Block 1 - Pages-> clear JSON formatted for vector store insertion
- Type / Role
- n8n-nodes-base.set - set
- Config choices
- Version 3.4
This workflow is provided as-is. Please review and test before using in production.
Click here to access this Workflow for free . Make your website the knowledge base of your LLM chatbot This wor...
n8n-nodes-base.set, n8n-nodes-base.scheduletrigger, n8n-nodes-base.stickynote, n8n-nodes-base.httprequest, n8n-nodes-base.aggregate, n8n-nodes-base.merge, @n8n/n8n-nodes-langchain.vectorstorepinecone, @n8n/n8n-nodes-langchain.embeddingsopenai
This workflow is cataloged by N8N Workflows and links back to its original n8n.io source page by Theo Marcadet.
Original n8n.io sourceClick here to access this Workflow for free.
This workflow automatically syncs your WordPress content (Pages and Posts) into a vector database like Pinecone, making it searchable and usable by AI agents through embeddings. This allows your AI chatbot to stay continuously up to date with your latest site content — without manual uploads or retraining steps.
It pulls all published and private content via WordPress's REST API, processes the data into clean JSON, and sends it to Pinecone using vector embeddings (OpenAI by default, but swappable). This enables a dynamic AI knowledge base that grows with your website.
Best of all, you don’t need to modify your workflow: keep managing your content in WordPress — this automation takes care of the rest.
Configure WordPress Access:
pages and posts.Connect Pinecone and OpenAI:
Set Your Schedule:
Check the Nodes:
Once the workflow is active:
You’ll have a continuously refreshed, vectorized copy of your WordPress site content.
This catalog entry is organized from the workflow JSON. The node-level section below shows the executable blocks available for review before importing the template.
Showing the first 24 of 32 workflow blocks. Download the JSON for the full node graph.
| Workflow | Export Wordpress to PineCone vector store |
|---|---|
| Complexity | advanced |
| Nodes | 32 |
| Categories | Engineering, AI RAG |
| Author | Theo Marcadet |
| Published | 15 Apr 2025 |
Use the JSON export at /data/workflows/3557/3557.json as the source template for this automation.
Open n8n, import the downloaded JSON, and review each node before activating the workflow.
Replace placeholder credentials, API keys, webhook URLs, account IDs, and environment-specific values with your own settings.
Run the workflow manually or in a staging workspace, inspect node output, and confirm downstream systems receive the expected data.
Enable the workflow only after testing, then monitor executions, errors, and rate limits during the first production runs.
Review imported nodes carefully before activation. This catalog entry is intended to help you inspect the workflow structure, understand required services, and find related templates faster.
Node names, credentials, schedules, webhook paths, and external service limits may need adjustment for your workspace.
Click here to access this Workflow for free . Make your website the knowledge base of your LLM chatbot This wor...
Review the workflow JSON, configure any required credentials in n8n, and test the automation in a safe workspace before using it in production.
Yes. Use the block-by-block analysis and the downloadable JSON to inspect each node, then adjust credentials, prompts, schedules, filters, or destinations for your Engineering, AI RAG use case.