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.