Load help center pages into a Pinecone knowledge base with OpenAI embeddings

Quick overview This workflow manually fetches help pages (or demo docs), chunks them, generates OpenAI embeddings, and upserts the vectors into a Pinecone index so they can be used for retrieval-based support or chat. How it works 1. Starts when you run the workflow manually. 2. Reads configuration to decide whether to load URLs from DOCURLS or use the built-in demo knowledge base. 3. Fetches each public document URL with retries, converts the HTML to plain text, and records any pages that fail or contain too little text. 4. Splits each document into overlapping text chunks with stable chunk IDs so re-running updates existing vectors instead of duplicating them. 5. If OpenAI/Pinecone loading is not enabled (for example, preview mode or missing configuration), outputs the chunks that would be embedded and upserted without writing anything. 6. Otherwise, batches chunks, calls the OpenAI Embeddings API to create vectors, and skips any batches that fail to embed after retries. 7. Upserts each successfully embedded batch into Pinecone with title, source, and chunk text stored as metadata, and reports either the upsert counts or the Pinecone error. Setup 1. Add an OpenAI API credential in n8n and set USEOPENAI to true in the config node. 2. Create a Pinecone index whose vector dimension matches EMBEDDIMENSIONS, then add a Pinecone HTTP Header Auth credential (Api-Key) and set PINECONEINDEXHOST and PINECONENAMESPACE in the config node. 3. Add your public documentation URLs to DOCURLS and adjust CHUNKWORDS/CHUNKOVERLAP if you want different chunking behavior. 4. Switch out of preview mode (set TESTEMAIL for test mode or set TESTRUN to false) so upserts are enabled.