Build a Personal AI Chatbot from Your Resume with Google Gemini & Pinecone
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Automate resume ingestion into a searchable AI knowledge base using Google Drive, Gemini, and Pinecone. Deploy a chatbot to answer questions about your profile.
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About This Workflow
How it works
This workflow automates the process of turning your personal resume or CV into a conversational AI agent.
- Training Stage: When new or updated CV files are detected in a designated Google Drive folder (
SEAN-RAG-FOLDER), the workflow downloads them. - Data Processing: The downloaded files are loaded and then split into manageable text chunks using a Recursive Character Text Splitter.
- Vectorization: These text chunks are converted into numerical embeddings using Google Gemini's embedding model and then stored in a Pinecone vector database index (e.g.,
seanrag). This makes your resume content searchable by semantic similarity. - Chatbot Deployment: A webhook endpoint (
/chat) is exposed, allowing users to ask questions. The workflow uses Google Gemini's chat model to understand queries, retrieve relevant information from the Pinecone vector store using a custom tool (Resume lookup : Vector Store Tool), and generate context-aware responses. - Memory & Conversation: Chat memory is managed using a
Chat Memory - Window Buffernode to maintain conversational context. Optionally, conversation history can be saved to NocoDB via a separate webhook (/update-conversation). - Reporting (Optional): A scheduled trigger can daily retrieve conversation history from NocoDB and send an email summary.
Key Features
- Automatically ingest and process resume/CV documents from Google Drive.
- Leverage Google Gemini for advanced text embedding and conversational AI.
- Utilize Pinecone for efficient semantic search and retrieval of your profile data.
- Deploy a chatbot endpoint to answer questions about your resume.
- Implement chat memory for a more natural conversational experience.
- Optional: Save conversation history to NocoDB and schedule daily email reports.
How To Use
- Google Cloud Project & Vertex AI API: Create a Google Cloud project and enable the Vertex AI API.
- Google AI API Key: Obtain an API key from Google AI Studio.
- Pinecone Account: Create a Pinecone account and obtain your API key. Create an index named
seanrag(or your preferred name). - Google Drive Setup: Create a dedicated folder in Google Drive to store your resume/CV documents (e.g.,
SEAN-RAG-FOLDER). - n8n Credentials: Configure the following credentials in your n8n instance:
- Google Drive OAuth2
- Google Gemini (PaLM) API (using your Google AI API key)
- Pinecone API (using your Pinecone API key)
- NocoDB API Token (if using conversation saving)
- Import Workflow: Import this workflow into your n8n instance.
- Configure Workflow:
- Update the
Google Drive - Resume CV File CreatedandGoogle Drive - Resume CV File Updatednodes to watch your specific Google Drive folder. - Ensure the Pinecone nodes (
Pinecone - Vector Store forr CV Content,Resume Vector Store (Retrieval)) are configured with your Pinecone index name (seanrag). - If using conversation saving, ensure the NocoDB credentials and table name (
mk9sfu217ou392s) are correctly set in theSave Conversation - NocoDBnode.
- Update the
- Deployment: Activate the workflow. The webhook endpoints (
/chatand/update-conversation) will become active.
Apps Used
Workflow JSON
{
"id": "e49b6b3a-8868-421a-a9cc-14008a07c41e",
"name": "Build a Personal AI Chatbot from Your Resume with Google Gemini & Pinecone",
"nodes": 0,
"category": "AI & LLM",
"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: e49b6b3a-8868...
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