Build AI Voice Assistent with VAPI

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        "content": "## Post-sale Agent\nGet a WooCommerce user's orders and shipping tracking"
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        "content": "## General info\n\nExtract information saved in the vector database\n\nTo build the RAG system please view [this Workflow](https://n8n.io/workflows/7647-build-a-self-updating-rag-system-with-openai-google-gemini-qdrant-and-google-drive/)"
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        "content": "## STEP 1\nCreate two \"API Request\" tools in VAPI:\n- WooCommerce tool: connect the n8n webhook and set two parameters as the request body: 'email' and 'n_order'\n- Rag tool: connect the n8n webhook and set the 'search' parameter as the request body\n\n## STEP 2\n- Create a wizard by generating a system prompt that includes the functionality of the two tools created in STEP 1.\n- Set the voice in the Voice tab.\n- Include the two tools created in STEP 1 in the 'Tools' tab.\n\n## STEP 3\n- Create or import a phone number from Twilio, setting it as \"Inbound.\"\n- Associate the phone number with the wizard."
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        "content": "## Build AI Voice Assistent with VAPI & Twilio for WooCommerce Post-Sales & RAG\nThis workflow integrates a **Retrieval-Augmented Generation (RAG) system** with a **post-sales AI agent for WooCommerce**. It combines vector-based search (Qdrant + OpenAI embeddings) with LLMs (Google Gemini and GPT-4o-mini) to provide accurate and contextual responses.\n\nBoth systems are connected to **VAPI webhooks**, making the workflow usable in a **voice AI assistant** via **Twilio** phone numbers.\n\nThe workflow receives JSON payloads from VAPI via webhooks, processes the request through the appropriate chain (Agent or RAG), and sends a structured response back to VAPI to be read out to the user."
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