🔍 PDF proposal knowledge base with S3, OpenAI GPT-4o & Qdrant RAG agent
⚡ 1,130 views · 🔍 AI RAG & Knowledge Retrieval
Description
This template has a two part setup:
- Ingest PDF files from S3, extract text, chunk, embed with OpenAI embeddings, and index into a Qdrant collection with metadata.
- Provide a chat entry point that uses an Agent with OpenAI to retrieve from the same Qdrant collection as a tool and answer proposal knowledge questions.
What it does
- Lists objects in an S3 bucket, loops through keys, downloads each file, and extracts text from PDFs.
- Chunks text and loads it into Qdrant with metadata for retrieval.
- Exposes a chat trigger wired to an Agent using an OpenAI chat model.
- Adds a retrieve as tool Qdrant node so the Agent can ground answers in the indexed corpus.
Why it is useful
- Simple pattern for building a proposal or knowledge base from PDFs stored in S3.
- End to end path from ingestion to retrieval augmented answers.
- Easy to swap models or collections, and to extend with more tools.
Setup notes
- Attach your own AWS credentials to the two S3 nodes and set your bucket name.
- Attach your Qdrant credentials to both Qdrant nodes and set your collection.
- Attach your OpenAI credentials to the embedding and chat nodes.
- The sanitized template uses placeholders for bucket and collection names.
🔗 Nodes Used
AWS S3, AI Agent, Embeddings OpenAI, OpenAI Chat Model, Recursive Character Text Splitter, Extract from File
📥 Import
Download workflow.json and import into n8n:
Workflow menu → Import from File