Complete stack RAG driven document intelligence assistant developed using FastAPI and React. Features multi-format ingestion support (PDF, DOCX, and scanned images through EasyOCR), with FAISS vector indexing, and 6 LLM driven modes operating on Groq’s Llama 3.3 70B model. Dockerized application via GitHub actions CI pipeline for automatic linting and build validation with sub-2 second response time in local tests. FAISS is chosen over Pinecone for quick in-memory computation without relying on cloud infrastructure, while Groq is selected over other options for quicker and cheaper inference than GPT-4 alternatives.

How do you persist the FAISS index across Docker restarts?

I designed an automated Navision → n8n → Odoo → Peppol workflow. Navision generates a daily Excel export, which n8n automatically retrieves, parses, validates, and maps before using the Odoo API to create customer invoices with their corresponding invoice lines.

The workflow also includes error handling, logging, validation, secure API authentication, scheduling, and failure notifications. Once invoices are created in Odoo, its standard Peppol functionality handles electronic delivery to B2B customers. The solution reduced manual invoice processing, created a more transparent and traceable workflow, and provided a scalable foundation for the company's transition from Navision to Odoo. It also prepared the invoicing process for Peppol e-invoicing while keeping Navision as the primary accounting system during the transition.

I'd want a reconciliation check between each daily Excel export and the invoices Odoo actually created. If one row fails after the others succeed, does the next run resume just that row?