations, which I can definitely achieve with my experience in AI evaluations and memory management. I specialize in Python and have worked with FastAPI and Playwright, so I’m well-equipped to build the structured test cases you need while ensuring seamless integration with your chosen technologies. You can count on me for speedy communication and a fast turnaround. Let me know if you are available for a quick chat! Regards, Wonita

@GenomyR

Hi, For automated data extraction built around Playwright, the priority is handling rate limits, session rotation, and anti-bot checks cleanly so the pipeline never silently fails. We will deploy a turnkey Automated Data Extraction System with modular selectors, resilient retry logic, and direct structured exports into your required format (JSON, CSV, Google Sheets, or database). We can have the Stage 1 extraction system and live sample dataset ready for you to inspect in 1 to 2 business days. What is your target website, and how frequently does this extraction system need to run? Best regards, Tumisang