Automating Knowledge into Conversations with AI
This project implements a Retrieval-Augmented Generation (RAG) Pipeline using n8n, OpenAI, and Pinecone to build a chatbot that can:
- Ingest documents from Google Drive
- Convert them into embeddings
- Store them in a vector database (Pinecone)
- Answer user queries with contextually accurate responses
Businesses and teams often struggle with scattered documentation. Searching for answers manually is slow and inefficient.
This project solves that by automating document ingestion + retrieval + contextual answering in real-time.
Steps:
- Trigger: Google Drive upload event
- Data Loader: Extract file contents
- Embedding: Generate vector embeddings using OpenAI
- Storage: Store embeddings in Pinecone Vector DB
- User Query: Chat agent triggers OpenAI model
- Context Retrieval: Pinecone fetches relevant chunks
- Response: AI chatbot provides contextual answer
Tech Stack:
- n8n (workflow automation)
- OpenAI API (embeddings + LLM)
- Pinecone (vector database)
- Google Drive API (document source)
- 📂 Automated Document Ingestion from Google Drive
- 🔎 Semantic Search powered by Pinecone
- 🧠 Contextual Q&A via OpenAI Chat Model
- ⚡ Low-latency responses for real-time interactions
- 🔄 Scalable & modular workflow built on n8n
- ⏱️ 80% faster knowledge retrieval compared to manual search
- 📉 Reduced dependency on manual FAQ maintenance
- 🤝 Usable for customer support, internal knowledge bases, or research
- Multi-source ingestion (Notion, Slack, Confluence)
- Fine-tuned models for domain-specific Q&A
- Frontend chatbot widget integration
- Response caching for efficiency
Muskkan Iyer
AI Product Analyst | Data Enthusiast | Automation Builder
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