🔬 Chat with PDF, CSV, and JSON documents using Google Gemini RAG
⚡ 118 views · 🔬 Document Extraction & Analysis
Description
Overview
Turn documents into an AI-powered knowledge base.
Upload PDF, CSV, or JSON files and ask natural-language questions about their content using a Retrieval-Augmented Generation (RAG) workflow powered by Google Gemini. The workflow extracts, embeds, and semantically searches document data to generate accurate, source-grounded answers.
Designed as a simple and extensible starting point for building AI document assistants.
Key Features
- Upload and analyze PDF, CSV, and JSON
- AI chatbot with semantic document search
- Retrieval-Augmented Generation (RAG) architecture
- Answers grounded in uploaded documents
- Beginner-friendly workflow with clear documentation
- Easy to extend for production use
How It Works
- Upload a document via form trigger
- Content is split into searchable chunks
- Gemini generates embeddings
- Data is stored in a vector store
- The chatbot retrieves context and answers questions
Requirements
- Google Gemini API credentials
Notes
- Uses an in-memory vector store (data resets on restart)
- Can be replaced with Pinecone, Supabase, Weaviate, or other persistent databases
- Gemini API usage may incur costs depending on document size and query volume
🔗 Nodes Used
AI Agent, Simple Memory, Recursive Character Text Splitter, Simple Vector Store, n8n Form Trigger, Default Data Loader
📥 Import
Download workflow.json and import into n8n:
Workflow menu → Import from File