PDF proposal knowledge base with S3, OpenAI GPT-4o & Qdrant RAG agent

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 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.