RAG (Retrieval-Augmented Generation) is an AI approach that combines a language model (like ChatGPT) with real data from your website, database, or files to generate more accurate answers.
Instead of guessing, it retrieves relevant content from your knowledge base before answering.
📁 Step 1: Upload Your Data
Website content (HTML, text)
PDF, Word, Excel, CSV, JSON
Database exports (SQL, NoSQL)
📚 Step 2: Index & Embed
Split content into chunks
Convert text into vector embeddings
Store in a vector database (Pinecone, FAISS, etc.)
💬 Step 3: Ask Questions
Bot retrieves relevant chunks
Passes them to GPT for response
Answer is grounded in your data
⚙️ Tools & Frameworks
LangChain / LlamaIndex - RAG orchestration
Pinecone / FAISS / Chroma - Vector DB
OpenAI API - GPT answering
Streamlit / React / Custom UI - Chat frontend
✅ Chatbot Capabilities
Answer FAQs from your website or docs
Query internal systems or databases
Stay factual by using your real content
Combine multiple sources (CRM + docs + SQL)
Odoo Development (confidential).
Odoo Development (confidential)
as a Odoo Developer
🐍 Developed over 15 custom modules in Odoo v12 to v16, across CRM, Accounting, Inventory, and HR systems.
🧩 Customized core modules such as sale, purchase, stock, and account to meet client-specific workflows.
📊 Built dynamic reports using QWeb (PDF invoices, stock valuation reports) and custom dashboards.
🔄 Integrated Odoo with third-party platforms via REST APIs (e.g., Shopify, PayMongo, Viber SMS).
🛡️ Implemented access controls and record rules for multi-user, multi-company environments.
🚀 Migrated legacy Odoo v12 instance to v16 with full data preservation and test coverage.
⚙️ Automated invoice generation and scheduled email reminders via cron jobs.>