Equipment Troubleshooting Assistant using Agentic AI and RAG

## Project Overview I am looking for an AI Engineer to develop an MVP Equipment Troubleshooting Assistant that helps operators and technicians diagnose equipment issues using technical manuals and maintenance documentation. The system should allow a user to describe a problem or enter an equipment error code and receive troubleshooting guidance generated from relevant documentation. This is an MVP project intended to demonstrate Agentic AI and Retrieval-Augmented Generation (RAG) capabilities. ## Functional Requirements ### Input * Equipment manuals (PDF) * Maintenance guides (PDF) * Error code documentation * User question or error code ### Example Queries * "What does error E102 mean?" * "The machine stops after startup. What should I check?" * "How can I calibrate the sensor module?" ### Agent Workflow 1. Understand user intent. 2. Retrieve relevant sections from manuals. 3. Identify likely causes. 4. Suggest troubleshooting steps. 5. Provide source references from documentation. ### Output * Root cause suggestions * Step-by-step troubleshooting instructions * Relevant manual references * Confidence level (optional) ## Technical Requirements Preferred Stack: * Python * FastAPI * LangGraph or similar agent framework * ChromaDB or FAISS * OpenAI, Anthropic, or Groq API ## Deliverables * Working backend API * Document ingestion pipeline * Vector database setup * Agent workflow implementation * README with setup instructions * Sample test dataset ## Nice to Have * Multi-step reasoning * Error-code-specific workflow * Follow-up questioning by the agent I have received too many ai generated submission, so please write apple in the begginning. ## Timeline 2–3 days