PDF proposal knowledge base with S3, OpenAI GPT-4o & Qdrant RAG agent
Needs changesNeeds changes. This template uses 1 thing(s) that n8n 3.0 removes: AI Agent v1 (old agent modes). 1 breaking item(s) found. Static check, last run 2026-10-19.
What this workflow does
This AI workflow has 14 nodes and runs manually, when you click “Execute workflow” (plus 1 other trigger). Along the way it connects Qdrant Vector Store, AWS S3, Embeddings OpenAI and OpenAI Chat Model.
How it runs
Trigger: Manually, when you click “Execute workflow” (plus 1 other trigger).
- Get Files from S (AWS S3): get many
- AI Agent: runs an AI agent, with model: OpenAI Chat Model; 1 tool (Qdrant Vector Store)
- Loop Over Items (Loop Over Items (Split in Batches)): loops over the items in batches
- Download Files from AWS (AWS S3): uses AWS S3
- Extract from File: extracts data from a file (pdf)
- Qdrant Vector Store: stores documents in the Qdrant vector store, with embeddings: Embeddings OpenAI; document loader: Default Data Loader
Services it uses
Before you upgrade to n8n 3.0
1 thing in this workflow is removed in n8n 3.0. Fix it before you upgrade:
- AI Agent v1 (old agent modes) in AI Agent (v1.9): replace with Latest AI Agent node (Tools Agent). For SQL Agent, use a Postgres or MySQL tool with a recent AI Agent.
Rather not rewrite it? Many of these flows can be rebuilt in Make (free plan).
| Removed in 3.0 | Replace with |
|---|---|
| AI Agent v1 (old agent modes) AI Agent (v1.9) | Latest AI Agent node (Tools Agent). For SQL Agent, use a Postgres or MySQL tool with a recent AI Agent. |
Nodes used (11)
- Manual Trigger
n8n-nodes-base.manualTrigger - Loop Over Items (Split in Batches)
n8n-nodes-base.splitInBatches - Extract from File
n8n-nodes-base.extractFromFile - Qdrant Vector Store
@n8n/n8n-nodes-langchain.vectorStoreQdrant - AWS S3
n8n-nodes-base.awsS3 - Embeddings OpenAI
@n8n/n8n-nodes-langchain.embeddingsOpenAi - Default Data Loader
@n8n/n8n-nodes-langchain.documentDefaultDataLoader - Recursive Character Text Splitter
@n8n/n8n-nodes-langchain.textSplitterRecursiveCharacterTextSplitter - Chat Trigger
@n8n/n8n-nodes-langchain.chatTrigger - AI Agent
@n8n/n8n-nodes-langchain.agent - OpenAI Chat Model
@n8n/n8n-nodes-langchain.lmChatOpenAi
From the author
This template has a two part setup: 1. Ingest PDF files from S3, extract text, chunk, embed with OpenAI embeddings, and index into a Qdrant collection with metadata. 2. Provide a chat entry point that uses an Agent with OpenAI to retrieve from the same Qdrant…