Quick overview
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. An n8n automation workflow template by Alex Raj.
Alex Raj
web
free
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