{"id":27076163,"url":"https://github.com/devbauti/embeddingchat","last_synced_at":"2026-02-11T13:02:33.391Z","repository":{"id":284262300,"uuid":"954358380","full_name":"DevBauti/EmbeddingChat","owner":"DevBauti","description":"n8n workflow that downloads a PDF from Google Drive, generates embeddings with Cohere, and stores them in Supabase. 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It also features a chat-based question-and-answer system powered by xAI's Grok model.\n\n![EmbeddingWithChatBasicExample](EmbeddingWithChatBasicExample.png)\n\n## Description\n\nThe workflow downloads \"Química - Raymond Chang - 12va Edición.pdf\" from Google Drive, splits the text into chunks, generates embeddings with the `embed-multilingual-v2.0` model from Cohere, and stores them in a Supabase vector database. A chat trigger enables question-answering using the `grok-2-1212` model from xAI, retrieving relevant context from the vector store.\n\n## Features\n\n- Downloads files from Google Drive.\n- Generates embeddings with Cohere (`embed-multilingual-v2.0`).\n- Stores embeddings in Supabase vector store.\n- Chat-based Q\u0026A with xAI (`grok-2-1212`).\n- Configurable chunk size for text splitting (default: 3000).\n\n## Prerequisites\n\n- n8n instance with required nodes installed.\n- Credentials for:\n  - Google Drive OAuth2 API.\n  - Cohere API.\n  - Supabase API.\n  - xAI API (Grok).\n\n## Setup\n\n1. Import the workflow JSON into your n8n instance.\n2. Configure the credentials for Google Drive, Cohere, Supabase, and xAI in the respective nodes.\n3. Adjust the `Token Splitter` chunk size if needed (default: 3000).\n4. Activate the workflow and test it using the \"Test workflow\" trigger or chat input.\n\n## Usage\n\n- Trigger manually to process the PDF and store embeddings.\n- Use the chat interface to ask questions based on the document content.\n\n# Flujo de Trabajo de Embedding y Q\u0026A con n8n\n\nEste flujo de trabajo de n8n automatiza la descarga de un PDF desde Google Drive, la generación de embeddings multilingües con Cohere y su almacenamiento en un almacén vectorial de Supabase. También incluye un sistema de preguntas y respuestas basado en chat, impulsado por el modelo Grok de xAI.\n\n## Descripción\n\nEl flujo descarga \"Química - Raymond Chang - 12va Edición.pdf\" desde Google Drive, divide el texto en fragmentos, genera embeddings con el modelo `embed-multilingual-v2.0` de Cohere y los almacena en una base de datos vectorial de Supabase. Un desencadenador de chat permite responder preguntas usando el modelo `grok-2-1212` de xAI, recuperando contexto relevante del almacén vectorial.\n\n## Características\n\n- Descarga archivos desde Google Drive.\n- Genera embeddings con Cohere (`embed-multilingual-v2.0`).\n- Almacena embeddings en un vector store de Supabase.\n- Sistema de Q\u0026A por chat con xAI (`grok-2-1212`).\n- Tamaño de fragmentos configurable (predeterminado: 3000).\n\n## Requisitos Previos\n\n- Instancia de n8n con los nodos necesarios instalados.\n- Credenciales para:\n  - Google Drive OAuth2 API.\n  - Cohere API.\n  - Supabase API.\n  - xAI API (Grok).\n\n## Configuración\n\n1. Importa el JSON del flujo de trabajo en tu instancia de n8n.\n2. Configura las credenciales para Google Drive, Cohere, Supabase y xAI en los nodos correspondientes.\n3. Ajusta el tamaño de fragmentos en el `Token Splitter` si es necesario (predeterminado: 3000).\n4. Activa el flujo y pruébalo con el desencadenador \"Test workflow\" o la entrada de chat.\n\n## Uso\n\n- Activa manualmente para procesar el PDF y almacenar embeddings.\n- Usa la interfaz de chat para hacer preguntas basadas en el contenido del documento.\n\n## Licencia\n\nLicencia MIT - siéntete libre de usar, modificar y distribuir.","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdevbauti%2Fembeddingchat","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fdevbauti%2Fembeddingchat","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdevbauti%2Fembeddingchat/lists"}