Mission-critical industries such as aviation, healthcare, banking, and public-sector IT depend on high-quality software testing. QA engineers frequently need instant, domain-specific guidance for automation frameworks, CI/CD practices, and compliance-driven test strategies.
Traditional knowledge sources (forums, docs, vendor platforms) are fragmented, slow, or locked behind proprietary APIs.
Research Gap: Few AI chatbots are optimized for QA engineering, let alone designed to run offline, securely, and with reproducibility guarantees.
1.2 What is GenQAChat-RAG-AI?
GenQAChat-RAG-AI is an open-source Generative AI chatbot designed to empower QA engineers.
Key features:
Retrieval-Augmented Generation (RAG) for QA-specific knowledge
Local embeddings via HuggingFace MiniLM (no OpenAI API needed)
Proprietary AI assistants introduce data privacy risks
No offline, compliance-friendly solution exists for QA automation
Solution (GenQAChat-RAG-AI):
Domain-specific embeddings trained on QA automation knowledge
Local FAISS-based retrieval (no vendor lock-in)
Lightweight deployment in Codespaces or enterprise CI/CD pipelines
Compliance-ready traceability with reproducible markdown knowledge base
2. System Architecture & Key Features
2.1 At a Glance
Core: FastAPI backend + LangChain retrieval
Embeddings: HuggingFace MiniLM
Vector Store: FAISS for semantic search
Frontend: Next.js + Tailwind CSS
Deployment: Streamlit demo and GitHub Codespaces
2.2 Architecture
flowchart TD
A["Next.js Frontend UI"]
B["FastAPI REST API"]
C["LangChain Retriever + HuggingFace MiniLM Embeddings"]
D["FAISS Vector Store (semantic search)"]
E["QA Knowledge Base (Markdown / Text files)"]
A --> B --> C --> D --> E
2.3 Feature Breakdown
Privacy-First AI: Works offline, no external API calls
