Build a dual-stage RAG chat assistant with Google Drive, Ollama, Pinecone and Gemini
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Alex Raj
Description
Quick overview
This template builds a dual-stage RAG system that ingests PDFs/text from Google Drive into a Pinecone knowledge-base index with Ollama embeddings, and serves chat queries via a Pinecone semantic cache that falls back to a Google Gemini AI Agent with vector retrieval.
How it works
Triggers on a Google Drive folder update (or manually) to list and download files from a specified Drive folder.
Detects whether each file is a PDF or plain text and extracts the document text accordingly.
Splits extracted content into overlapping chunks, embeds them with Ollama (nomic-embed-text), and inserts the vectors into a Pinecone index for the knowledge base.
Triggers when a chat message is received and embeds the user query with Ollama to search the Pinecone semantic-cache index for the closest prior answer.
Returns the cached answer immediately when the best match score is at least 0.88.
On a cache miss, uses a Google Gemini AI Agent with conversation memory and a Pinecone vector-store retrieval tool to generate a grounded answer from the knowledge-base index.
Stores the new query-and-answer pair back into the Pinecone semantic-cache index for faster responses to future similar questions.
Setup
Create two Pinecone indexes using cosine similarity and 768 dimensions: one for the knowledge base (e.g. nomic-embed-text) and one for the semantic cache (e.g. rag-semantic-cache).
Set up Pinecone credentials in n8n and select the correct index names in the Pinecone vector store nodes.
Run Ollama and pull the nomic-embed-text model, then configure Ollama credentials/base URL in n8n.
Add Google Drive OAuth2 credentials and replace YOUR_GOOGLE_DRIVE_FOLDER_ID with the folder you want to ingest.
Add a Google Gemini API key and confirm the model name (for example models/gemini-1.5-flash) in both Gemini chat model nodes.
If you use the chat trigger via webhook, copy the chat/webhook URL from n8n and configure your client to send messages to it.
This workflow was created by Alex Raj and shared publicly on the n8n community template library. n8nFind indexes it for discovery — the workflow remains the property of its original creator.