BigQuery RAG with OpenAI for Documentation Q&A - n8n Workflow | Neura Market
BigQuery RAG with OpenAI for Documentation Q&A
Implements Retrieval-Augmented Generation (RAG) using BigQuery vector search and OpenAI embeddings to answer questions about documentation accurately.
This n8n workflow demonstrates a powerful RAG pipeline leveraging BigQuery's built-in vector capabilities and OpenAI models. It starts by embedding user queries with OpenAI, performs semantic search in a BigQuery table storing document chunks and embeddings (includes a public n8n docs table for instant testing), retrieves relevant context, and generates precise answers via OpenAI's LLM. No dedicated vector database needed—ideal for teams already using BigQuery.
Key benefits include cost-efficie
Platform
n8n
Category
Internet of Things
Price
$22.99
Creator
BestWorkflows
RAG
BigQuery
OpenAI
Embeddings
Vector Search
AI Chatbot
Documentation QA
Semantic Search
Knowledge Base
How to import this workflow into n8n
1Purchase or download the workflow to get the n8n workflow JSON file.
2In your n8n instance, open Workflows and choose "Import from File" (or paste the JSON with Ctrl+V on the canvas).
3Open each node marked with a credential warning and connect your own accounts and API keys.
4Run the workflow once manually to verify the data flow, then toggle it to Active.