Intelligent Content Q&A with WordPress & RAG
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Empower your audience with instant, AI-driven answers directly from your WordPress content. This workflow leverages Retrieval Augmented Generation (RAG) to create an intelligent Q&A system that understands and responds using your website's information.
About This Workflow
Unlock the power of your WordPress content with this advanced RAG (Retrieval Augmented Generation) and Generative AI workflow. This solution transforms your website's articles, posts, and pages into a dynamic knowledge base, allowing users to ask questions and receive accurate, context-aware answers. It intelligently processes your WordPress content, generates embeddings for efficient retrieval, and uses a powerful LLM to craft human-like responses. The workflow also incorporates chat memory, enabling context-aware conversations and a more personalized user experience. Ideal for support bots, internal knowledge bases, or enhancing user engagement on your website.
Key Features
- AI-Powered Q&A: Provides instant, intelligent answers to user queries based on your WordPress content.
- RAG Integration: Utilizes Retrieval Augmented Generation for accurate and contextually relevant responses.
- WordPress Content Ingestion: Seamlessly processes and indexes your website's published articles and pages.
- LLM Integration: Leverages state-of-the-art language models for natural language understanding and generation.
- Persistent Chat Memory: Maintains conversational context for a more fluid and personalized user interaction.
How To Use
- Trigger Setup: Configure the workflow to be triggered by a webhook (e.g., when a chat message is received) or a manual test.
- WordPress Content Fetching: Ensure your WordPress content is accessible and can be parsed. The workflow expects data with fields like
title,url,content_type,publication_date,modification_date,id, anddata(containing the main content). - Content Processing: The workflow uses a
Default Data Loaderto ingest and structure your WordPress content, followed by aToken Splitterto break it down into manageable chunks. - Embedding Generation: OpenAI Embeddings are generated for each chunk of content, creating a vector representation for efficient searching.
- LLM Integration: An
OpenAI Chat Modelis used to process user queries and generate responses. - Chat Memory: A
Postgres Chat Memorynode is configured to store conversation history, enabling context-aware interactions. - Response Formatting: The workflow formats the retrieved documents and the LLM's answer into a user-friendly response.
- Webhook Response: A
Respond to Webhooknode sends the generated answer back to the originating system.
Apps Used
Workflow JSON
{
"id": "b94526f0-c1cd-4890-94b3-369790263cbe",
"name": "Intelligent Content Q&A with WordPress & RAG",
"nodes": 17,
"category": "Operations",
"status": "active",
"version": "1.0.0"
}Note: This is a sample preview. The full workflow JSON contains node configurations, credentials placeholders, and execution logic.
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ID: b94526f0-c1cd...
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